Additive manufacturing (AM) (=3D printing) has emerged during the last few years as a powerful technological platform for fabrication of functional parts with unique complex geometries and superior functionalities that are next to impossible to achieve using conventional manufacturing techniques. Due to their importance in industrial applications and the maturity of the applicable AM techniques, metallic materials are at the forefront of the developments in AM. In this editorial, which has been written as a preamble to the special issue “Perspectives on Additively Manufactured Metallic Materials”, I will highlight some of the frontiers of research on AM of metallic materials to help readers better understand the cutting edge of research in this area. Some of these topics are addressed in the articles appearing in this special issue, while others constitute worthy avenues for future research. Additive manufacturing (AM) (=3D printing) is one of the most important developments in manufacturing since the turn of the century. Although the underlying technology has been under development for several decades, it is only during the last decade that the technology has matured to the level that is required for reliable fabrication of functional parts. The layer-by-layer manufacturing not only means that complex geometries could be achieved with no additional costs but also enables the realisation of geometries for which no feasible fabrication technique existed before. Moreover, the lead time is much shorter in AM as compared to many conventional techniques, while batch size makes little to no difference in terms of the production costs. A combination of all these advantages makes AM an extraordinarily promising manufacturing platform for a host of industrial applications. Metals and metallic alloys are the most commonly used types of materials in a wide range of industries. Moreover, research into AM of metallic materials started in the 90’s and has been continuing for more than two decades. As a result, the technology required for AM of metallic materials has matured and reached the level that enables direct fabrication of functional parts. That is one of the reasons why metal AM is currently receiving so much attention from both researchers in academia and actual industrial users.Given the relatively high manufacturing costs, the first applications of AM have been in high added value industries such as biomedical  and aerThe most commonly used metal AM processes, namely selective laser melting and electron beam melting being, are classified under the general category of powder bed fusion technologies. These techniques work based on layer-by-layer melting of selected areas within a powder bed. Direct energy deposition techniques are also used for processing of metals and metallic alloys, particularly when near net shape parts are desired, when the part being fabricated does not fit the build volume of the powder bed fusion machines, or when AM is used for the repair of already existing parts. Finally, some studies try to interface metal printing with other AM technologies such as those based on material extrusion to create hybrid materials .Metal AM is successful only if the laser processing parameters are properly selected so as to simultaneously optimize three objective functions: fidelity of the built parts (parts conforming to CAD design), density of the parent material (≈100%), and the mechanical properties of the built material. The usual approach for selecting the processing parameters is trial and error. This approach is time-consuming, expensive, and inaccurate, particularly for geometrically complex parts. Moreover, the obtained parameters are often not transferable from one machine or lab to another. This presents a major challenge for every new geometrically complex part that needs to be manufactured using AM, and is a barrier to widespread application of cost-effective AM. First-time-right AM is an ideal approach to metal AM where the built part is fabricated without any need for trial and error. There is currently no first-time-right AM technology available that guarantees direct success of the AM process regardless of the geometrical complexity or material type. The success of any such technological platform is likely to be dependent on developments in three core underlying technologies including process monitoring techniques that provide a rich set of data for controlling and predicting the AM process, process simulation models that are capable of using the data provided by the process monitoring systems and predict the best processing parameters, and control systems enabling on-the-fly adjustment of processing parameters. Although all three types of technologies have received increasing attention during the recent years, the level of maturity, integration, and precision required for first-time-right AM has not yet been realised. Enhancing the capabilities of these core underlying technologies as well as integrating them into a functional and reliable first-time-right AM platform constitutes one of the most impactful directions of future research and one that is being pursued by several research labs worldwide.Two main limitations set the AM of metallic materials apart from that of polymeric materials. First, there are few methods currently available for multi-material AM of metals. Second, the minimum feature sizes that could be achieved with metallic material is usually much larger for metals as compared to polymers. Development of processes and machines that allow for multi-material AM of metals with resolutions <50 µm should be a priority for future research. In particular, support materials that could be easily removed would enable fabrication of parts with clearances  and small-angle geometrical features that are not possible to realise with the vast majority of currently available machines. Increasing the production rate of metal AM techniques through addition of more energy sources  or re-design of build platforms are important areas that could benefit from further research too.Finally, hybrid manufacturing equipment that combines AM techniques with subtractive manufacturing techniques in one single apparatus could increase the production speed, (surface) quality of the fabricated parts, and geometrical fidelity. The design of hybrid manufacturing systems including not only the equipment but also optimized control/planning algorithms are the other topics of current interest that could facilitate the adoption of AM and hybrid manufacturing techniques by various industrial sectors.AM techniques have been successful in fabrication of  parts from a wide range of metallic materials including (but not limited to) titanium ,7,8,9 anA number of high profile studies into development of materials with exceptionally favourable properties have taken the pure materials science approach suggested here. For example, nanoparticle nucleants have been used in one study  to contrEven if the desired microstructure and mechanical properties are not achieved by adjusting the AM process itself , there Another approach for improving the mechanical properties of AM metals particularly their fatigue performance is the application of surface treatment techniques. Unmolten powders and the irregularities caused by the AM process could lead to the formation of a large number of potential crack initiation sites. For parts with simple geometries, the manufacturing irregularities could be decreased using such techniques as machining and polishing. For more complex parts, it is either expensive or infeasible to perform machining or polishing in which case (electro) chemical surface treatments could be helpful in smoothening the surface of AM parts. It has been shown that a treatment improving the surface of AM metals may significantly improve their fatigue performance . HoweverIn addition to improving the microstructure and mechanical properties of AM metals, post-AM surface treatments could be used to induce new functionalities. Although such additional functionalities could potentially be applied in various industries, the biomedical applications have received the most attention to date. For example, post-AM surface treatments and coatings have been used to induce antibacterial properties ,53,54 anComputational models are primarily used for two purposes, as far as the AM of metallic materials is concerned. The first purpose is modelling the AM process itself  to simuThe utility of computational models originates from their predictive power. This predictive power allows for better understanding of the relationship between the parameters of the AM process and the properties of the resulting material. In addition to helping us establish this generic understanding of process-property relationships, computational models could be used for on the fly optimization of the process parameters as well as for geometry- and process-specific optimization of the post-AM treatments.The main challenges in building computational models of the AM process are caused by the multi-physics, multi-scale nature of the involved problem. Modelling the interactions between the energy source  and the powder, capturing the physics and mechanics of the flow of the powder as a granular matter, simulating the heat transfer between the various types of involved materials, determining the shape and temperature profiles of the melt pool, relating the local thermal history of the melt pool to the solidification process and thermophysical changes in the microstructure of the powder material, predicting the spatial distribution of the residual stresses, describing the stochastic processes driving the formation of manufacturing irregularities, and modelling the relationship between the microstructure and the mechanical properties are all aspects that need to be taken into account when developing computational models of the AM process. Most currently available computational models capture only some of the above-mentioned effects. It is therefore essential to develop integrated models that take all of the physical phenomena determining the quality of the final AM metallic parts into account at different scales. This line of research is very challenging and has so far made only limited progress. More contributions from the groups possessing experience in computational material science, computational mechanics, physics of granular matter, and multi-scale computational modelling are needed to develop the type of computational models that could adequately address the complexity of the problem at hand.The batch-size-indifference and complexity-for-free features of AM materials respectively enable realisation of custom-made parts and designer materials . Custom-AM could also be used for development of the so-called designer materials with novel properties and, thus, functionalities that originate from their complex topological design at the nano-/micro-scale ,64. ThisSome of the frontiers of research in AM metallic materials were briefly reviewed in this editorial to identify the most important research questions that are worthy of further investigation by researchers. The relevant literature was also cited to enable the readers to more easily find the required background information and more extensive explanation of the research questions. I hope this editorial encourages further research in this exciting area of scientific inquiry and that the time and energy of the researchers are spent answering the questions that require the most attention.


The network real-time kinematic (RTK) technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI), and countless smart devices , robotic equipment, etc.) require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC) approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC) according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS) data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side. The network real-time kinematic (RTK) technique is one of the most popular global navigation satellite system (GNSS)-based precise positioning techniques, and provides instantaneous centimeter-level accuracy positioning solutions within its service area. The network RTK has a larger coverage area than the single reference station RTK system, and its coverage area can be further extended by installing more reference stations . TherefoIn the past, the network RTK technique was mostly oriented towards professional applications such as surveying, mapping, and deformation monitoring, etc., since the mass market had little high-precision positioning requirement. Most current provincial network RTK systems only support up to a few hundred concurrent online users. In recent years, a need for precise positioning has arisen in the massive market, driven by developments in artificial intelligence, unmanned aerial vehicles (UAVs), and robotics . Many auThe conventional RTK system is designed for radio transmission. Therefore, corresponding data transmission protocols, such as the Radio Technical Commission for Maritime services (RTCM), compact measurement record (CMR)  etc., arThe generation of VRSs is the key to the network RTK systems. The simplest network RTK system includes three reference stations and one VRS, as demonstrated in V with real observations from points A, B, and C. The VRS observations can be generated by adding corrections to the real measurements. The virtual observations at station V can be expressed by:The basic idea of the VRS is the generation of a set of observations virtually observed at point A and station V only contain the geometry difference and the propagation-specified error sources, which can be expressed as [Then, the inter-station difference observations between the station essed as ,21:(3)ΔPThe equation indicates the remaining terms in the inter-station differenced observations are the geometrical distance, propagation path specified errors and the ambiguity parameters.A, B, and C can be precisely predetermined, since they are considered still during the network RTK system operation period. Then the geometrical distance displacement term AB and AC. The baseline AB and AC are estimated in double-difference mode, which provides the double-differenced atmospheric delay p and q, while the subscript indicates the inter-station difference between stations A, B or A, C.The remaining problem is how to compute the single-difference measurements p is the reference satellite, the required single-difference atmospheric delay for satellite q can be expressed as:It is noted that the network processing can only provide a double-differenced atmospheric delay, while VRS requires a single-difference atmospheric delay. Assuming the satellite The double-differenced terms ) method , or the ) method . AlthougPPP-RTK) , networkPPP-RTK) . As the As discussed in previous section, the network RTK systems perform atmospheric delay interpolation and geometrical correction, and generate VRS observations for every online user in real-time mode. The computational burden will dramatically increase as the concurrent online user number increases. Moreover, the corrections of the network RTK systems have to be generated in real time, since the latency may degrade positioning performance on the user side. If the corrections from VRS are not synchronized with the observations at the user end, the propagation path-dependent error may increase since both ionospheric delay and tropospheric delay change over time. The remaining error will be assimilated by the coordinate parameters and thus degrade the positioning precision on the user side. The current network scheme is only suitable for supporting professional applications with limited concurrent users. When the network RTK technique is extended into the mass market, the smart vehicles, the robotics and the UAVs all become network RTK users. As a spatial infrastructure, the network RTK is responsible for providing a quality-ensuring differential correction service to support all clients. However, currently a user-oriented VRS service is not possible for supporting large numbers of concurrent users.One possible solution is employing more power computing resources such as grid computing or cloud computing, although this increases the system complexity and cost. A more cost-effective solution is the reduction of the computational burden by improving the algorithms.As stated, the major challenge of VRS generation is handling the propagation path-dependent biases. These biases are strongly correlated when two stations are close, and the spatial correlation weakens as the distance increases. Therefore, it is reasonable to share the same VRS information if two clients sit close to each other. In this way, we propose a spatial clustering algorithm, which clusters the clients into groups first and each group shares the same VRS information. After clustering, the unlimited client number can be mapped into limited service area. Hence, the maximum VRS number can be guaranteed with a limited network RTK service area. The concept of the spatial clustering approach can be illustrated by (1)The radius of cluster constraint: Since the spatial correlation decreases as the baseline length increases, the radius of the cluster has to be controlled to ensure the positioning performance.(2)The real-time constraint: the network RTK system is a real-time system, so the clustering algorithm has to be computationally efficient.(3)All users should be properly clustered and no negligible user is allowed.The clustering methods are highly non-unique, and can be generally classified as distance- or similarity-based clustering, hierarchical clustering, density-based clustering, graph theory-based theory, neural network-based clustering, kernel-based clustering, and the mixed clustering method ,27. WithThe famous DBSCAN algorithm and OPTICS algorithm may produce isolated points, so they cannot be directly used in this application. The DENCLUE algorithm is robust to noise , but it Algorithm 1. SOSC.(1)Create an empty cluster set (2)Get one arbitrary element (3)Check if If (4)Check if (5)Check if   Given alternative set 2). Thirdly, the algorithm can determine the center of clusters and the cluster number according to the spatial distribution of clients. The flowchart of the SOSC algorithm is depicted in The benefits of the SOSC algorithm are three-fold. Firstly, the SOSC algorithm is an unsupervised clustering algorithm. It only requires one parameter—the radius threshold. The remaining work can be automatically finished with the algorithm. Secondly, the algorithm is computationally efficient, since the complexity of SOSC algorithm is O, which is fairly efficient, but its time consumption still increases as the concurrent client number increases. This means that there is a breakdown client number, which leads to the SOSC algorithm becoming overdue with a given latency constraint. The network RTK system is a real-time system, so the latency is an important system measure. Therefore, the breakdown point defines the volume of the network RTK systems. In order to further extend the system volume, the advanced computing algorithm can be employed.The scheme of parallel computing can be depicted by According to Gustafson’s law, the potential speedup in latency of the whole task can be roughly estimated as in :(5)Sl(s)The proposed algorithm can be integrated into existing network RTK platform with the middleware technique. A demonstration of network RTK system with the middleware is presented in Although the SOSC algorithm is executed on the server side, it has potential impact on the positioning performance on the user side. Therefore, the performance of the spatial clustering approach should be evaluated from the server side and the user side, respectively. As for the server side, we focus on whether the clustering algorithm is efficient and whether the clustering results are reasonable, which can be examined with simulation data. At the user side, we determine a reasonable cluster radius for the SOSC algorithm with a real data test to avoid a potential negative impact on user positioning.On the server side, we focus on analyzing the benefit of the SOSC algorithm compared to existing algorithms. The benefit can be measured from two aspects: the computational efficiency and the reasonability of clustering. Since the network RTK system is a real-time system, it is unacceptable for incorporating algorithms with unpredictable latency. The most time-consuming task in the middleware is the SOSC algorithm, so computational efficiency affects bottlenecks in the real-time system. Another performance measure is the reasonability of clustering algorithm, which is measured by the cluster number and the mean distance to cluster center (MDTCC). The cluster number reflects whether the algorithm can efficiently group all clients within certain area into the same group. Improper cluster algorithm may split one cluster into many parts and thus produces a larger cluster number. The MDTCC is defined as:In order to evaluate the performance of the SOSC algorithm, a simulation is performed. In this study, the service area is simulated as a 200 km × 200 km square field. The cluster radius threshold for the SOSC algorithm is set as 10 km. In order to make the SOSC algorithm and the grid algorithm comparable, they should have the same cluster radius. Since no gap is allowed in the service area, the inter-station distance for the grid algorithm is 10For the clustering algorithm, we are only concerned with the location property of the clients so the simulation achieves exactly the same results as in practice, but the spatial distribution of client may affect the performance of the clustering algorithms. It is impossible to examine all possible distributions, so two typical distribution scenarios are considered: (1) uniform distribution; and (2) clustered distribution. The uniform distribution is the simplest case, where all clients are evenly distributed over the service area. This distribution is mathematically perfect, but this rarely happens in the real word. The clustered distribution means that the online users are gathered at several ‘hotspot’, these places may be construction site or fieldwork. This scenario is closer to the real world than the uniform distribution since people always clustered by their jobs or hobbies.At first, the same data set involving 1500 user positions is used to test the clustering performance. The users are uniformly distributed over the service region and the clustering results by the SOSC algorithm and the grid algorithms are presented in The two figures give a first impression on how do these clustering algorithms works. The following section will compare their performance more comprehensively. As stated, the computational efficiency is one of the most important performance indicators of the clustering algorithm to evaluate the computational efficiency. The time consumption depends on the client number, so we tested client numbers ranging from 10 to 10,000 to determine their computational efficiency. The time consumption of SOSC approach against different online user number is evaluated and the results are presented in The reasonability of clustering can be evaluated from two aspects: the cluster number and the mean distance to the cluster center (MDTCC).With the given client position set and radius threshold, the smaller cluster number means the clustering algorithm is more efficient and consequently less computation required at the server side. The mean distance to cluster center (MDTCC) is used to describe the optimality of the cluster center. With a given client position set, the clustering algorithm with minimum MDTCC should be the optimal clustering algorithm. However, due to the latency constraint, the SOSC is a sub-optimal clustering algorithm, which represents trade-off between the MDTCC indicator and the computational efficiency. Therefore, the SOSC algorithm cannot ensure production of the global smallest MDTCC. The MDTCCs of the SOSC approach and the grid approach are presented in The clustering-based approach can mitigate the computational load on the server side, but it also has a side effect on the user side. For the existing algorithms, the NTRIP server generates one VRS for each client, so the distance between the VRS and the clients can always be guaranteed. After clustering, the maximum distances between the VRS and the clients are constrained by the cluster radius threshold. A small cluster radius threshold means larger cluster number and heavier computational burden, while larger threshold means weaker spatial correlation between the client and VRS. Therefore, optimally determination of the cluster radius threshold is vital for the clustering-based approach. In this section, we attempt to address the side effects of longer distances between the VRS and the client on user side positioning, and find an optimal trade-off between the computational burden and positioning performance.Considering the complexity of the positioning scenario, the real GNSS data set is employed in user positioning assessment. We collected our own observation data and VRS corrections were obtained from the commercial continuous operating reference stations (CORS) network. The target of this experiment is to determine the optimal cluster radius threshold of the SOSC algorithm. The test includes the static test and kinematic test. Both of them involve VRSs from difference places.For the static test, we collect real data from a commercial CORS network to verify the impact of distance between VRS and users on the positioning performance. The CORS network is demonstrated in The true location of VRS can be extracted from type 1005 in the RTCM data stream and the true location of the monument is also precisely known. Then the positioning precision at the user side can be evaluated by comparing the positioning results and the truth. The cumulative distribution function (CDF) of the positioning error with different distances to the VRS is presented in Similar to the conventional RTK system, the prerequisite for fast precise positioning for the network RTK positioning is correctly resolving the integer ambiguity parameters. Due to the remaining modeling error and the interpolation error, the precision of the float solution in network RTK is not as good as in the conventional RTK techniques. The remaining residuals in the double-differenced observations will be assimilated by the coordinate parameter and ambiguity parameter. The long baseline length between the NTRIP clients and VRS introduces additional propagation path-specified error, which affects the positioning performance by reducing the reliability of ambiguity resolution. The reliability of ambiguity resolution is often tested by various ambiguity acceptance test and the most popular test is called ratio test, which is defined as in :(7)ratioIn practice, most NTRIP clients are moving rather than still, so testing the positioning performance in kinematic mode is necessary. In this experiment, the individual holds a RTK surveying device and walks around a small square for two laps. The observation is recorded by the receiver and the VRS correction is collected with different distance. The kinematic positioning is processed by post-processing with the same software and configuration for comparison purposes. For convenience, the test trajectory is aligned to a small square with an irregular shape. The positioning results are compared with the satellite images from Google Earth and the results are presented in The performance of SOSC algorithm is evaluated from both the server side and user side. At the server side, the SOSC algorithm is evaluated with respect to time consumption, cluster number, and MDTCC. Compared to the grid algorithm, the most obvious advantage of the SOSC algorithm is its elastic and automatic adaptation to the data set. Since the cluster center is predefined, its time consumption and MDTCC are independent of user distribution. Due to its insensitivity to the data set, it is almost impossible for the grid algorithm to obtain the best cluster center and cluster number for a particular data set. The advantage of the grid algorithm is its predictable time consumption and controllable cluster number. The SOSC algorithm inherits the benefits of the grid algorithm and becomes more elastic and adaptive to the dataset. The simulation results also indicate that the SOSC algorithm outperforms the grid algorithm under some particular distributions or under smaller data sets. This is because the SOSC algorithm makes changes with respect to different datasets. The results also reveal that the predefined grid approach cannot be the best choice under different scenarios.The second aspect of the evaluation is determining the optimal cluster radius with real GNSS data. All the cluster-based approaches have reduced adverse impacts on the user positioning performance, including the grid algorithm and the SOSC algorithm. However, determination of the impact and the best cluster radius threshold did not occur. The inter-station distance in the grid algorithm is empirically determined. In this study, we find that the baseline length between the cluster center and the client should be no larger than 10 km in terms of precision and reliability. The cluster radius threshold should be no larger than 3 km to pursue the highest positioning accuracy. This conclusion is also meaningful for the grid algorithm. A 10-km cluster radius threshold is equivalent to a 10This paper presents a self-organizing spatial clustering (SOSC) algorithm to support large-scale network RTK systems. The current network RTK system generates virtual reference station for every online client, which represents a time-consuming procedure. The computational burden will result in a bottleneck in the system for supporting huge numbers of concurrent online users in the future. The computational burden problem can be mitigated by clustering the online users to reduce the VRS generation burden. The SOSC algorithm can cluster all online users into groups by their proximity without supervision. The test results indicate that the SOSC algorithm is comparable to the grid algorithm, and outperforms it in many cases. The grid approach guarantees the cluster number and time consumption, but it cannot ensure reasonable cluster centers since they are predefined. In contrast, the SOSC algorithm determines the cluster number and cluster center according to the data set, so it gives more reasonable clustering results. The computational efficiency, cluster number, and mean distance to cluster center (MDTCC) of the SOSC algorithm are all changed under different data distributions and data size, while the grid approach is less variable. The paper also examined the impact of clustering on the positioning performance on the user side. After clustering, the MDTCC is much larger than the single VRS for each client mode. The increased baseline has negative impact on the positioning precision and reliability. According to static and kinematic data processing, it is noted that the cluster radius can be safely extended to 10 km without significantly reducing the precision and reliability. The cluster radius threshold should no larger than 3 km for use in the highest-precision applications. From the perspective of implementation, the proposed SOSC algorithm can be implemented as middleware and seamlessly integrated into current network RTK systems without modifying existing modules. For these very large network RTK systems, the SOSC algorithm can also be deployed on a parallel computation platform. Overall, we hope the SOSC algorithm can support the large-scale network RTK system better and help to popularize the network RTK technique in future mass-market applications.


Localization is one of the critical services in Underwater Acoustic Sensor Networks (UASNs). Due to harsh underwater environments, the nodes often move with currents continuously. Consequently, the acoustic signals usually propagate with varying speeds in non-straight lines and the noise levels change frequently with the motion of the nodes. These limitations pose huge challenges for localization in UASNs. In this paper, we propose a novel localization method based on a variational filtering technique, in which the spatial correlation and temporal dependency information are utilized to improve localization performance. In the method, a state evolution model is employed to characterize the mobility pattern of the nodes and capture the uncertainty of the location transition. Then, a measurement model is used to reflect the relation between the measurements and the locations considering the dynamics of the acoustic speed and range noise. After that, a variational filtering scheme is adopted to determine the nodes’ locations, which consists of two phases: variational prediction and update. In the former phase, the coarse estimation of each node’ location is computed based on its previous location; in the latter phase, the coarse location is optimized by incorporating the measurements from the reference nodes as precisely as possible. At last, an iterative localization scheme is applied, in which a node labels itself as a reference node if the confidence of its location estimation is higher than the predefined threshold. We conducted extensive simulations under different parameter settings, and the results indicate that the proposed method has better localization accuracy compared to a typical SLMP algorithm while maintaining relatively high localization coverage. Moreover, spatial–temporal variational filtering (STVF) is more robust to the change of the parameter settings compared to SLMP. Underwater Acoustic Sensor Networks (UASNs) have attracted much attention due to its application in many fields such as environmental monitoring, military defense, disaster prevention and ocean resource exploration ,2,3. In Although there has been a lot of research for localization in terrestrial wireless sensor networks, they can’t be directly applied to UASNs due to the following reasons: (1) the acoustic communication has long propagation delay, and the acoustic signals often propagate with varying speeds in non-straight lines due to the inhomogeneity in terms of temperature, salinity and pressure; and (2) underwater nodes move with tides and currents continuously, resulting in frequent changes of network topology. These unique characteristics of underwater environment pose huge challenges for localization in UASNs.Recently, many localization methods for UASNs have been widely explored ,6. Most In addition, ambient noise is an important factor that impacts localization performance, especially for range-based localization methods. Most existing works model the range noise as a Gaussian variable with fixed mean and variance, which can’t reflect the actual situations of harsh underwater surroundings. Generally, the range noises have two features: (1) the noises depend on the propagation path of the acoustic signal and change with the motion of underwater nodes ; (2) theThe characteristic of continuous mobility of underwater nodes has its own disadvantage and advantage. On one hand, a localized node will become unlocalized when beacon nodes move out of its communication range; on the other hand, the continuous movement of each node implies that the locations in its trajectory are dependent temporally. For example, the current location of one node is closely related to its locations at previous and next time instants. However, this temporal dependency among locations is neglected in most existing works.t. Localization with two beacons will calculate two candidate locations In fact, localization based on spatial correlation in UASNs, especially with insufficient beacon nodes in sparse deployments, can be improved significantly using temporal dependency information. As illustrated in To fuse spatial correlation and temporal dependency effectively, the approach commonly adopted is Bayesian filtering, which has been widely used in target tracking ,9,10,11.The rest of this paper is organized as follows: in Existing localization schemes for UASN can be divided into two categories: range-free and range-based. While the range-free scheme is rarely adopted in realistic environments due to its poor localization accuracy, the range-based scheme has been paid more attention in recent literature. Ranging methods include Time of Arrival (TOA), Time Difference of Arrival (TDOA) , Angle of Arrival (AOA) and Received Signal Strength Indicator (RSSI) . RSSI reThe TOA method gets the difference of message sending and receiving time, and then calculates distance by multiplying the time difference and acoustic speed. Its accuracy is vulnerable to multiple factors such as time synchronization, multipath effect and stratification effect. In SLSMP  and STSLOnce the distances to reference nodes are determined, a localization algorithm is executed to convert the distance estimations into the node location. In RLS , a new mMany localization algorithms reveal the weakness when they are applied in localization for large-scale UASNs. The situation of sparse beacon nodes brings new challenges. The unknown nodes can’t be localized due to a lack of sufficient beacon nodes. SLMP  providesThe algorithms mentioned above make the best of spatial correlation between nodes; however, none of them exploits temporal dependency. The characteristic of continuous movement with time can help to improve localization performance. In , an offlIn this section, we first describe the network model and then give the definition of the localization problem in the model.Traditional 3D localization algorithms for UASNs require at least four non-coplanar reference nodes within the communication range of one unknown node. This requires a high node density in the deployment area and thus results in an increase of the overall cost. To solve this problem, in this paper, the 3D localization problem is simplified to a 2D counterpart using a projection technique ,34. WithU unknown nodes with the identifiers i with the location of t, in which the superscript i is suppressed, as well as in what follows. The transition of its location is characterized by first-order Markov model j, j and k can be obtained. Considering that the redundant measurements may incur additional errors, we sort the measurements according to the updated confidence value  :(7)L(q)=We then substitute the factorized form of Equation  and condThis means the optimal solution is obtained by considering the log of the joint distribution over both the state and observation variables and then taking the expectation with respect to In the prediction phase, we seek to find a coarse estimation of the current state Equation . Assume Considering the constraint Equation , the disIt is observed that the approximate distribution quations  and 11)qt|t−1, we, we16), In the measurement model, the measurement The involved expectations have the expressions:The remaining expectations involve the joint optimization of the component distributions Equation :qt|t(st)We can see that the logarithm likelihood function is nonlinear over the variables For Finally, the component distribution Hence, we have:Equations –23) sug sug23) sAlgorithm 1: STVF localization algorithm.D represents the sum of the estimated distances to all the reference nodes:E represents the sum of errors between the estimated and measured distances for all reference nodes:Considering the hardware costs and the deployment difficulties, the beacon nodes usually account for a small proportion of all the nodes in most UASNs. The network has low localization coverage, i.e., many unknown nodes can’t be localized due to lack of sufficient beacon nodes, especially for large-scale UASNs. To solve this problem, an iterative localization scheme is adopted, in which an unknown node that has been localized with high confidence can serve as a reference node and broadcast beacon signals to localize other unknown nodes. The confidence value of an unknown node is not only related to its localization error, but also related to the confidence values of its reference nodes. In STVF, the confidence value is calculated as:If the confidence value of the localized node is higher than the confidence threshold The variational update procedure reveals that the measurements from the reference nodes are vital for improving localization accuracy. The confidence value of a reference node reflects its own localization accuracy, whereas the quality of its measurement to the unknown node is also subject to other factors such as distance and velocity. Herein, we consider the confidence value of each measurement based on that of a reference node. Next, we present the method for updating the confidence value of the measurements.i-th reference node. The function Intuitively, the long distance between the unknown node and the reference node will incur large errors, i.e., the measurement error increases with the distance. Similarly, the reference nodes with high velocities usually move a long distance between two consecutive time instants, which will increase the localization error as well. Thus, the confidence value of a measurement from the reference node Based on Equations –29), th, th29), In this section, we present the simulations and evaluate the performance of the proposed STVF algorithm.In our simulations, 200 sensor nodes are randomly deployed in the monitoring area with the range 500 m × 500 m. The initial proportion of the beacon nodes is set as 20%. The node density is defined as the expected number of the nodes within the communication range. All the nodes have the communication range of 80 m and the corresponding node density is 16. The acoustic signals propagate with the average speed 1500 m/s and the standard deviation 10 m/s. We assume the measurement errors increase with the distances, which follows the Gaussian distribution with the real propagation time as the expectation and the standard deviation to be two percent of the real propagation time. Each simulation lasts for 500 s. The localization period is set to 1 s. In a localization period, all the unknown nodes need to get their location estimations. Moreover, each simulation is run 50 times to relieve the impact of the deployment randomness. The simulation settings and the parameters involved in variational filtering are listed in In the simulations, we focus on two performance metrics: localization accuracy and localization coverage. Localization coverage is defined as the proportion of the unknown nodes that have been localized. Localization accuracy is defined as the average distance between the estimated locations and the real locations of all the localized nodes. We compare the performance of STVF with that of SLMP  because We evaluate the performance of STVF under different parameters, which include the threshold of the measurements’ number and the number of the samples.As described in the problem statement, the number of the measurements has an important impact on the localization accuracy and coverage. It is necessary to determine the threshold of the measurements’ number through simulations. By keeping the parameter settings in The number of the samples is another factor that can impact the localization results. Here, we simply select the same value for the samples’ number of calculating the expectations From t denotes time instants when the unknown node is localized, m is the number of all the time instants, t and t.Keeping the parameter settings in To illustrate the localization performance in the continuous movements with water currents, we select an unknown node with the RMSE value 5.03 and plot its real trajectory and estimated trajectory within 100 s. The unknown node starts from the coordinates  and stops at the coordinates . As shown in In addition, we evaluate the proposed method on computational complexity. Although the parameters of the posterior distribution need to be optimized by turns, the optimizations mostly converge in several iteration steps. As illustrated in We compare the performance of STVF with that of SLMP under different parameters, including the node density, the confidence threshold and the standard deviation of the measurements. The unique parameters of SLMP are set according to . The firKeeping the parameter settings in In this set of simulations, we keep the parameter settings in As shown in In this set of simulations, we evaluate the impacts of the standard deviation of the measurements on localization performance. The values of the standard deviations are normalized to the real propagation delays. By keeping the parameter settings in From Focusing on the problems in UASN localization, including continuous movements of nodes, varying acoustic speed and dynamic noises, we have presented STVF, a novel localization method that fuses spatial correlation and temporal dependency information. In STVF, the mobility patterns of the nodes are characterized by the general state evolution model, and the variation of the acoustic speed and the dynamics of the measurement noises are captured by the measurement model. The states of the unknown nodes are augmented by regarding the expectation, the precision, the acoustic speed and the measurement noise as variables. The posterior probability distribution of the state variables of each unknown node can be optimized jointly using a variational filtering technique. The localized nodes with high confidence are then labeled as new reference nodes. We evaluate the impacts of the threshold of the measurements’ number and the number of the samples. The simulation results give the suggestions of selecting proper values for the two parameters. We also evaluate the localization coverage and accuracy of STVF and SLMP under different parameters, including the node density, the confidence threshold and the standard deviation of the measurements. The simulation results show that STVF reduces the localization error dramatically while maintaining approximate localization coverage with SLMP. Moreover, STVF is robust to the change of the parameter settings and works well even in sparse UASNs.


High-strength carbon fiber reinforced polymer (CFRP) composites have become popular materials to be utilized in the aerospace and automotive industries, due to their unique and superior mechanical properties. An understanding of cutting temperatures is rather important when dealing with high-strength CFRPs, since machining defects are likely to occur because of high temperatures . The friction behavior at the flank tool-workpiece interface when drilling CFRPs plays a vital role in the heat generation, which still remains poorly understood. The aim of this paper is to address the friction-induced heat based on two specially-designed tribometers to simulate different sliding velocities, similar to those occurring along the flank tool-work interface in drilling. The elastic recovery effect during the drilling process was considered during the tribo-drilling experiments. The drilling temperatures were calculated by the analytical model and verified by the in-situ experimental results gained using the embedded thermocouples into the drills. The results indicate that the magnitudes of the interfacial friction coefficients between the cemented carbide tool and the CFRP specimen are within the range between 0.135–0.168 under the examined conditions. Additionally, the friction caused by the plastic deformation and elastic recovery effects plays a dominant role when the sliding velocity increases. The findings in this paper point out the impact of the friction-induced heat and cutting parameters on the overall drilling temperature. High-strength carbon fiber reinforced polymer (CFRP) composites have become one of the most popular materials in many industries, including aerospace, automobile, etc., due to their excellent mechanical properties of high specific tensile strength, fracture toughness, modulus and fatigue strength. Meanwhile, the CFRP materials meet the lightweight requirements for aircraft structures in order to reduce fuel consumption in regard to economic and environmental reasons while maintaining durability and safety standards ,2. GenerRegarding the development of materials, as well as the requirement of high efficiency/quality in drilling process, a large number of novel methods are being employed for the drilling of high-strength CFRP, such as laser beam drilling ,5, abrasIt is more challenging to drill high-strength CFRP composites reinforced with long fibers compared with some metal materials. For instance, the presence of uncut polyester fibers often leads to an increase of machined surface roughness . The chiA variety of temperature measuring methods utilized to monitor different machining processes, combining analytical, numerical and experimental techniques, were performed in the open literature. The method of embedding thermocouples into the drill bit remains the most-used and the earliest technique to capture the cutting temperatures and thermal gradients of the cutting edges during the drilling process. Samy et al.  analyzedHowever, issues relating to the heat generation in the material removal when drilling the fibrous composites are still not widely addressed in the literature. Regarding the conventional drilling process, the input energy is generally dissipated by two parts, namely the chip separation and the friction at the tool-chip and tool-work interfaces. Since the CFRP chips are separated by brittle fracture without undergoing elastoplastic deformations, the heat produced by the chip breakage is relatively low, which can be reasonably neglected. In contrast, a large amount of the cutting heat is dissipated by the chips. The powder-like composite chips would reduce the contact length of the tool-chip interaction zone and further suppress the frictional heat generation at the tool-chip interface. However, the interaction at the tool-work interface plays a vital role in the frictional heat generation because of the elastic recovery effects. Additionally, the friction-induced heat can easily accumulate at the tool flank face, which may dissipate into the machined CFRP surface particularly in the semi-closed drilling operation. The present study is an extension of our previous work  aiming tThe composite plates investigated in this work were multilayer high-strength CFRP laminates fabricated by carbon fibers and epoxy prepregs containing a 65% fiber volume fraction. The matrix is an X850 epoxy material and the reinforcement is T800 carbon fibers. The physical properties of the composite specimens are given in The HURCO VMX42 vertical machining center  as used for the drilling and pin-on-disc experiments, as shown in The thermocouples were embedded into the drill bits and the pins as illustrated in f = 0.01, 0.02, 0.03, 0.04 mm/rev) were adopted in the present work. Meanwhile, the cutting speeds were carefully selected to meet the conditions of the pin-on-disc tests. A number of drilling tests were arranged to figure out the correlations between the drilling temperatures and the cutting parameters. Through the literature survey, it is believed that low feed rates (0.01–0.05 mm/rev) are favorable for the drilling of fibrous composites . Hence, aR < 0.30 μm). Then, the surface condition of the pins is similar to that of a finely carbide cutting tool [To quantitatively evaluate the friction-induced heat, two types of tribometers were designed which were designated as pin 1 and pin 2. Being as a standard cemented carbide tool, pin 1 was utilized to replicate the frictional heat generated in the tool-work interaction zone during the drilling of CFRP composites. Additionally, pin 2, which was removed one half of its head, was adopted to avoid the elastic recovery effects during the drilling of CFRP composites. It is worth noting that the contact surfaces of the two pins were polished to guarantee a low surface roughness (ing tool . The maiing tool  is the rnF) and tangential load (tF). The thermocouples were embedded into the pins to record the temperature during a test. A schematic diagram showing the working conditions of the tribometer is given in In general, the pin-on-disc experiments were carried out following several steps to determine the interfacial friction coefficients while drilling the CFRP composites by referring to the work done by Mondelin et al. . FirstlytF) to normal force (nF) was defined as the interfacial friction coefficient (appμ), which signifies the macroscopic friction coefficient of the flank tool-work interface as expressed by the following equation.nF denotes the thrust force in the Z-axis direction and tF represents the force in the X-axis direction obtained by the dynamometer.The ratio of tangential force (appμ consists of two parts: One is the adhesion part (adhμ) and another is the plastic deformation part (defμ). The elastic friction coefficient (elaμ) corresponding to the elastic recovery part was considered in this paper, due to the occurrence of the elastic recovery effects on the machined surfaces (Equation (2)).The apparent friction coefficient  was calculated based on the microscopic forces measured by the dynamometer. These forces include on the one hand the adhesive part influenced by material properties, such as hardness and chemical reactivity, and on the other hand the plastic deformation of the specimen, which cannot be ignored under the severe contact conditions ,29. The μ) between the tool flank surface and the workpiece can be calculated byTherefore, according to our previous work , the intq is the heat flux density, λ is the thermal conductivity coefficient, and The prediction model of the frictional heat was established based on the Fourier’s law. The amount of heat transferred through a unit area per unit time is proportional to the temperature gradient, which can be expressed as follows:The frictional heat model was considered as a plane thermal source case.The average velocity of the main cutting edges was used in the model and the chisel edge length was neglected.The fracture-induced heat arising from two main fracture zones and the heat induced by the friction at the tool-chip contact surface were ignored.During the machining of CFRPs, the tribological interaction between the tool flank surface and the machined composite surface plays a vital role in heat generation, due to the elastic recovery effects arising from the highly abrasive carbon fibers. To evaluate the temperature rise induced from the drilling operation, a prediction model was established based on the following assumptions with a reference to formulas describing the average temperature increment of the tool-chip interface:hR is the percentage of heat transferred to workpiece in this case, hq is the heat flux density of the flank-workpiece interaction zone, wk is the heat conductivity of work materials, wa is the thermal diffusivity of work materials, wL is the length of flank-workpiece interaction zone, and cV is the average cutting velocity of the drill lips.ρ and wc are the density and specific heat capacity of the composite, respectively. Assuming that all the work done by friction is transformed into heat, thent = D/(2sin(φ/2)) is the length of the drill cutting edge; D is the drill diameter; φ is the drill point angle; nF is the component of the thrust force that is perpendicular to the cutting plane. Taking the area that is cut across by the drill edges per revolution as the heat source plane, the following equation can be obtained:A prediction model of the temperature rise for the composites drilling was derived from  as shownrT denotes the room temperature.Finally, the temperature localized at the CFRP drilling surface can be estimated byappμ) of two designed pins and the sliding velocity in the pin-on-disc tests, as well as the elastic friction coefficient (elaμ) calculated are given in appμ values obtained by Klinkova et al. [elaμ) were calculated around 0.04 with a very slight variation as the sliding velocity increases. Due to the elastic recovery effects between the machined surface and the tool flank surface, the elastic friction coefficient contributes to the increase of the frictional heat.The correlations between the apparent friction coefficients  and adhesive friction coefficient (adhμ) are found to show a decreasing trend with the elevated sliding velocity. This is attributed to the thermal softening effects as the sliding velocity increases during the drilling of CFRPs. Moreover, the magnitudes of the frictional temperatures can be calculated according to Equation (9), which are discussed in the following subsection.Evolutions of the adhesive friction coefficient, the interfacial friction coefficient of two pins and the appμ and adhμ, as discussed earlier in V = 30 m/min and f = 0.03 mm/rev. This can be explained by the fact that the tool-work interface, which features the tribological interaction between the tool flank face and the machined composite surface, plays a vital role in the frictional heat generation, due to the elastic recovery effects resulting from the abrasive machined carbon fibers.appμ and adhμ due to the effects of thermal softening.The interfacial friction coefficient shows a high sensitivity to the input drilling variables. Increasing the sliding velocity tends to reduce the magnitudes of both the cV = 30 m/min and f = 0.03 mm/rev. To reduce the temperature of the flank tool-work surface, low cutting speeds and high feed rates, as well as the efficient geometry design of tools should be adopted.The established analytical model can be used to predict the drilling temperatures of the tool-work interface under the condition of low feed rates. The experimental investigation confirms the presence of the contribution of the elastic recovery phenomenon to the interfacial friction coefficient, and the dominant source of heat generation when drilling CFRP composites is the friction taking place at the tool-work interaction zone, which contributes a percentage of around 71.6% to the overall drilling temperatures particularly under the cutting conditions of The present paper made an investigation on the tribo-drilling of high-strength CFRP composites by addressing the aspects of frictional heat and drilling temperatures. A particular focus was placed on the predictions of the frictional heat occurring at the flank tool-work interface and on the quantifying the contribution of the frictional temperature to the overall drilling temperature subjected to varying cutting conditions. According to the results obtained, the following key conclusions can be drawn.


In recent years, wearable devices have been popularly applied in the health care field. The electrocardiogram (ECG) is the most used signal. However, the ECG is measured under a body-motion condition, which is easily coupled with some noise, like as power line noise (PLn) and electromyogram (EMG). This paper presents a grey spectral noise cancellation (GSNC) scheme for electrocardiogram (ECG) signals where two-stage discrimination is employed with the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD) and the grey spectral noise estimation (GSNE). In the first stage of the proposed GSNC scheme, the input ECG signal is decomposed by the EMD to obtain a set of intrinsic mode functions (IMFs). Then, the noise energies of IMFs are estimated by the GSNE. When an IMF is considered as noisy one, it is forwarded to the second stage for further check. In the second stage, the suspicious IMFs are reconstructed and decomposed by the EEMD. Then the IMFs are discriminated with a threshold. If the IMF is considered as noisy, it is discarded in the reconstruction process of the ECG signal. The proposed GSNC scheme is justified by forty-three ECG signal datasets from the MIT-BIH cardiac arrhythmia database where the PLn and EMG noise are under consideration. The results indicate that the proposed GSNC scheme outperforms the traditional EMD and EEMD based noise cancellation schemes in the given datasets. The wearable device has been widely studied in recent years. In the related researches, it is essential to monitor the cardiac and physical activities in users with congestive heart issues at home. The electrocardiogram (ECG) is an important signal in monitoring the cardiac activity. A Holter ECG device is frequently applied to record long-term ECG signals and helps to find arrhythmic heartbeats within twenty-four hours . AnotherAn ECG patch is a wearable device, which not only records ECG signal, but also shows some cardiac information on the smart phone in real time . HoweverTraditionally, ECG noise cancellation methods applied a low-pass filter  to removThe basic idea for ECG noise cancellation consists of two main stages. First, an ECG signal is decomposed by the EMD into a set of IMFs through which the noisy IMFs are discriminated and discarded. Second, the ECG is reconstructed by the retained IMFs. By this doing, the noise in the ECG signal is reduced or cancelled. To be successful in the EMD-based noise cancellation, a fundamental problem is to estimate the noise energy appropriately in each IMF. In addition, the problem of mode mixing occurs during the EMD decomposition. The problem of mode mixing comes from either an IMF mingled with signals of different scales or other IMF appeared in the combination of other components. To overcome the mode mixing problem in the EMD, the EEMD  was propThe methods described above generally deal with white noise and the PLn only. Besides, All of them estimates the noise energy in the first-order IMF. However, only considering the noise energy in the first-order IMF might not be appropriate especially when the signal energy of the related IMF is high. This will lead to over-cancellation and result in distortion in the reconstructed ECG signal. Consequently, this paper considers noise energies in every IMFs through the noise magnitude spectrum.As we know, very few approaches to ECG noise cancellation are based on noise magnitude spectrum. In this paper, two-stage discrimination scheme is proposed to estimate and cancel the PLn and EMG noise in ECG signals according to IMFs’ noise magnitude spectrum where the EMD, the EEMD and the grey spectral noise estimation (GSNE) are employed. In the first stage, the EMD decomposes the input ECG signal into IMFs. Then the GSNE is used to estimate noise in IMFs and calculate the related noise energies through its noise magnitude spectrum. By a user-defined threshold, noisy IMFs are identified and put into the second stage. In the second stage, the noisy IMFs are reconstructed and decomposed by the EEMD. Then the IMFs are rechecked in a similar manner to the first stage. If an IMF is considered as noisy, it is discarded. The procedure is repeated for each IMF. At last, the ECG signal is reconstructed with all retained IMF components. To evaluate the performance, a noise energy ratio in dB  model is briefly reviewed. For details, one may refer ,23. The By LetK = 4.It can be shown that the solution of In this subsection, the grey spectral noise estimation (GSNE) based on GM is introduced where an additive signal model is assumed, that is, Step 1.Level up Step 2.K is the number of data used in GM model. K = 4 where the square refers to the overlapped sample.Divide Step 3.For each subset Step 4.Since the additive noise Step 5.Apply Fast Fourier Transform (FFT) on Step 6.Calculate the standard deviation of Note that the energy preserving property in Parseval’s theorem  which prSince the standard deviation In this section, the determination of noisy IMFs and the ECG noise cancellation based on the GSNE are described. In the experiment, we randomly select 100 ECG signal segments with the continuous duration of 10 seconds from the mitdb/100 dataset. Then By In order to justify the fixed threshold As for the EMG noise, To evaluate the performance of the proposed GSNC scheme, a performance index to evaluate the improvement based on the noise energy ratio in dB  beats are within the duration. The denoised results for the PLn and EMG noise  scheme for ECG signals. In the proposed GSNC scheme, two-stage discrimination for noisy IMFs was employed which included the EMD, the EEMD and grey spectral noise estimation (GSNE). In general condition, the lower order IMFs would be easily coupled with the noise. The GSNE was applied to estimate the noise energy through the standard deviation of noise magnitude spectrum


Spatial autocorrelation in the residuals of spatial environmental models can be due to missing covariate information. In many cases, this spatial autocorrelation can be accounted for by using covariates from multiple scales. Here, we propose a data-driven, objective and systematic method for deriving the relevant range of scales, with distinct upper and lower scale limits, for spatial modelling with machine learning and evaluated its effect on modelling accuracy. We also tested an approach that uses the variogram to see whether such an effective scale space can be approximated a priori and at smaller computational cost. Results showed that modelling with an effective scale space can improve spatial modelling with machine learning and that there is a strong correlation between properties of the variogram and the relevant range of scales. Hence, the variogram of a soil property can be used for a priori approximations of the effective scale space for contextual spatial modelling and is therefore an important analytical tool not only in geostatistics, but also for analyzing structural dependencies in contextual spatial modelling. This is why associations between the spatial patterns of kriged predictions and the physical processes that influence and control these patterns often remain hidden3.Environmental properties are the result of complex and non-linear interactions of physical, chemical and biological processes across space and time. Often, these interactions are so complex that the spatial variation of the environmental property cannot be explained deterministically. Geostatistical methods treat variation as if it were random in terms of spatially autocorrelated random fields or random processes4 describes the variation of a soil property Z(s) in terms of a deterministic component Z*(s) consisting of structural variation, a stochastic component ε′(s) consisting of (apparently) random variation that may be spatially correlated and ε′ consisting of spatially uncorrelated random noise:The universal model of spatial variation introduced by MatheronZ*(s) can be modelled by correlation between the target variable and other environmental variables following the concept of Jenny5. With such models, for example, soil properties are expected to be predictable in terms of their correlation to environmental covariates that represent the factors of soil formation: climate, organisms, relief, and parent material, as they influence the dominant soil forming processes at any location. The stochastic component ε′(s) consisting of spatially correlated variation is often modelled with kriging6.The structural variation of the deterministic component 15. Some of these contextual modelling approaches also facilitate pedological interpretation16. The increase in prediction accuracy, compared to methods that are not multi-scale, is understood to be related to the fact that the spatial variation observed in soil properties occurs simultaneously at many scales17 in response to soil-forming processes that themselves vary across multiple scales and are captured and described at multiple spatial resolutions16. It has been shown that modelling can directly, as well as indirectly, account for relevant spatial contextual influences stemming from interacting, nested, hierarchical and scale dependent processes19. However, it remains challenging to efficiently unravel the effective scale space for modelling20. Moreover, being able to estimate the effective scale space in advance of the modelling would be advantageous to help determine if multi-scale modelling is necessary and to improve the parsimony, accuracy and interpretability of the modelling16.Recent studies have shown that the use of multiple scales of covariates in environmental correlation models can increase prediction accuracyThe aims of this paper are to (i) present a method that derives the effective scale space, with defined lower and upper scale space limits, for contextual spatial modelling with machine learning and (ii) derive an a priori approximation of the effective scale space, using the variogram. Our hypothesis is that using covariates that encompass only the effective scale space is economic in terms of computational cost and provides parsimonious and interpretable models with prediction accuracies that are at least as good as a model that uses all scales.12. The analysis is based on four different soil datasets.To analyze the influence of environmental covariates that operate over multiple scales, terrain derivatives computed from Gaussian pyramid octaves of a Digital Elevation Model (DEM) were used12 was used to prepare the multi-scale terrain derivatives used for modelling. The Gaussian pyramid is a multi-scale signal processing method based on Gaussian filtering and down-sampling21. It can be used to decompose the scales of environmental covariates11. Each down-sampling step reduces the cell size by half while the Gaussian filter helps to reduce associated artifacts. During each down-sampling step every second row and every second column is removed from the raster dataset. The output of a down-sampling step is called an octave. For environmental modelling these octaves are ultimately up-sampled, i.e. interpolated, back to the original resolution, to ensure that all covariates used possess the same cell size as the original covariate11. In mixed scaling, the DEM is down-sampled to all possible octaves. Then, the terrain attributes are calculated for each octave and finally the terrain derivatives are up-scaled back to the resolution of the original DEM. Compared to other scaling approaches, mixed scaling has been demonstrated to produce the best prediction accuracy while still being interpretative12.Gaussian mixed scaling22:elevationslopesine transformed aspectcosine transformed aspectaverage curvatureprofile curvatureplanform curvatureThe following terrain attributes were calculated at each scale of a Gaussian pyramid based on the equations presented by Zevenbergen and Thorne24, was used as the machine learning model. Random Forests have been used in various pedometric studies over the past decade29 as well as in many other environmental assessments31. The modelling accuracy was evaluated with 10 times, 10-fold cross-validation using the caret package for R32.The Random Forests approach, as implemented in R12. This approach is exhaustive in that it iterates successively with the covariates from individual scales. For the additive models, coarser scale covariates were successively included in models and the prediction accuracy of each model, described by the coefficient of determination (R2), was computed. For the subtractive models, finer scale covariates were removed in succession from the entire set of scales, starting from the finest, and the R2 was computed after each step. Previous studies, including multi-scale analyses, have shown that, with the additive approach, the maximum prediction accuracy is achieved only after multiple coarser scales have been added12. With the subtractive approach, prediction accuracy is expected to be good at the start, because, at first, all scales are included in the model. But accuracy can be seen to decrease only slightly if finer scale covariates, which can represent noise rather than signal, are removed. Consequently, the maximum effective scale required for modeling can be identified when the accuracy in the additive approach no longer improves. Similarly, the minimum scale can be identified when prediction accuracy in the subtractive approach first begins to show a significant decrease. For a given data set, a combination of both the additive and subtractive analyses should make it possible to identify and select the most relevant range of scales of the covariates.We developed additive and subtractive multi-scale models based on mixed scaling to analyze the influence of different scales on the spatial modeling34 that appear spatially random at the scale of investigation and is the y-intercept of the variogram. The maximum variability between point pairs is represented by the sill. The nugget:sill ratio therefore represents the degree of spatial dependency. Smaller nugget:sill ratios indicate greater proportions of spatially dependent variation. The range of the variogram is the maximum distance up to which a soil property is spatially autocorrelated. Hence, it may be considered an indicator for the maximum scale of the contextual environmental processes relevant to soil formation. Several studies have suggested investigating the relationship between the variogram and structural dependencies of environmental covariates36.In geostatistics, the variogram is used to develop a theoretical model from empirical data that describes the degree of spatial autocorrelation of an environmental property. The parameters of the variogram model are the nugget, sill and range. The nugget describes the small-scale variability of the data and measurement errors34, can be approximately converted into a spatial scale. i.e. if the nugget is relatively large, resulting in weak spatial dependence, then the influence of fine-scale covariates should also be small. Conversely, if the nugget is relatively small, i.e. there is strong spatial dependence, then the fine-scale covariates should improve the prediction accuracy of spatial models. If they were absent, the small-scale differences could not be accounted for in the modelling.We used the range of the variogram of a soil property of interest to define the upper limit of the relevant range of scales and the nugget:sill ratio multiplied by the range to approximate the lower limit of the relevant range of scales. This lower limit approximation is based on the assumption that the variability of the soil property at the short-scale, as represented by the nugget effect37), to 85% of the diagonal length. We did this because the scales analyzed with the machine learning approach are relatively large and partially exceed the extent of the area covered by samples, for the study sites.The separation distance up to which point pairs are included in semivariance estimates of the variogram (cutoff) was extended, from the length of the diagonal of the box spanning the data divided by three 38 to test the efficacy of contextual modelling for reducing or eliminating spatial auto-correlation in the residuals at all steps of the additive and subtractive modelling. The MI ranges from −1 to 1, where full dispersion is indicated by −1, randomness by 0, and clustering by 1, respectively.We used Moran’s I The Lachlan dataset comes from an agricultural production and grazing area on the Lachan River Catchment in central western New South Wales, Australia. The climate ranges from sub-alpine to semi-arid conditions with rainfall ranging from 280 mm to over 1000 mm, while the geology in the catchment area is complex and has a significant impact on the soil. Modelling was based on using 300 samples of bulk density and terrain covariates computed from an SRTM DEM with 90 m resolution.12. Samples (n = 342) of the top-soil silt content (0–10 cm) were used. The spatial distribution of silt content is influenced by wind erosion and loess translocation from the Rhine-Main lowlands to the surrounding heights of Rhine-Hesse. The silt content was transformed using sqrt(max(silt) - silt) and a 20 m DEM was used.The Rhine-Hesse (Germany) data set is an example of a dataset with a strongly autocorrelated distribution of soil properties12. Soil samples (n = 321) of topsoil clay content (0–10 cm) were used for modelling. Soil formation patterns strongly reflect those of the underlying rock formations, strike and dip and subsequent erosion due to a relatively high precipitation. The clay content was transformed using sqrt(clay). An SRTM DEM with a resolution of 90 m was used.The Piracicaba study area describes a sugarcane growing region in BrazilR2) when the predictors representing the second scale are added to the predictors of the original (finest) scale. For the Piracicaba data set, adding the largest scale covariates results in a decrease in prediction accuracy. With the subtractive approach, the R2 generally starts to decrease at a certain scale, when relevant information represented by fine to medium scale covariates is excluded from the model. In all examples, however, it was noted that the R2 increases when the finest scales, containing irrelevant and noisy information, were removed.For all four study sites, successive addition of coarser scales of DEM-based covariates (additive) to a random forest model generally increased prediction accuracy, while successive removal of finer scales (subtractive) from the model generally decreased prediction accuracy after a certain point  scale covariates, the average increase in the explained variance when all scales were included is 33% Figs  and 3. T.2% Fig. .Figure 3The diagrams of Moran’s I are based on the residuals of the additive and subtractive models. They show that the continuous addition of coarser scales ultimately leads to complete disappearance of autocorrelation in the residuals . The maximum anisotropic scale, estimated from the variograms, corresponds well with the point at which the curve of the additive approach begins to flatten out, which is close to the maximum scale derived from the additive approach. This can also be seen when comparing the experimental variogram to the additive and subtractive approach Fig. . AlthougIn all cases, the first two or three scales (octaves) of DEM derived covariates seem to contain noisy or irrelevant predictors. For the Piracicaba data set, the coarsest investigated scale is also irrelevant Fig. . Figure It is important to identify the appropriate scale space in spatial modelling to improve parsimony, computational efficiency, and to remove noise and augment interpretability. We presented two approaches by which the relevant range of scales for spatial modelling may be identified. One is exhaustive and derives the relevant range using data-driven machine learning with different sets of multi-scale DEM-based covariates added and removed incrementally. The second approach is based on an analysis of the properties of the variogram derived from point data of the soil property being modelled. The first is accurate but computationally demanding, while the latter allows for the relevant range of scales to be approximated a priori and is relatively rapid to derive. Using the variogram, one could determine if multi-scale modelling is necessary. Depending on the size of the dataset this can therefore reduce modelling time with machine learning by hours or even days.Although there is a good correspondence between the two approaches to identify the relevant range of scales, the range of scales as determined by the variogram does not lead to an increase in the explained variance in all cases. This effect can be traced back to the generally longer-range scales identified by the Gaussian pyramid compared to the lower cutoff value determined by variogram. Additionally, non-linear interactions of covariates at very coarse scales may lead to non-stationary effects that cannot be explained by variography. If, however, one considers the empirical variograms, i.e. the raw data from the sample set and not the fitted variogram function, the optimum range could well be greater, in many cases, compared to the automatically fitted values. The latter would lead to larger, and more appropriate, maximum ranges for some datasets. For this reason, we recommend that the empirical variogram should also be considered when trying to determine the relevant range from the variogram for the soil property.R2 generally drops, while the MI increases above the value of the entire data set. This shows that the fine scales contain noise, which is the reason why selecting a reduced range of scales can lead to higher prediction accuracies. Interestingly, when including the very coarsest scales, negative autocorrelation can occur. Generally, little is known about negative spatial autocorrelation and especially the consequences of negative spatial autocorrelation for regression-based inference41. What can be seen from the subtractive model, is that the negative spatial autocorrelation effect gets stronger when fine scales are removed. This could be an effect of missing fine to medium scale information, which, when approximated from non-linear combinations of the coarser scale covariates in the machine learning models, leads to the negative spatial autocorrelation.The non-linearities observed towards the endpoints of the additive and subtractive models Fig.  may be dR2 in the subtractive models, compared to larger increases in the additive models, confirms that coarser scales are often more informative than those of finer scale covariates. This effect has also been observed in previous studies11, including the multi scale analyses of soil spectra42. The minimum scales required to achieve the most accurate model results are often coarser than the original (finest) scale of the selected (DEM) environmental predictors. This also supports similar findings from previous studies42. This is related to the effective scales of the physical processes which influence the development of soil properties, but may also be related to sampling density and sampling error. Higher density samples, taken at shorter distances apart, and covariates with finer resolutions may extend the relevant range of scales and thus help to better estimate the pedologically relevant range of scales.The considerably smaller decrease of 20, or in ecology to, describe animal habitat relationships43.Finally, the significance of our work is that we have shown that there is a good relationship between spatial dependence, as described by the variogram of a soil property, and the relevant scale space (maximum and minimum effective resolutions) in contextual spatial modelling with machine learning. Thus, the variogram of a soil property can be used as an analytical tool to inform multiscale contextual spatial modelling using machine learning. This method should have a broader appeal as it can also be used to describe the appropriate scales for modelling other environmental phenomena, for example in land-management


Realistic tool-tissue interactive modeling has been recognized as an essential requirement in the training of virtual surgery. A virtual basic surgical training framework integrated with real-time force rendering has been recognized as one of the most immersive implementations in medical education. Yet, compared to the original intraoperative data, there has always been an argument that these data are represented by lower fidelity in virtual surgical training. In this paper, a dynamic biomechanics experimental framework is designed to achieve a highly immersive haptic sensation during the biopsy therapy with human respiratory motion; it is the first time to introduce the idea of periodic extension idea into the dynamic percutaneous force modeling. Clinical evaluation is conducted and performed in the Yunnan First People's Hospital, which not only demonstrated a higher fitting degree (AVG: 99.36%) with the intraoperation data than previous algorithms  but also shows a universal fitting range with multilayer tissue. 27 urologists comprising 18 novices and 9 professors were invited to the VR-based training evaluation based on the proposed haptic rendering solution. Subjective and objective results demonstrated higher performance than the existing benchmark training simulator. Combining these in a systematic approach, tuned with specific fidelity requirements, haptically enabled medical simulation systems would be able to provide a more immersive and effective training environment. Minimally invasive surgery (MIS) is prevalent in the medical field due to the characteristics of efficiency, less bleeding, and faster recovery . Biopsy The structure and content of this article are organized as follows: we briefly make an introduction on biopsy surgery and the challenges of VR-based medical training in the above-mentioned part; after that, we review the previous related work on needle insertion biomechanics modeling. Thirdly, the biomechanics data-based force model, with clinical and VR training evaluation experiments, is designed in Methodology. Fourthly, the experimental fitting and evaluation results were demonstrated and discussed. Finally, we summarize the results and the potential contributions this paper makes.Currently, lots of researchers have studied the needle insertion and proposed many mechanical models based on the experimental data. The related work can be classified into two species according to data acquisition: in vivo and in vitro. In the first species, Brouwer et al.  and BrowAccording to the reviewed literature above, it can be summarized that two major flaws will appear when integrating these models with the biopsy simulation: most of the experimental data recording was performed under the static condition, without the respiratory motion; however, the breath action drives the human viscera up and down throughout the biopsy procedures. Moreover, there is no doubt that experimental force data from in vivo is more reliable than in vitro, but are these animal experimental data-based force models suited for the haptic rendering in the VR-based training simulator of human biopsy surgery? With respect to these points, we conducted the solutions with a dynamic biomechanical experiment platform and evaluated with clinical data in the following parts.Respiratory motion is an unavoidable influencing factor of the needle insertion precision during the biopsy surgery; nevertheless, most of the researchers focus the study on the static puncture in vitro. Force is recorded in the ideal without-breath condition, regardless of the movement of the organ by the respiration during the operation. To resolve this issue, this study designed a novel biomechanical platform to record the dynamic biopsy force with respiratory motion. According to Weiss et al.'s research , the priμm, and the velocity can be programmed to for control, with the controllable range of 1 mm/s-30 mm/s. There are two stepper motor drive systems: one is used to control the vertical velocity of the biopsy needle insertion and the other one is used to simulate the human respiratory motion to achieve dynamic puncture. The literature suggested that the velocity of biopsy needle insertion is usually in the range of 0.1 mm/s~10 mm/s [The biomechanics test platform mainly consists of the surgical instrument, force sensor acquisition system, stepper motor drive system, container for soft tissue, and computer, which are detailed and demonstrated in ~10 mm/s ; therefoThe mechanical properties of soft tissues are complex and usually exhibit characteristics such as nonlinearity and anisotropy . The stuAs far as we know, the needle puncture procedures are continuity, uncertainty, and independence, which may be hard to describe with the existing model. Therefore, it is necessary to find a mechanical model that can be applied to both the puncture process analysis and the general application. As a powerful function, Fourier is usually used in various fields; consequently, Fourier is applied to insertion force modeling in this paper. In order to incorporate the Fourier model into the mechanical modeling of percutaneous biopsy, we introduced the periodic extension idea to convert the nonperiodic puncture force into a periodic function while extending the function definition domain to the entire real field. Because the real-time puncture force is centered on the low-frequency part, according to Fourier definition, each periodic function can be decomposed of numbers of trigonometric periodic functions, which is the Fourier series. The equation is as follows:eix = cosx + isinx, we transformed the trigonometric function into an exponential form:Because of the Euler formula: Based on the above formula, the superposition coefficient of a function expression with a known periodic function can be obtained by integration. In our simulation, since the force model is unclear, we employed the function approach method to fit the experimental data:y(x) to describe the final model asAccording to this, we introduced finite triangular periodic functions approaching the puncture force function αn and βn are the Fourier coefficients, and N means the term number of the Fourier series. In N, which is set from 1 to 8.http://civ.ynnu.edu.cn/ChineseShow.aspx?ID=1). We chose the renal biopsy surgery as the clinical data validation procedure which is an invasive procedure to obtain a small piece of tumor sample from the renal tissue for pathologic examination. Clinical data collection trials are set up in the Division of Urology in Yunnan First People's Hospital, Kunming, Yunnan, China. The needle insertion usually can be divided into prepuncture and postpuncture; before insertion, the patient lies in the supine position, then the operator injects local anesthesia into the skin, through the subcutaneous tissue and down to around the kidney. After the preliminary work is prepared, a biopsy is performed by the operator with the aid of real-time medical imaging for guidance. It provides critical information for clinicians to determine the condition, treatment prescription, and postoperative estimation. In these procedures, what it is desired is more accuracy of insertion, reduced operation time, and less damage to surrounding tissue during the insertion. Because of one of the coauthors who is also a professional surgeon in the hospital, all clinical and data collection procedures referring to the patients and the surgery are anonymous and nonidentifiable. Clinical procedures are planned and conducted by professional urologists in the hospital and in accordance with associated safety and ethical approvals. The clinical data adopted in this paper was provided to the authors through a public link to a database on haptic force feedback  and a guide image recording component (ultrasound), as well as an audio recording component. The force acquisition component is implemented based on a standard trocar needle (18G COOK trocar needle), which is a flexible surgical instrument, consists of a needle core and needle sheath which is demonstrated in As the CT images used for visual reconstruction could be obtained before the operation, only the guided image recording is required in real time. Ultrasound images were recorded with a screen capture software as a guiding image. Each frame was recorded for the guide image in the proposed training environments. The recorded images are cropped to the preoperation part as the training interface needs to be overlaid with only a shadow's projection of the needle. On the audio recording part, we adopted a Sony PCM-D100 high-resolution recorder to capture the working audio of machines inside a theatre. We then replay these as the background audio in our training system. In the proposed training framework, the urologist guides the real-time C-ARM or ultrasound image navigation to locate the minimal invasive position for needle insertion. To provide an immersive haptic rendering of such dual-hand operation, we implemented both ultrasound force and needle insertion force within one haptic rendering loop. The first line in 18 novice and 9 expert urologists are invited to assess the virtual reality training platform. Before the training session starts, a didactic introduction of system operation is provided to both the expert and novice groups. Furthermore, a customized-built Global Rating Scale (GRS) questionnaire is introduced for the subjective evaluation –31. The U test (continuous variables) or the chi-square test  of the stored data in our system [t-tests. Spearman rank order correlations were calculated to determine correlations between objective simulator-derived scores and the GRS results. P value < 0.05 was considered statistically significant. Analyses were performed using the software package IBM SPSS Statistics, version 20.0 [Comparisons between groups (novice–expert) were analyzed using the Mann–Whitney r system , 32. Preion 20.0 , 34. TheAfter the experiments, the data were analyzed by MATLAB 2016 software. The nonlinear least squares were utilized to fit the insertion force by using the Fourier model proposed in A detailed piecewise fitting degree of five kinds of tissues is demonstrated in To verify the modeling method, the clinical trial setup is conducted in the Urology Department of the Yunnan First People's Hospital in Kunming; all the procedures related to the patients and the operation are designed and constructed by the professional surgeons according to the safety and ethical rules. Based on the result in Each expert participant could perform the puncture attempts on the model and successfully hit the provided tumor in the calyx. The survey showed that 7 participants (77.8%) would use our framework to train novices and 8 urologists (88.9%) considered the kidney model as an accurate anatomic representation with only 1 participant (11.1%) who did not agree with this statement. 6 participants (66.7%) considered the depicted the graphic simulation is as real as the real surgical scene. The other 3 were neutral about this statement. 5 (55.5%) participants thought the system had a high performance of haptic feedback; 2 (22.2%) of them were neutral. 8 attendants (88.9%) considered the X-ray-simulated image as an accurate representation of a real fluoroscopic image, and all of them (100%) felt that avoiding radiation was important for training; the detailed score of GRS and the objective evaluation on the VR-based training simulator are shown in For the system subjective questionnaire evaluation result, novices' appraisement demonstrated significantly higher than experts' in total performance. Details of the subjective evaluation of the VR-based training simulator are shown in For different tissue types, the stiffness force and the force distribution are differentFor the same tissue type, the insertion force curves vary from region to region, but the tendency of the force curve is similar; only the force peaks and slopes have a little diversityThe complexity of the organizational structure distribution is known from the shape of the mechanical curve by observing the fluctuation of the force curve during needle insertion. The distribution of the internal structure was simple when the fluctuation was calm, but vice versaAs medical surgery, percutaneous operation is often utilized for cancer treatment, and cytopathological examination, as the cancer treatment method, such as percutaneous ethanol injection therapy (PEIT) and radiofrequency ablation (RFA) is performed for liver cancer; both of them require the needle insertion into a specific part of the diseased area as precise as possible . For cytIn this paper, the experimental data were modeled using the above classical methods and the Fourier model proposed by us. As shown in From To sum up, the principal contribution of this paper is to implement and verify the modeling method, by obtaining the clinical data of the needle puncture and comparing with the experimental data of a dynamic framework, which illustrated that the dynamic insertion model can effectively simulate the real operation.For the system evaluation result, the face and content appraisal median value was 4 (1-5) by 9 experts, which is the same score as the existing percutaneous biopsy surgical training platform. Experts' performance is significantly higher than novices' in total performance. Posttest values of novice group after a few training sessions have shown significant improvement with respect to pretest GRS and objective scores. In all, In this paper, a novel force modeling methodology for the biopsy therapy with respiratory motion using direct clinical data is proposed; the dynamic biopsy biomechanics experiment architecture is designed and constructed to facilitate the accurate modeling of the insertion force. In addition, clinical evaluation is conducted and performed, and VR-based training evaluation based on the proposed haptic rendering solution is also performed, which demonstrated the validity of the mechanical model and the feasibility of the evaluation. It is the first time to propose a dynamic force modeling and introduce the idea of continuations modeling of real-time biopsy force rendering. The principal contribution of this paper is to establish a complete system for the study of percutaneous biopsy training, including experimental construction, theoretical analysis, and surgical laboratory validation and evaluation, which is finally based on the VR training simulation assessment, which is a comprehensive research and verification process. The Fourier model was used to model the experimental data of different tissue types, which illustrated that the dynamic force modeling can effectively apply to several tissue types; it is further verified that the mechanical model proposed in this paper has a certain generalization. The future work will include the following: the experimental equipment needs to be optimized, such as the simulation of human respiratory system, and experiments need to be performed to enrich experimental data and types.