The utilized method proves superior in performance and speed, accelerating the programming time using online and offline approaches by an estimate of 88% to 98%. The proposed work is a unique development, retrofitting current industrial manipulators into collaborative entities, securing human job resources, and achieving flexible production.The increased demand for cost-efficient manufacturing and metrology inspection solutions for complex-shaped components in High-Value Manufacturing (HVM) sectors requires increased production throughput and precision. This drives the integration of automated robotic solutions. However, the current manipulators utilizing traditional programming approaches demand specialized robotic programming knowledge and make it challenging to generate complex paths and adapt easily to unique specifications per component, resulting in an inflexible and cumbersome teaching process. Therefore, this body of work proposes a novel software system to realize kinesthetic guidance for path planning in real-time intervals at 250 Hz, utilizing an external off-the-shelf force–torque (FT) sensor. The proposed work is demonstrated on a 500 mm Intuitive robotic path planning for complex geometries;Adaption and interaction of the process per component variations;Deployment and utilization of big data analysis tools for process optimization.The growth of High-Value Manufacturing (HVM) sectors, such as defense, nuclear, aerospace, and marine, comes with a need to fabricate and inspect components with ever-increasing structural complexity and volume. This emerging industrial demand coupled with the high-reliability and precision demands in those sectors has grown their desire to adopt and integrate automated robotic solutions in their production cycle. Alongside the prevalence of Industry 4.0, paired with the latest technological paradigms, smart factories capable of delivering products with highly customized geometry and quality specifications are being sought . To meetAn important aspect to realize flexible automation is in the approach of robotic programming that generates motion path planning. As robotic path teaching is a vital task for all robotic applications, the efficiency of the programming method directly affects the production cycle. Online programming (OP) is predominantly used to manually teach, where the input of a trained operator is required to teach the path points in space through a teach pendant ,3. This Moreover, as it relies heavily on the component and environment CAD models, additional cumbersome calibration procedures are required to compensate for as-built part tolerances and to locate the workpiece in the environment accurately. Nonetheless, path planning generated from Visual Servoing (VS) guides the robotic arm with respect to a target object based on nominal CAD and on visual feedback vs. approaches that are characterized by a high computation load to deploy real-time image processing algorithms and a requirement for nominal CAD models . The useCurrently, industrial manipulators that perform heavy-duty tasks, such as welding, inspection, milling, painting, or pick-and-place operations, predominantly use OP approaches to teach robotic paths, remaining inflexible to complex-shaped components and with limited adaptability to fabrication variations. An intuitive way of robot programming originates from the concept of collaborative robots. Over recent years, collaborative robots have gained popularity in the global manufacturing sector owing to their design, as their joints are equipped with force–torque sensors, offering the ability to sense human interaction and work alongside human operators sharing the same working space ,10. ThisConsidering these challenges, there is an imperative need to transform current industrial robotic manipulators into collaborative entities able to be programmed with kinesthetic guidance to (i) minimize downtime between tasks, (ii) cope with high variability in components specification, and (iii) accelerate the production process from start to finish and, at the same time, not to be limited to the restricted loading and reachability constraints of collaborative manipulators.One particular technology that has been rapidly developed, and is part of the Industry 4.0 revolution that would benefit from this transformation would be metal Additive Manufacturing (AM) and, particularly, Wire–Arc Additive Manufacturing (WAAM) based on direct energy deposition . This teCurrent industrial robotic manipulators installed in HMV sectors characterized by high reachability (>2 m) and payload capabilities (>35 kg) can be utilized by humans as collaborative entities to intuitively teach the robotic path in the manufacturing ecosystem;All singularities that may arise and collisions with possible fixtures are realized during the path-planning process. Compared to traditional OP and OLP robotic programming, in kinesthetic path planning, a real-time motion is executed from start to end;The intuitive way of collaboratively performing the path planning for industrial robots achieves advanced performance over OP and OLP by decreasing the robotic programming time by 88% and 98%, respectively; see Adaptive FT motion for defects inspection is achieved through the deployed software system and the real-time feedback of the FT sensor to the kinematics generation. This feature enables the adaption of the robotic motion to complex curvatures by generating the kinematics in real-time (250 Hz) and avoiding the distortion of the motion trajectory profile due to the parallel deployment of FT sensory corrections upon the main robotic motion;Compared to the previous work of the authors in ,25, the This paper presents, for the first time, a novel software system for industrial robotic manipulators to realize collaborative path planning using kinesthetic guidance in real-time (at a 250 Hz interpolation rate) by utilizing a 6 DoF force–torque (FT) sensor mounted at the robot’s out-bore flange. As such, with the presented work:The capabilities of the developed system are demonstrated through a KUKA KR90HA with a reachability of 3.1 m and a payload of 90 kg . KinesthThe adaptive Non-Destructive Evaluation (NDE) UT inspection of a complex-shaped WAAM component demonstrated in this work utilizes (i) the kinesthetic guidance for intuitive path planning, described in this section, and (ii) the real-time kinematics generation, which was developed in previous work . In paraThe kinesthetic guidance for path planning is implemented through the Robotic Sensor Interface (RSI) developed by industrial robot manufacturer KUKA  in conjuReal-time kinesthetic guidance requires a control process algorithm to be executed cyclically at the robot controller and a User Datagram Protocol (UDP) connection between the controller and an external control target . This connection allows exchanging of process data, such as FT measured values, robot positional corrections, and Boolean input/outputs . Figure The construction of the control process algorithm, which executes at a 4 ms interpolation cycle rate in the KRC-4, consists of different modules, which can be found as functions on the toolbox of the RSI package. These range from signal processing  controller, and filters), logic , and communication  connection and digital IO) to math . In addition, the KUKA FT package comes with additional functions such, as integrating an ATI FT Sensor, transforming FT raw measurements to other coordinate systems, a PI controller to generate positional corrections, and a load determination module for the end effector. The interconnection and the flow of process information between these modules realizing the kinesthetic path teaching can be seen in In each interpolation cycle, the FT-measured forces and moments are transformed relative to the end-effector TCP. This transformation is based on the tool’s center of gravity, load, and the FT sensor’s origin transformation to the robot’s flange. This stage is essential to realize and interpret FT measurements from the TCP accurately to the sensor’s origin while the operator drives the end effector to the points of interest. The transformed forces and moments  are fed to the LabVIEW interface through a UDP connection in port A, as can be seen in During the kinesthetic teaching phase, all the force and moment setpoints are set to zero; hence, the motion of the end effector will oppose any externally applied force and/or torque, allowing for the operator to hand guide the end effector to a desired point in the workspace. In order to maintain a zero pre-set force and torque, the PI controller produces a positional correction in the tool coordinate frame. These positional corrections are generated based on the transformed FT values and the set proportional and integral gains. The generated corrections pass through a low-pass filter to remove any outliers from the sampling of raw measurements and produce a smooth motion for the end effector. Before these corrections are applied, they are multiplied with a Boolean array , set from the LabVIEW Graphical User Interface (GUI), which allows independently enabling the individual DoFs in the cartesian space.For example, an application requires a 2D plane surface to be covered by the end effector. In this case, a 6 DoF motion might not be required, and disabling the control of some axes could make it easier to maintain the end effector normal to the surface. Once these corrections are transformed into the base coordinate motion frame, they are applied to the robot motion trajectory. Furthermore, positional encoding takes place within the same interpolation cycle based on , and theDuring an interpolation cycle, positional corrections in the cartesian frame of the TCP motion are produced by the LabVIEW interface based on the set acceleration, speed, and end target . The PI The external program that controls the real-time process algorithm is developed in LabVIEW, and the GUI is depicted in Record Position, which appends the current position to the Motion Path 2D array.If the Boolean teaching control top left  is enablFollowing the kinesthetic teaching, the generation of kinematics between each taught point takes place once the KUKA Enable control is activated. The end-effector velocity and acceleration can be adjusted on the fly during the motion, while a separate numeric control sets the robot scan speed during UT inspection in accordance with the sampling rate and other acquisition parameters. The Boolean array FT_Enable_Control is generated next to the Motion Path array, allowing the user to enable the FT correction during motion, ensuring consistent contact with the specimen under inspection. Once contact is made, the UT data acquisition is automatically triggered. Additional inputs can be added in a similar manner to trigger different application-specific actions, such as turning on a welding torch or increasing the rotational speed of a milling tool. The FT_Enable_Repeat Boolean array allows the user to select which axes the FT-based correction should be applied to. Lastly, the FT_Setpoint repeat numeric control array is used to select the force and/or torque value for each cartesian axis in those points where the FT_Enable_Control is activated. The Boolean values for the DoF under FT motion control and the setpoint values are communicated cyclically with the real-time control process algorithm through the FT Axis Enable  Boolean array, as depicted in 2 footprint complex geometry consists of features commonly found in aerospace components and can be split into three different areas: a mixed curved–linear area (Area 1), a connected linear T joint (Area 2), and a linear section with reduced width (Area 3). Three bottom-drilled holes of a 2 mm diameter were embedded 5 to 10 mm below the surface of the sample. These embedded defects simulate a lack of fusion and keyholes that commonly occur during WAAM manufacturing [2.A UT inspection case study was carried out with a representative WAAM component, manufactured from Ti-6Al-4V of approximately a 21 mm wall height, to demonstrate complex geometry dry-coupled ultrasound NDE using the proposed kinesthetic teaching concept, as shown in acturing . A KUKA acturing ,36 with Before the initiation of the kinesthetic path planning: (i) the FT sensor was calibrated by running the KUKA load determination, estimating the end effector’s load and center of gravity relative to the origin of the FT sensor  and (ii)The end effector was driven from the user to the points of interest for each one of the three areas. Four clearance positions with a standoff 100 mm above the sample surface were recorded and added to the Motion Path at the start, between, and at the end of the three areas, as can be seen in Following the kinesthetic teaching, the adaptive UT inspection  was perfZ-axis), maintaining uniform coupling. The inspection scan speed was selected to be 2 mm/s, aligning the encoded positional data of the roller probe with the generated live beamforming ultrasound data [During the inspection, a constant force of 100 N was required for dry-coupling of the probe to the sample surface and was set to the FT_Setpoint Repeat array with the direction towards the sample  can take between 33 and 40 min of programming time compared to the kinesthetic approach, which takes only 4.45 min. The programming time is influenced by the experience of the robot programmer. Nonetheless, the number of taught points required can vary between 17 and 25 based on the approximation of the complex geometry by a mix of linear and arc motions and the number of FT programs for each motion.Utilizing OLP for path planning requires an accurate design of the manufactured WAAM component, which, by the nature of WAAM manufacturing technology, deviates from the original CAD by material . Taking In this paper, the design and demonstration of a new novel kinesthetic guidance software system were presented to enable rapid human-taught collaborative inspection of complex components using traditional industrial manipulators.The described software system was based on the feedback of a 6 DoF FT sensor in real-time intervals of 4 ms, allowing the user to drive the end effector to the points of interest and generate the path planning for complex-shaped geometries, such as the WAAM component. Following the path planning, the same system was utilized successfully to perform adaptive kinematics generation over the near-net-shaped WAAM component. For the inspection of defects, uniform coupling was achieved with a constant force of 100 N with a high-temperature dry-coupled PA roller probe. The control approach around the RSI interface of KUKA robots for real-time external control aligns with similar packages from common industrial robotic brands, such as Fanuc and ABB, enabling the proposed technology to be applied to a wide variety of industrial manipulators.Programming time;Number of points required;Need for base calibration;Ability to adapt to complex shape geometries.The benefits that accompany the proposed kinesthetic teaching concept were revealed through a quantitative comparison with OP and OLP programming approaches, proving that the proposed intuitive programming approach was superior overall in terms of:The programming time using the flexible kinesthetic path planning accounted only for 4.45 min. The authors estimate a significant decrease in time between 88% and 98% when compared to OP and OLP robotic teaching approaches, respectively. Moreover, the proposed concept allows the user to easily readjust the motion and redefine the points in space when the fabricated component deviates from the original CAD, which is not the case for the OLP approach. In addition, the ability to re-calculate the kinematics in real-time based on the FT feedback makes it feasible to adapt the setpoint force dynamically during the motion of the roller probe over the curved parts of the WAAM component and eliminate possible distortions to the generated trajectory profile.Future work will seek to integrate a strategy to interpolate and recognize complex curves with the minimum number of points recorded. In addition, digital controls on the robot flange will be added to increase the flexibility during teaching as well as the ability to change the gains of the PI controller on the fly for an easy transition between teaching and the deployment of motion.Overall, the presented research enables a collaborative and intuitive way to perform the cumbersome process of path planning for industrial manipulators. The concept originates from collaborative robots, which, due to their design, are still limited with short reachability (<2 m) and payload capabilities (<35 kg). In this way, this work can retrofit current industrial manipulators, which are designed to perform welding, metrology inspection, pick-and-place, or other heavy-duty tasks, into collaborative entities in the production line, securing human resources, and increasing production throughput.


The reprogramming of metabolism is a recognized cancer hallmark. It is well known that different signaling pathways regulate and orchestrate this reprogramming that contributes to cancer initiation and development. However, recent evidence is accumulating, suggesting that several metabolites could play a relevant role in regulating signaling pathways. To assess the potential role of metabolites in the regulation of signaling pathways, both metabolic and signaling pathway activities of Breast invasive Carcinoma (BRCA) have been modeled using mechanistic models. Gaussian Processes, powerful machine learning methods, were used in combination with SHapley Additive exPlanations (SHAP), a recent methodology that conveys causality, to obtain potential causal relationships between the production of metabolites and the regulation of signaling pathways. A total of 317 metabolites were found to have a strong impact on signaling circuits. The results presented here point to the existence of a complex crosstalk between signaling and metabolic pathways more complex than previously was thought. The link between metabolism and cancer has been well known since almost one century ago , with thThe profusion of detailed biological knowledge on signaling and metabolism, as stored in pathway repositories such as KEGG , ReactomHere, a study on the crosstalk between metabolism and signaling has been carried out in BRCA, using mechanistic models to simultaneously infer from gene expression data the production of metabolites ,22 and tGene expression data for BRCA and controls were downloaded from the TCGA portal and processed as described in Materials and Methods R2 score and mean squared error (MSE). As expected, not all tasks (circuit activities) can be predicted with reliability. However, there are no clear signs of overfitting, as can be seen by inspecting the joint distribution plot  show that the variability decreases as the predictive performance increases (less is better), for both the test and train fold sets. As with the R2 score, the error trend is clearly linear and close to the diagonal (identity).As outlined in Methods, a 100-times repeated 5-fold cross-validation was carried out in order to measure the viability of the model in terms of the ion plot , left ofUsing the multi-task Gaussian process, a total of 317 metabolites were found to have a strong impact on the activity of signaling circuits in BRCA . These cR2 score > 0.5) and non-relevant (R2 < 0.5) predictions that have or have not common genes. As a result, signaling circuits predicted and metabolic circuits corresponding to the most predictive metabolite do not show any significant enrichment in common genes . Actually, if the circuits analyzed are expanded to those corresponding to the 20 best predictive metabolites, the scenario is the same . This discards common genes between metabolic and signaling pathways as an explanation for this cross-talk, reinforcing the hypothesis that the metabolites detected are triggers of signaling pathway activity.Since the activity of a number of signaling circuits can be predicted from the previous production of metabolites, it is likely that such metabolites are the ultimate trigger for this cross-talk. However, signaling and metabolic pathways share some genes  but could not be detected in the metabolomic profiles , but not using an experimental metabolomics approach , a protective amino acid against cancer  interval and returns output values in the same interval. Such results are non-dimensional values that, like gene expression values, can be interpreted in the context of a class comparison.c fluxes . The EquThe mechanistic model implemented in Hipathia infers signaling circuit activities from the expression levels of the genes corresponding to the proteins involved in the circuits.Similarly, Metabolica estimates the production activity of the metabolites using the reactions involved in the sub-pathways analyzed . It is it-test for paired samples is used to assess the significance of observed changes in metabolite concentrations or in metabolic production activity when samples of two conditions are compared.Finally, a Student’s A False Discovery Rate (FDR)  is used Relating the production of metabolites with the activity of signaling pathways involves solving several Multi-Task regression problems, one for each signaling pathway and tumor type (for machine learning tasks only tumor samples are used). Here, Gaussian Processes (GP)  were usesn signaling circuit activities) can be expressed as ln latent GPs of the inputs (mn metabolite production activities) mixed by W . The covariance function of the GP is built using a linear combination of the squared exponential  and linear covariance functions. The kernel hyperparameters are forced to be shared across all the inputs , the inducing variables are shared between all the inputs (in= 50 inducing points are used) and W is the identity matrix of dimension ln. Note that Sparse Variational Processes are known to correctly scale across the data and output dimensions and all the computations are possible due to the data generating processes  [The model assumes that the outputs (y input) .R2 scores  and mean squared errors (MSE) were calculated for each circuit to evaluate the performance of each model. Note that since the model hyperparameters are optimized as part of the GP modeling, R2 is indeed measuring the suitability of the model building procedure which is in line with recent recommendations for machine learning use in computational biology [R2 ranges from -infinite to 1 where 0 is the baseline prediction (predicting the mean of each output). The R2 score for a given signaling circuit and its prediction is given by:n is the number of samples, j indexes the signaling circuit, i the samples, and ^ refers to the mean prediction of the gaussian process model. While the mean squared error (MSE) is given by:To assess the quality of the model, a 100-times repeated 5-fold cross-validation schema was performed in order to measure the generalization performance of the model.  biology . Here thShapley values are a game theory approach to estimate the importance of any individual player as a part of a collaborative team , which cR2 score of the training and testing splits.Here, the kernel-SHAP method  (v0.24.0The functionality triggered by signaling circuits is assumed to correspond to the functionality of the effector protein ,33,86. TSince signaling circuits trigger specific functionalities in the cell, metabolites that potentially regulate these signaling circuits can be annotated with the functions corresponding to these circuits. The results presented here reinforce the idea that, beyond the well-known influence of signaling on the reprogramming of metabolism in cancer ,11, a no


The utilization of multiscale entropy methods to characterize vibration signals has proven to be promising in intelligent diagnosis of mechanical equipment. However, in the current multiscale entropy methods, only the information in the low-frequency range is utilized and the information in the high-frequency range is discarded. In order to take full advantage of the information, in this paper, a fault feature extraction method utilizing the bidirectional composite coarse-graining process with fuzzy dispersion entropy is proposed. To avoid the redundancy of the full frequency range feature information, the Random Forest algorithm combined with the Maximum Relevance Minimum Redundancy algorithm is applied to feature selection. Together with the K-nearest neighbor classifier, a rolling bearing intelligent diagnosis framework is constructed. The effectiveness of the proposed framework is evaluated by a numerical simulation and two experimental examples. The validation results demonstrate that the extracted features by the proposed method are highly sensitive to the bearing health conditions compared with hierarchical fuzzy dispersion entropy, composite multiscale fuzzy dispersion entropy, multiscale fuzzy dispersion entropy, multiscale dispersion entropy, multiscale permutation entropy, and multiscale sample entropy. In addition, the proposed method is able to identify the fault categories and health states of rolling bearings simultaneously. The proposed damage detection methodology provides a new and better framework for intelligent fault diagnosis of rolling bearings in rotating machinery. In modern industrial systems involved in major engineering fields such as aviation, electric power, the chemical industry, and mining, rotating machinery has been widely used as an integral part of such systems. With the continuous progress of modern science and technology, the complexity of rotating machinery systems is also getting higher . The rolDue to its cost-effectiveness, the vibration sensor has been widely used in bearing condition monitoring . In geneAs a statistical measure, information entropy can be used to quantify the internal information and complexity of a time series . UsuallyBased on vibration signals, some commonly defined information entropy has been successfully applied to the field of fault diagnosis and has yielded useful results. For example, Yan  used theFor more complicated diagnosis situations, the above-mentioned single-scale entropy is sometimes not sufficient. Accordingly, the multiscale entropy method has been proposed . MultiscMultiscale-based entropy feature extraction methods are widely used, but they still have two major drawbacks. From one aspect, multiscale entropy based on the traditional coarse-graining method has a large variance of entropy value when the scale is large, causing the reliability of the entropy assessment results to be reduced. On the other side, the current multiscale methods extract only the low-frequency information from the sequence, without considering the high-frequency information. In order to overcome the problem of a high variance of entropy value in multiscale entropy methods based on traditional coarse-graining methods, composite multiscale entropy was proposed . AlthougThe fuzzy dispersion entropy (FDE) has been proven to be a promising method for feature extraction . HoweverThe rest of this paper is organized as follows. The BCMFDE method is formed by combining the FDE method and the bidirectional composite coarse-graining process (BCCGP).The time series 2.Each element 3.The series 4.The fuzzy membership function is introduced in sequence 5.Each vector The FDE is a method used to characterize the complexity of a time series and estimate the dynamic changes of signal fluctuations. The calculation procedure is as follows :The time6.The probability of each dispersion pattern 7.Finally, the FDE is calculated according to the theory of Shannon’s entropy as follows:In general, the number of dispersion patterns that are attributed to each vector 1.For time series 2.The coarse-grained series form of operators 3.According to the definition of FDE, the BCMFDE is obtained byThe BCCGP is based on an improvement of the composite coarse-graining process , which hThe BCCGP with scale factors The advantage of BCMFDE is that the information of low- and high-frequency components at different scales is considered. However, the more sequence scales that are considered, the more information features that can be constructed, and consequently, the more computational effort that is required. To balance feature richness and the computational burden, it is necessary to make a reasonable selection of the extracted feature set. To this end, the RF-mRMR is used for feature selection.The RF is an ensemble learning algorithm composed of decision tree models as its basic units . The ess1.The corresponding OOB data are selected for each decision tree to calculate the OOB data error rate, denoted as 2.The OOB data error rate is calculated again after adding random noise interference to all samples of OOB data and is denoted as 3.The importance To evaluate the importance of a feature, the steps are as follows:The reason that given in , where tThe mRMR is acting as a filter for feature evaluation and selection ,32. The The mutual information amount is used to measure the similarity between variables, and the corresponding score is assigned to achieve feature selection according to the size of the score. For the two given random variables Assume that The defined maximum relevance criterion is able to find the feature subset In order to retrieve the feature sets with maximum relevance and minimum redundancy, The KNN  classifiThe KNN classifier is used for classification by measuring the distance between different feature data. The basic idea is that a sample Calculating the distance between the feature data of the test sample and the feature data of each training sample.Ranking the distance according to its magnitude.Selecting the Calculating the frequency of occurrence of the category in which the top Returning the category with the highest occurrence frequency among the top The basic steps of KNN include:K affects the results of the model, as shown in The value of the nearest neighbor number Step 1: Signal acquisition as shown in Step 2: Feature sets construction as shown in Step 3: Failure identification and classification as shown in Based on the above discussions, we propose a rolling bearing fault feature sets construction method based on the BCMFDE feature extraction method combined with the RF-mRMR feature selection method, aiming at extracting features in the whole frequency range of the signal while minimizing the information redundancy. And, it is combined with the KNN classifier to form an intelligent fault diagnosis framework. The procedures of the proposed intelligent fault diagnosis framework are depicted in In this section, simulated signals of rolling bearings with different faults are used to evaluate the effectiveness of the proposed signal processing framework . SimulatAssuming at time Then, the impact force excited by the local defect on the inner ring can be expressed as:In Equations (18)–(20), The vibration amplitude decay envelope function due to damping can be expressed as:The amplitude transfer function is expressed as:The load distribution can be expressed as:The simulated outer race fault signal can be expressed as:The simulated inner race fault signal can be expressed as:The simulated roller fault signal can be expressed as:In order to simulate the actual working conditions, Gaussian white noise is added into the analog signal, and the signal noise ratio is 5 dB. The time history of the simulated bearing vibration response with normal, roller fault, inner race fault, and outer race fault are plotted in The vibration signal is processed according to the procedures outlined in The feature sets constructed by different entropy methods are visualized by using the t-SNE  algorithIn order to quantitatively evaluate the feature extraction ability of different entropy methods, the classification effect of the feature set was tested. The KNN classifier was used in the evaluation, and the test accuracy was adopted as the evaluation measure.The KNN classifier was trained first. Eighty percent of the feature sets were randomly selected to form the training set and the rest were used as the test set. The training sets were used to train the classifier model first. The test sets were then fed to the trained model to validate the classification accuracy. This process was repeated 10 times and the mean, standard deviation, and mean time values of the test accuracies were calculated and listed in It can be seen from The confusion matrix can intuitively show the category and number of samples that were misclassified. The confusion matrix of the fifth test result was visualized and analyzed, as shown in Although the method based on BCMFDE improves classification accuracy, its disadvantages are an increased computational burden and the risk of feature information redundancy. Compared with traditional coarse-graining, bidirectional composite coarse-graining considers not only the information of low-frequency components but also the additional information of high-frequency components, which will double the number of extracted features. To avoid redundancy of feature information while minimizing the computation cost, the RF-mRMR is used to select important and sensitive features  in In order to verify the effectiveness of the proposed fault diagnosis method in diagnosing different fault categories, as well as the fault severities of rolling bearings, the experimental verification is carried out on the rolling bearing vibration dataset of Case Western Reserve University (CWRU). The rolling bearing fault simulation test rig is shown in The accelerometer is placed near the drive end of the motor and is used to acquire vibration signals. The vibration signals are collected with a 16-channel DAT recorder. The sampling frequency is 12.00 kHz. Single-point faults are introduced to the test bearing parts using EDM with fault diameters of 0.18 mm, 0.36 mm, and 0.53 mm, respectively. The fault depth is 0.28 mm.The bearing vibration signal acquired at a motor speed of 1797 rpm is selected as the data sample for analysis. Data samples consist of 10 categories of bearing conditions, including health baseline and different failure categories and severities, as listed in In this section, the vibration signals corresponding to the 10 categories of bearings obtained in the experiments are processed according to the procedure outlined in The evaluation procedure used is similar to For the quantitative evaluation, the test results of the BCMFDE method and the other entropy feature extraction methods on the KNN classifier are listed in The confusion matrix of the results of the fifth test was visually analyzed, as shown in In the RF-mRMR-BCMFDE process, the results of the calculation of importance For quantitative evaluation, the test results of the RF-mRMR, RF, and mRMR feature selection methods on the KNN classifier are shown in For condition assessment of rolling bearings in rotating machinery, a combination of the BCMFDE-based feature extraction method and RF-mRMR feature selection method is proposed for the construction of rolling bearing fault feature sets. The BCMFDE can extract richer feature information from high-frequency components and low-frequency components for characterizing bearing fault features while The application of RF-mRMR can effectively select features with high importance and sensitivity suitable for classification, thus improving efficiency in the classification and identification of fault categories and reducing the redundancy of the feature sets. The validation results of numerical simulations and two experiments demonstrate that using the proposed framework, i.e., the combination of the fault feature sets construction process and the KNN classifier, is able to automatically identify bearing fault categories, in addition to bearing fault severity. The proposed framework provides a new perspective for intelligent bearing fault diagnosis. With some modifications, it is expected that the framework can be expanded to the intelligent diagnosis of other types of faults such as gear damage or to the intelligent condition monitoring of a complete system.


Local field potential (LFP) recording is a valuable method for assessing brain systems communication. Multiple methods have been developed to collect LFP data to study the rhythmic activity of the brain. These methods range from the use of single or bundled metal electrodes to electrode arrays that can target multiple brain regions. Although these electrodes are efficient in collecting LFP activity, they can be expensive, difficult to build, and less adaptable to different applications, which may include targeting multiple brain regions simultaneously. Here, the building process for a 16-channel customizable multielectrode array (CMEA) that can be used to collect LFP data from different brain regions simultaneously in rats is described. These CMEA electrode arrays are lightweight (<1 g), take little time to build (<1 h), and are affordable ($15 Canadian). The CMEA can also be modified to record single-unit and multiunit activity in addition to LFP activity using both wired and wireless neural data acquisition systems. Moreover, these CMEAs can be used to explore neural activity (LFP and single-unit/multiunit activity) in preliminary studies, before purchasing more expensive electrodes for targeted studies. Together, these characteristics make the described CMEA a competitive alternative to the commercially available multielectrode arrays for its simplicity, low cost, and efficiency in collecting LFP data in freely behaving animals. Dysregulated neural oscillatory activity is present in multiple neuropsychiatric and neurodegenerative disorders. To accurately diagnose and treat these disorders, it is important to understand the mechanisms that underlie neural dysfunction. Several types of neural pathologies have been studied including LFP activities, which represent a summation of population-level neuronal activity recorded from within the brain. Although several commercially available electrodes for studying LFP activity exist, they are expensive, not easily adaptable, and difficult to build. Here, we describe a simple CMEA that is lightweight, easy to build, affordable, and adaptable to a number of applications.in vitro and in vivo. In vitro, APs can be studied in brain slices using tools such as glass micropipettes  techniques, can be used. An example of an in vivo noninvasive technique is electroencephalography (EEG), one of the oldest techniques for investigating neural function . Download Check each piece to be sure the cannula holes and the pinholes are well centered and not too close to the edges, which might make them more likely to break.Select one bottom and one top plastic piece A,B.ChecUsing a needle (27 or 30 gauge), push with a twisting motion until the needle goes all the way through the existing holes.Enlarge the cannula holes if necessary.Be sure that the end of the dowel is flush with the flattest side of the bottom plastic piece (which will now be the bottom side).You may need to enlarge the beginning of the hole slightly with a smaller-gauge needle to help start the dowel.Insert a plastic dowel into the larger/center hole of the bottom plastic piece C.Be surSlide the top plastic piece onto the plastic dowel D,E.For the most efficient cutting, measure and mark each length needed on a piece of tape on the lab bench. Lay the cannula along the markings and cut with a new razor blade. Check the cut lengths with calipers.x), the thickness of the bottom plastic piece (y), and how far the metal electrode (wire) will extend outside the cannula (z). The length of the cannula was therefore calculated using the formula: x + y – z.The cannula length can be determined by identifying the dorsoventral coordinate of the brain region of interest (Cut the cannula to the appropriate length.Cut the wire from the spool into 2 inch lengths.Thread a single wire through each precut cannula F. RepeatInsert the cannula with wire into the underside of the bottom plastic piece and be sure the top of the cannula is flush with the top side of the bottom plastic piece (opposite of the plastic dowel).Using forceps and a magnifying glass make threading the wire easier.Use the electrode map/schematic  to confiInsert the wire in each cannula into the corresponding hole in the top plastic piece.Insert the correct length cannula with wire into the corresponding holes in the top and bottom plastic pieces G. RepeatInsert the pin with the wire toward the outside of the top plastic piece. This ensures the wire is stabilized against the thickest wall and will make the best connection with the pin.Be sure the pin holds the wire in place in the pinhole. If the wire is very fine, and the pin goes fully into the pinhole without any resistance, it may not shear the protective coating on the wire and therefore not make a connection with the pin. A gold female pin may need to be used instead to make a good connection.Insert a female pin, previously removed from a Mill-Max connector strip, into the pinhole from the top of the top plastic piece I.InsertSome designs (tetrodes or single-wire designs to access multiple regions with similar AP and ML coordinates but different DV coordinates) may call for multiple wires to be threaded through one cannula. In this case, the wires can be bundled and held together with dextrose and subsequently threaded through the cannula and the top plastic piece.The electrode wires could be threaded for either unipolar or bipolar recording. Thread one wire (ground wire) through the designated hole on the top plastic piece and insert a pin to hold it in place. Be sure to deinsulate the free end of the ground and reference wires using a lighter.Repeat with each cannula until all wires are threaded and pinned.Insert pins into any remaining empty pinholes in the top plastic piece J.Pull wires taut from the nonpinned ends to decrease the space between the top and bottom pieces.Push the top and the bottom plastic pieces closer together once all wires have been threaded and pined J.Pull wBe sure both wires are firmly connected to a 9 V battery. Fill one well on a plastic well plate with saline and insert the end of one wire attached to the 9 V battery into the saline. Hold the wire ends of the electrode in the saline and, one by one, touch the end of the other wire connected to the battery to the top of each pin of the electrode. Watch for bubbles on or coming from the tip of the electrode wire that corresponds to the pin being touched.If no bubbles (or very few) form, the connection between the wire and pin may be weak.Look for bubbles coming from wires not being touched as this may indicate cross-talk between pins or wires.A magnifying glass or microscope may be helpful.Evaluate the connection between each pin and wire using the bubble test.After identifying the weak connections or nonconnections, gently push each pin out from the bottom of the top plastic piece.Pull out the electrode from the corresponding hole.Cut a new electrode of the same length and thread it through the cannula corresponding to the pinhole from which the pin was pushed.Insert the cannula with the electrode into the corresponding hole in the bottom plastic piece, ensuring that the cannula is flush with the top part of the bottom plastic piece.Guide the electrode through the bottom part of the top plastic piece into the corresponding pinhole.Afterward, push the pin into the pinhole in the top plastic piece as done previously.Retest until all pins have good/strong connections with corresponding wires and ensure there is no cross-talk between neighboring pins and/or wires.Fix weak connections or nonconnections and retest.Too much glue can pull/shift the cannula as it dries, but too little will not hold the cannula in place.Avoid getting glue in the ends of the cannula to prevent clogging.Carefully apply superglue to the bottom of the cannula and the underside of the bottom plastic piece to hold it in place.Apply superglue to the dowel around the top plastic piece, the bottoms of the pins, and the wires as they enter the cannula at the top of the bottom plastic piece. Be careful not to get glue inside the pins on the top plastic piece.Hold the plastic pieces in place as the superglue dries.Apply superglue to all movable parts using a 1 ml syringe and a 27 gauge needle.Use a magnifying glass, a pair of scissors, and forceps.Grasp the end of the wire with forceps and gently pull it taut.Afterward, make a circular motion with the electrode wire while keeping the gentle tension. This will create a pressure point in the wire where it is pinched between the plastic and the pin, and the electrode wire will break cleanly at this point leaving no tail that could create cross-talk.In case the electrode wire does not break cleanly, use scissors to cut extra wire.Remove the ends of the wires protruding from around the pins on the top of the customized multielectrode array J, inset.z mm (from formula above) past the end of each cannula.Cut at The total length of the cannula and wire from the bottom of the bottom plastic piece can also be measured to be sure it is equal to the bregma depth specified in the design.Measure and trim the ends of the wires at the end of each cannula J.Cut atAll connections between pins and wires are good and produce bubbles via the bubble test.The ends of wires around the pins are trimmed off to prevent cross-talk.z mm past the end of the cannula.The ends of wires protruding from the cannula are trimmed to All ends of wires are trimmed.both plastic pieces to the dowel,cannula to the bottom plastic piece,pins to the top plastic piece,wires where they enter the cannula.All movable parts are glued in place as follows:The end of the ground and reference wire(s) are stripped.All pins are clear, and a male end of a Mill-Max connector can be plugged in.All cannulas and wires are as straight as they possibly can be.The top plastic piece is well aligned with the bottom plastic piece.All cannulas and wires are the proper lengths to hit each targeted structure.For the best chance of success, ensure each electrode meets all the following steps.Place empty weigh boats in the UV sterilizer about 30 min prior to sterilizing the CMEA, and turn on the sterilizer.Place the completed multielectrode array on one side in the weigh boat while the UV light is on.With sterile gloves, turn the CMEA onto the other side after 30 min.The CMEA will be ready to implant after another 30 min. Depending on the size of the UV sterilizer, multiple CMEAs can be sterilized simultaneously.For sterilization with ultraviolet light using a UV sterilizer, the steps are the following: The CMEA can also be sterilized via alternative methods such as ethylene oxide gas sterilization.The unipolar CMEA was constructed to target different brain regions with coordinates obtained from the Paxinos rat brain atlas . To alloUsing a wire cutter, cut a strip of the male/male ZIF-Clip to Mill-Max connector with nine pairs of gold pins .Insert the pins from this precut Mill-Max connector into the sockets in the ZCA-Mil16 head stage adapter.Connect the precut Mill-Max connector attached to the ZCA-Mil16 head stage adapter to the animal by inserting the protruding pins into the sockets in the CMEA on the head of the animal.Attach the ZIF-Clip analog head stage to the ZCA-MIL16 head stage adapter and ensure that the ZIF-Clip is tightly secured on the head stage adapter A.Interface the CMEA (16 channels) with the RZ10 Tucker Davies Technology (TDT) system as follows:Using a wire cutter, cut a strip of the male/male ZIF-Clip to Mill-Max connector with 10 gold pins (including one pin for ground).Insert the pins from this precut Mill-Max connector into the sockets in the wireless W2100-HS116 head stage.Connect the precut Mill-Max connector attached to the wireless head stage to the implanted multielectrode array B.The signal is collected using a wireless W2100 system (MultiChannel Systems).Interface the CMEA (10 channels) with the MultiChannel Systems as follows:Local field potential data were recorded using either the wired RZ10 Tucker Davis Technology (TDT) system or the wireless MultiChannel Systems W2100.LFP data collected can be analyzed using the Chronux software package for MATLAB (MathWorks). Example traces of acquired LFP data are shown in Advances in electrophysiological recording have been crucial in understanding brain systems communication . HoweverWith all the required components available, it takes ∼1 h to build one probe for someone with no electrode-building experience. This time reduces significantly after gaining experience with the assembly. Moreover, the CMEA costs less than $16 Canadian for a 16-channel electrode, which is significantly cheaper than the cost of Michigan, Utah, and Neuropixels electrodes . These pIn this study, LFPs were recorded from three bilateral brain regions simultaneously. However, the plate was designed to enable the acquisition of LFP data from eight bilateral regions, regardless of their dorsoventral, anteroposterior, and mediolateral coordinates, using a 16-channel electrode. This makes it ideal for use in studies to explore neural activity in several brain regions simultaneously, and the interplay between these regions . This CMEA can also be used to record single-unit and multiunit neural activities if the 50 µm stainless steel metal wires being used as electrodes are replaced with bundles of four 12.5 µm (or less) metal electrodes. In this setting, these bundles would act as tetrodes but would not be drivable.In general, how long an implanted electrode remains attached to the skull depends on a number of factors associated with the stereotaxic surgery performed to implant it, including attention to sterility during the surgery and how secure the implanted electrode was on the skull . Lack ofOne important feature of neural probes that could significantly affect the outcome of studies involving animal models is their weight. Probes must be lightweight to allow the animals to behave normally without any impairments and to obtain good-quality data for analysis and interpretation. The proposed CMEA is <1 g in weight ; thus, it is unlikely to affect the behavior of the animals following implantation. Animals have performed several tasks successfully without showing any signs of impairments in their behavior following implantation of the proposed CMEA . MoreoveDespite the advantages the proposed CMEA has over other commercially available ones, there are some limitations that are worth noting. First, because of the design of the top and bottom plastic plates, the number of sites that can be recorded simultaneously is limited. Unlike other commercially available electrodes like the Michigan and Utah electrodes, which can record from several hundred sites simultaneously, the proposed CMEA can record from a maximum of 16 sites at a time. Another limitation is that the proposed CMEA, once implanted in the brain, is fixed. It can therefore not be used in studies where multiple layers of specific brain regions are to be studied.The proposed multielectrode array is cheap, easy, and fast to build and is mainly suited for LFP activity recording in awake, freely behaving animal models. Because of these advantages, they could be used in studies to investigate LFP and single-unit and multiunit activity in 16 brain regions simultaneously, and even those that require brain-stimulation-based approaches. This approach also allows electrophysiological studies to be performed in resource-restricted environments, making neuroscience accessible for the broadest possible range of institutions, especially in combination with other open-source tools (e.g., Open Ephys, OpenVape;


The reliability and safety of advanced driver assistance systems and autonomous vehicles are highly dependent on the accuracy of automotive sensors such as radar, lidar, and camera. However, these sensors can be misaligned compared to the initial installation state due to external shocks, and it can cause deterioration of their performance. In the case of the radar sensor, when the mounting angle is distorted and the sensor tilt toward the ground or sky, the sensing performance deteriorates significantly. Therefore, to guarantee stable detection performance of the sensors and driver safety, a method for determining the misalignment of these sensors is required. In this paper, we propose a method for estimating the vertical tilt angle of the radar sensor using a deep neural network (DNN) classifier. Using the proposed method, the mounting state of the radar can be easily estimated without physically removing the bumper. First, to identify the characteristics of the received signal according to the radar misalignment states, radar data are obtained at various tilt angles and distances. Then, we extract range profiles from the received signals and design a DNN-based estimator using the profiles as input. The proposed angle estimator determines the tilt angle of the radar sensor regardless of the measured distance. The average estimation accuracy of the proposed DNN-based classifier is over 99.08%. Therefore, through the proposed method of indirectly determining the radar misalignment, maintenance of the vehicle radar sensor can be easily performed. With the growing interest in autonomous driving systems, demand for automotive sensors, such as radar, lidar, and camera, is also increasing . Radar, These sensors provide various functions for driver assistance systems and driver safety, including advanced cruise control, collision damage mitigation, and pre-crash safety ,5,6,7,8.In the case of automotive sensor, the initial alignment may be distorted due to various external shocks and continuous vibration of the vehicle while driving . In partk-nearest neighbors algorithm =∑essed assd,Q[n]=∑m indicates the frequency index in the frequency-domain. The magnitude of the baseband signal in the frequency-domain is defined by the range profile. In general, the time delay of wave propagation due to the distance between the radar and the target (i.e., The distance to the target can be estimated from the frequency of  33]. Th. Th33]. In this section, we describe our experimental setup and analyze the obtained radar data. First, we describe the specifications of the radar system and our experimental environment. Then, we analyze how the received radar signals are affected by the tilt angle in the range profile.In our measurements, we obtained radar data using an AWR1642BOOST board manufactured by Texas Instruments . We usedWith this radar system, we obtained the radar sensor data from various tilt angles while changing the measurement distances, which is shown in A steel trihedral corner reflector with a side length of 20 cm and two vehicles are used as targets. First, the range profiles according to the measurement angles and distances are obtained using a corner reflector. Based on the acquired corner reflector data, the input of the proposed vertical tilt angle estimator is determined and the structure of the DNN is designed. Then, the measurement results on the actual vehicles are used for training and verification of the proposed DNN along with corner reflector data. Measurement scenarios are summarized in The range profiles according to various tilt angles for corner reflector are shown in As mentioned in After that, we define the training and test datasets to be used in training the proposed DNN-based classifier and validating its performance. In our measurements, 100 frames of data were obtained with 4 different channels. The range profiles are not exactly identical due to the phase difference that exists between each channel (i.e., In this section, we design a DNN-based classifier for estimating the misalignment angles from ax layer . We usedTo determine the appropriate number of hidden layers and the nodes used in each layer, we evaluated the estimation performance by varying the number of those hyper parameters. In addition, we estimated the training time of the deep neural network while varying the hyper parameters. In terms of training time, the case of using one hidden layer has an advantage over the case of using two hidden layers, but when only one hidden layer is used, the maximum accuracy is 90.83%, which is 8.25%p lower than the case of using two hidden layers. In addition, if three hidden layers are used, the maximum accuracy is only slightly higher than when using two hidden layers, with a difference of less than 1%p. However, the training time required for the network with three hidden layers increases by more than 10%, which may not be worth the marginal improvement in accuracy. Therefore, we decided to utilize the highest accuracy DNN classifier structure with two hidden layers to determine the vertical tilt angle of the automotive radar. The structure with the highest accuracy uses 11 nodes for the first hidden layer and 28 nodes for the second hidden layer, as shown in First, when a new radar signal is received, the FFT is applied to obtain the range profile in the frequency-domain. Then, an input vector of length 34 containing all signal components for the target is acquired and utilized as an input for the proposed DNN-based classifier, as shown in Moreover, the performance comparison was conducted using the decision tree , which iIn this paper, we proposed an efficient method for estimating the vertical misalignment state of automotive radar system by using the DNN-based classifier. The signal received through the automotive FMCW radar was converted into a range profile and used as the input. Then, we extracted input vectors containing signal components between 0 and 4 m where the target exists and passes them through the proposed DNN. Finally, the DNN estimates the misalignment angle from the input vector. When evaluating the performance with the acquired radar datasets, the proposed DNN-based misalignment angle estimator classified the sensor data for 7 tilt angles with an average accuracy of 99.08% regardless of the measurement distance. In addition, our proposed method showed 100% of misalignment condition discrimination accuracy. Furthermore, we compared the estimation performance with other feature-based machine learning algorithm and confirmed that our proposed method is superior. This suggests that the proposed DNN-based estimator can be used to effectively detect the misalignment of the automotive radar system with a simple inspection that does not require a professional technician and avoids the need for disassembling the vehicle. However, as the proposed method uses data measured at 15 degree intervals, it is possible to determine the misalignment condition, but there is a limit to precisely estimating the radar mounting angle. Therefore, future work through data measured at smaller angular intervals should be conducted.


This work presents the first attempt to create a physics-based digital twin model for predictive analysis of damage evolution during the use of ground stone tools (GSTs) in transformative tasks, encompassing the processing of raw resources for nutritional and non-alimentary purposes. The proposed methodology introduces a digital twin of the GSTs developed from 3D models generated using a photogrammetric technique based on Structure-from-Motion and Multi-View Stereo reconstruction. These models serve as the foundation for the development of the finite element (FE)-based digital twin model of the GSTs that exploits a contact formulation and the phase-field approach to simulate tool damage during pounding and grinding tasks. Defining the initial relative positions of the stones, their mechanical behaviour, and controlling the movement of the active stone in a way as close as possible to the real one, the digital twin model has been devised to evaluate how the surface damage is affected by perturbations in the loading conditions. The simulated damage is compared with the surface traces observed from experiments. The developed digital twin model aims at demonstrating its potentials for the GSTs investigations, as a supporting tool for experiments and for simulated tests on the archaeological records. In this study the focus is on unmodified stones, including cobbles, river pebbles, and slabs, used by Anatomically Modern Humans during the Upper Palaeolithic to process plant organs, for various nutritional and non-alimentary purposes. This involved transforming them into flour, softening them for easier chewing and digestion, or separating fibres to create treats. Typically, Upper Palaeolithic GSTs used in plant processing were used in pairs, with a lower steady passive stone where the resource to be processed was placed, and an active, movable tool held in one hand for grinding, pounding, and pulverising tasks.This work exploits the potential of digital twin model and predictive analysis for ground stone tools (GSTs) studies. GSTs represent a category of artefacts that encompass a diverse range of domestic toolkits manufactured and/or used across various daily activities. These tasks involve the processing of different raw materials for various purposes. Examples of such activities include the production of mineral pigments, bone fragmentation, nut cracking, wood pounding for fibre extraction, plant grinding for flour production, leather preparation, ceramic vase polishing, and numerous other tasks that necessitate actions such as percussion, pounding, cracking, grinding, abrasion, polishing, chopping, pecking, and softening .Experimental reproduction, through repeated trials, can be exploited to gain insights and enhance the understanding of the kinetics of tools, specifically the underlying motion and forces that contribute to the formation of use-wear traces. By replicating and closely observing the motion and forces involved in the use of these tools, researchers can uncover valuable information about the wear patterns and the mechanisms and processes involved in their formation. Additionally, the tribological mechanisms that occur when the two lithic surfaces and the medium come into contact can be analysed and used as a reference for a deeper understanding of the functional aspects of the GSTs and the activities they were employed for  to determine the tools' applicability, efficiency, and durability in specific tasks by attempting to replicate ancient technology and gestures. However, this was carried out in a laboratory setting to control and verify the impact of variables in the experiment9 and to ensure a high level of internal validity10. As emphasised in11, we cannot fully recreate or reconstruct prehistory because nobody truly knows how it was. Nevertheless, to address the concern raised by Lycett and Eren in 201310 regarding the low external validity of experimental replicas, our collection was carefully designed with control over morphometric and petrographic characteristics to ensure compatibility with archaeological contexts9.When designing an experimental ground stone collection, various approaches can be employed, which have been classified and categorised by scholars based on different parameters14). These setups allow for reproducibility and offer control over factors that are challenging to monitor in a manual experiment, such as the number of percussions, their direction, and the applied pressure19. However, it is fundamental to acknowledge that the design of the machinery itself can introduce variables that may not exist in actions performed by humans and may also exclude the introduction of variabilities that occur during manual use, moving away from the characteristics of real archaeological records. Therefore, to enhance the level of internal validity without compromising the connection to the relevant archaeological context, we also explore different approaches involving mathematical and mechanical models that can be controlled and manipulated with high precision. With this scope, the present work proposes a new path for the GSTs analysis that has not been attempted so far: developing a finite element (FE)-based digital twin model to investigate their past usage.In the GSTs study, a higher internal validity can be achieved in mechanical setups within laboratory-controlled environments .The digital twin (DT) model can be, in general, defined as a physics-based dynamic computer representation of an actual item, which includes the historical information, geometrical mechanical and structural characteristics of a physical object, and simulates how the virtual model reacts to external stimuli26, which is traditionally applied to engineering problems including structural analysis, heat transfer, fluid flow, mass transport, etc. In fact, the deformation of solids exposed to mechanical loads, as well as their interactions, can be mathematically described using partial differential equations (PDEs), accounting for the external constraints to which the solids are subjected and their mechanical properties. Analytical solutions of the PDEs become very difficult to achieve for complicated shapes. However, a numerical approximation of the solution can be achieved by discretizing the structural system in small units called "finite elements" to obtain the solids mesh. In this way, the FE method allows the solution of the PDE, computation of displacement and force distributions within the solids. More details can be found in the In the present work, we modelled the mechanical behaviour and the damage of the GSTs using the FE method27 dealing with Clovis projectile points and aiming at understanding the role of fluting. The projectile points were modelled using a model geometry not acquired from the real tool. The same approach is considered in28, where 3D models of arrowheads belonging to the Turk Khaganate, one of the foremost cultures in Eurasia, were simulated striking an armour plate belonging to this period. Other applications can be found in30, where Hellenist ceramic amphoras have been simulated to interpret damage and failure patterns found in the archaeological assets. The FE model has been used to simulate their stacking in cargo ships and understand how the mechanical loads and the contact interactions could cause fracture propagation. Damage was estimated based on a linear elastic stress analysis, by comparing the obtained stress distributions within the amphoras with the experimental ceramics' strength.Examples of FE models of archaeological records can be found in the research workphase-field variational approach , which introduces a further unknown variable in the PDEs describing the solids mechanics, i.e., the phase-field variable taking values between 0, corresponding to the intact material, and 1 where the material is broken. This method has been applied in the present investigation to reproduce the evolution of damage, and eventually material removal, of the tools’ surfaces during the contact process. The authors coded the phase-field governing equations within the commercial FE software Abaqus (ver. 6.14) with a user-defined routine, as detailed in the 34 and references therein).In this work, a more advanced approach to evaluate the damage is proposed, the so-called This study aims to develop a GSTs digital twin model that reproduces accurately the stone tools’ geometry and exploits a high-fidelity mechanical model that simulates the tribological behaviour and the damage pattern left on the surfaces during the tools' usage. The proposed DT model can be used to investigate the original usage of these artefacts in substitution or conjunction with the existing research strategies described above.Starting from acquiring the stones' shape through optical methodologies, the DT model has been exploited to simulate the grinding tasks, defining the initial relative positions of the stones, their mechanical behaviour, and controlling the movement of the active stone during the act to reproduce the real one. The DT model offers the same advantages as the laboratory's experimental tests but can also investigate the role of the variability typical of human actions. Additionally, it can reduce contamination by comprehending how the experimental design affects the outcomes. Moreover, while the traditional approaches can be applied only to experimental replicas, the digital twin model can be directly constructed on the original archaeological artefacts, starting from their digital models, and following the same procedure described in this paper.Rumex crispus achenes processing. Processing achenes (dry fruits with shelled pericarps containing a single seed) involves grinding actions, including horizontal, bidirectional, and sometimes circular movements. The gesture can be divided into steps, starting with an initial vertical action where the two lithic surfaces come into contact and subsequently, the motion transitions to horizontal. The action is interrupted by frequent pauses to group the achenes that disperse onto the passive tool's surface upon contact with the active stone. Then the cycle of movement repeats. As a result of the medium's dispersion on the passive surface there are repeated direct contacts between the two lithic surfaces, which determine an intense surface depletion35. Data collection took place across multiple scales, involving the recurrent documentation of features prior to and after the experimental use, by acquiring 3D models and molds of the stone surface, the latest subsequently observed through various types of microscopes .This article specifically focuses on a pair of replicated tools referred to as GS18 and GS17 which are shown in Fig. 35 and shortly summarised in the Method section. The STL surface mesh of the passive stone GS17 is shown in Fig. The digital model was created to replicate the damage caused by the replicative use of the stone tools and it has been devised starting from the geometrical data contained in the STL file produced with photogrammetric techniques prior to use, as detailed inFinally, the FE mesh has been imported in Abaqus where the model has been enriched with the material properties and the fracture parameters necessary to simulate the damage process of the stones using the phase-field approach for fracture, see The following paragraphs report results from different simulations conducted on the obtained DT to understand how damage on the tools’ surface changes with the location of the impact and the direction the active tool is pushed against the base, which are typically varying in time during the manual operation, while they are fixed in laboratory tests carried out with mechanical devices. The action has been divided in two steps: (1) the first phase consists in the motion that brings the stone tools in contact (similar to the pounding motion), where the active stone is pushed with a vertical or inclined direction towards the passive stone; (2) the second phase involves the active stone’s horizontal displacement (the proper grinding motion) on the passive stone’s surface, keeping the contact between the tools.far-field displacement to distinguish it from the displacements within the solids that represent the solution of the FEM problem) applied on the active stone tool; an animation of the DT model for this example is available in Supplementary Video The first case to be analysed considers the initial phase of the gesture, simulated applying a vertical displacement 35; Fig. The deformation of the stones obtained with the DT model has been compared in Fig. 9, even if the simulated deformed areas are smaller than the experimental counterpart because the DT reproduces only a single movement, while the experiment consisted of multiples contacts between the stones in multiple points due to the randomness of the manual operations. As already mentioned in the introduction, the FE digital twin does not only provide the deformation of the solids, but it also computes the phase-field variable The deformed areas match the material depletion areas analysed in35. In fact, while the phase-field parameter reaches 1 on the GS18 surface  phase; an animation of the DT model for this example is available online as Supplementary Video pounding and grinding tasks.This work presented the very first attempt in the literature to create a FE-based DT model for Upper Palaeolithic ground stone tools, which can be used for quantitative analyses of these archaeological items. The DT model simulates the replicas compatible with archaeological GSTs to investigate the damage patterns on the surface produced from Rumex crispus achenes processing, documenting the surface modifications during the experimental test through photogrammetric acquisition processes. The 3D model of the stone tools at the initial state has been used as input for the development of the DT model. The CAD surface triangulation has been edited to obtain a FE mesh optimised for achieving a good accuracy of the results with a reasonable computational time for the simulations.A pair of experimental tools have been employed for The resulting DT model conjugates an accurate geometry of the stone tools and an advanced computational method to simulate damage of the tool during its use: the phase-field approach to fracture has been employed to simulate the progressive damage of the stones. The physics-based DT predicts the deformation of tools and their damage by controlling the initial position of the stones, the direction with which the active tool is pushed against the passive one, and the inclination angle of the motion. The DT model offers high control on the test settings and can be used to evaluate the effect of the variability of the human actions which cannot be easily controlled in actualistic tests or replicated within machine-controlled experimental tests.The DT model has been exploited to simulate the manual operations, evaluating the amount of damage caused by these actions on the surfaces and comparing the damage patterns left on the physical objects during the experimental tests. The observed damage patterns significantly vary with the different configurations. This leads to an important guideline for archeologists for the design of controlled experiments under force control in the lab: the mechanical devices should consider perturbations in the location of the impact and in the inclination angle to lead to damage effects consistent with the actual manual use of the stone tools. The preliminary results on the grinding simulations showed that the DT model can capture the damage evolution due to the active stones horizontal movement which enlarges the traces left on the stones' surface with respect to the damage caused by the vertical stroke.The presented DT model represents a first step in the direction of a more realistic DT of the GSTs, which can be enriched by considering the impact dynamics, a wear model for a more accurate simulation of the effects of cyclic movements. A third body involved in the simulated task  has so far neglected because the present study focuses on the first stone impact and the following grinding motion, however presence of the vegetal resources could be introduced in the DT in future investigations on wear. Moreover, by producing videos and digital reproductions of the prehistoric use of these tools, the DT model also provides an instrument to enhance the exhibition of these items in museums, increasing visitors' interest in these archaeological objects.38. These images are then utilised to identify reference points, and through the triangulation process, the relative positions of these points in 3D space are determined. Structure-from-Motion and Multi-View Stereo reconstruction techniques were employed to obtain detailed 3D models of both passive and active stone tools before and after the experimental tests. The design of an ad hoc setup specifically tailored for these samples is extensively documented in35. It was guaranteed that each time the stones were removed from the acquisition setting for use in replicative experiments, they would be placed back in space with the same position and orientation. The acquired photographs, 114 per object, were then subjected to photogrammetric processing using Agisoft Metashape software, enabling the generation of highly refined 3D models of known accuracy. Finally, the resulting models were exported in STL format, preparing them for further analysis and visualisation  the presence of defects in the STL file, as holes, overlapping faces, and non-manifold edges; (2) the huge dimension of the resulting FE mesh to lead to computationally feasible simulations. Regarding the latter issue, we remark that the surface triangulation provided by the STL file resulting from the high-fidelity acquisition techniques described in the previous section is usually very fine to capture even the smallest features of the object. Since the DT model will be exploited for contact mechanics and near-surface damage simulation on specific portions of the tool boundary, the fine scale model should be retained especially on that zone.The DT model of the stones has been devised starting from the geometrical data contained in the STL file, transforming the surface triangulation in FE volume mesh discretization. The challenge here regards the fact that, even if a robust open source software like GMSH40 and MeshLab41 to regularise the mesh, design the elements dimensions and fill the holes in the STL mesh and to deal with defects like overlapping faces and non-manifold edges.These issues have been tackled and successfully resolved through two software for STL editing developed mainly for 3D printing applications: Autodesk MeshMixerFinally, the FE mesh has been imported in Abaqus (ver. 6.14) to simulate the damage process of the stones using the phase-field approach for fracture developed by the authors and implemented as a user element UEL subroutine, see Supplementary Information 1.Supplementary Legends.Supplementary Video 1.Supplementary Video 2.


Stock market forecasting is one of the most challenging problems in today’s financial markets. According to the efficient market hypothesis, it is almost impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to predict the direction of the stock market. First, these models are trained and validated using the traditional methodology on a historic data captured over a 1-day time frame. Then, the models are trained using the proposed methodology. Following the traditional methodology, Logistic Regression achieved the highest accuracy of 85.51% followed by XG Boost and Random Forest. With the proposed strategy, the Random Forest model achieved the highest accuracy of 91.27% followed by XG Boost, ADA Boost and ANN. In the later part of the paper, it is shown that only classification report is not sufficient to validate the performance of ML model for stock market prediction. A simulation model of the financial market is used in order to evaluate the risk, maximum draw down and returns associate with each ML model. The overall results demonstrated that the proposed strategy not only improves the stock market returns but also reduces the risks associated with each ML model. Stock markets being one of the essential pillars of the economy have been extensively studied and researched . ForecasThe stock market can only move in one of the two directions: upwards (when stock prices rise) or downwards  . GeneralThe difficulty of the stock market prediction drew the attention of numerous researchers worldwide. A number of papers have been presented that could predict the stock prices based on ML models. These models include Artificial Neural Network (ANN) , DecisioDifferent ML models like RF and stochastic gradient boosting were used to predict the prices of Gold and Silver with an accuracy of more than 85% . A novelDifferent ML models perform differently on the same historical data. Their performance depends on the type of data and the duration for which the past data is available. In many recent papers, multiple ML models were used on the same financial time series data to predict the future price of the stock to see the performance of each ML model –24. CompIn some recent studies, hybrid models (a combination of different ML models) are used to forecast stock prices. A hybrid model designed with the SVM and sentimental-based technique was proposed for Shanghai Stock Exchange prediction . This hyIn almost all the proposed ML-based systems, a primary limitation has been observed in the empirical results. The performance of the ML models were only gauged by their classification ability. Although, it is one of the important parameters being used for the evaluation of the ML model, but it is insufficient to determine the performance of the ML model for stock market prediction. The classification metrics do not take into the account some important factors like returns, maximum draw down, risk-to-reward ratio, transactional cost and the risks associated with each ML model. These factors must be considered in the evaluation of ML models for stock market predictions.The following are the major contributions of paper:A performance comparison of nine ML models trained using the traditional methodology for stock market prediction using both performance metrics and financial system simulations.Proposing a novel strategy to train the ML models for financial markets that perform much better than the traditional methodologies.Proposing a novel financial system simulation that provides financial performance metrics like returns, maximum drawdown and risk-to-reward ratio for each ML model.The rest of the paper is organized as follows: The next section explains the proposed methodology used in training nine ML models for stock market prediction. Section III analyses the outcomes of simulation models in detail. This section consists of ML models simulation as well as Financial models simulations. The conclusions and future directions are discussed in Sections IV and V respectively.In this paper, a software approach is used to apply different ML algorithms to predict the direction of the stock market for Tesla Inc. . This prThe flowchart of the methodology is illustrated in Traditionally, the stock price at the end of the day (EOD) is used in ML-based systems. The variation in the stock price is usually the most in the first hour after the market is open. So, stock price within this hour is more effective than the EOD stock price. The direction of the market is set by the business done in this hour. So, in this paper, the stock price after 15 minutes, when the stock market is open, will also be extracted. The results from the stock price at EOD will be compared with the results from the proposed 15 minutes strategy.Once the stock price data has been extracted, the subsequent stage involves computing various input features from the technical indicators and statistical formulas. Nine input features, listed in Following the overfitting tests, the input data is divided into training and testing data. The data is then normalized using Min-Max normalization technique to prevent the biasing phenomenon. Normalization is performed using the following The input features and output variables are provided to the ML models in order to detect the patterns within the training data. Various ML models have been employed in this study. In the next step, the outcome of the trained ML models is assessed using some performance metrics. There are a number of classification metrics that can be used to evaluate the performance of an ML algorithm . UsuallyFor evaluation purposes the accuracy, ROC_AUC and F1_score are useful measures, however, they are not sufficient for all problems. Recall and precision are two additional well-known metrics for classification problems , 48. TheAdditionally, a confusion matrix is used to summarize the performance of each ML model. It provides detailed insight into ML predictions by indicating False Positives (FP), True Positives (TP), False Negatives (FN) and True Negatives (TN) . False PIn the next step, a novel financial model is developed and simulated to analyze the performance of the trained ML models. The financial performance metrics like Sharpe ratio, maximum drawdown, cumulative return and annual return  are usedp = Return of portfolio, Rf = Risk free rate, σ = Std of portfolio excess return, P = Peak value before largest drop, and L = Lowest value before new high.The Sharpe ratio is the measure of risk-free return while the maximum drawdown is the greatest decline in the value of the portfolio . The equAnnual return is the return gained during the period of one year while the cumulative return is the total return on the invested capital within any specific time frame. The expressions for annual return and cumulative return are shown in Eqs  and 9)..9).AnnuTesla Inc. is a major American automobile company producing technologically advanced electric vehicles. The company has recently obtained a lot of attention due to its stock prices. A drastic increase in revenue in the year 2021 made Tesla stocks very appealing for capitalists and investors around the world as shown in The stock prices for Tesla Inc. from 2016 to 2021 are considered for experimental evaluations in this paper. Furthermore, the data is split into training data and test datasets. First, the optimal parameters settings for the nine ML models are selected through GridSearchCV. The selected optimal parametric settings for each model are shown in The simulations for stock market prediction are performed using Python on a Jupiter notebook. ML models were evaluated using Tesla Inc. stock prices for a 1-day time frame and 15-min time interval strategy. These models were first trained on the data from Jan 01, 2016 to Nov 15, 2020. The trained models were then validated on the test data from Nov 16, 2020 to Dec 31, 2021 as shown in Tables The confusion matrix in Based on the discussion above, it can be seen that the performance of Logistic Regression model is better than the rest of the models for 1-day time frame. Even though its accuracy among the nine ML models is only 85.51%.The graphical illustration of the predictions made by the Logistic Regression model for a 1-day time frame can be seen in In this paper, a novel 15-min time interval strategy has been proposed. In this strategy, the initial 15-min time interval is filtered out from 1-day time frame. Then the filtered 15-min time frame is used to train and validate the ML models in order to make prediction for the time frame of 1-day.The confusion matrix in The graphical illustration of the predictions made by the Random Forest model is shown in In this section, a novel financial simulation model is built that is able to make investment based on the decision of the ML model. Each ML model is evaluated using financial parameters to validate their performance and suitability for real-time stock market trading. The performance of ML models is gauged using cumulative return, annual return, maximum drawdown, Sharpe ratio and capital in hand at the end of the investment period.Initially, a USD 10k is invested. A commission fee of 0.1% (Alpaca standard commission fee) is set for each buy or sell trade. Based on the prediction by the ML model, a decision regarding buying, holding or shorting a share is taken. A single share is bought or sold on each trade to validate the performance of ML models.Figs The simulated outcomes of the ML models to forecast the stock price of Tesla Inc. for a 1-day period are displayed in The reason for better revenue generation by the Random Forest model is the quality of each True Positive and True Negative outcome. Even though the accuracy of the model is inferior to the Logistic Regression, each of its correct prediction resulted in more profit. The annual growth of Tesla Inc. from 2020 to 2021 is more than 70% as shown in The portfolio performance of the ML models using the proposed approach of a 15-min time interval strategy is shown in The above discussion shows that KNN is the worst performing model on the proposed strategy. Although, Random Forest is the best model in terms of portfolio returns but ANN is the most rewarding model with a Sharpe ratio of 0.91 on the proposed 15-min time interval strategy.In this paper, nine ML models are used to predict the direction of the Tesla Inc. stock prices. The performance of this stock is first assessed for a 1-day time frame followed by a proposed 15-min time interval strategy. Following the traditional methodology, the Logistic Regression achieved the highest accuracy of 85.51% while Naive Bayes model is found to be the least accurate model with an accuracy of 73.49%. The proposed strategy significantly improved the classification performance of the ML models. With this strategy, the Random Forest model achieved the highest accuracy of 91.93% followed by XG Boost and ADA Boost. Conversely, the KNN model is found to be the least accurate model with an accuracy of 80.53%.In this paper, it was shown that only classification metrics are not enough to justify the performance of ML models in the stock market. These metrics do not consider important factors like risk, maximum draw down and returns associate with each ML model. A simulation model of the financial market is used to simulate the trained ML models so that their performance is gauged with actual investment strategies. The evaluated results revealed that although some models are performing well in terms of portfolio returns on a traditional methodology but models on the proposed 15-min time frame strategy are significantly better in terms of risk to reward ratio and maximum drawdown. The evaluated result shows that Random Forest outperformed other models in terms of returns in both 1-day and 15-min time interval strategy.Some other interesting observations are revealed by the comparison of the classification and financial results. The Logistic Regression model has the highest accuracy for a 1-day time frame data. So, it was expected that this ML model will generate the highest revenue. However, the outcome of the financial simulations showed different results. Similarly, the accuracy of the Random Forest model for a 15-min time interval strategy was much higher than the accuracy of the Random Forest model for a 1-day time frame. But instead of generating higher revenue on 15-min time frame strategy, it generated higher revenue on 1-day time frame. The above discussion revealed that however, the accuracy of the ML models is an important factor but the quality of each true positive outcome and true negative outcome is an equally important factor in the performance evaluation of the ML models for stock market prediction.The overall results show that the proposed strategy has not only improved classification metrics but it also enhanced the stock market returns, risks and risk to reward ratio of each ML model. Additionally, the results also revealed that how important it is to consider both classification as well as financial analysis to evaluate the performance of the ML model on stock market.S1 Filehttps://github.com/AzazHassankhan/Machine-Learning-based-Trading-Techniques/.The data and script has been uploaded to GitHub. It can be accessed using the following link: (IPYNB)Click here for additional data file.