With the progression of marine exploration and exploitation, as well as the advancements in mechanical intelligence, the utilization of the unmanned surface vehicle (USV) and the design of their guidance system have become prominent areas of focus. However, the stern ramp recovery of the USV is still in its infancy due to its unique attitude requirements and automation design. Furthermore, few studies have addressed the impact of maritime disturbances, with most research limited to simulations. To enhance the efficiency and accuracy of stern ramp recovery, this paper presents the development and construction of a novel recovery system. By incorporating physical modeling of disturbance forces acting on USVs at sea, the practicality of the system is improved. Additionally, an optimized genetic algorithm is introduced in the navigation module to improve convergence rates and subsequently enhance recovery efficiency. A line-of-sight (LOS) algorithm based on average velocity is proposed in this paper to ensure the attainment of unique attitude requirements and to improve the effectiveness of stern chute recovery. This paper provides a detailed description of the independently designed USV hardware system. Moreover, simulations and practical experiments conducted using this experimental platform are presented, offering a new solution for the USV’s stern ramp recovery. If you ask chatGPT how much of the ocean has yet to be explored by mankind, it will tell you that over 80% of the ocean remains unmapped, unobserved, and unexplored. To be honest, we have no idea exactly how much of the ocean is uncharted by humanity. But we cannot deny that there has been an increasing enthusiasm and confidence among researchers in the development of ocean exploration technologies in recent years. Driven by oceanographic research and other marine equipment requirements, the unmanned surface vehicle (USV) has gained worldwide attention for its remarkable autonomy and mission assistance capabilities.Compared to other vessels, the USV has the advantage of being smaller and operating more agilely, requiring only sufficient space for the relevant sensors and auxiliary navigation devices to make sure the vehicle functions properly. Moreover, the USV can be applied in extreme conditions independently, such as strong waves, tides, and radiation leaks, by means of a predefined system or remote control by professionals; thus, the safety of the operators can be ensured. Due to a variety of these aforementioned advantages, the USV has been well-accepted in both civilian and military fields in recent years ,2,3,4, aUnlike maritime navigation, the recovery situation of the USV does not need to consider the International Regulations for Preventing Collisions at Sea (COLREGS) for its immediate environment, close to the stern ramp of its large target ship, without the interference of other vessels. The intelligence of the stowage and release process of the USV will have a direct impact on the efficiency of its mission performance. It is mainly divided into hoist-based recovery, well-deck recovery, and stern ramp recovery. Examples are shown in The stern ramp recovery technology exhibits superior overall performance in terms of the mother ship’s hull structure requirements, adaptability to water surface conditions, and stowage efficiency, making it highly suitable for the rapid autonomous recovery of USVs. This method not only allows the mother ship to retract and release small boats at higher speeds, but also adapts to sea conditions of level 6. This advantage allows the mother ship to choose the appropriate timing for flushing. The primary operating principle of the stern ramp recovery system is that during recovery, the USV navigates a predetermined trajectory toward the mother ship’s aft section. During this approach, the USV continuously adjusts its heading angle to ensure alignment with the centerline of the mother ship’s stern ramp. In addition, it maintains a specified distance from the aft section of the mother ship while staying on course. However, achieving precise centering and control near the stern ramp remains challenging. The low dependency on manual assistance from the mothership during the recycling process results in high autonomous navigation accuracy and strong resistance to wake interference from the USV. This places higher demands on the convergence rate of the USV path planning algorithm and the accuracy of the recovery system. The development of autonomous recovery technology for USVs is still in its nascent stages.Due to the special requirements of stern ramp recovery for the path and USV’s attitude, the question of which path planning algorithm to choose is particularly important for the establishment of the recovery system. We hope that factors such as resistance to ocean current disturbance, smooth motion, accurate heading angle, and reasonable dynamic parameters of the USV are comprehensively considered in the proposed recycling system. Therefore, the algorithm used should have a multidimensional evaluation system rather than a single optimization method. In comparison with other intelligent algorithms, the genetic algorithm (GA) has significant advantages due to its adaptive evaluation indicators. Also, the genetic algorithm does not fall into the local optimum problem as easily as gradient-based optimization algorithms. For this reason, we chose this algorithm and optimized it to be combined with the recycling system. Furthermore, a suitable control system for the dynamics of the USV is imperative from a hardware perspective. To enhance the realism of the simulation in a maritime environment, we also conducted modeling of the interference environment and incorporated it into the tracking algorithm. Our main contributions are summarized as follows.We conducted physical modeling of currents and waves to analyze the impact of USV movement in a water environment from a force perspective. Subsequently, we applied these findings to our tracking algorithm in order to enhance the accuracy and effectiveness of the tracking process.To meet the specific requirements of stern ramp recovery, we enhanced the genetic algorithm by incorporating three-dimensional modeling and genetic manipulation techniques. This resulted in an improved convergence rate of the algorithm. Additionally, we designed a multi-dimensional evaluation index fitness function to streamline the screening mechanism.We improved the tracking algorithm and introduced a new tracking algorithm specifically designed for average speed based on line of sight (LOS). This exclusive algorithm ensures the effectiveness of waypoint tracking.We independently constructed the experimental platform and devised a hardware connection system within the USV. This system serves as a solution for implementing the algorithm in the actual operation of the USV.Based on the detailed introduction of the recycling method provided in the previous section, this section will primarily focus on presenting the relevant methods employed by scholars for unmanned boat path planning. Furthermore, we will elucidate the rationale behind our selection of the genetic algorithm and LOS as the research methodology.Currently, path planning algorithms used for USVs in domestic and international research can be classified into four main categories: graph search algorithms, virtual potential field methods, random sampling algorithms, and intelligent algorithms.Graph search algorithms are considered classical path planning algorithms; however, their usage has declined in recent years due to the slow computational speed of individual underlying logic. Among these algorithms, the A* algorithm has garnered attention for its remarkable scalability. Singh et al.  considerThe basic concept of the virtual potential field (VPF) method is to construct a virtual potential field within the map using certain techniques. The path is then generated using the gradient descent algorithm. However, this method suffers from inherent limitations such as local minima and oscillations. Kim et al.  integratRandom sampling methods exhibit varying convergence rates, making them extensively employed in the context of Multi-Agent Path Finding (MAPF) due to their inherent efficacy. Lee et al.  introducIn terms of obstacle avoidance, Khan et al. introduce a penalty term in the objective function, so that the tracking control and obstacle avoidance problems are unified into a constrained optimization problem, and active rewards are given to obstacle evaders . As objeThe purpose of tracking control is to manipulate the propulsion system, such as the propeller and rudder, based on a rational tracking control law. This enables the USV to navigate along the intended trajectory determined by the path planning module. Depending on whether the trajectory includes a time dimension, the problem can be divided into two categories: path tracking and trajectory tracking. The stern ramp recovery involves dynamic obstacle avoidance and requires a higher speed than the mother ship during the slope flushing phase. Therefore, it is a trajectory tracking problem.Pettersen  used theTo improve the tracking stability and accuracy of the USV under environmental disturbances such as wind, waves, and currents, Caharija  introducLiu  proposedDue to the particular environment of the autonomous recovery using a stern ramp, most of the recovery strategies have less concern about interference caused by currents as well as the effects of malposition on recovery effects. For a successful ramp approach, it is essential to have sufficient power and to approach the ramp at an optimal angle. This combination guarantees a seamless and proficient transition onto the ramp surface, facilitating a smooth and efficient docking process. To ensure the safety and efficiency of the recovery process, we have developed an innovative stern ramp recovery system that prioritizes three key factors: precise visibility, seamless guidance, and stable navigation. The recovery system structure is shown in Enter the initial parameters for the planning algorithm into the upper computer, wait for it to complete the calculations, and then package and transmit the set of waypoint information generated by the genetic algorithm to the USV end. This set of information includes the coordinates, real-time velocities, and headings of each waypoint. Upon receiving the information, the PID controller actuates the electronic speed regulators to regulate the propellers, thereby adjusting the speed and heading of the USV. Meanwhile, the LOS controller accurately tracks the waypoint to prevent interference such as currents from causing the USV to deviate from the planned route. In the meantime, the actual position information of the USV obtained by the Global Positioning System (GPS) will also be sent to the upper computer together with the current speed and the heading.The recovery process of the USV is affected by external environmental disturbances, which have a significant impact on the speed and stability of the centering procedure. Therefore, it is essential to develop a suitable mathematical model to account for these external disturbances. Factors such as sea currents and waves particularly affect the autonomous guided recovery motion.The motion of the USV can be summarized into 6° of freedom, and in order to accurately describe the motion state of the USV, a combination of a geodetic coordinate system and follower coordinate system is used to describe the position and attitude information, as shown in m denotes the quality of the USV, v and u present the transverse and longitudinal velocities, r is the bow-swinging angular velocity, Assuming that the center of gravity of the USV aligns with the center of the follower coordinate system, we can deduce the equation of motion governing the forces exerted on the USV during its movement. This equation is derived from the Newton–Euler equations for rigid body motion in a fluid medium and is given as Equation .(1)mu˙−The disturbance force and disturbance moment experienced by the USV due to sea currents can be attributed to two main components: 1) viscous resistance, arising from the friction and pressure difference between the USV and the surrounding fluid; (2) inertial resistance, resulting from the circulation patterns near the USV and the influence of the free liquid surface. The equations are given by Equation ξ=hwThe quality, efficiency, and convergence of path planning algorithms are crucial factors to consider. The genetic algorithm, known for its intelligent nature and multi-evaluation index mechanism, offers significant advantages in global path planning. However, traditional genetic algorithms rely on two-dimensional raster graphs, which suffer from time-consuming computations, unstable algorithmic architectures, and non-uniqueness in convergence rate performance. To address these limitations, we propose a novel approach that combines the characteristics and requirements of stern chute recovery, focusing on optimizing waypoint distribution, improving population initialization, and utilizing advanced genetic methods to establish a more suitable path planning algorithm.Considering the wide range of actual distances in latitude and longitude recorded by GPS, it becomes necessary to perform additional processing on the coordinates. In our simulation process and program design, we use the following coordinate information transformation formula for actuation:It should be noted that there is a trade-off between the computational time and the resolution ; if the Considering the sparsely distributed obstacles on the water surface, we have opted to use circular bounding boxes to represent these obstacles, as illustrated in Upon introducing the time dimension into the modeling, the bounding boxes of the obstacles are represented as oblique or straight cylinders in the x-y-t coordinate system. The expression of the three-dimensional geometry is depicted in Equation .(8)X−XoIn order to simplify the mathematical modeling of obstacle avoidance, we made the following assumptions.Dynamic obstacles move in uniform linear motion.The USV performs segmented uniform acceleration linear motion, with each consecutive pair of waypoints forming a segment.By solving for temporal intersections between the motion trajectory of the USV and the movement trajectory of the obstacles, we can determine whether the proposed planning will result in a collision. The relevant equation is given by Equation .    (9)Given the particularity of stern ramp recovery, we adopt the horizontal line representing the stern ramp as the x-axis in a 2D plane. This division separates the water into two symmetrical parts, one above and one below the stern ramp. The distribution of obstacles and the performance of the planned path are shown in In this paper, the genetic algorithm selects the population as the solution set for the recovery paths. Each path is represented by a distinct genetic individual. These individuals encompass three-dimensional coordinate information of multiple waypoints, storing the X, Y, and T coordinates along with real-time speed. The enhanced global planning algorithm autonomously determines the number of gene loci based on the input start and end points. This effectively enhances the computational capability of the genetic algorithm. Rather than generating the waypoints randomly, we designate the X-axis direction as the step direction and establish the range of intervals for each waypoint based on the step size n represents the number of bits in the binary sequence and N represents the total number of waypoints. An example of the encoding is shown in After that, the waypoints will then be generated in each interval in turn. The code conversion between binary and decimal is shown below.The binary sequence serves as the fundamental basis for genetic algorithm mutation. However, a common issue arises with value overflow subsequent to decoding. Consequently, a mapping procedure is introduced to the binary sequence, involving binary conversion, decoding, and subsequent restoration of the original decimal sequence through inverse mapping. This measure effectively addresses the challenge of overflow encountered in the process.The collision avoidance operations of the traditional genetic algorithm, based on 2D raster maps, consume significant memory resources and require additional time. To address this issue, as mentioned in the previous section, we employed a memory-efficient obstacle design during the 3D modeling process. This approach enables collision avoidance operations during population initialization. As illustrated in The traditional genetic algorithm does not achieve complete global convergence. During the genetic variation process, each individual has the possibility of undergoing crossover and mutation, resulting in changes in their sequence and original expression type. This mechanism serves as the primary means for the genetic algorithm to converge. However, this approach may cause individuals with excellent phenotypes to lose their advantageous genes, thereby reducing the retrieval speed .Many scholars have demonstrated that the genetic algorithm with Elitism Strategy achieves global convergence . After pDue to the requirements of rollover prevention caused by inappropriate heading and large turns in the surface environment, we add dynamic metrics to the fitness function to evaluate the performance of USVs. In cases where the generated path does not satisfy the constraints of obstacle avoidance and dynamic metrics, the fitness function is assigned a value of 0. By implementing the Elitism Strategy, individuals with superior genetic traits are preserved, thus minimizing the impact of discarding individuals with undesirable traits on the overall population. In addition, the presence of defective individuals during the evolutionary process consumes computational memory resources, resulting in reduced computational efficiency and increased traversal time of the algorithm . To addrIndividuals with a fitness function of 0 are treated as individuals that have died after the population iteration, and their memory is released when the algorithm is implemented. Subsequently, a local initialization scheme is employed to allocate a new path planning to the discarded genetic individuals, considering them as newly generated offspring within the population. In the subsequent genetic mutation operation, these regenerated offspring are hybridized with other genes present in the population. Simulation experiments demonstrate that this operation reduces the convergence time by approximately 10 s and minimizes the number of iterations required, thereby endowing the genetic algorithm with a notable real-time advantage in dynamic obstacle avoidance.The genetic algorithm is designed to seek optimal solution outcomes. In the event that the quality of the generated path fails to meet the threshold established by the program, the iteration process will persist. In order to uphold real-time planning efficacy, we have imposed a maximum iteration limit of 500. Through extensive simulations and empirical observations, we have determined that this value significantly surpasses the number of iterations necessary to yield results that align with the desired objectives. The pseudo code is shown in Algorithm 1.Using an ex ante method to analyze, the computational complexity of the proposed genetic algorithm is The implementation of a fitness function aims to enhance the initially subtle distinctions among individuals in the population through calibration and dynamic mapping. This process facilitates the clear manifestation of the variations in phenotypic excellence between different individuals and enables a more effective selection of genetically qualified individuals.Algorithm 1 Optimized Genetic AlgorithmInput: Output: getPath: the planning path which consists of waypoints;1:set 2:Foundation; //generate initial population3:4:while do5:    6:while do    7:if isCollision, their performance is not always satisfactory. Achieving constraint optimality through penalty functions relies on the discovery of appropriate penalty parameters. Even so, determining these parameters often necessitates numerous experiments and has posed a challenge for researchers . Khan etWe propose a set of adaptation functions that incorporate a penalty weight allocation mechanism. A novel nonlinear fitness relation is established by penalizing the total distance, time spent, degree of alignment, and amount of cornering in recovery planning. In addition, this approach combines the constraints related to collision avoidance with those concerning the dynamics of the USV. While we currently cannot overcome this limitation, our approach simplifies this process by introducing various indicators.a,b,c,d are parameters set by the researcher.Quantitative indicators and fitness can be calculated by the following equation:The fitness function mechanism is the key to ensuring good convergence and screening mechanism in the genetic algorithm. We restricted many variables relating to dynamics in the fitness function. To control the motion attitude and smoothness of the vehicle, we optimized the acceleration of each route segment and screened the randomly generated waypoint velocities.Due to the fact that all waypoint information is packaged through the communication system to the USV end, which is controlled and processed by controllers to optimize path details and tracking effects, optimizing variables such as turns and malposition in path planning algorithms can not only reduce the computational burden of controllers such as LOS, but also facilitate the effective control of the mechanical motion of the propeller by controllers such as PID. The USV’s motion is based on controlling the left and right propellers, and smooth propeller motion is conducive to dealing with ocean current disturbances.The trajectory tracking algorithm calculates the heading angle and velocity of the USV based on its current position, velocity state, and desired trajectory curve. This algorithm guides the USV to continuously approach the target trajectory, similar to missile guidance principles. Notable guidance laws for trajectory tracking include LOS, Follow the Carrot (FTC), Constant Bearing (CB), and Proportional Navigation (PN). Among these, the LOS algorithm stands out for its ease of use, reliability, and robustness, which have been extensively validated through research investigations . TherefoAs shown in The current LOS and its improved algorithms are limited to guidance in the spatial domain only, which means that it can only derive a target heading from the forward viewpoint and has a very limited ability to handle speed. This means that it can only derive a target heading from the forward viewpoint, and has a very limited ability to handle speed. For this purpose, we introduce an enhanced algorithm called Average Velocity Line of Sight (AV-LOS).R are As shown in In order to enhance the anti-interference capability of the tracking algorithm, we conducted data tests on simulated ocean waves using the aforementioned methods. Wind and wave data for various sea conditions, such as wave height, were obtained from the International Sea State Scale. Since it is not feasible to measure the level of interference caused by waves during actual operation, we could only apply pre-set compensation measures against such interference. Multiple experiments were conducted in the ship pool laboratory, revealing a maximum deviation of 4.94° for the heading angle and 0.16 m for alignment.After performing calculations based on the formula presented in In this section, we will present the hardware model of the stern ramp recovery system along with the underlying framework logic. Additionally, we will showcase the outcomes of simulation and field experiments conducted using the platform. The results will be analyzed using quantitative indicators and graphical representations for a comprehensive evaluation.At the mother ship’s end, a fixed steel transom chute is installed at the shore to replicate the mother ship for simplicity. The steel transom chute features an upward-sloping U-shaped opening with cylindrical rolling sheaves on the inside, as depicted in As shown in To streamline the development process, we opted for Visual Studio 2022  and utilized the C# language to create a Windows application. The upper computer was developed using the C# language, and its software interface is illustrated in After constructing the hardware/software system for the recovery system as outlined in the preceding section, a series of tests were conducted in lake waters. The primary objective was to validate the fundamental functionalities of the recovery system. Upon successfully debugging the system’s hardware and software components, a dedicated recovery scenario was devised to facilitate a series of recovery tests. These tests aimed to evaluate the effectiveness of the improved genetic algorithm (IGA) path planning algorithm and the AV-LOS tracking algorithm.By distinguishing between the length of the route plan, the size of the corners, and the number of surrounding obstacles, we designed four arithmetic examples with different starting points, as shown in By incorporating control variables, we have formulated these computational scenarios. The simulation outcomes will be depicted in After generating each set of operating algorithms, we applied curve fitting using Moreover, the deficiency of a multidimensional evaluation system has contributed to the notably poor performance of the EG3 item across all four algorithms. It is apparent that there are numerous challenging corners in practical implementation, resulting in compromised path smoothness. In conjunction with the previously mentioned evaluation metrics formulas, we conducted a comprehensive comparison of the evaluation metric values for malposition and large angles in the IGA and EG3 samples across the four examples. The results have been carefully tabulated and are presented in This is because the convergence and screening mechanism of the genetic algorithm is closely related to the design of the fitness function. The fitness function of IGA strictly limits the recovery attitude requirements for stern ramp recovery to moderate and heading. In addition, EG3 also lacks constraints on the path smoothness parameter indicators, resulting in uneven speed and frequent sharp turns.To validate the effectiveness of the preheating process in improving the genotype quality of the initial population and consequently reducing the number of iterative generations required, we conducted 100 experiments utilizing an arithmetic case sample with a starting point of . These experiments were carried out both with and without the preheating operation. We set the number of elite groups for the experiments to 10. Moreover, to ensure better visual interpretation of the results, we enforced a fixed number of iterations per dataset at 500, which significantly exceeds the number of iterations typically needed for path planning. By recording the average fitness function values for the elite group in each generation, we analyzed and presented the results in Based on the experimental results, it is evident that the example utilizing the preheating process exhibits a higher fitness function value in the initial stage of the algorithm, indicating its effectiveness in improving the genotype quality. As depicted in The Elite Strategy and SSIH contribute to an accelerated convergence rate primarily by enhancing genetic manipulation techniques, as evidenced by the time taken for computational planning displayed in It is evident that the incorporation of the Elite Strategy or SSIH yields advantages in enhancing the convergence rate. As the planning difficulty increases, there is a notable increase in computation time. However, these methods operate through distinct mechanisms. The former safeguards the retention of high-quality individuals during the genetic process by preserving their genotypes in each generation. On the other hand, the latter facilitates genetic diversity by releasing redundant memory and generating novel samples.Based on the comparison of dimensionless evaluation metrics, it is observed that the planning results achieved by IGA exhibit superior performance in terms of smoothness, path distance, and time consumption compared to the other two algorithms. However, it should be noted that there are instances where the differences in metrics for some data are not particularly significant. To improve the overall quality of the planned path, our fitness function adopts a weighting strategy. Even if the fitness function values of the final screening are significantly different, some qualitative indicators may be similar.R = 3, and the following assumptions are made:The USV utilizes a fixed control period T for speed and heading control. At the start of each period, the desired heading and desired speed are determined by solving the guidance law.The hysteresis effect in heading control is disregarded, and changes in speed are treated as linear transformations that either increase or decrease over time.The AV-LOS algorithm simulation is implemented using MATLAB. As an illustrative example, the desired trajectory S in  space is composed of three motion segments concatenated together. The segments consist of The simulation results are shown in The formula for trajectory deviation It is evident that the deviation of the 2D trajectory is minimal. Nonetheless, owing to the inclusion of the forward view distance, the actual trajectory undergoes smooth transitions with certain curvature during right-angle turns. Consequently, a deviation emerges at the termination point of the 3D trajectory. The LOS trajectory deviation takes the form of a three-step staircase since it directly adopts the isochronous target speed as the desired speed. Employing a random function, generate 50 sets of initial coordinates and repeat the simulation 10 times for each set. Calculating the average trajectory deviation for each algorithm, the resulting average deviation for the LOS algorithm is determined to be 2.0402 m, whereas, for the AV-LOS algorithm, it is found to be 0.8161 m.We selected the initial three samples, which demonstrate significant variations in their planning characteristics, for conducting field experiments. In order to investigate the trajectory tracking performance of AV-LOS under various wind and wave interference scenarios, we recorded the first set of field-tested waypoint planning data and replicated the experiment twice at different times. The resulting actual paths of each experiment are denoted as Real path1, Real path2, and Real path3, respectively. The real GPS coordinates obtained from these experiments are visualized using a It is evident that the actual planning results closely resemble the simulation results, with a slight deviation observed during larger corners. This discrepancy can be attributed to varying disturbance conditions, which result in different magnitudes of translation for the USV during the cornering process. This phenomenon is closely associated with the tracking radius R set in the AV-LOS algorithm. As depicted in Additionally, it is observed that the actual trajectory tends to have larger corners compared to the planning path. This is due to the necessity for the USV to undergo a dynamics adjustment process while making turns in a water environment. However, it is crucial for path planning to avoid sharp corners, as they can render the waypoint tracking ineffective during the dynamics adjustment process and result in a significant deviation from the intended course. Although the simulation results demonstrate that AV-LOS exhibits a small tracking error, a strategy solely focused on speed-oriented tracking is not an optimal solution for this issue.To provide a more comprehensive analysis of the system’s actual planning effectiveness, we conducted a thorough examination of the collected heading and speed data, as shown in From We removed the process of rapid speed changes during the start and end phases and analyzed the speed changes of the USV during the driving phase. It can be observed from This paper presents an improved genetic algorithm for a stern ramp recovery system of the USV. The genetic algorithm, known for its global planning capabilities and retrieval efficiency, has proven to be highly practical. However, traditional two-dimensional modeling approaches and single fitness evaluation systems significantly increase the time required for path planning.In the realm of algorithm optimization, we validated the viability of a genetic algorithm utilizing 3D modeling alongside a novel coding mechanism. By enhancing genetic operations to boost progeny diversity, we achieved a substantial improvement in the convergence rate. Furthermore, the multifactor screening of the fitness function enhances efficiency, albeit placing higher demands on the formulation of linear coefficients. By incorporating the forces exerted on the USV in real water environments through accurate modeling, we have integrated them into the enhanced AV-LOS algorithm. Furthermore, a method diagram for the AV-LOS algorithm is provided. Through simulation experiments, it has been demonstrated that the AV-LOS algorithm outperforms the traditional LOS algorithm in terms of tracking performance.We have built a USV experimental platform and designed a hardware system. In practice, the optimized genetic algorithm demonstrates adaptability when dealing with longer distances that involve larger detours. However, due to surface disturbances, the actual path generated using the IGA might experience partial shifts, potentially resulting in less favorable detours. Nonetheless, it possesses an advantage in terms of malposition and being able to return to the original route promptly with the assistance of AV-LOS.However, our system still has several shortcomings. One is that the movement of the obstacles set up in the experiment follows a regular pattern, which may not fully reflect real-world scenarios. The second is that the attitude adjustment of the USV during navigation may occasionally widen the turning path segment, even though the disturbance of the water surface environment is taken into account. And finally, when planning paths with larger turns and smaller total lengths, minor speed fluctuations may occur. In future efforts, we aim to further optimize the hardware system and the structure of the algorithm, while proposing reasonable solutions to deal with these limitations.


The saturating relationship between phytoplankton growth rate and environmental nutrient concentration has been widely observed, yet the mechanisms behind the relationship remain elusive. Here, we use a mechanistic model of phytoplankton and show that the saturating relationship between growth rate and phosphorous concentration can be interpreted by intracellular macromolecular allocation. At low nutrient levels, the diffusive nutrient transport linearly increases with the phosphorous concentration, while the internal phosphorous requirement increases with the growth rate, leading to a non-linear increase in the growth rate with phosphorous. This increased phosphorous requirement is due to the increased allocation to biosynthetic and photosynthetic molecules. The allocation to these molecules reaches a maximum at high-phosphorous concentration, and the growth rate no longer increases despite the rise in phosphorous concentration. The produced growth rate and phosphorous relationships are consistent with the data of phytoplankton across taxa. Our study suggests that the key control of phytoplankton growth is internal, and nutrient uptake is only a single step in the overall process.The Monod equation has been used to represent the relationship between growth rate and the environmental nutrient concentration under the limitation of this respective nutrient. This model often serves as a means to connect microorganisms to their environment, specifically in ecosystem and global models. Here, we use a simple model of a marine microorganism cell to illustrate the model’s ability to capture the same relationship as Monod, while highlighting the additional physiological details our model provides. In this study, we focus on the relationship between growth rate and phosphorus concentration and find that RNA allocation largely contributes to the commonly observed trend. This work emphasizes the potential role our model could play in connecting microorganisms to the surrounding environment while using realistic physiological representations. The equation suggests that extracellular nutrient concentration is the limiting factor of phytoplankton growth rates. Subsequent studies illustrated these nutrient dependencies on growth rates by applying Monod’s theory to various marine plankton and substrates  was recently developed to explore the relationship between growth rate, elemental stoichiometry, and macromolecular allocation  in phytoplankton  Fig. 1)Fig. 1). 43− concentration and growth rate often modeled by Monod kinetics? (2) What leads to the saturating relationship between the growth rate and phosphorus concentration? (3) How does this relationship differ from that between growth rate and nitrogen concentration? Here, we focus on the relationship between the growth rate and the concentration of one of the major nutrients, PO43−, using data of phytoplankton across taxa. The model provides a macromolecular-based interpretation of the widely observed saturating relationship.In this study, we focus on CFM-Phyto’s ability to provide physiological context and use it to interpret the saturating relationship between the growth rate and phosphorus concentration. We developed the model to address the following questions: (1) Can CFM-Phyto capture the saturating relationship between PO43− concentrations for 15 organisms  leads to higher photosynthetic and biosynthetic molecules , leading to higher allocation of P to these molecules, until the N switches to C limitation where N availability may no longer have an impact. Under both N and C limited cases, luxury uptake of P may occur, and the magnitude of the P storage would depend on multiple factors, including the availability of P, growth rate, and the individual capacity of P storage , converging on the best solution with visual trial-and-error. We recorded the resultant values of the maximum growth rate and the half-saturation constant for each data set  and the stoichiometric ratio for the cellular photosynthetic enzyme nitrogen to chlorophyll ratio  for the model runs which was equal to the respective light intensity used in each experiment.Similar to the Monod optimization, we used the Metropolis-Hastings algorithm with CFM-Phyto to converge to the best representation of the data, predicting the best values for the affinity to phosphorus (o APho; . We alsonitrogen , for a s


Biomechanical simulation allows for in silico estimations of biomechanical parameters such as muscle, joint and ligament forces. Experimental kinematic measurements are a prerequisite for musculoskeletal simulations using the inverse kinematics approach. Marker-based optical motion capture systems are frequently used to collect this motion data. As an alternative, IMU-based motion capture systems can be used. These systems allow flexible motion collection without nearly any restriction regarding the environment. However, one limitation with these systems is that there is no universal way to transfer IMU data from arbitrary full-body IMU measurement systems into musculoskeletal simulation software such as OpenSim. Thus, the objective of this study was to enable the transfer of collected motion data, stored as a BVH file, to OpenSim 4.4 to visualize and analyse the motion using musculoskeletal models. By using the concept of virtual markers, the motion saved in the BVH file is transferred to a musculoskeletal model. An experimental study with three participants was conducted to verify our method’s performance. Results show that the present method is capable of (1) transferring body dimensions saved in the BVH file to a generic musculoskeletal model and (2) correctly transferring the motion data saved in the BVH file to a musculoskeletal model in OpenSim 4.4. Biomechanical simulation enables in silico estimations of biomechanical parameters such as muscle, joint and ligament forces. Musculoskeletal models, which depict the human body as a multi-body system, are used to carry out the simulations. This makes the models an interesting tool for different scientific questions throughout many disciplines. Musculoskeletal simulations are utilized for medical investigations such as knee- or hip-implant simulations ,2,3, forOpenSense, a toolkit implemented in the software OpenSim, is presented by Al Borno et al. . OpenSenA method for analysing captured motion data measured by either marker-based or IMU-based systems in real-time is presented by Stanev et al. . The metKaratsidis et al.  exportedA BVH file consists of two parts—a header and a data section. The header section provides information about the hierarchy and the initial pose of the skeleton as well as information about degrees of freedom and rotation sequences. The hierarchy consists of multiple segments that are structured in a parent-child relationship. The initial segment, which has no parent segment, is referred to as the root. In most cases, the pelvis depicts the root. The segments are connected through joints, each consisting of a channel and an offset. The channel defines the number of degrees of freedom of the joint. The offset defines the segment length to this joint and, therefore, the parent segment length. Each joint is named after its child segment. Final joints do not have a following segment and thus are called “EndSite”. Karatsidis et al.  used theCurrently, only tools for specific IMU full-body measurement systems are available in order to transfer IMU data to musculoskeletal models in OpenSim. Other solution approaches enable the transfer of IMU-based motion data into musculoskeletal simulation programs other than OpenSim . Up to now, there is no universal way to transfer IMU data from arbitrary full-body IMU measurement systems into arbitrary digital human modelling software. The objective of this study was to enable the transfer of collected IMU motion data stored as a BVH file to the digital human modelling software OpenSim 4.4 to visualize and analyse the motion using musculoskeletal models. We verified our method for motion using both upper and lower body motions. For the upper body, we analysed an arm-lifting and a reaching motion. For the lower body, we investigated a squat motion. Further, we evaluated the accuracy of our IMU-based motion capture system.The method for transferring and analysing motion stored in the BVH file format to a digital human modelling software is described below. The method is presented using the OpenSim 4.4 software as an example. To simplify the description, we have divided the method into four subsections see . A gener(1)In the first step, the header part of the BVH file is used to create a stick figure model in OpenSim, which represents the skeleton information of the BVH file. Hereby, each segment of the skeleton is represented by an ellipsoid. The initial joint is the root joint, which is implemented as a 6 degrees of freedom (DOF) free joint between the model and the ground. Each following joint is implemented as a 3 degrees of freedom ball joint. Thus, the exact number of degrees of freedom of the model depends on the number of joints, which again depends on the skeleton hierarchy of the BVH file. After the stick figure model has been created, virtual markers are placed onto the stick figure model. The markers are placed into the rotation centers of each joint, and for each segment, one virtual marker is placed outward of the segment in order to be able to measure translations and rotations for every coordinate axis see .(2)The motion data contained in the BVH file is converted into the sto file format, which is readable by OpenSim. For that, the data are recalculated to match the OpenSim ball joint definition. For each 3 DOF joint, the sto file contains three joint angle values for every time frame. For the root joint, information about 6 degrees of freedom is stored .(3)Using the sto motion data, the stick figure can execute the experimentally measured motion. For each time step, the position of each virtual marker with respect to the global coordinate system is extracted and saved into a trc marker file. This file then corresponds to marker trajectory files measured by a conventional marker-based motion capture process.(4)To scale the generic musculoskeletal model, motion data measurements of a person standing in static T-pose are necessary. These data can then be used to perform the conventional marker-based scaling approach of OpenSim. In order to do that, markers corresponding to the virtual markers placed onto the stick figure have been placed on a generic musculoskeletal OpenSim model. The joint markers of the stick figure are placed in the origin of body frames of the model in which the corresponding joints are defined. The stick figure’s segment markers are placed in body frames so that their position is perpendicular to the connecting line of the joint markers between which the segment marker is placed see . The virThe experimental data were collected at the motion capture laboratory of the Institute of Engineering Design of Friedrich-Alexander-Universität Erlangen-Nürnberg. Three participants  volunteered for the study. Written consent was provided by all participants prior to data collection.The IMU-based motion capture system Perception Neuron Studio  was used in this study. A total of 17 sensors were placed on the head, the upper arms, the forearms, the hands, the upper back, the pelvis, the thighs, the shanks and the feet of each participant using size-adjustable straps. The placement of the sensors followed the guidelines given in the user manual of the system . Data reManual measurements of the participants’ anthropometry were taken before the motion capture process took place. These data were used as input in the Axis Studio software in order to scale the generic mannequin model of Axis Studio for each participant. Measured dimensions included: palm length, forearm length, upper arm length, shoulder width, hip width, head length, neck length, torso length, upper and lower leg length, ankle height and foot length. Additionally, the following functional body dimensions were measured: body height, inseam height, arm span width and grip height while standing.Each participant completed the following motion tasks: squat, raising arms into T-pose and a reaching motion. For the reaching motion, the subjects stood in front of a box with their feet 0.21 m apart. The distance between the heel of the feet to the edge of the box amounted to 0.68 m. The dimensions of the box were 0.35 × 0.33 × 1.34 m . Markers on the floor indicated the desired foot position. The arms were lifted upwards until the middle finger of the hand was at the same height as the top edge of the box. The measurement setup is shown in The BVH model, which is exported by the Perception Neuron System, has 60 joints in total. The system is able to measure detailed finger motion in the case where special motion-capturing gloves are used. As exact finger motions were not of interest for this work, we did not use the measurement gloves. Thus, our model has 22 moveable joints.We verified our method using both qualitative and quantitative measures. To detect possible error sources and weaknesses of the method, each step of the method was evaluated separately. First, the manual measurements, the BVH file and the OpenSim stick figure were compared quantitatively using specific segment lengths measured both in the BVH file and the OpenSim stick figure model. Investigated segment lengths included: body height, upper and lower leg length, foot (ankle) height, torso length, upper arm length, forearm length, palm length and head length. The segment lengths of the BVH stick figure can be taken directly from the BVH file itself. The segment lengths of the OpenSim stick figure model were computed by obtaining the joint marker positions in the OpenSim ground frame. We performed this comparison to verify that the Axis Studio Software does work as intended and no information is distorted or lost through the method. Additionally, the manual measurements, the BVH file and the OpenSim musculoskeletal model were compared using the same specific segment lengths. This was carried out to verify that the method is able to scale the generic musculoskeletal model correctly according to the body dimension values saved in the BVH file. Next, the position of the stick figure’s virtual markers and their corresponding positions in the generated trc marker file were qualitatively examined. For that, the stick figure model was loaded into the OpenSim graphical user interface, and the motion file—and simultaneously the trc marker file—was loaded into the model. Before the motion was investigated quantitatively, a first visual inspection of the corresponding marker positions examined the agreement with the model for the whole motion. In order to confirm that the motion stored in the BVH file was correctly transferred to the OpenSim stick figure model, the stick figure’s motion in OpenSim and the original BVH motion were compared qualitatively by visually comparing the pose of the models at certain points during the motion. Both motions were also quantitatively compared by comparing specific distances at certain points during the motion. The absolute distances between wrist joints, knee joints and ankle joints were compared for one participant for two poses during a squat motion. We also analysed the overall anthropometric error, which describes the difference between manually measured body dimension values of the participants and the corresponding values of the musculoskeletal model. Defined body lengths were chosen to compare the model with reality to quantify the size of the anthropometric error. For that, we compared functional dimensions, values that were not used for the scaling approach in the method, including the overall body height, the inseam height, the grip height while standing and the arm span. To evaluate the quality of the kinematic data transfer from the stick figure to the musculoskeletal model, the root mean square error (RMSE) and the maximum error of the inverse kinematics method were investigated. Additionally, position coordinate values of a marker on the tip of the middle finger (left and right hand) stored in the trc marker file and the position of the corresponding markers in the motion file, generated by the inverse kinematics approach, were compared. To evaluate the accuracy of the IMU-based motion capture system, the location of a hand grip marker within the musculoskeletal model was compared with the known position of the hand in reality. The position of the hand in reality was measured for each participant before the motion capture process took place. On the musculoskeletal model, the hand grip marker lies on the intersection of the grip axis of the palm and the longitudinal axis of the middle finger. Joint angle trajectories of both the lower and upper body were also investigated. Lower limb joint angle trajectories  were analysed for the squat motion and joint angle trajectories of the upper limb joints  were analysed for the reaching and the arm-raising motions to evaluate whether or not feasible motion results were generated.A comparison of specific body dimensions between manual measurements, the skeletal system information of the BVH file and the dimensions of the OpenSim stick figure model showed good agreement between all three values for all participants see . The OpeA comparison of specific body dimensions between manual measurements, the skeletal system information of the BVH file and the dimensions of the musculoskeletal model showed very good agreement between all three values for all participants see . The OpeA qualitative comparison of the BVH file and the OpenSim stick figure model at two certain points in time during the squat motion showed good agreement. For both points in time, the poses of the OpenSim stick figure model and the BVH file match very well see . A qualiAfter verifying that our method does correctly transfer both the skeletal and the motion data stored in the BVH file onto the OpenSim stick figure model, the results of the inverse kinematics method were evaluated. For the squat, the reaching and the arm-lifting motion, the RMSE was smaller than 0.0126 m, 0.0112 m and 0.0104 m, respectively. Maximum errors were smaller than 0.0329 m, 0.0292 m and 0.0246 m, respectively. The trajectories of the most relevant joints and degrees of freedom for each of the three investigated motions  are presented. All joint angle trajectories have been filtered using a third-order Butterworth filter with a cut-off frequency of 6 Hz. In this paper, we presented an easily applicable method that enables the transfer of BVH files measured with an IMU-based motion capture system to musculoskeletal models in OpenSim 4.4. The method shown in  was veriThe main drawback of our approach is its reliance on the BVH file format. The simplified joint and segment definition of the BVH file results in data loss and an unrealistic representation of the human locomotor apparatus. The joint definitions differ slightly between the BVH file (or stick figure) and the musculoskeletal model. The BVH model consists of only three DOF ball joints. Naturally, this does not depict the physiologically realistic human shoulder joint. The musculoskeletal model attempts to recreate the shoulder joint using joint definitions that are as physiologically realistic as possible. Our musculoskeletal model includes the dynamic upper limb model presented by Saul et al. . Hereby,When looking at the joint angle trajectories resulting from the inverse kinematics method, the following is noticeable. For the arm-raising motion, the shoulder elevation angle shows a smooth cyclic trajectory with consistent minimum and maximum angle values see . As expeThe accuracy of our IMU-based motion capture system in combination with our method was evaluated by comparing hand positions between the musculoskeletal model and reality for a reaching motion. As most deviations between model and reality were of reasonable size, one can say that our method generates decent results. But, for participant 1, deviations for the right hand were quite large (19 cm). When comparing the video material—which was taken during the motion capture process—with the captured IMU data, one can see that the participant followed the given instructions directly, because his right hand clearly touches the upper edge of the box see . NeverthOur experimental protocol had two main limitations—a small sample size and a limited number of analysed motions. Additionally, it was not possible to use a marker-based motion capturing system as a reference system. Because of this, another experimental study is needed to validate our approach and to obtain more detailed knowledge about the joint angle result accuracy of IMU-based motion capture systems in combination with the present method.We presented an easily applicable method that enables the transfer of motion data captured with an IMU-based measurement system and stored in BVH file format to musculoskeletal models in OpenSim 4.4. We extracted the skeletal system information that is stored in the BVH file to generate a corresponding stick figure model in OpenSim. Virtual markers were placed onto the stick figure model. Using the motion data stored in the BVH file, we generated a virtual marker file. Afterwards, this marker file was used analogous to a conventional experimental marker file. First, a generic musculoskeletal model was scaled. After that, an inverse kinematics analysis was conducted. The method generated satisfactorily good results, even though our approach was influenced by some limitations. The body dimensions of the resulting musculoskeletal models corresponded very well to the skeletal information of the BVH file. The captured motion was correctly and reliably transferred from the BVH file to the musculoskeletal model. Joint angle trajectories were overall consistent, and marker errors were overall small.In the next steps, we want to further enhance the method and reduce limitations. We want to conduct another experimental study with a larger sample size and simultaneously capture both IMU- and marker-based motion capture data to validate the kinematic and dynamic results of the method. Additionally, we want to investigate if the extension of the method to multimodal motion measurements in order to compensate for errors such as measurement noise and sensor drift leads to more reliable and accurate motion capture results. The effect of the alignment of the stick figure model to the musculoskeletal model on the quality of the kinematic results is also to be investigated. We now have a concrete approach available that enables us to transfer IMU-based motion capture data onto a musculoskeletal model in OpenSim. IMU-based motion capture systems are less expensive and generally more easily applicable than marker-based systems. As our approach uses the BVH file format to transfer the data, it is independent from the motion capture measurement system used. As long as the system is able to export the motion data as a BVH file, our approach can be used to analyse IMU data using a musculoskeletal model. Thus, this makes musculoskeletal models more accessible for people who do not have access to a marker-based motion capture system or whose activities of interest cannot be measured in a gait laboratory.


Traffic state data are key to the proper operation of intelligent transportation systems (ITS). However, traffic detectors often receive environmental factors that cause missing values in the collected traffic state data. Therefore, aiming at the above problem, a method for imputing missing traffic state data based on a Diffusion Convolutional Neural Network–Generative Adversarial Network (DCNN-GAN) is proposed in this paper. The proposed method uses a graph embedding algorithm to construct a road network structure based on spatial correlation instead of the original road network structure; through the use of a GAN for confrontation training, it is possible to generate missing traffic state data based on the known data of the road network. In the generator, the spatiotemporal features of the reconstructed road network are extracted by the DCNN to realize the imputation. Two real traffic datasets were used to verify the effectiveness of this method, with the results of the proposed model proving better than those of the other models used for comparison. Today, improvements in the economic level and the rise in per-capita vehicle ownership have improved travel efficiency while further accelerating the pace of social development. This increasing vehicle ownership presents great challenges in the use of road network resources, such as the rising incidence of traffic congestion and accidents in urban areas. Therefore, the macro-level control of traffic resources is particularly important. In daily life, travel modes and the macro-level control of traffic management departments depend on data gathered through traffic detection systems. These datasets are utilized across various sectors, including predicting traffic flows and their distributions. Due to both external factors  and internal factors  the detectors arranged in the traffic network may not always be able to collect data at all times . For exaThree primary categories of missing data exist . When thNumerous approaches have been suggested for addressing missing data, and can be broadly categorized into three types ,7,8: preDeepwalk is used to reconstruct the road network and obtain the node set with the highest spatial correlation of each node as the neighborhood of the node reconstructed to improve the temporal and spatial correlation.DCNN is used to capture the dynamic spatial characteristics and consider the forward and reverse traffic flow diffusion to further enhance adaptability and accuracy.During experimental analysis, two real datasets and several classical missing traffic state data imputation models are selected for comparative experiments to verify the accuracy and robustness of the proposed model.Tp address the existing problems with the above approaches, we propose a method for enhancing the accuracy and stability of imputation results. The primary contributions of the proposed method are outlined below:Generation models can use missing data for training in order to understand the distribution of sample data. This approach minimizes the challenge of acquiring sample data and generates data that closely resemble real samples . TherefoGANs were first proposed by Goodflow in 2014. A number of subsequent models have been proposed on this basis, such as Deep Convolutional Generative Adversarial Network (DCGAN) , ConditiG and discriminator D. The generator extracts a feature distribution and attempts to generate data that approximate the real data. Meanwhile, the discriminator evaluates whether the input data originate from real data. The objective of a GAN is represented by Equation (A GAN includes a generator Equation :(1)minmaGANs have recently found broad application in the transportation sector. For example, a GAN was applied to predict the distribution of travel times using trajectory data in . Xu applGraph-based analysis tasks have found extensive utilization in real-world scenarios, such as social networks, transportation networks ,35, multThe overall framework for the imputation method of traffic state data based on DCNN-GAN is shown in i and detector j, then i at time t and T indicates the duration of data collection.The road network is defined as H as the minimum time, and the data are divided into T of each detector to construct the traffic state dataset X, is set comprising values of 0 and 1. The traffic state data matrix containing missing data X and M.During data preprocessing, the traffic state data are divided according to The Deepwalk algorithm was proposed in 2014, and was initially applied to graph embedding. This method can express the nodes within a graph as a vector with potential features by acquiring the hidden characteristics of the network. It comprises two primary parts: random walk and skip-gram.jth node in the random walk process with node k represents the length of the random walk. The nodes which the random walk selection path pass by can be described as follows. Starting from the initial point of the walk, each subsequent node randomly selects an edge connected to the current node and moves to the next node along the selected edge. For each detector, the random walk is repeated k.First, a random walk is carried on the original road network w represents the size of the selection window. The optimization objective function is expressed as Equation min−lThe complex formula above can be transformed into Equation  by PearsIn the skip-gram algorithm, layered softmax is used to can avoid calculating the softmax of all words when calculating the probability, improving the training efficiency of the model. A Huffman tree is used to replace the mapping from the hidden layer to the output softmax layer, which is constructed according to the frequency of nodes. The count of leaf nodes in the Huffman tree is the count of nodes. In the Huffman tree, the negative class (recorded as 1) represents walking along the left subtree, while the positive class (recorded as 0) represents walking along the right subtree. Using a Huffman tree reduces the amount of calculation and makes higher-frequency nodes easier to find. Let Equation :(4)Prup∣Equation  is obtaim road segment sets with the highest relationship and the obtained road segment sets are used as the reconstructed adjacency detector of the detector. We let the reconstructed road network adjacency matrix be Through the above optimization calculation and the objective optimization function in Equation , the vecG and discriminator D. The generator G learns the data distribution mapped from the input data, and the discriminator D tries to determine whether the data are from the real data distribution or the generated data distribution. During model training, the generator G and discriminator D continuously improve their learning ability; as the generator G generates more realistic data distributions, the discriminator D further improves its discrimination ability and finally becomes able to generate effective data distributions. Due to a number of problems, traditional GANs are difficult to train; thus, we adopted the Wasserstein GAN (WGAN) for model training.After extracting the representation information of each detector using Deepwalk and reconstructing the adjacency relationship of the detectors, the generation of missing traffic state data is realized via GAN. The framework of the GAN includes a generator The generator imputes the complete data by learning the features of the graph structure through a diffusion convolution neural network (DCNN), then uses a sparse adjacency matrix to reduce the computational complexity. The reconstructed graph S is the maximum diffusion step, f represent different activation functions, The complete generator calculation process is shown in Equations (6)–(8):X or The discriminator, which is composed of multi-layer full connection layers, is mainly responsible for distinguishing whether the data is real or generated data using the calculation formula in Equations (9) and (10):After defining the generator and discriminator models, their respective loss functions need to be designed in order to facilitate the effective training of the models. The loss function of the generator is mainly composed of the basic loss function and the data repair loss function, while the loss function of the discriminator is the difference between the real data and the generated data, as shown in Equations (11) and (12):To ensuring that the experimental results reflected a real scenario, two real-world datasets were selected for experimental verification and analysis. These datasets were the PEMS-BAY flow dataset from California and the Seattle speed data set. The PEMS-BAY dataset consists of data collected from 24 detectors in zone 7 of the PEMS open source website from California. The data sampling period is 5 min, including flow data from 1 May 2014 to 30 June 2014; therefore, each detector contains 17,568 sampling data. The Seattle speed dataset is collected from 323 detectors on four continuously connected highways in Seattle: I-5, I-405, I-90, and sr-520. It records the traffic speed data for the entire year of 2015 at a 5 min sampling interval. To better simulate a real data loss scenario in the experiment, the data loss modes in the dataset were divided prior to the experiment into missing completely at random (MCAR) and missing completely at random in time (MCART) through mask masking. MCAR refers to data loss that occurs randomly in space and time, while MCART means that all detectors are missing data at a certain time or time interval. In m = 4; for the Seattle dataset, the number of highly relevant detectors selected was m = 10. In the Deepwalk module, the fixed parameters were the size of the context window of the node w = 5, number of random walks of each node 10, length of random walks k = 40, and embedded vector dimension d = 64.The parameters of the DCNN-GAN model are mainly composed of two parts: Deepwalk and GAN. In the Deepwalk module, for PEMS-BAY dataset, the number of highly relevant detectors selected was In the GAN module, the parameter settings of PEMS-BAY dataset and Seattle dataset were the same. The generator included the full connection layer and DCNN layer. In the DCNN layer, the diffusion convolution step was set as the best parameter in n represents the amount of missing data, y represents the real data corresponding to the missing data, and The dataset was divided into a training set and test set, with the first 80% of the data used to train the model and the other 20% used to test the effectiveness of the model. The training batch size of the model was 512 and the number of training rounds was 20,000. The following model performance evaluation indicators were selected: mean absolute error MAE, root mean square error RMSE, and mean absolute error MAPE. The calculation formulas are shown in Equations (13)–(15):The deduction effect of the model can be further verified by selecting an appropriate and effective comparison model. In this experiment, typical algorithms for data repair were selected for comparative experiments. The comparison models were DSAE (denoising stacked auto encoders) , BayesiaThe DSAE model had two hidden layers, the number of hidden layer units was 128 and 64, the training batch size of the model was 128, and the number of training rounds was 1000. For the BGCP model, the third-order tensor  was used as the model input. For the GAN model, the generator and discriminator used three fully connected layers as the hidden layer. Because WGAN was used for training, the output layer in the discriminator did not use an activation function. The training batch size was 512 and the number of training rounds was 20,000.First, in order to prove the influence of the DCNN layer in DCNN-GAN on the the spatial dependence of the road network and select the best diffusion convolution step S, a parameter determination experiment was carried out on the PEMS-BAY dataset. Several diffusion convolution steps were selected for comparison; the evaluation indexes were RMSE, MAE, MAPE, and training time. In this experiment, the other parameters were the same except for the diffusion convolution step.It can be seen from In order to prove the effectiveness of Deepwalk embedding learning in the model, the reconstructed road network obtained by Deepwalk embedding learning and the original road network without Deepwalk embedding learning were compared under different data loss rates. The comparison index was MAPE. The experimental comparison results are shown in In MCAR mode, when the deletion rate is less than 40%, the prediction performance of DCNN-GAN and DCNN-GAN (no embedding) remains relatively stable. When the deletion rate exceeds 40%, the repair error gradually increases. This is because in MCAR mode, when the data deletion rate is too high the surrounding detectors are in the state of high data deletion; as a result, too little information is obtained in space and time. DCNN-GAN is always better than DCNN-GAN (no embedding).In MCART mode, it can be seen that as the deletion increases rate, the repair effect of DCNN-GAN grows significantly better than DCNN-GAN (no embedding). As the data deletion rate increases, the repair error of DCNN-GAN gradually tends towards stability, because the model reconstructs the adjacency matrix according to spatial correlation. In MCART mode, when the time period of the missing data gradually increases, the model is able to maintain its repair performance through spatial dependence, showing good robustness.In order to further compare the deduction and generation results of DCNN-GAN, the deduction and generation results of DCNN-GAN and GAN were compared, and the repair performance of DCNN-GAN was further described by comparing the thermal diagram of original data with the thermal diagrams of DCNN-GAN and GAN after data repair in the two missing data modes. In order to verify the effectiveness and applicability of the DCNN-GAN model, a comparative experimental analysis was carried out on the Seattle dataset. Similar to the PEMS dataset, the deduction generation errors of different models on the Seattle dataset are summarized in In addition, it can be seen that on both the PEMS and Seattle datasets, the repair errors of the models are generally higher in MCAR missing data mode than in MCART mode. This is because the spatiotemporal information in the traffic state data is more incomplete in MCAR mode, increasing the difficulty of data deduction and repair.In this paper, we utilize DeepWalk to reconstruct the road network structure. We use DCNN as the internal structure of the generator for better extraction of the spatial features, and achieve effective data imputation through adversarial training using WGAN. In experiments on two datasets, we compare the performance of the model under different missing data modes and rates. The results demonstrate that DCNN-GAN can effectively perform traffic state data imputation under both MCAR and MCART missing data modes and maintain good performance even at higher missing data rates.In this paper, a deduction and generation method based on DCNN-GAN for repairing missing road network data is proposed to solve the problem of detectors installed in road networks failing to capture data due to uncontrollable reasons. These data may be required for subsequent work in traffic management. The method that we propose uses a graph embedding algorithm to construct a road network structure based on spatial correlation instead of the original road network structure; through the generation of a network for confrontational training, it is possible to generate the missing data based on the known data of the road network. In the generator part of the network, the spatiotemporal features of the reconstructed road network are extracted by diffusion convolution to realize the generation of missing data. In comparison experiments conducted on two real traffic datasets, the proposed model’s data repair results are better than other comparison models.


Lumbar puncture is an important medical procedure for various diagnostics and therapies, but it can be hazardous due to individual variances in subcutaneous soft tissue, especially in the elderly and obese. Our research describes a novel robot-assisted puncture system that automatically controls and maintains the probe at the target tissue layer through a process of tissue recognition.The system comprises a robotic system and a master computer. The robotic system is constructed based on a probe consisting of a pair of concentric electrodes. From the probe, impedance spectroscopy measures bio-impedance signals and transforms them into spectra that are communicated to the master computer. The master computer uses a Bayesian neural network to classify the bio-impedance spectra as corresponding to different soft tissues. By feeding the bio-impedance spectra of unknown tissues into the Bayesian neural network, we can determine their categories. Based on the recognition results, the master computer controls the motion of the robotic system.The proposed system is demonstrated on a realistic phantom made of ex vivo tissues to simulate the spinal environment. The findings indicate that the technology has the potential to increase the precision and security of lumbar punctures and associated procedures.In addition to lumbar puncture, the robotic system is suitable for related puncture operations such as discography, radiofrequency ablation, facet joint injection, and epidural steroid injection, as long as the required tissue recognition features are available. These operations can only be carried out once the puncture needle and additional instruments reach the target tissue layer, despite their ensuing processes being distinct. Lumbar puncture (LP), sometimes called a spinal tap, is a critical step in accessing the spinal region and is irreplaceable in many medical diagnosis and treatment procedures, such as sampling spinal fluid for meningitis tests. The procedure has two main steps: the insertion of the spinal needle into the subarachnoid space filled with cerebrospinal fluid (CSF) and the subsequent therapeutic or diagnostic operations  technology has manifested its value in different applications. EBI technology is a cluster of methods which aims to analyze EBI data from biological tissues. By fitting the Fricke and Morse 's bio-elWe refer to some related studies using various approaches to detect the subarachnoid space. Several methods use tactile devices that monitor the force feedback and its change or derivative as the needle tip penetrates through different soft tissue layers , radiofrequency ablation (RFA), and discography. When applied to ESI, users only need to detect the epidural fat layer. As for RFA and discography, the needle can stop at the corresponding position.In our research, we developed a robotic system with one degree-of-freedom that uses a bio-impedance needle as the sensing unit. Instead of finding a proper frequency, our system acquires the whole impedance spectrum as the feature of the tissues. We developed a method for classifying and identifying soft tissue layers based on sampling bio-impedance spectrum data from 1 to 349 kHz and applying a Bayesian neural network (BNN) to the sampling data. Halonen et al.  has provTo avoid traumatic puncture, the needle tip should not travel too far into the subarachnoid space, which is a very thin ring-shaped layer inside the spinal column. We define the comfortable travel distance (CTD) as the distance from the first contact of the needle tip with the CSF and its final stopping point. According to Holsheimef and Barolat , the minThe remainder of this paper is organized as follows. Section 2 describes the robotic system in detail and tests its performance. Section 3 illustrates how the BNN is implemented on the master computer. Section 4 presents the results of phantom experiments for the preliminary verification of the tissue-identifying system for robot-assisted LP by operating and recording the needle penetration process. Finally, Section 5 concludes the paper with a discussion of the test results and expectations for future applications and development.Figure 3, the system consists of a bio-impedance needle, an impedance spectrum analyzer, a closed-loop stepper motor, and a master control panel. Our design improves the accuracy of LP by stopping the needle as soon as it reaches the subarachnoid space.Robot-assisted surgery has developed rapidly since the first platform was used in 1985  are usually defined as a standard normal distribution. Therefore, the trained parameters are the mean and standard deviation of the normal distribution. Instead of maximum likelihood estimation, the learning process identifies the maximum a posteriori of the probability of random weights MacKay, .Serving as a classifier, the output units from the BNN are also random. However, we choose the maximum from the means of the output units to determine the classification result. The error corresponding to the maximum mean value determines the confidence level. For a classifier, a popular cost function for the back-propagation method is the cross-entropy of the dataset.The GUI in the master computer is based on the Qt framework and plays the role of control panel and monitor. As shown in To generate a BNN classifier, we must first build a dataset. The dataset includes samples with labels and features. In the dataset, the samples are recorded by rows while their labels and features are arranged in columns.Z| are remapped to the conductivity σ. The remapping function of a concentric electrode is generated by measuring the impedance of a saline solution of known concentration |ZS|, because conductivity σS is a dielectric constant of saline solutions that only varies according to concentration.Impedance spectroscopy captures the bio-impedance of the tissues, but it is the conductivity of the tissues that leads to different bio-impedance. Moreover, the captured bio-impedance data are influenced by different concentric needle electrodes and their manufacturing tolerances. To calibrate the differences introduced by such deviations, our previous research  to cover the impedance range of the target tissues. S/cm) and are independent of the excitation current frequency. The impedance values are measured in ohm (Ω) and vary with frequency. Each impedance value is the median of 500 samples from the corresponding saline solution.We used eight salinecenter solutions with different concentrations , torchbnn (version 1.2), an Nvidia 3070ti GPU, and CUDA11.7 (GPU toolkit). Thus, the BNN was trained using a GPU. The fitting process is described in the following steps:mean = 0 and std = 1 as the prior distribution. As a classification task, the output is a one-hot encoding categorical variable marked by units in the output layer, so there are four units corresponding to the four targets.1. We set up a BNN with two hidden layers, and set the weight priors to obey a standard normal distribution using the torchbnn framework. This neural network has one input layer with 15 units, one output layer with four units, and two hidden layers. Each of the hidden layers contains 10 units. Under a Bayesian approach with a forward function, the weights are assumed to be normally distributed, and all weights are set to 2. The loss function is regularized by the Kullback–Leibler divergence (KLD), also called the relative entropy, from the weights. A popular way of regularizing the loss function is to add the KLD multiplied by a coefficient of influence to the cross-entropy (CE):batch_size is the number of samples in a training batch. These functions are integrated in the torchbnn framework. As the dataset is small, we take the whole dataset as a single batch. Thus, the batch size is the number of samples.where 3. The fitting process is iterated over 2,000 epochs using the back-propagation method, as shown in This section describes the results of phantom experiments performed to check the performance of the robotic system and the BNN classifier in a simulation environment.S/cm.As shown in In experiment I, we simulated part of the LP procedure. The target is CSF, so the needle did not stop moving forward until it arrived inside the balloon. The operator could start the procedure and check the needle tip position in real time through the graphical bio-impedance spectrum and textual real-time results on the monitor of the master computer. The master computer also generated a log file of the detection data and textual results during the experiment. The detection data and results are visually presented in It can be seen that the saline solution has leaked out in In experiment II, we simulated the puncture procedure of ESI. In this experiment, the balloon should not be impaled, the needle tip should stop at the epidural fat layer and the final position of the needle tip is marked by ink. We set fat as the target tissue and introduced a time delay for the tissue-identifying program to avoid detecting subcutaneous fat. Once the needle had stopped, we injected a small drop of ink into the cannula to mark the tissue. We then disassembled the phantom environment to check the location of the ink, as shown in ex-vivo animal tissue, and showed that the system was feasible and effective for tissue identification.This study has presented a novel tissue identification system for robot-assisted LP. The system can distinguish different tissues during LP procedures by measuring the bio-impedance spectrum at the needle tip. This multi-classification method provides more information to the users, as they know exactly which tissue layer the needle tip has reached in real time. We performed preliminary experiments simulating LP and ESI on a realistic phantom made of One problem encountered in the preliminary experiment was the deformation of the bio-impedance needle. In further research, a stronger structure should be considered for the junction between the cannula and the slider. The robotic performance of this system is also limited in the absence of robotic motion compensation for respiratory movement.Further developments will include improvements to the needle probe design, refinement of the mechanics, and a more persuasive experimental environment. Regarding the needle probe, a refined electrode design of the inner needle would contain three or four electrodes so that the voltage signal could be sampled without any excited current interference in the measurements (Grimnes and Martinsen, The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the Ethic Committee of Guangdong University of Technology. Written informed consent to participate in this study was provided by the participants. This study also uses animal samples obtained from public source. The Ethic Committee of Guangdong University of Technology did not require the study to be reviewed or approved by an ethics committee because the source of the animal samples follows the the local law.JL: Conceptualization, Data curation, Investigation, Software, Writing—original draft, Writing—review and editing. ZH: Investigation, Validation, Writing—review and editing. BZ: Investigation, Writing—review and editing. ZC: Conceptualization, Formal analysis, Methodology, Supervision, Writing—review and editing. JG: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing—review and editing. HL: Writing—review and editing.