Simulation Experiments Research Articles

A Hybrid Integration Method Based on SMC-PHD-TBD for Multiple High-Speed and Highly Maneuverable Targets in Ubiquitous Radar

Based on the characteristic of ubiquitous radar emitting low-gain wide beam, a method of long-time coherent integration (LTCI) is required to enhance target detection capability. However, high-speed and highly maneuverable targets can cause Doppler frequency migration (DFM), range migration (RM), and velocity ambiguity (VA), severely degrading the performance of LTCI. Additionally, the number of targets is unknown and variable, and the presence of clutter further complicates the target tracking problem. To address these challenges, we propose a hybrid integration method to achieve joint detection and estimation of multiple high-speed, and highly maneuverable targets. Firstly, we compensate for first-order RM using the keystone transform (KT) and generate corresponding sub-range-Doppler (SRD) planes with different folding factors to achieve VA compensation. These SRD planes are then stitched together to form an extended range-Doppler (ERD) plane, which covers a broader velocity range. Secondly, during the track-before-detect (TBD) process, tracking is performed directly on the ERD plane. We use the sequential Monte Carlo (SMC) approximation of the probability hypothesis density (PHD) to propagate multi-target states. Additionally, we propose an amplitude-based adaptive prior distribution method and a line spread model (LSM) observation model to compensate for DFM. Since the acceleration of the target is included in the particle state, using particles to search for DFM does not increase the computational load. To address the issue of misclassifying mirror targets as real targets in the SRD plane, we propose a particle space projection method. By stacking the SRD planes to create a folding range-Doppler (FRD) space, particles are projected along the folding factor dimension, and then, the particles are clustered to eliminate the influence of the mirror targets. Finally, through simulation experiments, the superiority of the LSM for targets with acceleration was demonstrated. In comparative experiments, the proposed method showed superior performance and robustness compared to traditional methods, achieving a balance between performance and computational efficiency. Furthermore, the proposed method’s capability to detect and track multiple high-speed and highly maneuverable targets was validated using actual data from a ubiquitous radar system.

Research on the Design of a MIMO Management System for Lithium-Ion Batteries Based on Radiation–Conductivity–Convection Coupled Thermal Analysis

In this study, the heat transfer model of a radiation–conduction–convection coupled lithium-ion battery pack is established through theoretical analysis. The temperature distribution and flow field distribution inside the battery pack are obtained by simulation using ANSYS Fluent software 2022 R1, and the reasonableness of the simulation model is verified with an experiment. This study also analyzes in detail the improvement effect of adding heat dissipation ribs, applying heat dissipation coatings, and adjusting the fan speed on the heat dissipation performance of the system. Under the same heat sink rib height conditions, the relationship between its thickness and total heat dissipation and thermal efficiency is studied in depth, and the temperature distribution of the cell under different rib thicknesses is obtained. At the same time, the emissivity of the heat sink coating under different coating thicknesses was measured by infrared thermography, and the relevant design values were determined through simulation experiments. Finally, based on the experimental test results of fan performance, a corresponding control strategy is proposed to construct an efficient and high-performance multiple-input multiple-output (MIMO) battery thermal management system. The experimental results show that optimizing the structure of the forced air cooling system through the above measures can ensure that the Li-ion battery operates within the efficient operating temperature range, thus extending its cycle life, improving its stability, and reducing the risk of thermal runaway. Meanwhile, the problem of excessive temperature difference between different modules is improved, and the output capacity of the energy storage system is increased.

Event‐triggered leader‐following consensus of multi‐agent systems with guaranteed performance specifications

AbstractThe article investigates the leader‐following consensus problem for second‐order multi‐agent systems (MASs) with guaranteed performance specifications. First, some event‐triggered strategies are introduced for systems with and without disturbances, which can effectively reduce the frequency of controllers' update and no Zeno‐behavior is occurred. Especially, the self‐triggered strategy proposed for MASs without disturbances does not require continuous communication between agents. Second, based on Lyapunov theory, we have demonstrated that the proposed distributed controllers can achieve the predefined performance specifications of the system, that is, the position tracking error can meet the steady‐state error accuracy requirements within an appointed time, while ensuring that the cumulative position difference (CPD) converges to the region specified by the performance function. Finally, the effectiveness of the theoretical results is demonstrated through simulation experiments.

Ten-electrode bioelectrical impedance analysis (BIA) system: Sensitivity investigation by simulation and phantom experiment

Sensitivity of Bioelectrical Impedance Analysis (BIA) measurement was investigated in this study. Limb segments and the trunk of the body are usually assessed for the whole body impedance in the common-used eight-electrode system. However, even though the trunk is the largest part, the measurement sensitivity of the trunk part is extremely low resulting in low trunk impedance. In this study, a ten-electrode BIA system is proposed to improve the sensitivity of the trunk region by adding two more electrodes at the abdomen and proposing a new measurement scheme. Simulation results showed that the proposed system improved the sensitivity in the trunk region by 3.4–4.2 times. With the eight-electrode system, the content of a body segment could also affect the measured impedance of the other segments, but this effect was substantially reduced in the proposed system. The simulation results were confirmed by a phantom experiment on a piece of pork.

A Robust AR-DSNet Tracking Registration Method in Complex Scenarios

A robust AR-DSNet (Augmented Reality method based on DSST and SiamFC networks) tracking registration method in complex scenarios is proposed to improve the ability of AR (Augmented Reality) tracking registration to distinguish target foreground and semantic interference background, and to address the issue of registration failure caused by similar target drift when obtaining scale information based on predicted target positions. Firstly, the pre-trained network in SiamFC (Siamese Fully-Convolutional) is utilized to obtain the response map of a larger search area and set a threshold to filter out the initial possible positions of the target; Then, combining the advantage of the DSST (Discriminative Scale Space Tracking) filter tracker to update the template online, a new scale filter is trained after collecting multi-scale images at the initial possible position of target to reason the target scale change. And linear interpolation is used to update the correlation coefficient to determine the final position of target tracking based on the difference between two frames. Finally, ORB (Oriented FAST and Rotated BRIEF) feature detection and matching are performed on the accurate target position image, and the registration matrix is calculated through matching relationships to overlay the virtual model onto the real scene, achieving enhancement of the real world. Simulation experiments show that in complex scenarios such as similar interference, target occlusion, and local deformation, the proposed AR-DSNet method can complete the registration of the target in AR 3D tracking, ensuring real-time performance while improving the robustness of the AR tracking registration algorithm.

A novel ship trajectory reconstruction approach based on low-rank tensor completion

The Automatic Identification System (AIS) enhances maritime safety and environmental monitoring by providing crucial ship trajectory data. However, this data is often compromised by missing or abnormal values due to signal blockage, transmission errors, and equipment failures, jeopardizing safety and the accuracy of environmental analyses. Addressing these challenges, we propose a novel Customized Tensor-Based Maritime Trajectory Reconstruction (CTMTR) framework that leverages the self-similarity of ship trajectories to reformulate trajectory reconstruction as a matrix rank minimization issue. The CTMTR framework consists of three steps: data preprocessing, trajectory matrix construction, and trajectory reconstruction. We conducted simulation experiments using a dataset comprising vessel trajectories from the east coast of Dover in the United States in 2022. To validate its effectiveness, the CTMTR is compared with four advanced methods (LRMC, LRTC-TNN, TRPCA, and TNN-DCT) under diverse missing and anomalous scenarios. The results substantiate that the performance of CTMTR outperforms other approaches, especially in anomalous cases. Our method outperforms existing methods by an order of magnitude under random missing combined with anomaly scenarios. The CTMTR framework thus has the potential to advance maritime trajectory reconstruction methodologies, providing a solid foundation for future innovations in maritime data analysis and navigation safety technologies.

In Silico Exploration of Isoxazole Derivatives of Usnic Acid: Novel Therapeutic Prospects Against α-Amylase for Diabetes Treatment.

Diabetes mellitus (DM) a metabolic disorder characterized by high blood sugar levels causing damage to various organs over time. Current anti-diabetic drugs have limitations and side effects, prompting a search for new inhibitors targeting the α-amylase enzyme. This study aims to discover such inhibitors from thirty isoxazole derivatives of usnic acid using in silico approaches. The potential inhibitory effects of compounds were investigated using ADMET, molecular docking, molecular dynamic simulation, principal component analysis and density functional theory studies. ADMET analysis exhibited a wide range of physicochemical, pharmacokinetic, and drug-like qualities with no significant side effects which were then investigated using molecular docking experiment to determine the lead compound with the best binding affinity for the α-amylase enzyme. All compounds showed good binding affinity against α-amylase enzyme (-7.9 to -9.2 kcal/mol) where compound-13 showed the best binding affinity of -9.2 kcal/mol forming hydrogen bonds with Leu162, Tyr62, Glu233 and Asp300 amino acids. Furthermore, the binding posture and the stability of the compound-13-α-amylase enzyme complex was confirmed by molecular dynamic simulation experiment. Moreover, compound-13 showed binding energy value of -27.92 ± 5.61 kcal/mol, which indicated it could be an α-amylase inhibitor. Additionally, the reactivity of compound-13 was further confirmed by density functional theory analysis. The above findings suggest compound-13 to be a potential α-amylase inhibitor in DM. And setting the stage for further in vitro and in vivo experimental validation.

Two Acceleration-Layer Configuration Amendment Schemes of Redundant Robot Arms Based on Zhang Neurodynamics Equivalency

Two innovative acceleration-layer configuration amendment (CA) schemes are proposed to achieve the CA of constrained redundant robot arms. Specifically, by applying the Zhang neurodynamics equivalency (ZNE) method, an acceleration-layer CA performance indicator is derived theoretically. To obtain a unified-layer inequality constraint by transforming from angle-layer and velocity-layer constraints to acceleration-layer constraints, five theorems and three corollaries are theoretically derived and rigorously proved. Then, together with the unified acceleration-layer bound constraint, an enhanced acceleration-layer CA scheme specially considering three-layer time-variant physical limits is proposed, and a simplified acceleration-layer CA scheme considering three-layer time-invariant physical limits is also proposed. The proposed CA schemes are finally formulated in the form of standard quadratic programming and are solved by a projection neurodynamics solver. Moreover, comparative simulative experiments based on a four-link planar arm and a UR3 spatial arm are performed to verify the efficacy and superiority of the proposed CA schemes. At last, physical experiments are conducted on a real Kinova Jaco2 arm to substantiate the practicability of the proposed CA schemes.

A Study on the Impact of Window Partition Walls on the Spread of Fire on Building Facades

This paper investigates the impact of window partition walls on the spread of fire on building facades under the impact of environmental wind through Fire Dynamics Simulator simulation experiments. A four-story building model was constructed using a Fire Dynamics Simulator incorporating six different wind speed conditions and six different partition wall widths. The fire-blocking performance of window partition walls of varying widths was systematically compared and analyzed, and the data indicated: (1) Under calm wind conditions, the installation of window partition walls is observed to facilitate the vertical spread of facade fires. Moreover, as the width of these partition walls increases, this facilitative effect becomes increasingly prominent; (2) Under wind speeds of 0 to 5 m/s, the temperature on the leeward side is lower when window partition walls are present than when they are absent. This indicates that window partition walls inhibit the horizontal spread of building facade fires, and wider window partition walls have better horizontal fire resistance performance.

Multi-Factor Quality Assessment of Digital Speckle Pattern for Speckle Projection Profilometry

Abstract The digital speckle pattern (DSP) is an essential component in the speckle projection profilometry (SPP) task, its quality directly affects the results of three-dimensional (3D) shape reconstruction. However, the SPP field lacks specialized numerical metrics for evaluating speckle quality. To address this issue, this study introduces a multi-factor metric (MFM) for comprehensive DSP assessment. Through comparing the metric, optimal parameter ranges for DSP design and the advisable matching subset size can be determined for SPP algorithm. A global indicator named valid feature distribution (VFD) based on scale-invariant feature transform (SIFT) and Delaunay triangulation, is defined to analyze the overall information distribution in DSPs. In addition, MFM incorporates a local metric called mean subset intensity gradient (MSIG), which aids in selecting the suitable radius for different DSPs to balance the accuracy and efficiency. The quality assessment targets the speckle scene images, allowing for the reverse adjustment of the most suitable DSP according to different scenes. The performance of DSPs can be evaluated based on the accuracy and completeness of 3D reconstruction results. By conducting simulation experiments on the 3ds Max platform, the recommended parameter range for DSP can be inferred, including speckle density ratio, speckle diameter, and random variation rate. Appropriate subset sizes for different scenes are also investigated. Furthermore, the MFM is verified on a real binocular speckle device, demonstrating that the measurement standard deviation of a complex workpiece can be reduced to 0.078 mm using the recommended DSP.

Task-Motion Planning System for Socially Viable Service Robots Based on Object Manipulation

This paper presents a software architecture to implement a task-motion planning system that can improve human-robot interactions by including social behavior when social robots provide services related to object manipulation to users. The proposed system incorporates four main modules: knowledge reasoning, perception, task planning, and motion planning for autonomous service. This system adds constraints to the robot motions based on the recognition of the object affordance from the perception module and environment states from the knowledge reasoning module. Thus, the system performs task planning by adjusting the goal of the task to be performed, and motion planning based on the functional aspects of the object, enabling the robot to execute actions consistent with social behavior to respond to the user’s intent and the task environment. The system is verified through simulated experiments consisting of several object manipulation services such as handover and delivery. The results show that, by using the proposed system, the robot can provide different services depending on the situation, even if it performs the same tasks. In addition, the system demonstrates a modular structure that enables the expansion of the available services by defining additional actions and diverse planning modules.

Calculation Model for the Exit Decision Sight Distance of Right-Turn Ramps on the Left at Interchange

Given the rapid construction of freeways in developing countries such as China, land use is constantly under strict constraints, leading to challenges in adopting conventional layouts for interchanges. Implementing right-turn ramps on the left (RTRL) at interchanges can minimize land occupancy; however, the traffic safety level in this type of diversion area design requires extra attention. This study examines the decision sight distance for right-turn exit ramps on the left side. Utilizing unmanned aerial vehicle (UAV) video and the YOLOv3 target detection algorithm, the original trajectory data of vehicles in the diversion area is extracted. Employing Kalman filtering and Frenet coordinate system conversion reveals microscopic vehicle lane-change patterns, velocities, and time headways. Furthermore, the driving simulation experiment assesses driver behaviors in RTRL, with subjective, task performance, and physiological measure indicators. Ultimately, the range of the decision sight distance is defined, and establishing a calculation model involves determining relevant parameters based on measured data and simulation outcomes. The results indicate potential insufficiencies in the decision sight distance when standardized values are applied to RTRL.

Research on single target search and rescue in underwater environment based on machine learning

Machine learning has demonstrated its exceptional performance in addressing complex problems across various domains. This paper proposes an underwater search-rescue system based on the artificial immune algorithm, which effectively solve the problem of finding the optimal local search network in unknown underwater environments and shorten searching time. The system comprises multiple underwater search-rescue robots that form a global search network. Once target victims are identified, the global search network is broken down to form one or more local search networks to track and rescue them. To evaluate the robustness and effectiveness of the system, three indicators were designed: searching time, energy consumption, and tracking distance, and a simulation experiment with a single target is conducted. The results demonstrate that under the identical conditions, the artificial immune algorithm outperforms other methods in underwater target search-rescue tasks.

Biomechanical effects of posterior lumbar interbody fusion with vertical placement of pedicle screws compared to traditional placement.

The pedicle screw technique is widely employed for vertebral body fixation in the treatment of spinal disorders. However, traditional screw placement methods require the dissection of paraspinal muscles and the insertion of pedicle screws at specific transverse section angles (TSA). Larger TSA angles require more force to pull the muscle tissue, which can increase the risk of surgical trauma and ischemic injury to the lumbar muscles. To study the feasibility of zero-degree TSA vertical pedicle screw technique in the lumbosacral segment. Finite element models of vertebral bodies and pedicle screw-rod systems were established for the L4-S1 spinal segments. A standard axial load of 500 N and a rotational torque of 10 N/m were applied. Simulated screw pull-out experiment was conducted to observe pedicle screw resistance to pull-out, maximum stress, load-displacement ratio, maximum stress in vertebral bodies, load-displacement ratio in vertebral bodies, and the stress distribution in pedicle screws and vertebral bodies. Differences between the 0-degree and 17-degree TSA were compared. At 0-degree TSA, the screw pull-out force decreased by 11.35% compared to that at 17-degree TSA (P < 0.05). At 0-degree and 17-degree TSA, the stress range in the screw-rod system was 335.1-657.5 MPa and 242.8-648.5 MPa, separately, which were below the fracture threshold for the screw-rod system (924 MPa). At 0-degree and 17-degree TSA, the stress range in the vertebral bodies was 68.45-78.91 MPa and 39.08-72.73 MPa, separately, which were below the typical bone yield stress range for vertebral bodies (110-125 MPa). At 0-degree TSA, the load-displacement ratio for the vertebral bodies and pedicle screws was slightly lower compared to that at 17-degree TSA, indicating slightly lower stability (P < 0.05). The safety and stability of 0-degree TSA are slightly lower, but the risks of screw-rod system fracture, vertebral body fracture, and rupture are within acceptable limits.

Using unsupervised learning based convolutional neural networks to solve Digital Image Correlation

Digital Image Correlation (DIC) is a robust, non-contact method for full-field deformation measurement widely used in photomechanics. Based on subset image matching and an iterative optimization architecture, traditional DIC deformation field analysis requires manual selection of such calculation parameters as subset size, rendering it challenging to balance spatial resolution and measurement resolution in non-uniform field measurements. The requirement of manual parameter adjustment is lowered by a recently emerged supervised learning-based neural network DIC method, as it establishes a direct correlation between images and deformation fields using deep neural networks (DNN). However, its generalization and accuracy are contingent on the scope and variety of the dataset because it requires training with a large number of simulated speckle images of deformation fields with known standards. This paper proposes an unsupervised learning neural network based DIC measurement method that circumvents the need for standard deformation fields and achieves high precision analysis of deformed fields. It constructs a loss function based on the photometric consistency assumption of speckle images before and after deformation and allows the neural network model to directly extract displacement information from the gray data of the “reference image-deformed image” pair. Meanwhile, the model is enhanced accordingly by further introducing displacement continuity constraints to enhance accuracy and mitigate noise interference. Validation through both simulated and real experiments shows that the proposed approach surpasses traditional subset DIC in measuring non-uniform deformation fields with higher precision. It also offers superior flexibility and adaptability over supervised learning-based neural network DIC methods by eliminating the need for a large training dataset, allowing for immediate application in displacement field analysis.

Inference for ARMA time series with mildly varying trend

Statistical inference is studied for ARMA time series with a mildly varying smooth trend. Following properly calibrated B-spline estimation of the trend, approximates of the unobserved ARMA series are used instead to estimate ARMA parameters by maximum likelihood. The parameter estimates are shown to be oracally efficient in the sense that they are asymptotically as efficient as if the true trend function was known and removed to obtain the true ARMA series. The mildly varying trend function is estimated by Nadaraya–Watson method with asymptotically correct simultaneous confidence band (SCB). The SCB sprawls over an interval that eventually covers the half real line, instead of a bounded interval in the existing literature, with asymptotic width of the SCB dependent on ARMA coefficients. Simulation experiments corroborate the theoretical findings.

Research on identification of flight cadets’ cognitive load based on multi-source physiological data and CGAN-DBN model

Modern aircraft cockpit system is highly information-intensive. Pilots often need to receive a large amount of information and make correct judgments and decisions in a short time. However, cognitive load can affect their ability to perceive, judge and make decisions accurately. Furthermore, the excessive cognitive load will induce incorrect operations and even lead to flight accidents. Accordingly, the research on cognitive load is crucial to reduce errors and even accidents caused by human factors. By using physiological acquisition systems such as eye movement, ECG, and respiration, multi-source physiological signals of flight cadets performing different flight tasks during the flight simulation experiment are obtained. Based on the characteristic indexes extracted from multi-source physiological data, the CGAN-DBN model is established by combining the conditional generative adversarial networks (CGAN) model with the deep belief network (DBN) model to identify the flight cadets’ cognitive load. The research results show that the flight cadets’ cognitive load identification based on the CGAN-DBN model established has high accuracy. And it can effectively identify the cognitive load of flight cadets. The research paper has important practical significance to reduce the flight accidents caused by the high cognitive load of pilots.

The distributed strategy for asynchronous observations in data-driven wildland fire spread prediction

Background Asynchronous observations refer to observations that are obtained at multiple moments. The observation moments of fire fronts may differ throughout an entire wildfire area. Asynchronous observations include historical data, which hinders the effectiveness of data assimilation due to the lack of timely updates on changing fire fronts. Aims This paper proposed a distributed strategy combined with the Ensemble Transform Kalman filter (ETKF-distributed) for asynchronous observations. It can assimilate fire fronts immediately at any location by using new matching schemes between prediction and observation. Methods The ETKF-distributed undergoes testing using a wildland fire generated based on real terrain, vegetation, and historical weather data from the local area. In addition, the ETKF and ETKF-centralised proposed in our previous work were employed as comparisons. Observing System Simulation Experiments were conducted to generate asynchronous observation fire fronts. Key results The benefit of immediate assimilation enables the new method to maintain high accuracy predictions. Conclusions The allocation of observation resources can be focused in regions with high rates of speed when employing ETKF-distributed. Implications The ETKF-distributed has high efficiency and adaptability, making it highly promising for implementation in wildfire prediction.

Fire Identification Based on Novel Dense Generative Adversarial Networks

Increasing death rates, damage to properties, and loss of trees can be caused by fires. In Australia and the United States of America, many fire incidents are reported annually. Due to that, both governments struggle from the devastation beyond plants, buildings, and infrastructure. A lot of people have lost their properties and land. Various innovations in fire detection technologies have been implemented to minimize the impacts of fires on the economy and lives. Some of these solutions are costly, while others lack accuracy. In this article, a novel deep-learning model to detect fires is presented. This model is based on new Novel Dense Generative Adversarial Networks (NDGANs) and image preprocessing technologies for fire detection through a continuous monitoring system. This system produces alarms if a fire or smoke is detected. The proposed approach was trained and tested on five datasets. This system was evaluated using four performance quantities, which are accuracy, sensitivity, dice, and F-score, and attained 98.87%, 97.64%, 98.82%, and 98.69% for the considered quantities, respectively. In addition, the proposed method was compared with other developed approaches and outperformed these methods. The presented New Dense Generative Adversarial Networks technology is useful in fire detection as shown from the conducted simulation experiments on MATLAB.

A Combined Discrete Event Simulation and Factorial Design Experiment for the Scheduling Problem in a Hybrid Flow Shop

Production plants have always been confronted with the problem of scheduling, as this has a direct impact on production time and therefore on production costs. This is especially true in today's world, where it is necessary to produce a quality product at a low price and in a short time, while at the same time responding flexibly to customer demands. Due to the complexity and for economic reasons, testing different variants in a real production environment is insufficient and ineffective. For this reason, this paper proposes a new method that combines discrete event simulation and factorial design experiment to find the optimal schedule in hybrid flow shop. It was tested with the goal of achieving the minimum makespan. The results show that this method makes it possible to find improved solution very quickly. Compared to the original production schedule, the makespan could be reduced by 3 hours and 31 minutes which is reduction of 16.3%. The proposed methodology can be used in many discrete and process production plants.