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The combination processing of molds and electronic product joint inspection introduce the mandatory concurrent operations on the same machine (MCDSM), which breaks the constraint that the same machine cannot process different jobs at the same time in the job shop scheduling (JSP) To solve this type of job shop scheduling problem, this paper proposed an adaptive hybrid initialization genetic algorithm (AHIGA). Firstly, this paper defined the problem as a generalized JSP considering mandatory concurrent operations on the same machine (GJSPMCO) problem, and established an optimization model using mixed integer programming with the objective of minimizing the maximum completion time. Next, it designed corresponding encoding and decoding techniques to support the representation and resolution of feasible scheduling plans within the constraints of MCOSM. Meanwhile, it established a population hybrid initialization method to generate high-quality feasible solutions. This paper also designed novel crossover and mutation operation methods to ensure the feasibility of newly generated solutions under the constraint of MCOSM. Additionally, it constructed an adaptive crossover and mutation operators to enable the adaptive updating of offspring, thereby enhancing the algorithms global search capability. Finally, it constructed 40 test cases based on JSP benchmark, and used these test cases and an example of joint inspection of electronic products for experiments. The results show that the proposed model and algorithm can effectively solve GJSPMCO problem, and the improvement strategies can enhance the quality of solutions, demonstrating the feasibility of the model and the superiority of AHIGA. [ABSTRACT FROM AUTHOR]

When forklifts are used to move stored crops in a storage environment, the positioning system is severely affected by the presence of multiple stored crops and shelves and other complex factors in the environment. Aiming at the problems of low positioning and navigation accuracy and large accumulated error of forklift system, a Lidar/IMU integrated navigation and positioning method is proposed in this paper, which can improve the positioning accuracy of forklift truck in storage environment. Meanwhile, the improved EKF filtering algorithm is proposed in this paper which can optimize the navigation and positioning system. This method first extracts the environmental information obtained from Lidar scan measurements and the attitude information collected by the IMU. Then the output data from the two sensors are processed with the improved EKF filtering algorithm, which can improve the navigation and positioning accuracy when the forklift is working. The Lidar/IMU integrated navigation and positioning method proposed in this paper is validated by experiments simulating forklifts working in a warehouse environment in the laboratory. Through simulation experiments, it is verified that the improved EKF filtering algorithm in this paper can improve the positioning accuracy of forklift truck, accuracy of forklift movement trajectory, closer to the expected trajectory. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of the adaptive neural network tracking control of nonlinear cyber–physical systems (CPSs) subject to unknown deception attacks with prescribed performance. The considered system is under the influence of unknown deception attacks on both actuator and sensor networks, making the research problem challenging. The outstanding contribution of this paper is that a new anti-deception attack-prescribed performance tracking control scheme is proposed through a special coordinate transformation and funnel function, combined with backstepping and bounded estimation methods. The transient performance of the system can be ensured by the prescribed performance control scheme, which makes the indicators of the controlled system, such as settling time and tracking accuracy, able to be pre-assigned offline according to the task needs, and the applicability of the prescribed performance is tested by selecting different values of the settling time (0.5 s, 1 s, 1.5 s, 2 s, 2.5 s, and 3 s). In addition, to save the computational and communication resources of the CPS, this paper uses a finite-time differentiator to approximate the virtual control law differentiation to avoid "complexity explosion" and a switching threshold event triggering mechanism to save the communication resources for data transmission. Finally, the effectiveness of the proposed control strategy is further verified by an electromechanical system simulation example. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive neural network (NN) tracking control problem for stochastic nonlinear systems with multiple actuator constraints and full-state constraints. The issue of system full-state constraints is tackled by a generalized barrier Lyapunov function (GBLF), and the output constraints of the system are considered to be in the form of time-varying functions, which are more in line with the needs of real physical systems. The NN approximation technique is utilized to overcome the influence of the uncertainty term on controller design due to randomness. Based on the backstepping technique, a neural adaptive fixed-time tracking control strategy is designed. Under the designed control strategy, the tracking accuracy of the controlled system can reach the expectation in a fixed time. The multi-actuator constraints are converted into a generalized mathematical model to simplify the controller design process. Using the characteristics of the hyperbolic tangent function, a new function called practical virtual control signal is designed using the virtual control signal as the input. Due to the saturation constraint property of the hyperbolic tangent function, it is theoretically ensured that no state of the system exceeds the constraints through to the new form of the virtual controller. Using the adaptive controller constructed in this paper, the controlled system is semi-global fixed-time stabilized in probability (SGFSP). Finally, the effectiveness of the proposed control strategy is further verified by simulation examples. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper through the redundant monitoring unit reflecting the real-time temperature change of the array, an adaptive refresh circuit based on temperature is designed. Design/methodology/approach: This paper proposed a circuit design for temperature-adaptive refresh with a fixed refresh frequency of traditional memory, high refresh power consumption at low temperature and low refresh frequency at high temperature. Findings: Adding a metal oxide semiconductor (MOS) redundancy monitoring unit consistent with the storage unit to the storage bank can monitor the temperature change of the storage bank in real time, so that temperature-based memory adaptive refresh can be implemented. Originality/value: According to the characteristics that the data holding time of dynamic random access memory storage unit decreases with the increase of temperature, a MOS redundant monitoring unit which is consistent with the storage unit is added to the storage array with the 2T storage unit as the core. [ABSTRACT FROM AUTHOR]

In the islanded microgrid, the mismatch in line impedance hinders the traditional droop control from achieving an equal distribution of reactive power among Distributed Generation (DG) sources. To address this problem and enhance the flexibility and reliability of the controller, this paper analyzes the reasons behind the failure of traditional droop control and proposes an adaptive droop coefficient that can be dynamically adjusted to meet the conditions for reactive power sharing. Additionally, a dynamic distributed observer is designed and its convergence is proven, which allows DG sources to obtain necessary information flexibly and reliably in a distributed manner. Finally, the proposed control strategy is verified through simulations in four different scenarios, and the results demonstrate its superiority and effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive maximum correntropy cubature Kalman filter based on multiple fading factors (MAMCKF) is proposed to address the problem of inaccurate process noise covariance and unknown measurement noise covariance together with outliers in target tracking. Although there are many adaptive filters and robust filters have been proposed to handle unknown measurement noise covariance or measurement outliers, most filters cannot deal with both unknown noise covariance and outliers simultaneously. In this article, we propose an adaptive and robust cubature Kalman filter. The modified measurement noise covariance matrix (MNCM) and innovation covariance matrix are used to construct multiple fading factors for correcting the prediction error covariance matrix (PECM), which can achieve adaptability. Then, the maximum correntropy criterion (MCC) is introduced to suppress outliers, which further enhances the robustness. Compared with the existing approaches, the proposed approach improves the performance by at least 5% in unknown time-varying noise, unknown time-varying heavy-tailed noise, and non-Gaussian heavy-tailed noise scenarios. The simulation results show that the proposed approach can effectively suppress inaccurate process noise covariance and unknown time-varying measurement noise together with outliers. Compared with the existing filtering approaches, the proposed approach exhibits both adaptability and robustness. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) possess an inherent three-order structure, prompting increased interest in extracting 3D features. Tensor analysis and low-rank representations, notably truncated higher-order SVD (T-HOSVD), have gained prominence for this purpose. However, determining the optimal order and addressing sensitivity to changes in data distribution remain challenging. To tackle these issues, this paper introduces an unsupervised Superpixelwise Multiscale Adaptive T-HOSVD (SmaT-HOSVD) method. Leveraging superpixel segmentation, the algorithm identifies homogeneous regions, facilitating the extraction of local features to enhance spatial contextual information within the image. Subsequently, T-HOSVD is adaptively applied to the obtained superpixel blocks for feature extraction and fusion across different scales. SmaT-HOSVD harnesses superpixel blocks and low-rank representations to extract 3D features, effectively capturing both spectral and spatial information of HSIs. By integrating optimal-rank estimation and multiscale fusion strategies, it acquires more comprehensive low-rank information and mitigates sensitivity to data variations. Notably, when trained on subsets comprising 2%, 1%, and 1% of the Indian Pines, University of Pavia, and Salinas datasets, respectively, SmaT-HOSVD achieves impressive overall accuracies of 93.31%, 97.21%, and 99.25%, while maintaining excellent efficiency. Future research will explore SmaT-HOSVD's applicability in deep-sea HSI classification and pursue additional avenues for advancing the field. [ABSTRACT FROM AUTHOR]

Delusions are usually depicted in one of two contrasting ways. They are either characterized as harmful and dysfunctional beliefs or as fostering engagement with the environment and sometimes even psychological wellbeing in the face of psychological or biological difficulties – something which, according to some accounts, would make them biologically adaptive. It is this “adaptive hypothesis” that I focus on in this paper, by empirically investigating the adaptiveness of delusions in a sample of people suffering from OCD. The paper shows that delusions in OCD entertain complex and heterogeneous relationships with psychological wellbeing and engagement with the environment, highlighting some future directions that clinical and philosophical research on the topic should take. [ABSTRACT FROM AUTHOR]

In this paper, a low quiescent current structure with self-regulation impedance buffer and bandgap amplifier is presented. With the bandgap amplifier, the function of voltage reference and error amplifier can be achieved simultaneously, which can efficiently reduce the consumption. The low-dropout regulator has been implemented in a 0.18µm process with die size 0.03mm². The circuit module designed in this paper are simulated by the Cadence software and the simulation results are validated. Experimental result shows that the overshoot and undershoot of line transient response are less than 30mV/V. The load regulation is about 0.1%/A, and line regulation is about 0.07mV/V at no load condition. Through the test results, it is found that the simulation results are consistent with the measured data. This conclusion ensures the stability and reliability of this module in the subsequent application scenarios. [ABSTRACT FROM AUTHOR]

3D reconstruction technology is gradually applied to underwater scenes, which has become a crucial research direction for human ocean exploration and exploitation. However, due to the complexity of the underwater environment, the number of high-quality underwater images acquired by underwater robots is limited and cannot meet the requirements of 3D reconstruction. Therefore, this paper proposes an adaptive 3D reconstruction algorithm for underwater targets. We apply the frame interpolation technique to underwater 3D reconstruction, an unprecedented technical attempt. In this paper, we design a single-stage large-angle span underwater image interpolation model, which has an excellent enhancement effect on degraded underwater 2D images compared with other methods. Current methods make it challenging to balance the relationship between feature information acquisition and underwater image quality improvement. In this paper, an optimized cascaded feature pyramid scheme and an adaptive bidirectional optical flow estimation algorithm based on underwater NRIQA metrics are proposed and applied to the proposed model to solve the above problems. The intermediate image output from the model improves the image quality and retains the detailed information. Experiments show that the method proposed in this paper outperforms other methods when dealing with several typical degradation types of underwater images. In underwater 3D reconstruction, the intermediate image generated by the model is used as input instead of the degraded image to obtain a denser 3D point cloud and better visualization. Our method is instructive to the problem of acquiring underwater high-quality target images and underwater 3D reconstruction. [ABSTRACT FROM AUTHOR]

This paper presents in-depth research and analysis of the optimization method of tourist traffic routes using the adaptive cuckoo algorithm. The traditional cuckoo algorithm has the disadvantages of slow convergence speed and easy falling into local extremes. This paper proposes the iterative adaptive CHSACS algorithm based on iteration. Based on the original CS algorithm, a dynamic adaptive step control amount and a segmented weighted position update formula are introduced to give a class of improved CS algorithms to coordinate the problem of local search and global search of the CS algorithm and speed up the convergence speed at the later stage. To address the problem that the out-of-bounds nests interfere with the convergence of the algorithm, a memory strategy is introduced to relocate the out-of-bounds nests in the search space to improve the stability of the algorithm. Experiments are conducted on the iterative adaptive-based conductive CHSACS algorithm with test function sets. Compared with the original CS algorithm, and ACO algorithm, the CHSACS algorithm has faster convergence, higher search accuracy and a better ability to avoid local optima when dealing with continuous function optimization problems. For the dynamic travel path problem of travel time optimization, the road travel time and sightseeing time of different periods are predicted based on historical data, and the most traveled distance of travel time between two sights is dynamically searched by using the search mechanism based on time windows. The iterative adaptive CHSACS algorithm is used to continuously find the tourist traffic route with the shortest travel time travel path from all combinations of tour sequences. This paper combines the characteristics of bus vehicle scheduling itself, takes into account the interests of both bus companies and tourists, establishes a bus vehicle scheduling model with the departure interval as the independent variable, introduces this hybrid cuckoo algorithm into bus scheduling, verifies the scientific and feasibility of the algorithm through examples, and provides a new idea for solving bus scheduling optimization problems. [ABSTRACT FROM AUTHOR]

Existing methods for filter pruning mostly rely on specific data-driven paradigms but lack the interpretability. Besides, these approaches usually assign layer-wise compression ratios automatically only under given FLOPs by neural architecture search algorithms or just manually, which are short of efficiency. In this paper, we propose a novel interpretable task-inspired adaptive filter pruning method for neural networks to solve the above problems. First, we treat filters as semantic detectors and develop the task-inspired importance criteria by evaluating correlations between input tasks and feature maps, and observing the information flow through filters between adjacent layers. Second, we refer to the human neurobiological mechanism for the better interpretability, where the retained first layer filters act as individual information receivers. Third, inspired by the phenomenon that each filter has a deterministic impact on FLOPs and network parameters, we provide an efficient adaptive compression ratio allocation strategy based on differentiable pruning approximation under multiple budget constraints, as well as considering the performance objective. The proposed method is validated with extensive experiments on the state-of-the-art neural networks, which significantly outperforms all the existing filter pruning methods and achieves the best trade-off between neural network compression and task performance. With ResNet-50 on ImageNet, our approach reduces 75.49% parameters and 70.90% FLOPs, only suffering from 2.31% performance degradation. [ABSTRACT FROM AUTHOR]

We propose an adaptive fractional multi-scale optimization optical flow algorithm, which for the first time improves the over-smoothing of optical flow estimation under the total variation model from the perspective of global feature and local texture balance, and solves the problem that the convergence of fractional optical flow algorithms depends on the order parameter. Specifically, a fractional-order discrete L1-regularization Total Variational Optical Flow model is constructed. On this basis, the Ant Lion algorithm is innovatively used to realize the iterative calculation of the optical flow equation, and the fractional order is dynamically adjusted to obtain an adaptive optimization algorithm with strong search accuracy and high efficiency. In this paper, the flexibility of optical flow estimation in weak gradient texture scenes is increased, and the optical flow extraction rate of target features at multiple scales is greatly improved. We show excellent recognition performance and stability under the MPI_Sintel and Middlebury benchmarks. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of particle-based fluid surface reconstruction, this paper proposed a perspective-grid-based adaptive narrow-band surface particle extraction method. Compared with the object-space-based method, this scheme adaptively extracted the surface particles closest to the viewpoint within the frustum according to the particle density, dispersion coefficient, so that the number of surface particles and memory consumption were related to the visible surface area, rather than the entire fluid surface or simulation domain. In addition, this paper presented an adaptive thickness estimation method based on particle density by taking advantage of the arrangement of perspective grids along the view. Experiments show that this scheme effectively reduces surface particles by 40% to 76% and memory overhead by 30% to 50%,solves the problems of surface particle redundancy and holes, and quickly obtains thickness information at a lower cost. This scheme brings significant performance improvement for surface reconstruction and rendering, and can better handle the surface reconstruction and rendering of large-scale particle sets. [ABSTRACT FROM AUTHOR]

This study advances dynamic force reconstruction in the time domain through the precise integration method (PIM), particularly effective for matrix exponential numerical solutions. Traditional PIM approaches, typically reliant on linear or step function assumptions within integration time steps, face limitations in handling large integration time step loads. Addressing this, our research introduces an innovative method employing velocity and displacement sensors in simple and complex structures, significantly enhancing load fitting accuracy, especially for nonlinear loads. We propose a refined version of PIM: the Chebyshev polynomial integration method (CPIM) and the adaptive precise integration method (APIM) based on wavelets. These methods are designed to fit force functions of varying orders across different time steps, thereby bolstering signal reconstruction robustness. The efficacy of these approaches is validated given adequate measurement response data. Furthermore, recognizing the growing emphasis on uncertainty in engineering, our work incorporates the collocation method for uncertainty quantification in load identification. To counteract the erratic results from polynomial fitting, particle swarm optimization (PSO) thresholding is employed to eliminate high-frequency noise, thus enhancing the Signal-to-Noise Ratio and minimizing signal distortion. Notably, we demonstrate that the Shannon wavelet surpasses the Chebyshev polynomial in fitting ability due to its tighter branch, rendering APIM more suitable for engineering applications compared to CPIM. APIM adeptly minimizes errors related to acquisition frequency by adaptively adjusting the number of discretization points and wavelet convergence levels to fulfill convergence criteria and matrix norm requisites. This paper proposes a multi-source uncertainty-oriented dynamic Force Reconstruction Framework, integrating adaptive fitting precise integration with PSO. Our numerical examples illustrate that APIM, as discussed herein, offers broader applications and is more time efficient than CPIM. [ABSTRACT FROM AUTHOR]

This paper proposes a wind-speed-adaptive resonant piezoelectric energy harvester for offshore wind energy collection (A-PEH). The device incorporates a coil spring structure, which sets the maximum threshold of the output rotational frequency, allowing the A-PEH to maintain a stable output rotational frequency over a broader range of wind speeds. When the maximum output excitation frequency of the A-PEH falls within the sub-resonant range of the piezoelectric beam, the device becomes wind-speed-adaptive, enabling it to operate in a sub-resonant state over a wider range of wind speeds. Offshore winds exhibit an annual average speed exceeding 5.5 m/s with significant variability. Drawing from the characteristics of offshore winds, a prototype of the A-PEH was fabricated. The experimental findings reveal that in wind speed environments, the device has a startup wind speed of 4 m/s, and operates in a sub-resonant state when the wind speed exceeds 6 m/s. At this point, the A-PEH achieves a maximum open-circuit voltage of 40 V and an average power of 0.64 mW. The wind-speed-adaptive capability of the A-PEH enhances its ability to harness offshore wind energy, showcasing its potential applications in offshore wind environments. [ABSTRACT FROM AUTHOR]

With the advent of technological advancement in learning, such as context-awareness, ubiquity and personalisation, various innovations in teaching and learning have led to improved learning. This research paper aims to develop a system that supports personalised learning through adaptive content, adaptive learning path and context awareness to meet individual learner's requirements and promote the effectiveness and performance of the learning process. Furthermore, the study used an experimental control group to ascertain the significance of learners' learning styles and preferences in improving learning performance. The model considers different modules such as personalisation, location support, learning plan and context awareness while relying on the proper classification of learners' learning styles. The system was experimented using 127 learners of a Computer Science Course. The result obtained using 10-fold cross-validation on various algorithms shows better performance in ROC, Kappa and Accuracy, which were all used to evaluate the quality of classification outcome and compared with learners' performances in assessments. During classification, Naïve Bayes Model has the highest average ROC value of 0.979, TP Rate of 0.951 and FP Rate of 0.045. The results obtained show an improvement in learning. [ABSTRACT FROM AUTHOR]

This paper investigates the path planning problem of the coal mine solid-filling and pushing mechanism and proposes a hybrid improved adaptive genetic particle swarm algorithm (AGAPSO). To enhance the efficiency and accuracy of path planning, the algorithm combines a particle swarm optimization algorithm (PSO) and a genetic algorithm (GA), introducing the sharing mechanism and local search capability of the particle swarm optimization algorithm. The path planning of the pushing mechanism for the solid-filling scenario is optimized by dynamically adjusting the algorithm parameters to accommodate different search environments. Subsequently, the proposed algorithm's effectiveness in the filling equipment path planning problem is experimentally verified using a simulation model of the established filling equipment path planning scenario. The experimental findings indicate that the improved hybrid algorithm converges three times faster than the original algorithm. Furthermore, it demonstrates approximately 92% and 94% better stability and average performance, respectively, than the original algorithm. Additionally, AGAPSO achieves a 27.59% and 19.16% improvement in path length and material usage optimization compared to the GA and GAPSO algorithms, showcasing superior efficiency and adaptability. Therefore, the AGAPSO method offers significant advantages in the path planning of the coal mine solid-filling and pushing mechanism, which is crucial for enhancing the filling effect and efficiency. [ABSTRACT FROM AUTHOR]

In order to provide new technical support for the variable temperature drying process and realize the adaptive control of drying temperature based on the change of moisture content, the study designed a temperature adaptive control system with the function of online detection of material moisture content. A moisture content online detection model with weight detection value, air velocity, the temperature of load sensor elastic substrate, airflow impingement distance as inputs, and the real material weight as outputs was established by using a convolutional neural network. A validation test of the moisture content online detection model was carried out. The results showed that the model meets the accuracy requirements of online moisture content detection in the variable temperature drying process, and the coefficient of determination R² and root mean square error (RMSE) of the five groups of model validation tests were 0.993 4 and 1.20% in that order. In this reasearch, an improved neural network-PID (INN-PID) controller was designed to realize temperature control in the variable temperature drying process. The dynamic performance of PID, neural network-PID (NN-PID), and INN-PID controllers was simulated in MATLAB software with unit step signal as input. The three controllers were tested for drying temperature control at 50-55 ℃. The results showed that the control stability and regulation time of the INN-PID controller were significantly better than the other two controllers in the simulation test, the drying temperature control results had approximately the same law with the simulation results, and the peak time of the INN-PID controller was 208.00 s, the regulation time was 120.59 s, and the maximum overshooting was 4.87%, which meets the requirements of temperature control in the variable-temperature drying process. In this paper, a temperature adaptive control system was built in the air-impingement dryer, and the temperature adaptive control test based on the moisture content change was carried out. The results showed that the system could quickly and effectively regulate the drying temperature in the variable temperature drying process based on the change in moisture content. This research is of great significance for improving the automation level of drying equipment, developing new variable-temperature drying processes, and providing reference for multi-information fusion detection and control strategy research in other fields. [ABSTRACT FROM AUTHOR]

Elastic kinetic structures are a recent approach to design transformable structures. Their transformation is based on elastic bending, that is compliant component behavior of structural members. This principle can be used to realize transformable structures with a stable deployment process. Regardless of a stable transformation, elastic kinetic structures are prone to static and dynamic loads due to their lightweight design. However, most of current research on these structures solely focuses on the principles of transformation. This paper proposes a concept for an active hybrid roof structure with a transformation based on elastic kinetics and rigid-body motion. The concept exhibits a stable structural deployment and active control components to counteract static and dynamic disturbances. Furthermore, this paper includes the realization and experimental evaluation of a mid-scale prototype structure. [ABSTRACT FROM AUTHOR]

The measurement of hot rolled steel plates is an important part of steel-plate production lines. A structured light vision system for online measurement of steel-plate width is developed in this paper. A camera plane calibration algorithm is proposed to transform pixel points into 3D coordinate points, and an adaptive image processing range algorithm is proposed to improve the accuracy and speed of laser stripe image center extraction. The gray-gravity method is also improved according to the width range of steel plates produced by a production line. The distortion points in the image are identified, and the actual width of the board is measured. An experimental platform is built, the results of which show that the structured light vision system developed in this paper and the proposed laser stripe center extraction algorithm can provide accurate real-time online detection of steel-plate width. [ABSTRACT FROM AUTHOR]

MySQL used configuration parameters to adjust the linear pre-read threshold and the cold/hot ratio of the cold/hot LRU algorithm, resulting in a performance bottleneck in the buffer. Aiming at the above problems, this paper proposed an adaptive buffer management method, the method designed an adaptive threshold adjustment algorithm and an adaptive hot and cold cache replacement algorithm through the reinforcement online learning technology of regret minimization. First of all, this paper made an in-depth study of the pre-read algorithm and the hot and cold cache replacement algorithm in MySQL, and clarified the specific impact of the pre-read threshold and the hot and cold ratio on the two algorithms. Secondly, this paper designed a set of parameter evaluation process by FIFO history queue and adding auxiliary fields to evaluate whether the current parameter is large or small in real time. Finally, this paper designed a parameter adjustment model, which uses the performance monitoring indicators of MySQL’s native pre-read algorithm and cache replacement algorithm to achieve reasonable parameter adjustment. This paper conducted 900 simulation experiments on FIU datasets, the experimental results show that compared with MySQL’s native benchmark pre-read algorithm and hot and cold cache algorithm, the two adaptive algorithms could effectively reduce disk I/O by 8% and increase cache hit ratio by 24% without sacrificing algorithm speed. Compared with the latest cache replacement algorithm, the adaptive hot and cold cache replacement algorithm improved the speed to 1.6 times while ensuring the cache hit ratio. [ABSTRACT FROM AUTHOR]

Keypoint-based detectors generate a large number of false positives due to incorrect keypoint matching in the object detection task. In this paper, we propose an adaptive center keypoint selection method (ACKSNet) to address the false-positive drawback. We first roughly group detected corners by associative embeddings, which flexibly localize objects of various shapes and scales to obtain a large number of initial candidate proposals. Then, ACKSNet associates introduced extra center keypoints with corner pairs through the geometric method to add location information in the candidate regions. And it independently generates a threshold for each center keypoint according to their statistical characteristics to ensure the high quality of center keypoints. Furthermore, we enrich the scale information of the output feature maps by equipping the backbone network with dilated convolution modules. On the MS COCO dataset, our model achieves an AP of 44.5%, surpassing most existing anchor-free detectors. [ABSTRACT FROM AUTHOR]

AR assembly instruction is a method that helps users with manual assembly by overlaying virtual information on the real environment. However, users have to manually change the information display form, which fails to adapt to their varying information needs in different stages of the assembly task. This study proposes a user-centered adaptive visualization method for AR manual assembly that can provide the necessary information according to the user's intention in the assembly process. This paper develops a system (UIAVS) through an information hierarchy mechanism and a user intention recognition method based on the user's eye gaze, the spatial location, and current assembly task, which can adaptively adjust the visibility of information, how much information is displayed, and the change of visualization form. UIAVS was tested for the first time in a small engine assembly task. Twenty-four participants were randomly assigned to different task familiarity (novice and expert) and different information visualization response methods for assembly task experiments. The results show that UIAVS has better task performance and cognitive performance as well as user experience than traditional AR assembly instruction method, especially for experts. The research results have certain guiding significance for the design of AR assembly instructions, which extends the application of AR technology in practical complex assembly tasks. [ABSTRACT FROM AUTHOR]

Aiming at the challenge of defining the subspace dissimilarity in traditional K-means soft subspace clustering techniques, this paper proposed a novel probability distance-based subspace difference representation model as the basis for an adaptive projection clustering algorithm. Firstly, based on the subspace clustering theory, the proposed method formulated a formula to describe the dissimilarities between associated soft subspaces. Secondly, by combining this formula with soft sub-space clustering, it defined a clustering objective optimization function and provided a detailed clustering algorithm process according to a local search strategy. A series of experiments on both synthetic and real-world datasets demonstrate that the introduction of subspace comparison can lead to learning a more optimal soft subspace for clusters. Compared to existing mainstream subspace clustering algorithms, the proposed algorithm significantly improves the clustering accuracy, making it suitable for high-dimensional data clustering analysis. [ABSTRACT FROM AUTHOR]

In order to reduce the clock offset caused by network delay jitter, network asymmetry, clock drift and clock frequency difference in the clock synchronization process of IEEE1588, a modified Strong Tracking Adaptive Kalman Filter (MSTAKF) algorithm was proposed. The Kalman filter model established by the algorithm is based on clock offset, network delay, frequency deviation and asymmetry ratio. The filter includes the modified strong tracking algorithm based on fuzzy logic and hypothesis test proposed in this paper, which can effectively eliminate the influence of outliers, and finally make the model tracking effect better. In addition, the modified Sage‐Husa algorithm based on Cholesky decomposition is also included, which makes the measurement noise more stable and ensures the positivity of the measurement noise covariance. The experimental results show that the proposed MSTAKF is smoother than Karman filter (KF), Sage‐Husa adaptive Kalman filter (AKF) and strong tracking Sage‐Husa adaptive Kalman filter (STAKF). Compared with these filter algorithms after testing in the LAN environment, the precision improvement and accuracy rate are significantly improved. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Large-sized rotating machines usually contain weak feature information of rub-impact fault, which is hard to extract. General scale transformation stochastic resonance (GSTSR) can match input signals with different frequencies by using the optimal barrier height and boost the weak fault feature in signals. The performance of GSTSR is determined by systemic parameters. When a rub-impact fault occurs between rotor and stator, vibration signals are often accompanied by an impact. Therefore, the paper takes advantage of sensibility of waveform factor to rub-impact fault information and margin factor to impact properties of signal and reconstructs a new signal evaluation index based on waveform and margin factors. The signal evaluation index is treated as the fitness function of grey wolf optimization (GWO) algorithm and combined with GSTSR to perform a comprehensive evaluation to rub-impact fault feature information. The result of comparison with conventional method (with signal to noise ratio (SNR) as fitness function) indicates that in case of extracting rub-impact fault features, the proposed method identifies a rotor-stator rub-impact fault more precisely than the classical method. [ABSTRACT FROM AUTHOR]

In the task of human behavior recognition, most of the video frames do not include important discrimination information, which seriously affects the accuracy of application. Key pose frames can effectively express the video and reduce the amount of computation. Furthermore, bone data contains richer information than RGB image. Therefore, this paper proposes an action recognition approach based on adaptive partition and association of key-frame nodes. Firstly, it constructs an adaptive pooled deep network to evaluate frames importance and obtain key pose sequence. Then, it establishes association between nodes in unnatural connection state by self-learning model. Finally, it applies the improved spatio-temporal information on STGCN and uses SoftMax for classification. This paper evaluates the effectiveness of the proposed approach by comparing with several typical technologies on the open-source and large-scale datasets of NTU-RGB+D and Kinetics. Experimental results show that it can reduce the amount of redundant data and retain key action information, and obtain higher average accuracy by 0.63% ～ 11.81% than the compared methods. [ABSTRACT FROM AUTHOR]

The traffic flow prediction task is essential to the urban intelligent transportation system. Due to the complex correlation of traffic flow data, insufficient use of spatiotemporal features will often lead to significant deviations in prediction results. This paper proposes an adaptive traffic flow prediction model AD-GNN based on spatiotemporal graph neural network. The gated temporal convolutional network captures the temporal dependence between layers. Moreover, the diffusion graph convolutional network simulates the spatial relationship between nodes. Then, the parameterized adjacency matrix is used to construct an adaptive convolutional network to adaptively mine the implicit global deep spatial dependence. The experimental results show that the model has good prediction performance on three real public datasets and can sufficiently meet real needs. [ABSTRACT FROM AUTHOR]

In this paper, we propose and analyze a second-order partitioned method with multiple-time-step technique for the nonstationary Stokes-Darcy model. This method allows different time steps in different subdomains and improves the accuracy by the time filters. Besides, by designing new error estimate and time step adjustment strategy, we extend this method to variable timestep and develop single and double adaptive algorithms. Constant and variable time step tests are given to confirm the theoretical analysis and illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We propose in this paper a new minimization algorithm based on a slightly modified version of the scalar auxialiary variable (SAV) approach coupled with a relaxation step and an adaptive strategy. It enjoys several distinct advantages over popular gradient based methods: (i) it is unconditionally energy diminishing with a modified energy which is intrinsically related to the original energy, and thus no parameter tuning is needed for stability; (ii) it allows the use of large step-sizes which can effectively accelerate the convergence rate. We also present a convergence analysis for some SAV based algorithms, which include our new algorithm without the relaxation step as a special case. We apply our new algorithm to several illustrative and benchmark problems and compare its performance with several popular gradient based methods. The numerical results indicate that the new algorithm is very robust, and its adaptive version usually converges significantly faster than those popular gradient decent based methods. [ABSTRACT FROM AUTHOR]

The traditional parallel Sparse matrix vector multiplication (SpMV) method has been optimized by an application-specific or compression format-specific. However, a single compression format cannot deal with all sparse matrix types effectively in practical applications. To solve this problem, an adaptive compression format based on Bagging ensemble learning algorithm is proposed in this paper. Experiments show that the adaptive compression format has higher prediction and computational performance on NVIDIA V100 and NVIDIA RTX 2080Ti. Compared with SpMV of the four compression formats, SpMV based on adaptive compression format reduces the execution time of 1.5×, 6.6×, 9× and 1.1×, respectively. [ABSTRACT FROM AUTHOR]

This paper is mainly concerned with the leaderless consensus problem for nonlinear multi-agent systems (MAS) with unknown mismatched nonlinear dynamics and external disturbances. First, a novel adaptive controller is designed to achieve bounded consensus with a linear feedback term, the RBF neural network (RBFNN) adaptive approximation term, a discontinuous feedback term, and a state constraint term. Furthermore, on this basis, an observer-based robust uniform control scheme against disturbance is proposed to achieve the leaderless consensus of MAS with unmeasurable states under the directed graph. Compared with the previous works, the proposed controller is less conservative. Finally, two simulation-based examples are provided to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In recent years, image denoising methods based on total variational regularization have attracted extensive attention. However, the traditional total variational regularization method is an approximate solution based on convex method, and does not consider the particularity of the region with rich details. In this paper, the adaptive total-variation and nonconvex low-rank model for image denoising is proposed, which is a hybrid regularization model. First, the image is decomposed into sparse terms and low rank terms, and then the total variational regularization is used to denoise. At the same time, an adaptive coefficient based on gradient is constructed to adaptively judge the flat area and detail texture area, slow down the denoising intensity of detail area, and then play the role of preserving detail information. Finally, by constructing a nonconvex function, the optimal solution of the function is obtained by using the alternating minimization method. This method not only effectively removes the image noise, but also retains the detailed information of the image. The experimental results show the effectiveness of the proposed model, and SNR and SSIM of the denoised image are improved. [ABSTRACT FROM AUTHOR]

Today, most humanoid mechanical fingers use an underactuated mechanism driven by linkages or tendons, with only a single and fixed grasping trajectory. This paper proposes a new multi-mode humanoid finger mechanism based on linkage and tendon fusion transmission, which is embedded with an adjustable-length tendon mechanism to achieve three types of grasping mode. The structural parameters of the mechanism are optimized according to the kinematic and static models. Furthermore, a discussion was conducted on how to set the speed ratio of the linkage driving motor and the tendon driving motor to adjust the length and tension of the tendon, in order to achieve the switching of the shape-adaptive, coupled-adaptive, and variable coupling-adaptive grasping modes. Finally, the multi-mode functionality of the proposed finger mechanism was verified through multiple grasping experiments. [ABSTRACT FROM AUTHOR]

To address the low efficiency and high false alarm rate in detection of DDoS (Distributed Denial of Service) flood attacks, this paper proposes a DWT (Discrete Wavelet Transform) and AKD (Adaptive Knowledge Distillation) self-encoder neural network based approach to detect DDoS attacks.The approach uses the DWT to extract frequency features, the auto-encoder neural network to encode and classify the features, and the AKD to compress the model in order to achieve efficient detection of DDoS attacks.The results show that the approach has high detection efficiency for proxy server attacks, database vulnerabilities & TCP flood attacks, and UDP flood attacks, with low false alarm rate. [ABSTRACT FROM AUTHOR]

In dense Wi-Fi networks, achieving load balancing is critical to optimize network utilization and provide equitable network consumption among the users. Traditional Wi-Fi networks have issues in attaining effective load balancing. Software-Defined Networking (SDN) has presented a viable solution by isolating the data plane and control plane, enabling more agile and cost-effective networks. In this paper we put forward an Adaptive Symmetrical Load Balancing (ASLB) scheme to ensure fairness of load symmetry in Software Defined Wi-Fi Networks (SD-Wi-Fi), while also optimizing the flows transition process using an Analytical Hierarchal Process (AHP). User activity is monitored by access points (APs), which operate under OpenFlow standards. Three essential features, packet volume, packet category and delay hindrance, are used for flow assignment to various controllers. The controllers are arranged in two tiers, universal and regional controllers. The universal controller (UC) handles the workload statistics of regional controllers (RC) in the form of clusters. Extensive simulations using OMNeT++ simulator are performed. The performance parameters taken into consideration are throughput, delay, packet loss rate, network transition count and workload distribution. Our findings demonstrate that the ASLB technique effectively optimizes the network utilization and ensures equitable network consumption among the end users. The proposed scheme outperforms the Mean Probe Delay scheme (MPD), Channel Measurement-based Access Selection scheme (CMAS), Received Signal Strength Indicator-based scheme (RSSI) and Distributed Antenna Selection scheme (DASA), being 40% higher in throughput and 25% lower in delay. [ABSTRACT FROM AUTHOR]

This paper proposes an arithmetic optimization algorithm that integrates the sine-cosine strategy to address the problems of low solution accuracy, slow convergence speed, and easy fall into local optima in arithmetic optimization algorithms. The algorithm adaptively adjusts the math optimizer acceleration (MOA) accelerator function based on the change information of individual fitness, balancing the global exploration and local exploitation abilities of the algorithm. The improved sine-cosine algorithm is introduced into the local development stage of the algorithm, increasing the population diversity in the later iterations, avoiding the algorithm from falling into local optima, and effectively improving the solution accuracy and convergence speed of the algorithm. Simulation experiments on 14 benchmark test functions show that the improved algorithm has significant improvements in solution accuracy, convergence speed, and robustness. Finally, the improved algorithm is applied to the optimization of support vector machine (SVM) parameters, and a student knowledge level prediction model is established, which further verifies the practicality and superiority of the algorithm. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous development of new energy, how to efficiently use wind energy has received more and more market attention. Due to cost advantages, the development of small wind turbines is accelerating. Among them, the design and research of the airfoil design and research of the lift vertical axis wind turbine has matured, but because of the aerodynamic characteristics of the lift airfoil structure, it is impossible to start itself at low wind speed, resulting in the waste of low wind speed energy. Although the drag wind turbine has good self-starting performance, the wind energy utilization efficiency in the high-speed state is inefficient. Each has its own unique shortcomings, which directly affects the marketization of small wind turbines. In order to solve these problems, this paper presents a drag-lift hybrid type wind turbine structure based on an NACA0018 symmetrical airfoil, which can adaptively change the blade shape. This design can keep the blade in the drag shape under static and low speed conditions, and adaptively change the lift shape with the increase of speed. In addition, through the research method of CFD numerical simulation combined with physical experiments, the proposed wind turbine design is studied and analyzed from multiple angles. At the same time, the "6DOF + dynamic grid" setting is used to study the influence of the opening angle factor of the drag-lift hybrid blade on the self-starting performance, and the study shows that the design of the drag-lift hybrid blade proposed in this paper has a higher self-starting torque and lower starting wind speed than the traditional lifting blade, and it is observed that the drag-lift hybrid blade has the best self-starting performance when the opening angle of the blade is 80°. At the same time, the problem of switching the blade morphology of the drag-lift hybrid blade is also analyzed, along with how to use the spring to control all this adaptively. In order to better analyze the advantages of the drag-lift hybrid design proposed in this paper, a wind tunnel test was also carried out using the physical model, and the relationship between the leaf tip speed ratio and the wind energy utilization rate was obtained, which intuitively showed the improvement of the wind energy utilization rate of the drag-lift hybrid design compared with the traditional lift blade. [ABSTRACT FROM AUTHOR]

Joint decision-making can be seen as the synchronization of actions and emotions, usually via nonverbal interaction between people while they show empathy. The aim of the current paper was (1) to develop an adaptive computational model for the type of synchrony that can occur in joint decision-making for two persons modeled as agents, and (2) to visualize the two persons by avatars as virtual agents during their decision-making. How to model joint decision-making computationally while taking into account adaptivity is rarely addressed, although such models based on psychological literature have a lot of future applications like online coaching and therapeutics. We used an adaptive network-oriented modelling approach to build an adaptive joint decision-making model in an agent-based manner and simulated multiple scenarios of such joint decision-making processes using a dedicated software environment that was implemented in MATLAB. Programming in the Unity 3D engine was done to virtualize this process as nonverbal interaction between virtual agents, their internal and external states, and the scenario. Although our adaptive joint decision model has general application areas, we have selected a therapeutic session as example scenario to visualize and interpret the example simulations. [ABSTRACT FROM AUTHOR]

It is challenging to ensure satisfying co-evolution efficiency for the multi-agents in dynamic environments since during Actor-Critic training there is a high probability of falling into local optimality, failing to adapt to the suddenly changed environment quickly. To solve this problem, this paper proposes a multi-agent adaptive co-evolution method in dynamic environments (ACE-D) based on the classical multi-agent reinforcement learning method MADDPG, which effectively realizes self-adaptive new environments and co-evolution in dynamic environments. First, an experience screening policy is introduced based on the MADDPG method to reduce the negative influence of original environment experience on exploring new environments. Then, an adaptive weighting policy is applied to the policy network, which accordingly generates benchmarks for varying environments and assigns higher weights to those policies that are more beneficial for new environments exploration, so that to save time while promoting adaptability of the agents. Finally, different types of dynamic environments with complexity at different levels are built to verify the co-evolutionary effects of the two policies separately and the ACE-D method comprehensively. The experimental results demonstrate that, compared with a range of other methods, the ACE-D method has obvious advantages helping multi-agent adapt to dynamic environments and preventing them from falling into local optima, with more than 25% improvement in stable reward and more than 23% improvement in training efficiency. The ACE-D method is valuable and commendable to promote the co-evolutionary effect of multi-agent in dynamic environments. [ABSTRACT FROM AUTHOR]

The of robustness in cloud computing is becoming mainstream due to unprecedented growth in the cloud. In order to enhance cloud service reliability, various fault tolerance techniques have been suggested in the literature. Many cloud service reliability and availability issues are arising due to the failure of virtual machines (VMs). Therefore, this study contributes a new Threshold-Based Adaptive Fault Tolerance (TBAFT) approach in the cloud by amalgamating proactive and reactive approaches. Furthermore, in this paper, VM migration and replication are being utilized in the proactive approach, whereas retry is used as a reactive approach. Additionally, experimental validation is performed by comparing three benchmark fault tolerance approaches by incorporating the first fit and least full host selection policies. The obtained experimental results of the proposed approach are presented in terms of different metrics such as failure rate, throughput, migration, and execution time using CloudSim simulator. [ABSTRACT FROM AUTHOR]

[Display omitted] • We propose a black-box adversarial text generation scheme called TextGuise, which can be used for almost all text classification tasks. The experiments show that TextGuise is superior to the state-of-the-art text adversarial example generation methods in terms of its attack success rate and attack efficiency when the perturbation ratio does not exceed 0.2.The adversarial examples generated using TextGuise retain the semantic information and strong readability of the text. None of the current defense methods can effectively defend against TextGuise. Adversarial examples greatly compromise the security of deep learning models. The key to improving the robustness of a natural language processing (NLP) model is to study attacks and defenses involving adversarial text. However, the current adversarial attack methods still face problems, such as the low success rates of attacks on some datasets, and the existing defense methods can already successfully defend against some attack methods. As a result, such attacks are unable to dig deeper into the flaws of NLP models to inform further defense improvements. Hence, it is necessary to design an adversarial attack method with a wider attack range and stronger performance. Aiming at the advantages and disadvantages of existing methods, this paper proposes a new adaptive black-box text adversarial example generation scheme, TextGuise. First, we design a keyword selection method in which word scores are calculated by combining context semantics to select the appropriate keywords to modify. Second, to maintain semantics, new keyword substitution rules are designed in combination with the characteristics of text and popular text expressions. Finally, the best modification strategy is adaptively selected through a querying model to reduce the magnitudes of disturbances. TextGuise can automatically select replacement keywords and replacement strategies that efficiently generate adversarial examples with good readability for various text classification tasks. Attack experiments conducted with TextGuise on 5 datasets yield high attack success rates that can surpass 80% when the perturbation ratio does not exceed 0.2. In addition, we present and discuss experiments focusing on defense, text similarity, query times, time consumption, etc., to test the attack performance of TextGuise. The results show that our attack method can achieve a good balance among various metrics. [ABSTRACT FROM AUTHOR]

To improve the accuracy of phase recovery in quantitative phase imaging, a novel two-stage synthetic denoising method is proposed in this paper. In this method, to avoid the loss of effective detail information, the mode of fixed attenuation parameter setting is changed into two stages of self-adaptation setting: the first attenuation parameter is defined as the convolution of filtered pixels in the window of interest, the second attenuation parameter is defined as the noise standard deviation of each pixel after filtering in the first stage. Moreover, the Pearson coefficient expression is redefined to get a more accurate similarity evaluation, and the algorithm's operational efficiency has been improved using cross-correlation functions and fast Fourier transform. The comparative results of simulations and experiments show that this method is more effective in reducing speckle noise in quantitative phase imaging and enhancing phase recovery accuracy, providing a new digital image denoizing method for efficient and accurate imaging. [ABSTRACT FROM AUTHOR]

Dry eye disease (DED) is a complex disorder of the ocular surface with a loss of tear film homeostasis, causing an imbalance in the tear–air interface and leading to ocular discomfort, pain, and vision issues. Immune control issues are a primary factor in dry eye disorder’s origin, progression, and management. The aim of managing DED is to reduce symptoms and improve the life quality of those affected. Despite the diagnosis, up to half of the patients do not get proper care. The scarcity of successful treatments for DED is worrisome, and it is of increasing importance to comprehend the root causes and create more effective therapies to alleviate the distress of those afflicted with the disorder. Therefore, the role of the immune system in the initiation and progression of DED has become the research focus. This paper reviews the current insight into the immune response in DED, the existing treatment methods, and ongoing research to search for better treatments. [ABSTRACT FROM AUTHOR]

Summary: Servo‐hydraulic actuators are typically employed in real‐time hybrid simulation (RTHS) to impose dynamic loads on a specimen. The success and reliability of an RTHS depend on the accurate tracking of the command displacements by the actuators. This paper introduces a new adaptive compensation approach, adaptive discrete feedforward (ADF) controller, based on the principles of model‐based feedforward and feedback (MFF) controller. While the MFF controller relies on an accurate model of the controlled system obtained through system identification, the ADF controller updates its parameters to adapt to the changing dynamics of the controlled system during the test. This paper presents an experimental parametric investigation that determines the parameters required for implementing the ADF controller, such as the buffer size and the controller updating frequency. The performance of the ADF controller was compared with two other compensation methods: MFF controller and adaptive time series compensator in a series of predefined displacement tracking tests and RTHSs considering both linear and nonlinear specimens. The results are discussed in detail, where the ADF controller was shown to considerably improve the capability of the servo‐hydraulic actuator in tracking the command displacements. [ABSTRACT FROM AUTHOR]

In this paper, we propose a gradient recovery method for the direct discontinuous Galerkin (DDG) method. A quadratic polynomial is obtain by using the local discrete least-squares fitting to the gradient of numerical solution at certain sampling points. The recovered gradient is defined on a piecewise continuous space, and it may be discontinuous on the whole domain. Based on the recovered gradient, we introduce a posteriori error estimator which takes the L 2 norm of the difference between the direct and post-processed approximations. Some benchmark test problems with typical difficulties are carried out to illustrate the superconvergence of the recovered gradient and validate the asymptotic exactness of the recovery-based a posteriori error estimator. Most of the test problems are from the US National Institute for Standards and Technology (NIST). [ABSTRACT FROM AUTHOR]

Infrared small target detection is widely used for early warning, aircraft monitoring, ship monitoring, and so on, which requires the small target and its background to be represented and modeled effectively to achieve their complete separation. Low-rank sparse decomposition based on the structural features of infrared images has attracted much attention among many algorithms because of its good interpretability. Based on our study, we found some shortcomings in existing baseline methods, such as redundancy of constructing tensors and fixed compromising factors. A self-adaptive low-rank sparse tensor decomposition model for infrared dim small target detection is proposed in this paper. In this model, the entropy of image block is used for fast matching of non-local similar blocks to construct a better sparse tensor for small targets. An adaptive strategy of low-rank sparse tensor decomposition is proposed for different background environments, which adaptively determines the weight coefficient to achieve effective separation of background and small targets in different background environments. Tensor robust principal component analysis (TRPCA) was applied to achieve low-rank sparse tensor decomposition to reconstruct small targets and their backgrounds separately. Sufficient experiments on the various types data sets show that the proposed method is competitive. [ABSTRACT FROM AUTHOR]

In this paper, a novel finite-time fault-tolerant trajectory tracking controller is created to render a marine vehicle to enhance tracking performance in the presence of complex unknown variables, including model uncertainties, environmental disturbances, and actuator faults. An adaptive fault-tolerant finite-time sliding mode controller is designed by introducing adaptive control techniques, the non-singular fast terminal sliding mode (NSFTSM) function, and a radial base function (RBF) neural network. The radial base function neural network (RBFNN) is developed to eliminate the influences of model uncertainties and environmental disturbances. A fault compensation by integrating the adaption technique is designed to reduce the effects of actuator faults and approximation errors. Suffering from uncertainties and actuator faults, the proposed finite-time tracking controller can track the desired trajectory with high precision. Simulation results and compared simulations indicate the efficiency and superiority of the proposed controller. [ABSTRACT FROM AUTHOR]