Search

This paper proposes a predictive power control strategy for the three‐phase, six‐switch Vienna rectifier without grid voltage sensors to reduce the hardware cost and complexity of a high‐power PWM rectifier system. Firstly, an algorithm for calculating the AC‐side voltage in the αβ coordinate system is derived according to the operating principle of the Vienna rectifier, and a voltage observer is constructed by combining a second‐order low‐pass filter to estimate the grid voltage. Secondly, a soft start method is designed to solve the problem that the rectifier is prone to inrush current when it is started. Furthermore, the control method of grid voltage sensorless is combined with predictive power control with good dynamic characteristics and simple parameter settings to form the control strategy proposed in this paper. Finally, simulation analysis and experimental verification are carried out on the proposed control strategy. Simulation and experimental results show that the grid voltage estimation has high accuracy, a good surge current suppression effect, unit power factor operation, low input current harmonic content, and good dynamic and steady‐state performance. Therefore, the correctness and effectiveness of the strategy proposed in this paper are verified. [ABSTRACT FROM AUTHOR]

Real‐time monitoring of the coal caving process in fully mechanized mining is crucial for achieving intelligent and efficient top‐coal caving. While the coal gangue identification method, employing vision and deep learning, has advanced in the realm of intelligent monitoring, it exhibits a dependency on high‐performance hardware. This reliance poses challenges for deploying identification equipment on mobile terminals, hindering the widespread application of this method. To address the issues above, the paper presents a lightweight algorithm, utilizing You Only Look Once version 5s (YOLOv5s), utilizing YOLOv5s for the real‐time perception of the top‐coal caving state in fully mechanized caving mining. We replace the backbone network of YOLOv5s with the ShuffleNetv2 structure that is more suitable for lightweight deployment, and add the Simple Attention Mechanism attention mechanism to the network structure to enhance the model's receptive field and feature expression ability, and reduce the impact of falling debris on the detection results. A dynamic experimental platform for top‐coal caving in fully mechanized caving mining for thick coal seams is set up, and preprocessing operations such as brightness, sharpening, and denoising are performed on the image data sets collected by high‐speed industrial cameras. Research results show that compared with the traditional YOLOv5s, the improved model's P, mAP, F1 score, and other indicators have increased by 3.4%, 2.1%, and 1.1%, respectively, the model size is 70% of the original, and the detection frames per second value has increased by 48.1%. The lightweight algorithm stabilizes the accuracy of coal gangue identification dramatically in real time. It dramatically reduces the computing pressure on the mobile terminal, providing basic theory and practice for real‐time monitoring of fully mechanized coal caving mining. [ABSTRACT FROM AUTHOR]

Load variation, distributed power output uncertainty and multi‐microgrids network complexity have brought great difficulties to the frequency stability of the whole microgrid. To address this problem, this paper uses a multi‐agent deep reinforcement learning(DRL) algorithm to design the controllers to control the frequency of the multi‐microgrids. Firstly, a load frequency control (LFC) model for multi‐microgrids was built. Secondly, based on the centralized training and decentralized execution (CTDE) multi‐agent reinforcement learning (RL) framework, the multi‐agent soft actor‐critic (MASAC) algorithm was designed and applied to the multi‐microgrids model. The state space and action space of multi‐agent were established according to the frequency deviation of every sub‐microgrid and the output of each distributed power source. The reward function was then established according to the frequency deviation. The appropriate neural network and training parameters were selected to generate the interconnected microgrid controllers through multiple training of pre‐learning. Finally, the simulation study shows that the MASAC controller proposed in this paper can quickly maintain frequency stability when the system is disturbed. Sensitivity analysis shows that the MASAC controller can effectively cope with the uncertainty of the system parameters. [ABSTRACT FROM AUTHOR]

Pixel‐level labeling of regions of interest in an image is a key step in building a labeled training dataset for supervised deep learning networks of images. However, traditional manual labeling of cancerous regions in digital pathological images by doctors is time‐consuming and inefficient. To address this issue, this paper proposes an automatic labeling method for whole slide images, which combines clustering and edge detection techniques. The proposed method utilizes the multi‐level feature fusion model and the Long‐Short Term Memory network to discriminate the cancerous nature of the whole slide images, thereby improving the classification accuracy of the whole slide images. Subsequently, the automatic labeling of cancerous regions is achieved by integrating a density‐based clustering algorithm and an edge point extraction algorithm, both based on the discriminated results of the cancerous properties of whole slide images. The experimental results demonstrate the effectiveness of the proposed method, which offers an efficient and accurate solution to the challenging task of cancerous region labeling in digital pathological images. [ABSTRACT FROM AUTHOR]

This paper investigates a backscatter communication (BackCom) based non‐orthogonal multiple access (NOMA) system in a multiple‐input and single‐output (MISO) scenario, where two decoding methods are deployed, including the sum‐capacity approach and QR decomposition. The goal is to maximize energy efficiency (EE) through the optimization of the beamforming matrix and the reflection coefficient of the BackCom devices. Two algorithms, Dinkelbach based on penalty semidefinite relaxation (SDR) and successive convex approximation (SCA), are proposed as high‐performance and low‐complexity solutions, respectively. Simulation results indicate that the combination of the sum‐capacity approach and Dinkelbach yields the best performance, though at the highest complexity, while the amalgamation of QR decomposition and SCA offers the lowest performance but with minimal complexity. [ABSTRACT FROM AUTHOR]

In this paper, the vector extension operation is proposed to replace the de Boor‐Cox formula for a fast algorithm to B‐spline basis functions. This B‐spline basis function based on vector extending operation is implemented in the class and shape transformation (CST) parameterization method in place of the traditional Bézier polynomials to enhance the local ability of control and accuracy to represent an airfoil shape. To calculate the k‐degree B‐spline function's nonzero values, the algorithm can improve the computing efficiency by 2k+1 times. [ABSTRACT FROM AUTHOR]

This paper proposes a new comprehensive load restoration (LR) method for electrical distribution networks. Since two main technologies of switching equipment are there in the modern distribution networks, namely manual switches (MSs) and remote‐controlled switches (RCSs), this article has benefited from this concept effectively. A two‐stage algorithm that provides the system operators with the ability to recover part of the loads in the shortest possible time by RCSs is proposed. After this step, the remaining loads will be restored by a combination of MSs and RCSs. The other strength of this algorithm is to provide accurate and practical solutions so that the sequence of switching actions is clearly defined. Also, using an innovative index called expected weighted energy not supplied as the objective function of the main problem will ensure the operators recover the maximum amount of load in the shortest time possible. This novel method was applied on a sample standard IEEE distribution test network. The simulation results proved the effectiveness of this proposed method. [ABSTRACT FROM AUTHOR]

License plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M. [ABSTRACT FROM AUTHOR]

To eliminate blind spots in the field of vision and achieve a safe and collision‐free path, this paper proposes a path planning method based on multivehicle collaborative mapping in the context of vehicle networking. First, a multi vehicle map merging strategy based on the fireworks algorithm is proposed. In this strategy, a dissimilarity objective function based on the concept of grid map similarity is established and an improved fireworks algorithm is used to quickly search for the maximum overlap between local maps, achieving multivehicle collaborative mapping. Second, a real‐time path planning method based on artificial potential field theory is proposed. The information obtained from multivehicle collaborative mapping is first combined with the potential field model to form a multifield coupled road environment model. Then, the obstacle repulsion potential field model is improved to address the issues of traditional artificial potential field methods that target unreachability and poor dynamic response. The feasibility and effectiveness of the collaborative path planning method and single vehicle path planning method are tested through simulation analysis. This paper demonstrates through simulation analysis that the proposed path planning method can effectively achieve beyond line of sight perception and safely and comfortably guide vehicles to complete path planning. [ABSTRACT FROM AUTHOR]

In addressing the challenge of ships being prone to occlusion in multi‐target situations during ship target detection, leading to missed and false detections, this paper proposes an enhanced ship detection algorithm for YOLOv7‐tiny. The proposed method incorporates several key modifications. Firstly, it introduces the Convolutional Block Attention Module in the Backbone section of the original model, emphasizing position information while attending to channel features to enhance the network's ability to extract crucial information. Secondly, it replaces standard convolution with GSConv convolution in the Neck section, preserving detailed information and reducing computational load. Subsequently, the lightweight operator Content‐Aware ReAssembly of Features is employed to replace the original nearest‐neighbour interpolation, mitigating the loss of feature information during the up‐sampling process. Finally, the localization loss function, SIOU Loss, is utilized to calculate loss, expedite training convergence, and enhance detection accuracy. The research results indicate that the precision of the improved model is 91.2%, mAP@0.5 is 94.5%, and the F1‐score is 90.7%. These values are 3.7%, 5.5%, and 4.2% higher than those of the original YOLOv7‐tiny model, respectively. The improved model effectively enhances detection accuracy. Additionally, the improved model achieves an FPS of 145.4, meeting real‐time requirements. [ABSTRACT FROM AUTHOR]

Communication security has become particularly crucial with the rapid development of the Internet of Things (IoT). Frequency hopping spread spectrum (FHSS) technology, a prevalent method in wireless communication, has a wide range of applications in the Internet of Things. Enhancing the security of frequency hopping sequences is an essential means to improve the security of frequency hopping communication in the Internet of Things, as the performance of frequency hopping sequences plays a crucial role in frequency hopping systems. This paper proposes constructing secure adaptive frequency hopping sequence sets based on the advanced encryption standard (AES) algorithm. As a block cipher algorithm with superior security, the AES algorithm can provide a fundamental guarantee for the security of the proposed frequency hopping sequences. The mapping methods from ciphertext sequences to frequency hopping sequences proposed in this paper can achieve the construction of frequency hopping sequences of any frequency set size to meet the requirements of adaptive frequency hopping. In addition, we also model and analyse the problem of overlapping spectrum band of the IoT groups in the industrial, scientific, and medical (ISM) band, aiming to achieve better packet transmission performance by adjusting the frequency set size. [ABSTRACT FROM AUTHOR]

In this paper, a power allocation algorithm of dual-function radar–communication system with limited power is proposed to obtain better overall system performance measured by the weighted summation of radar signal to interference plus noise ratio (SINR) and communication channel capacity. First, a power allocation model is established to maximize the radar SINR and communication channel capacity with limited transmitted power. Then, the Karush–Kuhn–Tucker (KKT) conditions are used to solve the optimal objective function under the condition that only radar SINR or communication channel capacity is considered, respectively. Finally, the optimal value is combined with the original model and transformed into a single objective optimization model, and the optimal power is obtained by solving the model through the iterative optimization algorithm. Simulation results show that, compared with other power allocation algorithms, the proposed algorithm can achieve better radar-communication integration performance under the same transmit power. [ABSTRACT FROM AUTHOR]

This paper studies the optimization of hydraulic parameters for pipeline system of hydropower station with super long headrace tunnel (HSSLHT) based on mayfly algorithm considering operational scenarios. Firstly, the state equation of HSSLHT under load disturbance is derived. The optimization design of hydraulic parameters for pipeline system based on mayfly algorithm is proposed. Then, the optimization of hydraulic parameters is conducted and analyzed. Finally, the effects of pipeline diameters and transfer coefficients of turbines on the optimization of hydraulic parameters for pipeline systems are revealed. The results show that the optimization of hydraulic parameters for pipeline system is a multiobjective problem, and the several objective functions exhibit significant conflicts. Compared to the firefly algorithm and genetic algorithm, the objective function under mayfly algorithm is improved by 14.7% and 5.1%, respectively. The mayfly algorithm can make the hydraulic parameters for the pipeline system reach the Pareto optimal solution under both load decrease condition and load increase condition. The diameter of penstock has an obvious influence on the robustness of dynamic performance of HSSLHT. When the diameter of penstock increases by 30%, the robustness of HSSLHT becomes worse and the robustness index deteriorates by 57%. The reason is that the flow inertia of penstock becomes smaller with the increase of diameter, and the flow inertia of penstock is favorable for resisting disturbance of HSSLHT. The coefficient of throttled orifice head loss and the lengths and head losses of headrace tunnel and penstock are the key hydraulic parameters for matching the operation of HSSLHT. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm based on sensitivity analysis and the Wolfe method to solve a sequence of parametric quadratic programming (QP) problems such as those that arise in quadratic model predictive control (QMPC). The Wolfe method, based on Karush–Kuhn–Tucker conditions, has been used to convert parametric QP problems to parametric linear programming (LP) problems, and then the sensitivity analysis is applied to solve the sequence of the parametric LP problems. This strategy obtains sensitivity analysis‐based QMPC (SA‐QMPC) algorithm. It is proved that the computational complexity of SA‐QMPC is O(Nn2)$O(Nn^2)$ for a region of the initial conditions and O(N2n2)$O(N^2n^2)$ for sufficiently small sampling time and all initial conditions, where N$N$ and n$n$ are the horizon time and dimension of the state vector, respectively. Numerical results indicate the potential and properties of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In recent years, with the development of low‐earth orbit broadband satellites, the combination of multi‐path transmission and software‐defined networking (SDN) for satellite networks has seen rapid advancement. The integration of SDN and multi‐path transmission contributes to improving the efficiency of transmission and reducing network congestion. However, the current SDN controllers do not support the multi‐path QUIC protocol (MPQUIC), and the routing algorithm used in current satellite networks based on minimum hop count struggles to meet the real‐time requirements for some applications. Therefore, this paper designs and implements an SDN controller that supports the MPQUIC protocol and proposes a multi‐objective optimization‐based routing algorithm. This algorithm selects paths with lower propagation delays and higher available bandwidth for subflow transmission to improve transmission throughput. Considering the high‐speed mobility of satellite nodes and frequent link switching, this paper also designs a flow table update algorithm based on the predictability of satellite network topology. It enables proactive rerouting upon link switching, ensuring stable transmission. The performance of the proposed solution is evaluated through satellite network simulation environments. The experimental results highlight that SDN‐MPQUIC significantly improves performance metrics: it reduces average completion time by 37.3% to 59.3% compared to QSMPS and by 52.8% to 72.4% compared to Disjoint for files with different sizes. Additionally, SDN‐MPQUIC achieves an average throughput improvement of 81.4% compared to QSMPS and 147.8% compared to Disjoint, while demonstrating a 26.3% lower retransmission rate than QSMPS. [ABSTRACT FROM AUTHOR]

In response to the low matching accuracy of stereo matching algorithms in image regions with specular reflection, this paper proposes a multidimensional fusion stereo matching algorithm named MFANet. The algorithm embeds a multispectral attention module into the residual feature extraction network, utilizing two‐dimensional discrete cosine transforms to extract frequency features. In the pyramid pooling module, a coordinated attention mechanism is introduced to capture relevant positional information. In the cost aggregation part, the MFANet algorithm incorporates a three‐dimensional attention mechanism, focusing on the more important semantic information in high‐level features. By combining detailed information from low‐level features, semantic information from high‐level features, and contextual information, the algorithm generates features that are more conducive to disparity prediction. The MFANet algorithm is evaluated on three standard datasets (SceneFlow, KITTI2015, and KITTI2012). Experimental results demonstrate its robustness against specular reflection interference, accurate prediction of disparities in specular reflection pathological regions, and promising application prospects. [ABSTRACT FROM AUTHOR]

Proper current sharing, DC bus voltage deviation reduction, and SOCs balancing, along with ensuring stability are the vital challenges of DC microgrids control algorithms. Addressing these challenges without communication links and a central controller is one of the priorities of control methods. Motivated by the above mentions, this paper presents a novel communication‐free control method. In this regard, a new parameter called "virtual current" is defined according to the unit current and its SOC. Then using a piecewise droop curve and the droop curve shift technique, the virtual current for each unit is determined. The units control coefficients and the relationship of the virtual current are allocated based on the location and power of the loads and RESs such that in the worst case; 1) SOCs are converged; 2) the DC bus voltage deviation is reduced; and 3) the current is appropriately distributed. The simulation and experimental results confirm that the proposed method can balance SOCs like SOC‐based methods and share power properly like piecewise droop methods while reducing DC bus voltage deviation. [ABSTRACT FROM AUTHOR]

Sidelobe cancellation (SLC) is a well-established beamforming technique for mitigating interference, particularly in the context of satellite communication (SATCOM). However, traditional SLC suffers from the issue of partially canceling the desired signal at high signal-to-noise ratio (SNR), primarily due to unconstrained beamforming processing. Extensive research has been conducted to address this problem; however, existing algorithms have limitations such as dependence on knowledge of signal array vectors or number of interferers and involve high computational complexity. In this paper, we propose a robust SLC algorithm based on beamforming vector norm constraint. Our proposal offers a practical solution by only requiring knowledge of the earth station antenna gain and maximum auxiliary array gain to the desired signal, both of which are fully known. Furthermore, compared to traditional SLC, our proposed method introduces additional computational complexity that only scales linearly with the size of the auxiliary array. Simulation results demonstrate comparable performance between our proposed method and existing techniques such as diagonal loading and spatial degrees-of-freedom control-based algorithms. [ABSTRACT FROM AUTHOR]

Accurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single‐diode model, the double‐diode model, the three‐diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV‐PWP201, STM6‐40/36, and STP6‐120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells. [ABSTRACT FROM AUTHOR]

Due to the robustness of skeleton data to human scale, illumination changes, dynamic camera views, and complex backgrounds, great progress has been made in skeleton‐based video anomaly detection in recent years. The spatio‐temporal graph convolutional network has been proven to be effective in modelling the spatio‐temporal dependencies of non‐Euclidean data such as human skeleton graphs, and the autoencoder based on this basic unit is widely used to model sequence features. However, due to the limitations of the convolution kernel, the model cannot capture the correlation between non‐adjacent joints, and it is difficult to deal with long‐term sequences, resulting in an insufficient understanding of behaviour. To address this issue, this paper applies the Transformer to the human skeleton and proposes the Spatio‐Temporal Enhanced Graph‐Transformer AutoEncoder (STEGT‐AE) to improve the capability of modelling. In addition, the multi‐memory model with skip connections is employed to provide different levels of coding features, thereby enhancing the ability of the model to distinguish similar heterogeneous behaviours. Furthermore, the STEGT‐AE has a single encoder‐double decoder architecture, which can improve the detection performance by the combining reconstruction and prediction error. The experimental results show that performances of STEGT‐AE is significantly better than other advanced algorithms on four baseline datasets. [ABSTRACT FROM AUTHOR]

Ensemble methods are among the most effective concept‐drift adaptation techniques due to their high learning performance and flexibility. However, they are computationally expensive and pose a challenge in applications involving high‐speed data streams. In this paper, we present a computationally efficient heterogeneous classifier ensemble entitled OMS‐MAB which uses online model selection for concept‐drift adaptation by posing it as a non‐stationary multi‐armed bandit (MAB) problem. We use a MAB to select a single adaptive learner within the ensemble for learning and prediction while systematically exploring promising alternatives. Each ensemble member is made drift resistant using explicit drift detection and is represented as an arm of the MAB. An exploration factor ϵ controls the trade‐off between predictive performance and computational resource requirements, eliminating the need to continuously train and evaluate all the ensemble members. A rigorous evaluation on 20 benchmark datasets and 9 algorithms indicates that the accuracy of OMS‐MAB is statistically at par with state‐of‐the‐art (SOTA) ensembles. Moreover, it offers a significant reduction in execution time and model size in comparison to several SOTA ensemble methods, making it a promising ensemble for resource constrained stream‐mining problems. [ABSTRACT FROM AUTHOR]

Representation‐based classification (RC) is an effective gauge of data similarity between a single instance and the whole dataset, which extends traditional individual‐wise distance metrics using representation coefficients. These coefficients show remarkable discrimination nature via various regularisation terms, but the interference from potentially uncorrelated objects involved in this single‐to‐global relation can degrade the effectiveness of the coefficients. In order to filter out those unproductive, or even counter‐productive, information from the decision making processes, this paper proposes a local representation‐based classification (LRC) algorithm to improve the classification accuracy or the RC approach. LRC uses a single‐to‐local relation induced by the local representation‐based neighbourhood (LRN) of each object, rather than the single‐to‐global relationship used by RC. Thanks to LRN, a compact and relevant dataset can be formed by selecting the most relevant data instances in the original dataset, to render a robust representation of a query. LRC was applied to multiple publicly available datasets, and the experimental results demonstrate the superiority of the proposed LRC algorithm as evidenced by the higher classification accuracy and more noise‐tolerant capability in reference to alternative RC approaches. Moreover, the sampling ability of LRN is also verified via a comparative study. [ABSTRACT FROM AUTHOR]

In a drone system with dual data links, this article presents a redundant data processing algorithm that can minimize flight control instability without increasing the weight and price of the aircraft by software processing of duplicate received messages. [ABSTRACT FROM AUTHOR]

Machine Learning and Data Mining algorithms are used extensively to enhance the performance of Intrusion Detection Systems. The number of training instances and the dimensionality of data are crucial factors affecting the performance of the model built during the training of any supervised learning algorithms. A sufficient proportion of instances having relevant features from all classes of attacks and normal traffic are considered most desirable while building the classification model that classifies the network traffic into attack and normal. This paper proposes a methodology to improve the accuracy of the model by giving importance to the relevant features that can contribute to model building. The feature selection using correlation‐based and information gain‐based techniques during training and testing contributes much to the detection of stealthier attacks and minority attacks. Then the features of the less detected attacks are identified as the second phase of the filter that is used to improve the performance. The relevant features of stealthy attacks are identified based on the correlation of corresponding features of the attack and normal data as the attacks are made stealthy mostly by making it resemble the normal traffic. Finally, the attacks that are rarely found in the training data are oversampled to improve their detection. CICIDS 2017 data set is employed as it comprises stealthier attacks generated using modern tools. NSL KDD data set is also used for evaluation to compare the proposed work with existing literature as it is used in most of the available literature. The results show superior performance with an accuracy of 99.8%, false positive rate of 0.2%, and a detection rate and 99.8%. [ABSTRACT FROM AUTHOR]

This study examines the challenges and opportunities of using group recommendation systems in an information overload scenario. Social network recommendation systems are increasingly important because they deliver users customized choices. Most existing solutions are geared for single users, making it difficult to propose for a group with different interests. This paper analyses group recommendation systems and exposes their flaws. This study tested whether the suggested approach outperforms the one without tagging information in recall, precision, and user satisfaction. Empirical evidence indicates that the algorithm exhibits appropriate levels of reliability and accuracy compared to conventional methods. The proposed approach has the potential to substantially enhance the existing state of social network group recommendation systems, thereby facilitating users in their quest to identify and participate in groups that align with their preferences. [ABSTRACT FROM AUTHOR]

The YOLO series of algorithms have made substantial contributions to the detection of insulator defects in power transmission line operations. However, existing target detection algorithms for the small target detection and low‐quality insulator images encounter difficulties in effectively capturing relevant features, resulting in a higher probability of target loss. To identify and classify defects in the operational state of insulators, an improved YOLOv8 target identification algorithm called DGW‐YOLOv8 is proposed in this paper. The deformable attention backbone of the DGW‐YOLOv8 target identification algorithm is designed by adding the deformable ConvNets v2 module and the global attention mechanism. This addition reduces the feature loss caused by the network feature processing, enhances the sensitivity of the algorithm to small‐scale targets, and reduces the impact caused by the different global positions of the targets. Additionally, to address the problem of low quality of captured images, WIoU v3 is used to replace CIoU in the original YOLOv8 target identification algorithm to optimize the loss function, reduce the degrees of freedom, and improve the network robustness. Experimental results demonstrate that the enhanced YOLOv8 algorithm can achieve an improvement of 2.4% and 5.5% in mAP and mAP50‐95, respectively, compared with the original algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the resource allocation (RA) for ultra‐dense network (UDN), where base stations (BSs) are densely deployed to meet the demands of future wireless communications. However, the design of RA in UDN is challenging, as the RA problem is non‐convex and NP‐hard. Therefore, this paper considers and studies a semi‐distributed resource block (RB) allocation scheme, in order to achieve a well‐balanced trade‐off between performance and complexity. In the context of semi‐distributed RB allocation scheme, the problem can be decomposed into the subproblem of clustering and the subproblem of cluster‐based RB allocation. We first improve the K‐means clustering algorithm by employing the Gaussian modified method, which can significantly decrease the number of iterations for carrying out the K‐means algorithm as well as the failure possibility of clustering. Then, bat algorithm (BA) is introduced to attack the problem of cluster‐based RB allocation. In order to make the original BA applicable to the problem of RB allocation, chaotic sequences are adopted to discretize the initial position of the bats, and simultaneously increase the population diversity of the bats. Furthermore, in order to speed up the convergence of BA, the logarithmic decreasing inertia weight is employed for improving the original BA. Our studies and performance results show that the proposed approaches are capable of achieving a desirable trade‐off between the performance and the implementation complexity. [ABSTRACT FROM AUTHOR]

An improved filtering algorithm, called the EttAF adaptive filtering, is proposed in this paper. Compared with the available algorithm, the equivalent observation error in the fusion centre from the external sensor is considered, which makes the observation noise matrix of the filter dynamically adjust according to the relative relationship between the fusion centre and the external sensor. The strong manoeuvring target is modelled based on the CS‐jerk model, and the error adaptive variation equation is derived for the external data. Simulation results show that compared to traditional algorithm the EttAF algorithm can effectively improve the accuracy of position and velocity by 10.8% and 22.3% respectively. [ABSTRACT FROM AUTHOR]

The vehicle's power battery is composed of a large number of battery cells series or in parallel. Due to the manufacturing process error and the different use environments, there are differences between the battery cells, and the battery pack will have inconsistency problems, which will increase the safety hazard. Therefore, it is of great practical significance to identify and warn about the inconsistency of power batteries. Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the sliding window (SW) to segment the dataset and update the data of the diagnosis model in real‐time. The scores of normal battery cells and abnormal battery cells were analyzed, and then the fault threshold was determined to be 0.75. The results show that the recall ratio and precision ratio of the algorithm are 0.91 and 0.95, respectively, which is more suitable for inconsistent battery cell identification than other methods. If the SW size is 15, the warning effect is the best. Before the vehicle alarm occurs, the algorithm can realize early fault warnings, thus effectively avoiding the safety problems caused by inconsistency faults. [ABSTRACT FROM AUTHOR]

At present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption. [ABSTRACT FROM AUTHOR]

This paper deals with the orbit tracking and formation motion problems with optimal energies and reduced computational cost. First, the orbit tracking and formation motion are decomposed into movements in the normal and tangent directions of level orbits, respectively, and simultaneously, the optimal value functions in both directions are defined. Then, to reduce the computational cost, a switching threshold event‐triggered (STET) mechanism is designed. Based on the STET mechanism, the optimal value functions are constructed to evaluate the optimal energies of orbit tracking and formation motion. Critic neural networks are then designed to approximate the optimal value functions, which yield the optimal policies along the normal and tangent directions of desired orbits, that is, a so‐called switching threshold event‐triggered critic algorithm (STET‐C). Theoretical analysis of system convergence is given in detail. Finally, two comparison simulations are given. The former intends to verify the optimal energy of STET‐C compared to the feedback controllers. The latter shows that STET‐C significantly reduces the computational cost in contrast with the non‐triggered actor‐critic algorithm, non‐triggered critic algorithm, and the relative threshold event‐triggered critic algorithm. [ABSTRACT FROM AUTHOR]

Developing safety and efficiency applications for Connected and Automated Vehicles (CAVs) requires a great deal of testing and evaluation. The need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly, possibly dangerous, and time‐consuming. As an alternative, researchers attempt to study and evaluate their algorithms and designs using simulation platforms. Modeling the behavior of drivers or human operators in CAVs or other vehicles interacting with them is one of the main challenges of such simulations. While developing a perfect model for human behavior is a challenging task and an open problem, a significant augmentation of the current models used in simulators for driver behavior is presented. In this paper, a simulation framework for a hybrid transportation system is presented that includes both human‐driven and automated vehicles. In addition, the human driving task is decomposed and a modular approach is offered to simulate a large‐scale traffic scenario, allowing for a thorough investigation of automated and active safety systems. Such representation through Interconnected modules offers a human‐interpretable system that can be tuned to represent different classes of drivers. Additionally, a large driving dataset is analyzed to extract expressive parameters that would best describe different driving characteristics. Finally, a similarly dense traffic scenario is recreated within the simulator and a thorough analysis of various human‐specific and system‐specific factors is conducted, studying their effect on traffic network performance and safety. [ABSTRACT FROM AUTHOR]

Aiming at the matter of poor precision in predicting the charge of lithium battery by applying conventional integer‐order models and offline parameter identification, this paper proposes a joint fractional‐order multi‐innovations unscented Kalman filter (FOUKF‐FOMIUKF) algorithm for predicting the cells' state of charge (SOC) online and uses the theory of singular‐value decomposition to tackle the issue of failure of the traceless transformation. Initially, the circuitry model of fractional order is built. The parameters of the model are recognized online by fractional‐order unscented Kalman filtering (FOUKF), and the obtained parameters are then transmitted to the method known as the fractional order multi‐innovations unscented Kalman filter (FOMIUKF) to calculate the SOC of the cell. The algorithm was validated under four working conditions such as FUDS (US Federal Urban Driving Distance), BJDST (Beijing Dynamic Stress Test), DST (Dynamic Stress Test), and US06 (Highway Driving Distance Test), respectively, and compared with the FOMIUKF, MIUKF, and FOUKF algorithms for offline identification. The conclusions demonstrate that the SOC estimated by the FOUKF‐FOMIUKF method is controlled within 0.5% of the mean absolute error under the four conditions and the root‐mean‐square error is controlled within 0.8%. It is not difficult to find that the FOUKF‐FOMIUKF algorithm estimates SOC with higher accuracy and robustness. [ABSTRACT FROM AUTHOR]

The paper reviews the evolution of Interactive Management, later referred to as Structured Democratic Dialogue, starting from the early 1970s up to this date. The authors propose a generational classification scheme consisting of five periods based primarily on whether some or all stages of the process were implemented synchronously or asynchronously and whether the participants' presence was physical, virtual or hybrid. Other aspects such as modifications in the steps of the process; the evolution of the software; domains of applications; file management; methods of collecting or recording contributions, votes, clarifications and preparation of reports; and key players are also considered and reported within the context of the primary scheme. The paper considers key advances achieved at each generational stage in terms of process or software, discusses associated challenges and concludes with a view towards the future of the emerging fifth generation. [ABSTRACT FROM AUTHOR]

A major advance in global bathymetric observation occurred in 2018 with the launch of NASA's ICESat‐2 satellite, carrying a green‐wavelength, photon‐counting lidar, the Advanced Topographic Laser Altimeter System (ATLAS). Although bathymetric measurement was not initially a design goal for the mission, pre‐ and post‐launch studies revealed ATLAS's notable bathymetric mapping capability. ICESat‐2 bathymetry has been used to support a wide range of coastal and nearshore science objectives. However, analysis of ICESat‐2 bathymetry in numerous locations around the world revealed instances of missing or clipped bathymetry in areas where bathymetric measurement should be feasible. These missing data were due to the ATLAS receiver algorithms not being optimized for bathymetry capture. To address this, two updates have been made to ICESat‐2's receiver algorithm parameters with the goal of increasing the area for which ICESat‐2 can provide bathymetry. This paper details the parameter changes and presents the results of a two‐phased study designed to investigate ICESat‐2's bathymetry enhancements at both local and global scales. The results of both phases confirm that the new parameters achieved the intended goal of increasing the amount of bathymetry provided by ICESat‐2. The site‐specific phase demonstrates the ability to fill critical bathymetric data gaps in open ocean and coastal settings. The global analysis shows that the area of potential bathymetry approximately doubled, with 6.1 million km2 of new area in which bathymetric measurements may be feasible. These enhancements are anticipated to facilitate a range of science objectives and close the gap between ICESat‐2 bathymetry and offshore sonar data. Key Points: Recent updates to ICESat‐2's receiver algorithm parameters have boosted the bathymetric measurement capabilitiesThe new updates allow ICESat‐2 to consistently measure bathymetry up to 41 m deepThe updates allow ICESat‐2 to potentially measure over 6.1 million km2 of new bathymetry in both coastal and ocean settings [ABSTRACT FROM AUTHOR]

The linear mixed‐effects model (LME) is a versatile approach to account for dependence among observations. Many large‐scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole‐brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed‐effects algorithm (FEMA) that makes whole‐brain vertex‐wise, voxel‐wise, and connectome‐wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross‐sectional and longitudinal effects of age on region‐of‐interest level and vertex‐wise cortical thickness, as well as connectome‐wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex‐wise cortical thickness and connectome‐wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/. [ABSTRACT FROM AUTHOR]

Due to the extensive host range of influenza A viruses, it is difficult to determine the best diagnostic algorithm to efficiently screen samples from a variety of host species for influenza A viruses. While there are some influenza diagnostic algorithms that are specific to host species, to our knowledge, no single algorithm exists for the characterization of influenza A viruses across multiple host species. In this paper, we propose an algorithm that can serve as a guide for screening human, animal, and environmental samples for influenza A viruses of high human and animal health importance. [ABSTRACT FROM AUTHOR]

To improve the performance of real‐time systems, heterogeneous asymmetric multiprocessors are proposed. Heterogeneous asymmetric multiprocessor architecture allows the system to better match computational resources to meet the needs and dynamic workload of each application. The benefits of system performance and reduced power consumption cannot be fully exploited unless proper task scheduling and task allocation methods are implemented at the operating system level.The EDZL scheduling algorithm has been shown to be a better scheduling algorithm in heterogeneous multiprocessor platforms. In this paper, the BSF‐EDZL (best speed fit for earliest deadline first until zero‐laxity) performance asymmetric multiprocessor scheduling algorithm is proposed, and an effective schedulability analysis is performed. The algorithm selects an appropriate processor for non‐zero relaxation highest priority task rather than the fastest processor when assigning tasks. Experimental results show that the proposed BSF‐EDZL scheduling algorithm can schedule a larger set of tasks than the ordinary EDZL scheduling algorithm. [ABSTRACT FROM AUTHOR]

Imbalanced image datasets consist of image datasets where there is a significant disparity in the number of samples across different classes. With imbalanced image datasets, learning algorithms often tend to be biased toward the majority class samples. This leads to poor classification of minority class samples as their training is not properly conducted. It becomes more complicated when the number of samples in the minority class is very low. In this paper, a novel hierarchical algorithm is proposed for generating new data using Capsule Generative Adversarial Networks (CapsGAN) to address the class imbalance problem in imbalanced image datasets. Unlike common GAN models, the proposed method incorporates an auxiliary CapsNet to identify high‐value images in both minority and majority classes. This identification is based on the ability to detect complex relationships between low‐level and high‐level features present in capsule networks. Furthermore, the proposed CapsGAN model is conditioned to generate minority class samples based on feature vectors of last capsule layer to achieve a more balanced image dataset. For evaluating the performance of the proposed model, an image dataset called CICS was collected and introduced. Extensive experiments were also conducted using various online image datasets from different domains, with varying numbers of classes and data sizes. The experimental results demonstrated that the proposed model can generate high‐quality samples in cases where the image dataset or the number of minority class samples is relatively small. Furthermore, the proposed model has maintained an accuracy of over 80% in an imbalanced ratio of 1:60. [ABSTRACT FROM AUTHOR]

Aim: This article aimed to explore the role of AI in advancing nursing practice, focusing on its impact on readiness for the future. Design and Methods: A position paper, the methodology comprises three key steps. First, a comprehensive literature search using specific keywords in reputable databases was conducted to gather current information on AI in nursing. Second, data extraction and synthesis from selected articles were performed. Finally, a thematic analysis identifies recurring themes to provide insights into AI's impact on future nursing practice. Results: The findings highlight the transformative role of AI in advancing nursing practice and preparing nurses for the future, including enhancing nursing practice with AI, preparing nurses for the future (AI education and training) and associated, ethical considerations and challenges. AI‐enabled robotics and telehealth solutions expand the reach of nursing care, improving accessibility of healthcare services and remote monitoring capabilities of patients' health conditions. [ABSTRACT FROM AUTHOR]

With the rapid development of integrated energy system (IES), it has become a trend to form multiple integrated energy systems (MIESs) in a certain region. However, there is a lack of coordination and cooperation among MIESs. This paper proposes an optimal operation model of MIESs with four cost objectives, which focuses on energy interaction among MIESs. As a non‐linear and large optimization problem, it is difficult for conventional mathematical methods to solve. Hence, this paper proposes a hybrid Taguchi‐compact salp swarm algorithm (TCSSA). The compact technique can save the operation memory of model. Taguchi method can improve the convergence speed and solution accuracy of model. The proposed algorithm is tested on 28 benchmark functions and applied to optimal operation of MIESs. Results demonstrate that: (1) compared with other famous algorithms, TCSSA can provide more efficient execution and better solutions. (2) The optimal operation of MIESs based on TCSSA can achieve the complementary of energy advantages, which effectively reduces the total cost of MIESs (up to 9.67%). [ABSTRACT FROM AUTHOR]

A 3D reconstruction enables an effective geometric representation to support various applications. Recently, learning‐based multi‐view stereo (MVS) algorithms have emerged, replacing conventional hand‐crafted features with convolutional neural network‐encoded deep representation to reduce feature matching ambiguity, leading to a more complete scene recovery from imagery data. However, the state‐of‐the‐art architectures are not designed for an indoor environment with abundant weakly textured or textureless objects. This paper proposes AttentionSPP‐PatchmatchNet, a deep learning‐based MVS algorithm designed for indoor 3D reconstruction. The algorithm integrates multi‐scale feature sampling to produce global‐context‐aware feature maps and recalibrates the weight of essential features to tackle challenges posed by indoor environments. A new dataset designed exclusively for indoor environments is presented to verify the performance of the proposed network. Experimental results show that AttentionSPP‐PatchmatchNet outperforms state‐of‐the‐art algorithms with relative 132.87% and 163.55% improvements at the 10 and 2 mm threshold, respectively, making it suitable for accurate and complete indoor 3D reconstruction. [ABSTRACT FROM AUTHOR]

Aiming at the problem of motion blur in the inspection image of wind power generation equipment, an improved fast motion blur removal method based on deblurganv2 is proposed in this paper. Firstly, according to the characteristics of disparate local motion blur degrees and different global motion blur directions of the wind power inspection image, the real wind power fuzzy image data set is collected and produced. Secondly, according to the characteristics of the single background of the wind power inspection image and the requirements of fast and effective processing, the lightweight network Ghostnet is redesigned as the backbone network of the Deblurganv2 generator to reduce the number of network parameters and calculations. Finally, five SE channel attention mechanism layers are added to GhostNet to strengthen feature extraction, and the upsampling process is optimized through bilinear interpolation, to improve the deblurring performance of the wind power inspection image. The experimental results show that compared with other algorithms, the proposed algorithm has a higher peak signal‐to‐noise ratio and structural similarity, the network model parameters are compressed to 6.1 MB, and the reasoning speed is improved to 0.42 s. [ABSTRACT FROM AUTHOR]

X‐ray flaw detection is widely used in non‐destructive area. The intuitive defect information can be obtained through the X‐ray film, which is usually digitized into high greyscale image by a 12‐bit or 16‐bit, called super 8‐bit, industrial scanner. When an ordinary 8‐bit monitor displays a super 8‐bit greyscale image, it appears loss of detail information, blurring of the image appears and other problems. Therefore, in this paper, a pseudo‐colour enhancement algorithm for displaying high‐bit RAW images on low‐bit monitor was proposed according to the chromatographic mapping relationship based on the visual characteristics of human eye. First, a high grey‐scale pseudo‐colour enhancement algorithm, called HOTM‐HGL, based on hot metal coding was proposed based on the standard film, whose enhancement effect is better than current mainstream algorithms. Second, aiming at the non‐standard film, a new stretching function called RAW‐Optical‐Stretching was reconstructed to improve HOTM‐HGL algorithm, called HOTM‐HGLS algorithm, whose display effect on ordinary monitors was improved in further. Finally, HOTM‐HGLS algorithm was applied in the detection of X‐ray film defect, which was convenient for the capture of defect information in the weld. Compared with the existing algorithms, various indicators have been greatly improved, enriching the amount of information and strengthening the image recognition effects. [ABSTRACT FROM AUTHOR]

Image restoration with impulse noise is an important task in image processing. Taking into account the statistical distribution of impulse noise, the ℓ1‐norm data fidelity and total variation (ℓ1TV$\ell _1TV$) model has been widely used in this area. However, the ℓ1TV$\ell _1TV$ model usually performs worse when the noise level is high. To overcome this drawback, several nonconvex models have been proposed. In this paper, an efficient iterative algorithm is proposed to solve nonconvex models arising in impulse noise. Compared to existing algorithms, the proposed algorithm is a completely explicit algorithm in which every subproblem has a closed‐form solution. The key idea is to transform the original nonconvex models into an equivalent constrained minimization problem with two separable objective functions, where one is differentiable but nonconvex. As a consequence, the proximal linearized alternating direction method of multipliers is employed to solve it. Extensive numerical experiments are presented to demonstrate the efficiency and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Despite the fact that machine learning (ML) algorithms to construct predictive models for pressure injury development are widely reported, the performance of the model remains unknown. The goal of the review was to systematically appraise the performance of ML models in predicting pressure injury. PubMed, Embase, Cochrane Library, Web of Science, CINAHL, Grey literature and other databases were systematically searched. Original journal papers were included which met the inclusion criteria. The methodological quality was assessed independently by two reviewers using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta‐analysis was performed with Metadisc software, with the area under the receiver operating characteristic curve, sensitivity and specificity as effect measures. Chi‐squared and I2 tests were used to assess the heterogeneity. A total of 18 studies were included for the narrative review, and 14 of them were eligible for meta‐analysis. The models achieved excellent pooled AUC of 0.94, sensitivity of 0.79 (95% CI [0.78–0.80]) and specificity of 0.87 (95% CI [0.88–0.87]). Meta‐regressions did not provide evidence that model performance varied by data or model types. The present findings indicate that ML models show an outstanding performance in predicting pressure injury. However, good‐quality studies should be conducted to verify our results and confirm the clinical value of ML in pressure injury development. [ABSTRACT FROM AUTHOR]

In this paper, a consensus based fully distributed optimization algorithm is proposed for solving economic dispatch problem (EDP) in smart grid. Since the incremental cost of all buses reach consensus when the optimal solution is achieved, it is selected as a consensus variable. An additional variable at each bus, called "surplus" is added to record the local power mismatch, which is used as a feedback variable to purse the balance between power supply and demand. Different from most of the existing distributed methods which require the communication network to be balanced, the algorithm uses a row random matrix and a column random matrix to precisely steer all the agents to asymptotically converge to a global optimal solution over a time‐varying directed communication network. Due to the use of a fixed step size, the proposed algorithm also outperforms other algorithms in terms of convergence speed. The graph and eigenvalue perturbation theories are employed for the algorithm convergence analysis, and the upper bound of the parameters required for convergence is given theoretically. Finally, the performance and scalability of the proposed distributed algorithm are verified by several case studies conducted on the IEEE 14‐bus power system and a 200‐node test system. [ABSTRACT FROM AUTHOR]

The paper proposes the method of uniform flow loading the improved consensus algorithm to achieve obstacle avoidance for multi‐UAV with 3‐D formation. In view of that the flow function is generally only applicable to two dimensional flow field, dimension reduction is to treat multi‐UAV formation as each UAV flows around along the circular surface of the projected cylinder of spherical obstacle at its different heights. To formation guaranteeing the smooth realization of obstacle avoidance of multi‐UAV formation centered on the leader, it is used the tactics of virtually expanding the radius of these static or moving equivalent cylindrical obstacles to reserve a certain space. The innovation of this strategy is to further reduce the possibility of collision between UAV formation and the edge of obstacles, meanwhile the problem of multi‐UAV formation avoiding obstacles is transformed into the problem of collision avoidance navigation for the leader treated as a particle. It is considered collision avoidance for multi‐UAV formation under three scenarios assumed are the most common situations, and the simulation experimental results of these scenarios are demonstrated on the validity of the designed experiments based on these algorithms. [ABSTRACT FROM AUTHOR]

This paper deals with parameter estimation problem of large‐scale systems. A recursive distributed parameter estimation algorithm, based on the minimization of the prediction estimation error method, is developed. Specifically, the class of large‐scale systems that are composed of several interconnected sub‐systems is considered. Each interconnected sub‐system is modelled by a linear discrete‐time state space mathematical model with unknown parameters. The convergence analysis is then achieved using the Lyapunov approach. The theoretical analysis and simulation results prove the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Finger vein recognition is a promising biometric technology that has received significant research attention. However, most of the existing works often relied on a single feature, which failed to fully exploit the discriminative information in finger vein images, and therefore led to a limited recognition performance. To overcome this limitation, this paper proposes an encoding coefficient similarity-based multifeature sparse representation method for finger vein recognition. The proposed method not only uses multiple features to extract comprehensive information from finger vein images, but also obtains more discriminative information through constraints in the objective function. The sparsity constraint retains the key information of each feature, and the similarity constraint explores the shared information among the features. Furthermore, the proposed method is capable of fusing all kinds of features, not limited to specific ones. The optimization problem of the proposed method is efficiently solved using the alternating direction multiplier method algorithm. Experimental results on two public finger vein databases HKPU-FV and SDU-FV show that the proposed method achieves good recognition performance. [ABSTRACT FROM AUTHOR]