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Letter to the Editor concerning "Describing center of pressure movement in stabilometry by ellipse area approximation" from Agnieszka Golab. Reply to "Describing center of pressure movement in stabilometry by ellipse area approximation" from Agnieszka Golab concerning the paper "A Review of Center of Pressure (COP) Variables to Quantify Standing Balance in Elderly People: Algorithms and Open Access Code" Our choice was actually to present the formula of the prediction ellipse area in the article, as it indeed does not strongly depend on the sample size as the confidence ellipse area does. [Extracted from the article]

The author presents comments on a paper on the use of machine learning to support the system dynamics method. Topics discussed include its interpretation of simulation models and explanation of policy analysis, and the emerging view whereby dynamic problems from endogenous feedback structures can be tackled via wider tools and methodological approaches. Also noted is the resulting potential for greater insights into the modelling process.

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]

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]

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]

Purpose: To evaluate the colour vision severity classification standard 'CIE 143:2001 International recommendations for colour vision requirements in transport' (CIE 143:2001), which has become out of date because of the lack of commercial availability of required colour vision tests., Methods: One-hundred-five subjects had colour vision tested and colour vision severity classified according to a modified CIE 143:2001 algorithm that included pseudoisochromatic plates (Ishihara's test and Hardy Rand Rittler (HRR) 4th edition), Optec 900 lantern and Farnsworth D-15. Subject's results and colour vision severity classification were compared to performance and colour vision severity classification on the computerized 'Colour Assessment and Diagnosis' (CAD) test., Results: According to CIE 143:2001, using Ishihara's test, Optec lantern and Farnsworth D 15, 11 subjects (10%) were category I (normal), 16 (15%) were category II (mild), 48 (46%) were category III (poor), and 30 (29%) were category IV (severe). Classified by CAD score, 10 (10%) were category I, 11 (10%) were category II, 41 (39%) were category III, and 43 (41%) were category IV. The correlation between the two estimates of the severity of colour vision loss (i.e. CIE 143:2001 and CAD) was high, with a Kendall's Tau test of 0.81 (τ = 0.81 p < 0.001). A suggested CIE 143:2001 classification including new CAD score limits improves the classification correlation to 0.90 (τ = 0.90 p < 0.001) for all diagnoses., Conclusion: The colour vision severity classification standard 'CIE 143:2001 International recommendations for colour vision requirements in transport', has not implemented new diagnostic tools with better accuracy. We propose three possible revisions to the CIE 143:2001 algorithm, based on the availability of CAD: (1) Replacing the current CIE 143:2001 algorithm using new CAD threshold limits, (2) Use of CAD as a secondary test to Ishihara's test and HRR or (3) Revising the current CIE 143:2001 algorithm using Ishihara's test, HRR, Optec 900 and FD15., (© 2020 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.)

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]

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]

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]

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]

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]

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]

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]

Objective: To characterize and validate the landscape of algorithms that use International Classification of Disease (ICD) codes to identify low‐acuity emergency department (ED) visits. Data Sources: Publicly available ED data from the National Hospital Ambulatory Medical Care Survey (NHAMCS). Study Design: We systematically searched for studies that specify algorithms consisting of ICD codes that identify preventable or low‐acuity ED visits. We classified ED visits in NHAMCS according to these algorithms and compared agreements using the Jaccard index. We then evaluated the performance of each algorithm using positive predictive value (PPV) and sensitivity, with the reference group specified using low‐acuity composite (LAC) criteria consisting of both triage and clinical components. In sensitivity analyses, we repeated our primary analysis using only triage or only clinical criteria for reference. Data Collection: We used the 2011–2017 NHAMCS data, totaling 163,576 observations before survey weighting and after dropping observations missing a primary diagnosis. We translated ICD‐9 codes (years 2011–2015) to ICD‐10 using a standard crosswalk. Principal Findings: We identified 15 papers with an original list of ICD codes used to identify preventable or low‐acuity ED presentations. These papers were published between 1992 and 2020, cited an average of 310 (SD 360) times, and included 968 (SD 1175) codes. Pairwise Jaccard similarity indices (0 = no overlap, 1 = perfect congruence) ranged from 0.01 to 0.82, with mean 0.20 (SD 0.13). When validated against the LAC reference group, the algorithms had an average PPV of 0.308 (95% CI [0.253, 0.364]) and sensitivity of 0.183 (95% CI [0.111, 0.256]). Overall, 2.1% of visits identified as low acuity by the algorithms died prehospital or in the ED, or needed surgery, critical care, or cardiac catheterization. Conclusions: Existing algorithms that identify low‐acuity ED visits lack congruence and are imperfect predictors of visit acuity. [ABSTRACT FROM AUTHOR]

Idiopathic pulmonary fibrosis (IPF) is a fatal progressive lung disease occurring in adults. In the last decade, the results of a number of clinical trials based on the updated disease classification have been published. The registration of pirfenidone and nintedanib, the first two pharmacological treatment options approved for IPF, marks a new chapter in the management of patients with this disease. Other nonpharmacological treatments such as lung transplantation, rehabilitation and palliation have also been shown to be beneficial for these patients. In this review, past and present management is discussed based on a comprehensive literature search. A treatment algorithm is presented based on available evidence and our overall clinical experience. In addition, unmet needs with regard to treatment are highlighted and discussed. We describe the development of various treatment options for IPF from the first consensus to recent guidelines based on evidence from large-scale, multinational, randomized clinical trials, which have led to registration of the first drugs for IPF. [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]

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]

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]

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]

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]

Traditional YOLOv4 object detection network is difficult to be applied to mobile embedded devices because it has some deficiencies such as complex structure and too many parameters. In this paper, the authors propose a lightweight object detection algorithm. Firstly, Mobienetv3 is used to replace the original feature extraction network, and the Mish function is used to be the activation function, which reduces the number of model parameters. Secondly, the dilated convolution is used to replace the maximum pooling operation in the original spatial pyramid pooling (SPP) structure. Then, a custom Dcn‐Dw structure is used to replace the convolution operation in the original PANet, which improves the accuracy of the model for irregular object detection and reduces the model size. Finally, a CBAM lightweight attention mechanism module is introduced in front of the YOLO Head, which further improves the model accuracy. An experiment on the VOC2007 dataset is carried out, and the results show that the mean average precision (mAP) is 80.3% and frames per second (FPS) is 15. At the expense of 3% accuracy, the detection speed is increased to two to three times, and the model size is reduced to one‐fifth of the original model. The lightweight object detection algorithm can suit for real‐time detection tasks on resource‐constrained embedded devices. [ABSTRACT FROM AUTHOR]

This paper provides a framework for identifying preferences in a large network where links are pairwise stable. Network formation models present difficulties for identification, especially when links can be interdependent, for example, when indirect connections matter. We show how one can use the observed proportions of various local network structures to learn about the underlying preference parameters. The key assumption for our approach restricts individuals to have bounded degree in equilibrium, implying a finite number of payoff‐relevant local structures. Our main result provides necessary conditions for parameters to belong to the identified set. We then develop a quadratic programming algorithm that can be used to construct this set. With further restrictions on preferences, we show that our conditions are also sufficient for pairwise stability and therefore characterize the identified set precisely. Overall, the use of both the economic model along with pairwise stability allows us to obtain effective dimension reduction. [ABSTRACT FROM AUTHOR]

Functional Magnetic Resonance Imaging (fMRI) is presently one of the most popular techniques for analysing the dynamic states in brain images using various kinds of algorithms. From the last decade, there is an exponential rise in the use of the machine and deep learning algorithms of artificial intelligence for analysing fMRI data. However, it is a big challenge for every researcher to choose a suitable machine or deep learning algorithm for analysing fMRI data due to the availability of a large number of algorithms in the literature. It takes much time for each researcher to know about the various approaches and algorithms which are in use for fMRI data. This paper provides a review in a systematic manner for the present literature of fMRI data that makes use of the machine and deep learning algorithms. The major goals of this review paper are to (a) identify machine learning and deep learning research trends for the implementation of fMRI; (b) identify usage of Machine Learning Algorithms and deep learning in fMRI, and (c) help new researchers based on fMRI to put their new findings appropriately in existing domain of fMRI research. The results of this systematic review identified various fMRI studies and classified them based on fMRI types, mental diseases, use of machine learning and deep learning algorithms. The authors have provided the studies with the best performance of machine learning and deep learning algorithms used in fMRI. The authors believe that this systematic review will help incoming researchers on fMRI in their future works. [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]

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]

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]

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]

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]

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]

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]

With the help of the analytical expression of the vertical gravity gradient (VGG) generated by a rectangular prism, an analytical algorithm for predicting seafloor topography using the VGG data has been studied. Nevertheless, ship sounding data are an essential constraint in solving the seafloor topography. This paper combines ship sounding data with VGG anomaly to predict the seafloor topography. The main research contents include the following: Using the ship soundings and VGG data in the study area, the observation equations about sea depth are established, and the stability of the equations are studied; furthermore, considering the influence of seafloor topography outside the study area on the observation equations, these effects are divided into boundary effects and far‐field effects, and different processing methods are proposed. Finally, the method is tested on the East Pacific Rise, only using VGG anomaly and adding the mean value to fix the boundary region, the RMS error of the results is 108.8 m; SIO's model is added to the boundary region and the seven maximum absolute errors are replaced by ship sounding data, the RMS error of the results can reach 94.2 m and the accuracy improvement is 13.42%. Plain Language Summary: The topographic fluctuation can be composed of many different rectangular cylinders, and the vertical gravity gradient generated by a rectangular cylinder on the sea level has an analytical expression. In this paper, the observation equations are established by using this analytical expression to predict the seafloor topography, and different methods are studied to control or weaken the influence of boundary effects and far‐field effects. This study will provide a new idea for the refinement of seafloor topography in the future. Key Points: A new method for predicting seafloor topography from vertical gravity gradient and ship soundings, and the effectiveness of the algorithm is verified by numerical simulationThe sources of interference errors are refined as "boundary effects" and "far‐field effects," and different processing methods are proposed; the algorithm is applied to the test area and the RMS error of prediction results can reach 94.2 m [ABSTRACT FROM AUTHOR]

This paper proposes a three‐stage algorithm based on clustering decomposition and task allocation—improved clustering planning algorithm (iK‐iD‐N), aiming at the optimization task allocation problem of drones in actual application to meet the task demand constraints. The algorithm solves the problem of the number of drones demanded and the initial delivery range of each drone by introducing dual‐objective planning into the clustering decomposition. Combining improved Dijkstra algorithm (iK‐D) with neighbourhood insertion algorithm into task allocation, to get high‐quality solutions and solve efficiently. Compared with the existing ant colony algorithm, the iK‐iD‐N algorithm proposed in this paper is more efficient and can obtain the best and stable solutions while evenly distributing tasks. Then it is compared with the improved clustering algorithm combined with the basic iK‐D to get better solutions of the iK‐iD‐N algorithm at any time, and compared with the basic clustering algorithm with the improved task allocation algorithm (K‐iD‐N) that iK‐ iD‐N can get a better solution with high probability. The thesis also simulates and analyzes the impact of uncertainty requirements on the solutions based on drone demand and task allocation models, and discusses the impact of drone load capability and endurance capability constraints on the final solutions. [ABSTRACT FROM AUTHOR]

In binocular stereo matching, there has been a problem of low matching accuracy and noise immunity in discontinuous regions and weak‐textured regions. This paper proposes a stereo matching algorithm based on an improved adaptive support window. In the cost computation stage, first, according to the preset arm length and colour threshold, a cross‐based arm is obtained, which centres on the pixel to be matched; then the adaptive regions of the vertical arm and the horizontal arm are constructed respectively, which have different shape and size. Finally, the union of the two adaptive regions is used as the final support window of Census transform. Performance evaluations on Middlebury stereo data sets demonstrate that the proposed algorithm outperforms other seven most challenging stereo matching algorithms. The mismatching rate of this algorithm is greatly reduced, and the anti‐noise performance is also improved considerably. Because the construction of the adaptive region is based on strict criteria and comprehensive consideration, the algorithm proposed in the paper can improve the matching accuracy in the weak‐textured regions and discontinuous disparity regions. [ABSTRACT FROM AUTHOR]

This paper uses a fuzzy set scheme to present an adaptive fractional differential algorithm for image edge enhancement and texture preservation. In the proposed algorithm, an image's membership function and area feature are used to calculate the fuzzy set of images. The function of adaptive fractional differential order (FAFDO) can be constructed by making the linear transformation of the fuzzy set. Then, the fuzzy adaptive fractional differential mask (FAFDM) is obtained by substituting the FAFDO into the fractional differential mask. Finally, the image edge and texture are enhanced and preserved by applying airspace filtering of the FAFDM convolution. The experimental results show that, compared to fractional differential or fuzzy set‐based image enhancement algorithms, the proposed algorithm can adaptively enhance the image edge and preserve the image texture by analysing the fuzziness of the image itself. [ABSTRACT FROM AUTHOR]

There are a lot of flammable materials in the textile workshop, and once a fire occurs, it will cause property damage and casualties. At present, smoke detection in textile workshops mainly relies on temperature‐sensing smoke sensors with low detection rate and poor real‐time performance, which cannot meet the task of smoke detection in complex environments. Therefore, this paper proposes an improved mixed Gaussian and YOLOv5 smoke detection algorithm for textile workshops. In order to reduce the interference of static background in smoke detection, an improved gaussian mixture algorithm is used to extract suspected smoke areas in video by using the dynamic characteristics of smoke. Then, an adaptive attention module is added to the feature pyramid infrastructure of the YOLOv5 target detection network to improve the multi‐scale target recognition ability. In addition, the focal loss function is used to reduce the impact of background and foreground class imbalances on the detection results. The experimental results show that the detection accuracy of the proposed method is 94.7%, and the average detection speed is 66.7 FPS. By comparing with the existing state‐of‐the‐art algorithms, the detection capability of this method has been significantly improved. At the same time, it has high real‐time performance and detection accuracy in smoke detection in textile workshops. [ABSTRACT FROM AUTHOR]

This paper aims to improve the efficiency of parameter identification of the nonlinear state‐space model (SSM). The commonly used particle Markov chain Monte Carlo (PMCMC) method is time‐consuming. The surrogate model is a useful acceleration strategy, but it is expensive to establish a global high‐precision surrogate model. This paper proposes an efficient algorithm that gradually estimates the unknown parameters in two stages. In the first stage, a reduced region is established based on the latest method. We train a local Gaussian Process regression (GPR) of the likelihood function in the reduced region based on the optimum Latin hypercube design (OLHD). In the second stage, we identify the unknown parameters more accurately based on the MCMC method. When the proposal sample is in the reduced region, we use GPR to estimate the likelihood; otherwise, BPF is used to estimate the likelihood. The reduced region is usually the high probability density region, which is why the algorithm is efficient. It is proved that the acceptance rate of any two samples based on the proposed algorithm is theoretically convergent to that of the PMCMC algorithm. Two examples demonstrate that the proposed method performs well in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of the Internet of Things (IoT) technology has provided a strong technical support for the technological transformation of the logistics industry. The informatization development of logistics industry increasingly relies on the Internet of Things technology represented by Radio Frequency Identification (RFID) technology. These technologies lead the whole business process to optimize the business process in the direction of accurate, efficient and real‐time. In order to solve the problem that the reader cannot identify the label information correctly due to the phenomenon of data collision in the application of RFID technology, this paper proposes an anti‐collision algorithm based on Slotted Random Regressive‐style Binary Search Tree (SR‐RBST). Based on Slotted ALOHA (SA) the method proposed in this paper uses the Regressive‐style Binary Search Tree (RBST) to process the RFID labels in the collision time slot. With the same size of tags, the SR‐RBST algorithm needs less total time slot and has higher efficiency and shorter identification time, while with the increase of the number of tags, the SR‐RBST anti‐collision algorithm has more obvious advantages. The SR‐RBST algorithm effectively improves the time slot utilization efficiency of the system. [ABSTRACT FROM AUTHOR]

In this paper, we develop algorithms to independently draw from a family of conjugate posterior distributions over the structural parameterization when sign and zero restrictions are used to identify structural vector autoregressions (SVARs). We call this family of conjugate posteriors normal‐generalized‐normal. Our algorithms draw from a conjugate uniform‐normal‐inverse‐Wishart posterior over the orthogonal reduced‐form parameterization and transform the draws into the structural parameterization; this transformation induces a normal‐generalized‐normal posterior over the structural parameterization. The uniform‐normal‐inverse‐Wishart posterior over the orthogonal reduced‐form parameterization has been prominent after the work of Uhlig (2005). We use Beaudry, Nam, and Wang's (2011) work on the relevance of optimism shocks to show the dangers of using alternative approaches to implementing sign and zero restrictions to identify SVARs like the penalty function approach. In particular, we analytically show that the penalty function approach adds restrictions to the ones described in the identification scheme. [ABSTRACT FROM AUTHOR]

Allpass transform is a key element of the discrete systems which produces signal processing in warped frequency domain. The paper presents a new short‐critical path algorithm for allpass transform, which essentially consists of calculating the outputs of allpass filters chain. The algorithm is based on the proposed dual allpass filter structure, which computes the outputs of a cascade of two allpass filters in one computational pass. The dual allpass filter structure is obtained using the state variable representation of the allpass filter. The efficiency of the proposed algorithm is analyzed in the context of allpass transform implementation using a general purpose CPU. The results of the experiment show that the use of the proposed algorithm accelerates the calculation of the allpass transform by 1.81 times. [ABSTRACT FROM AUTHOR]

In the blind reception of non‐cooperative signals in short‐time burst communication, signal time domain detection is the premise and key of signal modulation parameter estimation, signal demodulation, decoding, interpretation, information acquisition and interference guidance. In this paper, the semi‐blind demodulation method of short‐time burst FM‐MFSK signal is studied under the condition of low signal‐to‐noise ratio. A short‐time burst FM‐MFSK semi‐blind demodulation method based on STFT is proposed. When SNR is higher than 3 dB, the correct demodulation probability for FM‐MFSK signal can reach 90%. [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]