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In this paper work, three-dimensional terrain models were reconstructed from two dimensional contour lines. Firstly, spatial curves were generated from contour lines using parameterized cubic B-spline curve interpolation. Then surfaces were constructed by using B-spline ruled surface. In the reconstruction process, some issues such as keyholes and branching may arise. Therefore, we propose a method that handles the branching object to construct a bilinear patch by following the proposed data point’s extraction algorithm. We also solved keyholes issues by retaining the same knot vector condition on B-spline ruled surface. Results are also demonstrated for models with branching and without branching. [ABSTRACT FROM AUTHOR]

In order to better solve the problem of automatic identification of quality control line and detection line in the detection of gold standard test strip, this paper proposes to collect the image information of gold standard test strip after color rendering through CMOS sensor, preprocess the obtained information, transform RGB image into gray image, build cloud model in the CIELAB/HSV/HSL space, and apply the improved AdaBoost algorithm to determine the position of detection line and quality control line Place. Compared with the traditional template matching method, it improves the accuracy and accuracy of recognition. [ABSTRACT FROM AUTHOR]

At present, any industry that wanted to be considered a vanguard must be willing to improve itself, developing innovative techniques to generate a competitive advantage against its direct competitors. Hence, many methods are employed to optimize production processes, such as Lot Streaming, which consists of partitioning the productive lots into overlapping small batches to reduce the overall operating times known as Makespan, reducing the delivery time to the final customer. This work proposes carrying out a systematic review following the PRISMA methodology to the existing literature in indexed databases that demonstrates the application of Lot Streaming in the different production systems, giving the scientific community a strong consultation tool, useful to validate the different important elements in the definition of the Makespan reduction objectives and their applicability in the industry. Two hundred papers were identified on the subject of this study. After applying a group of eligibility criteria, 63 articles were analyzed, concluding that Lot Streaming can be applied in different types of industrial processes, always keeping the main objective of reducing Makespan, becoming an excellent improvement tool, thanks to the use of different optimization algorithms, attached to the reality of each industry. [ABSTRACT FROM AUTHOR]

Introduction This Special Issue of Algorithms is of a different nature than other Special Issue in the journal, which are usually dedicated to a particular subjects in the area of algorithms. Algorithms Editorial 2020 Selected Papers from Algorithms' Editorial Board Members Frank Werner Citation: Werner, F. 2020 Selected Papers from Algorithms' Editorial Board Members. Algorithms 2021,14, 32. https://doi.org/10.3390/a14020032 https://www.mdpi.com/journal/algorithms Algorithms 2021,14, 32 2 of 2 The paper [5] deals with parsing which has applications in many branches of computer science, e.g., in compilers or formal language theory. [Extracted from the article]

Privacy preserving utility mining (PPUM) aims to solve the problem of sensitive information leakage in utility pattern mining. In recent years, researchers have proposed algorithms to solve the privacy-preserving problem. However, these algorithms have high side effects, long sanitization time, and computational complexity. Although the FPUTT algorithm reduces the number of database scans, tree construction and traversal still take much time. The paper proposes a fast utility-list dictionary algorithm (FULD). The utility-list dictionary consists of all sensitive items. Through dictionary lookup, sensitive items can be found and modified. In addition, the novel concepts of SINS and tns are proposed to reduce the side effects of the algorithm. In this paper, the experiments show that the FULD algorithm has good performance, such as running time and side effects. The running time of the FULD is 15–20 times shorter than the FPUTT algorithm. It performs well both on sparse and dense datasets. [ABSTRACT FROM AUTHOR]

The article discusses recent advances in information-centric networks (ICNs) and their potential benefits for current network uses and mobile networks. It highlights the implementation of ICN through named data networking (NDN), which focuses on content rather than location. The article includes several research papers that address various issues related to ICN, such as in-network storage, resource efficiency, caching algorithms, satellite communications, and security. These papers propose innovative solutions and strategies to improve the performance and effectiveness of ICNs in different contexts. [Extracted from the article]

The utilization of ultraviolet (UV) absorption spectroscopy for monitoring the concentration of specific decomposition gas components in gas-insulated switchgear (GIS) can provide a means to assess its insulation status. Nevertheless, UV optical modules currently deployed in the field are susceptible to external interferences like ambient noise and equipment vibrations. Real-time spectral data acquisition often suffers from significant noise contamination, directly impinging on subsequent feature extraction and detection accuracy. This paper presents an optimized singular value decomposition (SVD) noise reduction method for mitigating noisy spectral signals. First, each singular value within the noisy signal is transformed into a component signal. Next, the highest frequency value in the signal serves as an indicator to characterize the signal. Finally, the primary frequency values are arranged based on the decreasing singular values of the original noisy signal. The singular value corresponding to the first primary frequency value surpassing a preset threshold is selected as the effective order for denoising. Random noise with varying intensities was intentionally introduced to the UV spectral signal of sulfur dioxide (SO2), followed by noise reduction procedures. It is shown that the improved SVD noise reduction algorithm proposed in this paper enhances the signal-to-noise ratio (SNR) by 18.02% and 16.86%, and reduces the root-mean-square error (RMSE) by 15.13% and 14.92%, respectively, compared with the singular value difference spectrum (SVDS) denoising method and wavelet transform denoising method under the condition of low SNR. Furthermore, there exists a linear relationship between the concentration of SO2 samples and the eigenvalues of the UV spectra, demonstrating a higher linear goodness with a coefficient of 0.99735. The denoising method proposed in this paper does not require the manual setting of various types of parameters, and has a better ability to deal with the noise of UV spectral signals in engineering sites with complex environments. [ABSTRACT FROM AUTHOR]

An autonomous place recognition system is essential for scenarios where GPS is useless, such as underground tunnels. However, it is difficult to use existing algorithms to fully utilize the small number of effective features in underground tunnel data, and recognition accuracy is difficult to guarantee. In order to solve this challenge, an efficient point cloud position recognition algorithm, named Dual-Attention Transformer Network (DAT-Net), is proposed in this paper. The algorithm firstly adopts the farthest point downsampling module to eliminate the invalid redundant points in the point cloud data and retain the basic shape of the point cloud, which reduces the size of the point cloud and, at the same time, reduces the influence of the invalid point cloud on the data analysis. After that, this paper proposes the dual-attention Transformer module to facilitate local information exchange by utilizing the multi-head self-attention mechanism. It extracts local descriptors and integrates highly discriminative global descriptors based on global context with the help of a feature fusion layer to obtain a more accurate and robust global feature representation. Experimental results show that the method proposed in this paper achieves an average F 1 score of 0.841 on the SubT-Tunnel dataset and outperforms many existing state-of-the-art algorithms in recognition accuracy and robustness tests. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The paper constructs a massive data mining model of distributed spatiotemporal databases for the Internet of Things. Then a homologous data fusion method based on information entropy is proposed. The storage space required by the tree structure is reduced by constructing the data schema tree of the merged data set. Secondly, the optimal dynamic support degree is obtained by using a neural network and genetic algorithm. Frequent items in the Internet of Things data are mined to achieve the normalization of the clustered feature data based on the threshold value. Experiments show that the F-measure of the data mining algorithm improves the efficiency by 15.64% and 18.25% compared with the kinds of other literatures respectively. RI increased by 21.17% and 26.07%, respectively. [ABSTRACT FROM AUTHOR]

Due to the unpredictability of the majority of green energy sources (GESs), particularly in microgrids (μGs), frequency deviations are unavoidable. These factors include solar irradiance, wind disturbances, and parametric uncertainty, all of which have a substantial impact on the system's frequency. An adaptive load frequency control (LFC) method for power systems is suggested in this paper to mitigate the aforementioned issues. For engineering challenges, soft computing methods like the bat algorithm (BA), where it proves its effectiveness in different applications, consistently produce positive outcomes, so it is used to address the LFC issue. For online gain tuning, an integral controller using an artificial BA is utilized, and this control method is supported by a modification known as the balloon effect (BE) identifier. Stability and robustness of analysis of the suggested BA+BE scheme is investigated. The system with the proposed adaptive frequency controller is evaluated in the case of step/random load demand. In addition, high penetrations of photovoltaic (PV) sources are considered. The standard integral controller and Jaya+BE, two more optimization techniques, have been compared with the suggested BA+BE strategy. According to the results of the MATLAB simulation, the suggested technique (BA+BE) has a significant advantage over other techniques in terms of maintaining frequency stability in the presence of step/random disturbances and PV source. The suggested method successfully keeps the frequency steady over I and Jaya+BE by 61.5% and 31.25%, respectively. In order to validate the MATLAB simulation results, real-time simulation tests are given utilizing a PC and a QUARC pid_e data acquisition card. [ABSTRACT FROM AUTHOR]

Low-Level Virtual Machine (LLVM) compiler infrastructure is a useful tool for building just-in-time (JIT) compilers, besides its reliable front end represented by a clang compiler and its elaborated middle end containing different optimizations that improve the runtime performance. This paper specifically addresses the part of building a JIT compiler using an LLVM with the scope of obtaining the hardware architecture details of the underlying machine such as the number of cores and the number of logical cores per processing unit and providing them to the NUMA-BTLP static thread classification algorithm and to the NUMA-BTDM static thread mapping algorithm. Afterwards, the hardware-aware algorithms are run using the JIT compiler within an optimization pass. The JIT compiler in this paper is designed to run on a parallel C/C++ application (which creates threads using Pthreads), before the first time the application is executed on a machine. To achieve this, the JIT compiler takes the native code of the application, obtains the corresponding LLVM IR (Intermediate Representation) for the native code and executes the hardware-aware thread classification and the thread mapping algorithms on the IR. The NUMA-Balanced Task and Loop Parallelism (NUMA-BTLP) and NUMA-Balanced Thread and Data Mapping (NUMA-BTDM) are expected to optimize the energy consumption by up to 15% on the NUMA systems. [ABSTRACT FROM AUTHOR]

A paper manufacturing plant minimizes its production cost by using long production runs that combine the demands from its various customers. As jobs are completed, they are released to distribution for delivery. Deliveries are made by railcars, each of which is dedicated to one customer. Long production runs imply that maximizing railcar utilization requires holding the cars over several days or holding completed jobs within the loading facility. Each of these methods imposes a cost onto the distribution function. We find how distribution can minimize its cost, given production's schedule. We then consider the problem of minimizing the company's overall cost of both production and distribution. A computational study using general data illustrates that the distribution cost is reduced by 25.80% through our proposed scheme, and that the overall cost is reduced an additional 4.40% through our coordination mechanism. An optimal algorithm is derived for a specific plant's operations. [ABSTRACT FROM AUTHOR]

Machine learning (ML), a form of artificial intelligence (AI), is being increasingly employed in neurology. Reported performance metrics often match or exceed the efficiency of average clinicians. The neurologist is easily baffled by the underlying concepts and terminologies associated with ML studies. The superlative performance metrics of ML algorithms often hide the opaque nature of its inner workings. Questions regarding ML model's interpretability and reproducibility of its results in real-world scenarios, need emphasis. Given an abundance of time and information, the expert clinician should be able to deliver comparable predictions to ML models, a useful benchmark while evaluating its performance. Predictive performance metrics of ML models should not be confused with causal inference between its input and output. ML and clinical gestalt should compete in a randomized controlled trial before they can complement each other for screening, triaging, providing second opinions and modifying treatment. [ABSTRACT FROM AUTHOR]

Author-supplied citations are a fraction of the related literature for a paper. The "related citations" on PubMed is typically dozens or hundreds of results long, and does not offer hints why these results are related. Using noun phrases derived from the sentences of the paper, we show it is possible to more transparently navigate to PubMed updates through search terms that can associate a paper with its citations. The algorithm to generate these search terms involved automatically extracting noun phrases from the paper using natural language processing tools, and ranking them by the number of occurrences in the paper compared to the number of occurrences on the web. We define search queries having at least one instance of overlap between the author-supplied citations of the paper and the top 20 search results as citation validated (CV). When the overlapping citations were written by same authors as the paper itself, we define it as CV-S and different authors is defined as CV-D. For a systematic sample of 883 papers on PubMed Central, at least one of the search terms for 86% of the papers is CV-D versus 65% for the top 20 PubMed "related citations." We hypothesize these quantities computed for the 20 million papers on PubMed to differ within 5% of these percentages. Averaged across all 883 papers, 5 search terms are CVD, and 10 search terms are CV-S, and 6 unique citations validate these searches. Potentially related literature uncovered by citation-validated searches (either CV-S or CV-D) are on the order of ten per paper - many more if the remaining searches that are not citation-validated are taken into account. The significance and relationship of each search result to the paper can only be vetted and explained by a researcher with knowledge of or interest in that paper. [ABSTRACT FROM AUTHOR]

A supply chain is an order of activities engaged which circulates, assembles and handles the products to move the benefits from a dealer under the control of the last customer. It is an interconnected compound network controlled by supply and demand. Cyber security in SCM is one of the segment of its estimates of protection which primarily gives attention in managing the essential virtual protection which comprises of system software of information technology. In the existing system, cloud services must need extra applications and assistances to locate, govern and protect data which initiate extra supply chain contributors. The manufacturing process data will mislead the manufacturing process in this system based on errors which are done manually. To Store and Maintain data in a protected manner, most algorithms such as DES (Data Encryption Standard) algorithm has disadvantages and threats which seems to be an upper hand for the hackers who are working to steal the data all around. In this paper, for securing the private and secret data, we applied and executed AES (Advanced Encryption Standard) algorithm and MD5 (Message-Digest algorithm 5) in supply chain management. We apply PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyzes) approach in domain of supply chain management, data security, and cyber security to screen the various methods and algorithms which are published in various journal papers and to select a unique and best approach to be used in supply chain management and its security. This method is basically a kind of literature survey to select a best topic for doing a project or a research paper. [ABSTRACT FROM AUTHOR]

The deep model is widely used and has been demonstrated to have more hidden security risks. An adversarial attack can bypass the traditional means of defense. By modifying the input data, the attack on the deep model is realized, and it is imperceptible to humans. The existing adversarial example generation methods mainly attack the whole image. The optimization iterative direction is easy to predict, and the attack flexibility is low. For more complex scenarios, this paper proposes an edge-restricted adversarial example generation algorithm (Re-AEG) based on semantic segmentation. The algorithm can attack one or more specific objects in the image so that the detector cannot detect the objects. First, the algorithm automatically locates the attack objects according to the application requirements. Through the semantic segmentation algorithm, the attacked object is separated and the mask matrix for the object is generated. The algorithm proposed in this paper can attack the object in the region, converge quickly and successfully deceive the deep detection model. The algorithm only hides some sensitive objects in the image, rather than completely invalidating the detection model and causing reported errors, so it has higher concealment than the previous adversarial example generation algorithms. In this paper, a comparative experiment is carried out on ImageNet and coco2017 datasets, and the attack success rate is higher than 92%. [ABSTRACT FROM AUTHOR]

This paper proposes and evaluates two robust sheet tension estimation algorithms based on unwind and rewind variables, respectively. The proper sheet tension estimation is guaranteed by a continuous monitoring of the differences between the actual estimated values and the values predicted from the last calibration of the estimation parameters. The evaluation was performed with the aid of a developed winder emulator that allowed the offline testing in a real-time field environment, including the initial setting of the estimation constants, and evaluation of both four- and six-shipping-roll cycles. The sheet tension estimation results confirm the precise and stable operation of the proposed robust estimation algorithms, in tests conducted with signals from a winder model, and with tests involving actual field signals from an operational winder. [ABSTRACT FROM AUTHOR]

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An algorithm for retrieval of vertical profiles from ground-based spectra in the near IR is described and tested. Known as GFIT2, the algorithm is primarily intended for CO2, and is used exclusively for CO2 in this paper. Retrieval of CO2 vertical profiles from ground-based spectra is theoretically possible, would be very beneficial for carbon cycle studies and the validation of satellite measurements, and has been the focus of much research in recent years. GFIT2 is tested by application both to synthetic spectra, and to measurements at two TCCON sites. We demonstrate that there are approximately 3° of freedom for the CO2 profile, and the algorithm performs as expected on synthetic spectra. We show that the accuracy of retrievals of CO2 from measurements in the 1.6μ spectral band is limited by small uncertainties in calculation of the atmospheric spectrum. We investigate several techniques to minimize the effect of these uncertainties in calculation of the spectrum. These techniques are somewhat effective, but to date have not been demonstrated to produce CO2 profile retrievals superior to existing techniques for retrieval of column abundance. We finish by discussing on-going research which may allow CO2 profile retrievals with sufficient accuracy to significantly improve on the results of column retrievals, both in total column abundance and in profile shape. [ABSTRACT FROM AUTHOR]

With the rapid advancement of Unmanned Aerial Vehicle (UAV) technologies in recent years, their uses have been increasingly adopted in the architecture, engineering, and construction industries. To satisfy the needs of various types of construction projects, a considerable amount of research work has been performed to implement and refine the operations, safety, and accuracy of UAVs. This paper presents the findings of a comprehensive literature review that focuses on UAV research in construction management during the timeframe of 2016 to 2021. A total of 95 papers were identified and collected from a list of 21 relevant journals and conference proceedings, and were then categorized by their research topic, sensor types, and targeted structures. The results of 47 exemplary studies were reported in two categories, namely UAV uses and construction uses. The research topics identified for UAV uses include algorithm, applications, operations, framework, and training, while research topics identified for construction use include inspection, surveying, safety, and monitoring. The connection between the research topics, sensor types, targeted structures, and other advanced technologies were also discussed. This paper summarizes the current results of UAV research in construction management, reviews the methodology, benefits, and limitations of the reviewed literature, and provides valuable knowledge for the future trend of UAV applications in the civil, infrastructure, and construction industries. [ABSTRACT FROM AUTHOR]

YOLOv5 is one of the most popular object detection algorithms, which is divided into multiple series according to the control of network depth and width. To realize the deployment of mobile devices or embedded devices, the paper proposes a lightweight aerial image object detection algorithm (LAI-YOLOv5s) based on the improvement of YOLOv5s with a relatively small amount of calculation and parameter and relatively fast reasoning speed. Firstly, to better detect small objects, the paper replaces the minimum detection head with the maximum detection head and proposes a new feature fusion method, DFM-CPFN(Deep Feature Map Cross Path Fusion Network), to enrich the semantic information of deep features. Secondly, the paper designs a new module based on VoVNet to improve the feature extraction ability of the backbone network. Finally, based on the idea of ShuffleNetV2, the paper makes the network more lightweight without affecting detection accuracy. Based on the VisDrone2019 dataset, the detection accuracy of LAI-YOLOv5s on the mAP@0.5 index is 8.3% higher than that of the original algorithm. Compared with other series of YOLOv5 and YOLOv3 algorithms, LAI-YOLOv5s has the advantages of low computational cost and high detection accuracy. [ABSTRACT FROM AUTHOR]

Personalized recommendation systems learn user preference characteristics by analyzing behavioral data such as ratings and comments generated by users in the Internet, and provide precise recommendations for individual users accordingly. However, in real life, users often conduct group activities like group buying and traveling together. How to recommend for groups has become a heated research topic in recent years. Most existing group recommendation algorithms are recommended for given divided groups by collectively combining the preferences of members in the group. However, in most cases, users' group properties are fickle. As the results of group detection are decisive to the performance of group recommendation, group detection is particularly important to the group recommendation algorithm. After analyzing problems of existing group recommendation algorithms, this paper proposes the density peak clustering group detection algorithm based on GRU-CNN and the group recommendation algorithm based on the mechanism. With respect to group detection, most of the existing group detection algorithms suffer from certain deficiencies: First, depending solely on the users' static preference features while ignoring the variation of users' interest over time when finding the group structure in the network; second, group division based on users' topic features extracted from reviews is difficult to support further digging of the in-depth features in reviews. To address the above-mentioned problems, this paper proposes a density peak clustering group detection algorithm based on CNN-GRU. It would first extract representative keywords in the reviews with LDA topic model, and then model time series information based on GRU attaining users' dynamic topic features. Coupling with deeper characteristics cored out by CNN, density peak clustering algorithm completes its group detection finally. Experiments on real dataset indicate that the features mined by the fusion depth neural network model effectively capture users' dynamic preferences, and yield better results of group detection than that of existing algorithms. [ABSTRACT FROM AUTHOR]

Consensus-based data aggregation in d-regular bipartite graphs poses a challenging task for the scientific community since some of these algorithms diverge in this critical graph topology. Nevertheless, one can see a lack of scientific studies dealing with this topic in the literature. Motivated by our recent research concerned with this issue, we provide a comparative study of frequently applied consensus algorithms for distributed averaging in d-regular bipartite graphs in this paper. More specifically, we examine the performance of these algorithms with bounded execution in this topology in order to identify which algorithm can achieve the consensus despite no reconfiguration and find the best-performing algorithm in these graphs. In the experimental part, we apply the number of iterations required for consensus to evaluate the performance of the algorithms in randomly generated regular bipartite graphs with various connectivities and for three configurations of the applied stopping criterion, allowing us to identify the optimal distributed consensus algorithm for this graph topology. Moreover, the obtained experimental results presented in this paper are compared to other scientific manuscripts where the analyzed algorithms are examined in non-regular non-bipartite topologies. [ABSTRACT FROM AUTHOR]

Multitarget positioning technology, such as FMCW millimeter-wave radar, has broad application prospects in autonomous driving and related mobile scenarios. However, it is difficult for existing correlation algorithms to balance high resolution and low complexity, and it is also difficult to ensure the robustness of the positioning algorithm using an aging antenna. This paper proposes a super-resolution and low-complexity positioning algorithm based on the orthogonal matching pursuit algorithm that can achieve more accurate distance and angle estimation for multiple objects in a low-SNR environment. The algorithm proposed in this paper improves the resolving power by two and one orders of magnitude, respectively, compared to the classical FFT and MUSIC algorithms in the same signal-to-noise environment, and the complexity of the algorithm can be reduced by about 25–30%, with the same resolving power as the OMP algorithm. Based on the positioning algorithm proposed in our paper, we use the PSO algorithm to optimize the arrangement of an aging antenna array so that its angle estimation accuracy is equivalent to that observed when the antenna is intact, improving the positioning algorithm's robustness. This paper also further realizes the use of the proposed algorithm and a single-frame intermediate frequency signal to estimate the position angle information of the object and obtain its motion trajectory and velocity, verifying the proposed algorithm's estimation ability when it comes to these qualities in a moving scene. Furthermore, this paper designs and carries out simulations and experiments. The experimental results verify that the positioning algorithm proposed in this paper can achieve accuracy, robustness, and real-time performance in autonomous driving scenarios. [ABSTRACT FROM AUTHOR]

There are six possible solutions for the surface normal vectors obtained from polarization information during 3D reconstruction. To resolve the ambiguity of surface normal vectors, scholars have introduced additional information, such as shading information. However, this makes the 3D reconstruction task too burdensome. Therefore, in order to make the 3D reconstruction more generally applicable, this paper proposes a complete framework to reconstruct the surface of an object using only polarized images. To solve the ambiguity problem of surface normal vectors, a jump-compensated U-shaped generative adversarial network (RU-Gan) based on jump compensation is designed for fusing six surface normal vectors. Among them, jump compensation is proposed in the encoder and decoder parts, and the content loss function is reconstructed, among other approaches. For the problem that the reflective region of the original image will cause the estimated normal vector to deviate from the true normal vector, a specular reflection model is proposed to optimize the dataset, thus reducing the reflective region. Experiments show that the estimated normal vector obtained in this paper improves the accuracy by about 20° compared with the previous conventional work, and improves the accuracy by about 1.5° compared with the recent neural network model, which means the neural network model proposed in this paper is more suitable for the normal vector estimation task. Furthermore, the object surface reconstruction framework proposed in this paper has the characteristics of simple implementation conditions and high accuracy of reconstructed texture. [ABSTRACT FROM AUTHOR]

Currently, regardless of the algorithm used, motion planners for dealing with dynamic obstructions need to rely on high‐precision sensors and high performance processors. The requirements for hardware increase as the density of dynamic obstructions in an area becomes higher. Additionally, motion planners are more prone to errors in complex environments. The Rapidly‐exploring Random Tree (RRT ∗) algorithm only considers static obstructions and cannot effectively avoid densely populated regions of dynamic obstructions. This paper develops an improved RRT ∗ algorithm that is capable of avoiding densely populated regions of dynamic obstructions. In this algorithm, the cost function of the traditional RRT ∗ algorithm is modified based on the density of dynamic obstructions, allowing the planned path to bypass densely populated regions. The algorithm also introduces reasonable penalty terms to penalize segments that pass through densely populated regions, while maintaining asymptotic optimality of the traditional RRT ∗ algorithm. Numerical experiments reveal that the improved RRT ∗ algorithm is able to successfully avoid densely populated regions of dynamic obstructions with minimal time cost and exhibits better robustness during the path search process in comparison to the traditional RRT ∗ algorithm. Thus, the improved RRT ∗ algorithm possesses the ability to adapt to more complex areas for path planning. [ABSTRACT FROM AUTHOR]

Some special manufacturing fields such as aerospace may encounter mixed production of multiple research and development projects and multiple batch production projects. Under these special production conditions resource conflicts are more severe, resulting in uncertain operating times that are difficult to predict. In addition, a single project may have tens of thousands of supporting products, making it difficult to effectively control the total construction process. To address these challenges this paper proposes new methods. A model, EMA-DCPM (dynamic critical path method) incorporating attention mechanisms in Enterprise Resource Planning and Mechanical Engineering Society) has been proposed. This model predicts product job time through machine learning methods and discovers the predictive advantage of the attention mechanism through data comparison. The CPM control algorithm was improved to enhance its robustness and an efficient modeling method, "5+X" was proposed. This new method is suitable for mixed line planning management in sophisticated manufacturing projects and has value for practical applications. [ABSTRACT FROM AUTHOR]

This paper focuses on predicting road passenger probability and optimizing taxi driving routes based on trajectory big data. By utilizing clustering algorithms to identify key passenger points, a method for calculating and predicting road passenger probability is proposed. This method calculates the passenger probability for each road segment during different time periods and uses a BiLSTM neural network for prediction. A passenger-seeking recommendation model is then constructed with the goal of maximizing passenger probability, and it is solved using the NSGA-II algorithm. Experiments are conducted on the Chengdu taxi trajectory dataset, using MSE as the metric for model prediction accuracy. The results show that the BiLSTM prediction model improves prediction accuracy by 9.67% compared to the BP neural network and by 6.45% compared to the LSTM neural network. The proposed taxi driver passenger-seeking route selection method increases the average passenger probability by 18.95% compared to common methods. The proposed passenger-seeking recommendation framework, which includes passenger probability prediction and route optimization, maximizes road passenger efficiency and holds significant academic and practical value. [ABSTRACT FROM AUTHOR]

In response to the challenges of small-size defects and low recognition rates in Printed Circuit Boards (PCBs), as well as the need for lightweight detection models that can be embedded in portable devices, this paper proposes an improved defect detection method based on a lightweight shared convolutional head using YOLOv8n. Firstly, the Spatial and Channel reconstruction Convolution (SCConv) is embedded into the Cross Stage Partial with Convolutional Layer Fusion (C2f) structure of the backbone network, which reduces redundant computations and enhances the model's learning capacity. Secondly, an adaptive feature selection module is integrated to improve the network's ability to recognize small targets. Subsequently, a Shared Lightweight Convolutional Detection (SLCD) Head replaces the original Decoupled Head, reducing the model's computational complexity while increasing detection accuracy. Finally, the Weighted Intersection over Union (WIoU) loss function is introduced to provide more precise evaluation results and improve generalization capability. Comparative experiments conducted on a public PCB dataset demonstrate that the improved algorithm achieves a mean Average Precision (mAP) of 98.6% and an accuracy of 99.8%, representing improvements of 3.8% and 3.1%, respectively, over the original model. The model size is 4.1 M, and its FPS is 144.1, meeting the requirements for real-time and lightweight portable deployment. [ABSTRACT FROM AUTHOR]

In abstract argumentation frameworks, the computation of stable extensions is an important semantic task for evaluating the acceptability of arguments. The current approaches for the computation of stable extensions are typically conducted through methodologies that are either label-based or extension-based. Label-based algorithms operate by assigning labels to each argument, thus reducing the attack relations between arguments to constraint relations among the labels. This paper analyzes the existing two-label and three-label enumeration algorithms for stable extensions through case studies. It is found that both the two-label and three-label algorithms are not precise enough in defining types of arguments. To address these issues, this paper proposes a four-label enumeration algorithm for stable extensions. This method introduces a m u s t _ i n label to pre-mark certain i n -type arguments, thereby achieving a finer classification of i n -type arguments. This enhances the labelings' propagation ability and reduces the algorithm's search space. Our proposed four-label algorithm was tested on authoritative benchmark sets of abstract argumentation framework problems: ICCMA 2019, ICCMA 2021, and ICCMA 2023. Experimental results show that the four-label algorithm significantly improves solving efficiency compared to existing two-label and three-label algorithms. Additionally, ablation experiments confirm that both the four-label transition strategy and preprocessing strategy enhance the algorithm's performance. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of trajectory planning for autonomous vehicles operating in complex, constrained environments. The proposed method enhances the hybrid A-star algorithm through back-end optimization. An adaptive node expansion strategy is introduced to handle varying environmental complexities. By integrating Dijkstra's shortest path search, the method improves direction selection and refines the estimated cost function. Utilizing the characteristics of hybrid A-star path planning, a quadratic programming approach with designed constraints smooths discrete path points. This results in a smoothed trajectory that supports speed planning using S-curve profiles. Both simulation and experimental results demonstrate that the improved hybrid A-star search significantly boosts efficiency. The trajectory shows continuous and smooth transitions in heading angle and speed, leading to notable improvements in trajectory planning efficiency and overall comfort for autonomous vehicles in challenging environments. [ABSTRACT FROM AUTHOR]

In the field of industrial inspection, accurate detection of thread quality is crucial for ensuring mechanical performance. Existing machine-vision-based methods for internal thread defect detection often face challenges in efficient detection and sufficient model training samples due to the influence of mechanical geometric features. This paper introduces a novel image acquisition structure, proposes a data augmentation algorithm based on Generative Adversarial Networks (GANs) to effectively construct high-quality training sets, and employs a YOLO algorithm to achieve internal thread defect detection. Through multi-metric evaluation and comparison with external threads, high-similarity internal thread image generation is achieved. The detection accuracy for internal and external threads reached 94.27% and 93.92%, respectively, effectively detecting internal thread defects. [ABSTRACT FROM AUTHOR]

In this paper, a consensus-based model predictive control (Cb-MPC) scheme is proposed to control the active power and voltage at all nodes in grid-connected active distribution networks (ADNs) with multiple distributed energy resources (DERs). The proposed design methodology is based on a multiple-input multiple-output (MIMO) model of an ADN which accounts for both the internal and external interactions among the control loops of the DERs. To achieve the control objective, each DER unit is equipped with a controller–observer system. In particular, the observer implements the consensus algorithm to estimate the collective system state by exchanging data only with its neighbors. The scope of the controller is to solve the MPC optimal problem based on its collective state estimate, and, due to the presence of an integral term in the control action, it is robust against any unknown scenarios of the ADN, which are represented by uncertainty in the model parameters. The results of numerical simulations validate the effectiveness of the proposed method in the presence of unknown changes in the operating conditions of the ADN and of communication using a sample and hold function. [ABSTRACT FROM AUTHOR]

Deep Cross-Modal Hashing (DCMH) has garnered significant attention in the field of cross-modal retrieval due to its advantages such as high computational efficiency and small storage space. However, existing DCMH methods still face certain limitations: (1) they neglect the correlation between labels, while label features exhibit high sparsity; (2) they lack fine-grained semantic alignment; (3) they fail to effectively address data imbalance. In order to tackle these issues, this paper introduces a framework named Semantic-Alignment Transformer and Adversary Hashing for Cross-modal Retrieval (SATAH). To the best of our knowledge, this is the first attempt at the Semantic-Alignment Transformer algorithm. Specifically, this paper first designs a label learning network that utilizes a crafted transformer module to extract label information, guiding adversarial learning and hash function learning accordingly. Subsequently, a Balanced Conditional Generative Adversarial Network (BCGAN) is constructed, marking the first instance of adversarial training guided by label information. Furthermore, a Weighted Semi-Hard Cosine Triplet Constraint is proposed to better ensure high-ranking similarity relationships among all items. Lastly, considering the correlation between labels, a semantic-alignment constraint is introduced to handle label correlation from a fine-grained perspective, capturing similarity on a global scale more effectively. Extensive experiments are conducted on multiple representative cross-modal datasets. In experiments with 64-bit hash code length, SATAH achieves average mAP values of 84.75%, 68.87%, and 68.73% on MIR Flickr, NUS-WIDE, and MS COCO datasets, respectively, outperforming state-of-the-art methods. The code is available at https://github.com/Daydaylight/SATAH. [ABSTRACT FROM AUTHOR]

As enterprises increasingly rely on cloud-based Human Resource Management Systems (HRMS) deployed on the Hadoop platform, the optimization of weighting algorithms becomes imperative to enhance system efficiency. This paper addresses the complex challenge of load balancing in the cloud environment by proposing Effective Load Balancing Strategy (ELBS) a hybrid optimization approach that integrates both Genetic Algorithm (GA) and Grey Wolf Optimization (GWO). The optimization objective involves the allocation of N jobs submitted by cloud users to M processing units, each characterized by a Processing Unit Vector (PUV). The PUV encapsulates critical parameters such as Million Instructions Per Second (MIPS), execution cost α, and delay cost L. Concurrently, each job submitted by a cloud user is represented by a Job Unit Vector (JUV), considering service type, number of instructions (NIC), job arrival time (AT), and worst-case completion time (wc). The proposed hybrid GA-GWO aims to minimize a cost function ζ, incorporating weighted factors of execution cost and delay cost. The challenge lies in determining optimal weights, a task addressed by assigning user preferences or importance as weights. The hybrid algorithm iteratively evolves populations of processing units, applying genetic operators, such as crossover and mutation, along with the exploration capabilities of GWO, to efficiently explore the solution space. This research contributes a comprehensive algorithmic solution to the optimization of weighting algorithms in enterprise HRMS on the cloud and Hadoop platform. The adaptability of the hybrid ELBS to dynamic cloud environments and its efficacy in handling complex optimization problems position it as a promising tool for achieving load balancing in HRMS applications. The proposed approach provides a foundation for further empirical validation and implementation in practical enterprise settings. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems are witnessing a paradigm shift with the integration of Cooperative Vehicular Networks. The transformations in Intelligent Transportation system in the realistic scenario has posed many research challenges to be addressed. This paper explores a profound survey on impact of vehicles' mobility within the context of real-time scenarios in Cooperative vehicular networks. The dynamic nature of vehicular mobility introduces unique challenges and opportunities for the design and implementation of cooperative systems. It delves into the key components that play a pivotal role for harnessing the full potential of Cooperative vehicular networks such as C-NOMA, Two-Way relaying, cluster formation and collaborative decision-making algorithms for improving latency, link reliability, cluster formation, and interference reduction etc. This paper outlines few surveys on each amalgamated technologies in Vehicular communication and conclude with the research problems in ITS due to vehicles mobility for real time scenarios. [ABSTRACT FROM AUTHOR]

This paper uses big data technology to extract the electromechanical fault characteristics of metro vehicles and analyze the current situation under different fault conditions to ensure the operation quality and safety of metro operations. It also establishes a simulation model to simulate the current waveform of metro vehicles under different fault conditions and analyze the fault phenomenon. The simulation test results show that: (1) The current waveform of a single transistor with the hard fault is compared with the simulated current waveform under a normal state. The upper part of the A phase current waveform is lost when T1 fails. When T2 fails, the current waveform in the lower half of the C phase current is lost. When T3 fails, the upper half of the B phase current waveform is lost. (2) The current waveform in the hard fault's upper and lower bridge arms will have phase loss. In the T25 fault, the C phase current is completely lost. In the T14 fault, the phase A current waveform is completely lost. In the T36 fault, the phase B current is completely lost. (3) The current waveform of a single transistor with a soft fault is complete, but the overall current amplitude is reduced. When a T1 fails, the A phase current tends to rise first and then fall. Compared to normal, the amplitude of the current decreases, and the peak decreases slightly. (4) The current values of phase B and phase C of the two transistors on the same bridge above and below the soft fault are mostly the same. The phase A current output value decreases in both the positive and negative half cycles. This paper aims to improve the monitoring ability of the monitoring system of electromechanical equipment of metro vehicles, which plays a specific role in maintaining the safety of subway operations and improving the quality of subway operations. [ABSTRACT FROM AUTHOR]

In this paper, the Hybrid Grey Wolf and Improved Bat Optimization Algorithm (HWIBO) is proposed to reduce the peak sidelobe level (PSLL) of linear symmetric array synthesis with aperture and element spacing constraints. The HWIBO utilizes both the Grey Wolf Optimization (GWO) and Improved Bat Algorithms (IBA) simultaneously to optimize PSLL. Each iteration generates two sets of results, and the optimal result is chosen for the next loop. Compared to other algorithms used in simulation of antenna sidelobe suppression, the HWIBO not only inherits the fast convergence advantage of the IBA which enhances population diversity but also possesses the strong global search capability of the GWO. This helps the IBA escape local optima and strengthens the global search capability during the later stages of algorithm iterations. Finally, the simulation results demonstrate the successful reduction of PSLL under various constraints, confirming the effectiveness of the hybrid algorithm. [ABSTRACT FROM AUTHOR]

This paper describes a 2-stage tactic for achieving an odd-order variable-bandwidth (OO-VBW) filter with absolutely guaranteed stability. The design methodology aims to minimize the p-norm amplitude-response error while maintaining the OO-VBW-filter's stability. In order to tune OO-VBW filter's amplitude response, we utilize a kind of functions of the bandwidth (BW)-tuning parameter to express the filter's coefficients. Because those functions have changeable function values, the designed OO-VBW-filter's amplitude response possesses variability. Another important concern in this paper is the stability issue. When the filter coefficient values expressed as function values are changed, the OO-VBW filter that has feedback structures may become unstable. This necessitates that the OO-VBW-filter's stability must always be preserved during real-time tuning. In order to stabilize such an OO-VBW filter that has feedback structures, a coefficient-conversion (CC) algorithm is adopted, and it is incorporated into the 2-stage methodology for obtaining the OO-VBW filter. To illustrate both the stability and high approximation accuracy, the design of an OO-VBW bandpass filter is simulated. The simulation details verify that both stability and high accuracy can be successfully achieved. [ABSTRACT FROM AUTHOR]

Aiming at the problems of traditional image super-resolution reconstruction algorithms in the image reconstruction process, such as small receptive field, insufficient multi-scale feature extraction, and easy loss of image feature information, a super-resolution reconstruction algorithm of multi-scale dilated convolution network based on dilated convolution is proposed in this paper. First, the algorithm extracts features from the same input image through the dilated convolution kernels of different receptive fields to obtain feature maps with different scales; then, through the residual attention dense block, further obtain the features of the original low resolution images, local residual connections are added to fuse multi-scale feature information between multiple channels, and residual nested networks and jump connections are used at the same time to speed up deep network convergence and avoid network degradation problems. Finally, deep network extraction features, and it is fused with input features to increase the nonlinear expression ability of the network to enhance the superresolution reconstruction effect. Experimental results show that compared with Bicubic, SRCNN, ESPCN, VDSR, DRCN, LapSRN, MemNet, and DSRNet algorithms on the Set5, Set14, BSDS100, and Urban100 test sets, the proposed algorithm has improved peak signal-to-noise ratio and structural similarity, and reconstructed images. The visual effect is better. [ABSTRACT FROM AUTHOR]

In this paper we propose an algorithm for testing whether the independent observations come from finite-variance distribution. The preliminary knowledge about the data properties may be crucial for its further analysis and selection of the appropriate model. The idea of the testing procedure is based on the simple observation that the empirical cumulative even moment (ECEM) for data from finite-moments distribution tends to some constant whereas for data coming from heavy-tailed distribution, the ECEM exhibits irregular chaotic behavior. Based on this fact, in this paper we parameterize the regular/irregular behavior of the ECEM and construct a new test statistic. The efficiency of the testing procedure is verified for simulated data from three heavy-tailed distributions with possible finite and infinite variances. The effectiveness is analyzed for data represented in time domain. The simulation study is supported by analysis of real vibration signals from rotating machines. Here, the analyses are provided for data in both the time and time-frequency domains. [ABSTRACT FROM AUTHOR]

Remote Sensing Image Object Detection (RSIOD) faces the challenges of multi-scale objects, dense overlap of objects and uneven data distribution in practical applications. In order to solve these problems, this paper proposes a YOLO-ACPHD RSIOD algorithm. The algorithm adopts Adaptive Condition Awareness Technology (ACAT), which can dynamically adjust the parameters of the convolution kernel, so as to adapt to the objects of different scales and positions. Compared with the traditional fixed convolution kernel, this dynamic adjustment can better adapt to the diversity of scale, direction and shape of the object, thus improving the accuracy and robustness of Object Detection (OD). In addition, a High-Dimensional Decoupling Technology (HDDT) is used to reduce the amount of calculation to 1/N by performing deep convolution on the input data and then performing spatial convolution on each channel. When dealing with large-scale Remote Sensing Image (RSI) data, this reduction in computation can significantly improve the efficiency of the algorithm and accelerate the speed of OD, so as to better adapt to the needs of practical application scenarios. Through the experimental verification of the RSOD RSI data set, the YOLO-ACPHD model in this paper shows very satisfactory performance. The F1 value reaches 0.99, the Precision value reaches 1, the Precision-Recall value reaches 0.994, the Recall value reaches 1, and the mAP value reaches 99.36 % , which indicates that the model shows the highest level in the accuracy and comprehensiveness of OD. [ABSTRACT FROM AUTHOR]

In response to the vulnerability of image encryption techniques to chosen plaintext attacks, this paper proposes a secure image communication scheme based on two-layer dynamic feedback encryption and discrete wavelet transform (DWT) information hiding. The proposed scheme employs a plaintext correlation and intermediate ciphertext feedback mechanism, and combines chaotic systems, bit-level permutation, bilateral diffusion, and dynamic confusion to ensure the security and confidentiality of transmitted images. Firstly, a dynamically chaotic encryption sequence associated with a secure plaintext hash value is generated and utilized for the first round of bit-level permutation, bilateral diffusion, and dynamic confusion, resulting in an intermediate ciphertext image. Similarly, the characteristic values of the intermediate ciphertext image are used to generate dynamically chaotic encryption sequences associated with them. These sequences are then employed for the second round of bit-level permutation, bilateral diffusion, and dynamic confusion to gain the final ciphertext image. The ciphertext image hidden by DWT also provides efficient encryption, higher level of security and robustness to attacks. This technology offers indiscernible secret data insertion, rendering it challenging for assailants to spot or extract concealed information. By combining the proposed dynamic closed-loop feedback secure image encryption scheme based on the 2D-SLMM chaotic system with DWT-based hiding, a comprehensive and robust image encryption approach can be achieved. According to the results of theoretical research and experimental simulation, our encryption scheme has dynamic encryption effect and reliable security performance. The scheme is highly sensitive to key and plaintext, and can effectively resist various common encryption attacks and maintain good robustness. Therefore, our proposed encryption algorithm is an ideal digital image privacy protection technology, which has a wide range of practical application prospects. [ABSTRACT FROM AUTHOR]

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The setting of parameter values will directly affect the performance of the neural network, and the manual parameter tuning speed is slow, and it is difficult to find the optimal combination of parameters. Based on this, this paper applies the improved Hunger Games search algorithm to find the optimal value of neural network parameters adaptively, and proposes an ATHGS-GoogleNet model. Firstly, adaptive weights and chaos mapping were integrated into the hunger search algorithm to construct a new algorithm, ATHGS. Secondly, the improved ATHGS algorithm was used to optimize the parameters of GoogleNet to construct a new model, ATHGS-GoogleNet. Finally, in order to verify the effectiveness of the proposed algorithm ATHGS and the model ATHGS-GoogleNet, a comparative experiment was set up. Experimental results show that the proposed algorithm ATHGS shows the best optimization performance in the three engineering experimental designs, and the accuracy of the proposed model ATHGS-GoogleNet reaches 98.1%, the sensitivity reaches 100%, and the precision reaches 99.5%. [ABSTRACT FROM AUTHOR]

This paper employs an integral reinforcement learning (IRL) method to investigate the optimal tracking control problem (OTCP) for nonlinear nonzero-sum (NZS) differential game systems with unknown drift dynamics. Unlike existing methods, which can only bound the tracking error, the proposed approach ensures that the tracking error asymptotically converges to zero. This study begins by constructing an augmented system using the tracking error and reference signal, transforming the original OTCP into solving the coupled Hamilton–Jacobi (HJ) equation of the augmented system. Because the HJ equation contains unknown drift dynamics and cannot be directly solved, the IRL method is utilized to convert the HJ equation into an equivalent equation without unknown drift dynamics. To solve this equation, a critic neural network (NN) is employed to approximate the complex value function based on the tracking error and reference information data. For the unknown NN weights, the least squares (LS) method is used to design an estimation law, and the convergence of the weight estimation error is subsequently proven. The approximate solution of optimal control converges to the Nash equilibrium, and the tracking error asymptotically converges to zero in the closed system. Finally, we validate the effectiveness of the proposed method in this paper based on MATLAB using the ode45 method and least squares method to execute Algorithm 2. [ABSTRACT FROM AUTHOR]

In Mobile Crowdsensing (MCS), sensing tasks have different impacts and contributions to the whole system or specific targets, so the importance of the tasks is different. Since resources for performing tasks are usually limited, prioritizing the allocation of resources to more important tasks can ensure that key data or information can be collected promptly and accurately, thus improving overall efficiency and performance. Therefore, it is very important to consider the importance of tasks in the task selection and allocation of MCS. In this paper, a task queue is established, the importance of tasks, the ability of participants to perform tasks, and the stability of the task queue are considered, and a novel task selection and allocation scheme (TSAS) in the MCS system is designed. This scheme introduces the Lyapunov optimization method, which can be used to dynamically keep the task queue stable, balance the execution ability of participants and the system load, and perform more important tasks in different system states, even when the participants are limited. In addition, the Double Deep Q-Network (DDQN) method is introduced to improve on the traditional solution of the Lyapunov optimization problem, so this scheme has a certain predictive ability and foresight on the impact of future system states. This paper also proposes action-masking and iterative training methods for the MCS system, which can accelerate the training process of the neural network in the DDQN and improve the training effect. Experiments show that the TSAS based on the Lyapunov optimization method and DDQN performs better than other algorithms, considering the long-term stability of the queue, the number and importance of tasks to be executed, and the congestion degree of tasks. [ABSTRACT FROM AUTHOR]

Typical satellite component detection is an application-valuable and challenging research field. Currently, there are many algorithms for detecting typical satellite components, but due to the limited storage space and computational resources in the space environment, these algorithms generally have the problem of excessive parameter count and computational load, which hinders their effective application in space environments. Furthermore, the scale of datasets used by these algorithms is not large enough to train the algorithm models well. To address the above issues, this paper first applies YOLOv8 to the detection of typical satellite components and proposes a Lightweight Typical Satellite Components Detection algorithm based on improved YOLOv8 (LTSCD-YOLO). Firstly, it adopts the lightweight network EfficientNet-B0 as the backbone network to reduce the model's parameter count and computational load; secondly, it uses a Cross-Scale Feature-Fusion Module (CCFM) at the Neck to enhance the model's adaptability to scale changes; then, it integrates Partial Convolution (PConv) into the C2f (Faster Implementation of CSP Bottleneck with two convolutions) module and Re-parameterized Convolution (RepConv) into the detection head to further achieve model lightweighting; finally, the Focal-Efficient Intersection over Union (Focal-EIoU) is used as the loss function to enhance the model's detection accuracy and detection speed. Additionally, a larger-scale Typical Satellite Components Dataset (TSC-Dataset) is also constructed. Our experimental results show that LTSCD-YOLO can maintain high detection accuracy with minimal parameter count and computational load. Compared to YOLOv8s, LTSCD-YOLO improved the mean average precision (mAP50) by 1.50% on the TSC-Dataset, reaching 94.5%. Meanwhile, the model's parameter count decreased by 78.46%, the computational load decreased by 65.97%, and the detection speed increased by 17.66%. This algorithm achieves a balance between accuracy and light weight, and its generalization ability has been validated on real images, making it effectively applicable to detection tasks of typical satellite components in space environments. [ABSTRACT FROM AUTHOR]

Object detection via remote sensing encounters significant challenges due to factors such as small target sizes, uneven target distribution, and complex backgrounds. This paper introduces the K-CBST YOLO algorithm, which is designed to address these challenges. It features a novel architecture that integrates the Convolutional Block Attention Module (CBAM) and Swin-Transformer to enhance global semantic understanding of feature maps and maximize the utilization of contextual information. Such integration significantly improves the accuracy with which small targets are detected against complex backgrounds. Additionally, we propose an improved detection network that combines the improved K-Means algorithm with a smooth Non-Maximum Suppression (NMS) algorithm. This network employs an adaptive dynamic K-Means clustering algorithm to pinpoint target areas of concentration in remote sensing images that feature varied distributions and uses a smooth NMS algorithm to suppress the confidence of overlapping candidate boxes, thereby minimizing their interference with subsequent detection results. The enhanced algorithm substantially bolsters the model's robustness in handling multi-scale target distributions, preserves more potentially valid information, and diminishes the likelihood of missed detections. This study involved experiments performed on the publicly available DIOR remote sensing image dataset and the DOTA aerial image dataset. Our experimental results demonstrate that, compared with other advanced detection algorithms, K-CBST YOLO outperforms all its counterparts in handling both datasets. It achieved a 68.3% mean Average Precision (mAP) on the DIOR dataset and a 78.4% mAP on the DOTA dataset. [ABSTRACT FROM AUTHOR]

The use of mobile robots in substations improves maintenance efficiency and ensures the personal safety of staff working at substations, which is a trend in the development of technologies. Strong electric and solid magnetic fields around high-voltage equipment in substations may lead to the breakdown and failure of inspection devices. Therefore, safe operation range measurement and coordinated planning are key factors in ensuring the safe operation of substations. This paper first summarizes the current developments that are occurring in the field of fixed and mobile safe operating range sensing methods for substations, such as ultra-wideband technology, the two-way time flight method, and deep learning image processing algorithms. Secondly, this paper introduces path-planning algorithms based on safety range sensing and analyzes the adaptability of global search methods based on a priori information, local planning algorithms, and sensor information in substation scenarios. Finally, in view of the limitations of the existing range awareness and path-planning methods, we investigate the problems that occur in the dynamic changes in equipment safety zones and the frequent switching of operation scenarios in substations. Furthermore, we explore a new type of barrier and its automatic arrangement system to improve the performance of distance control and path planning in substation scenarios. [ABSTRACT FROM AUTHOR]