Search

Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper's eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community's early reaction to a paper. [ABSTRACT FROM AUTHOR]

The simple vehicle routing problem (VRP) is a common topic of discussion in introductory operations research/management science courses. The VRP can be framed in a variety of ways, and it can be difficult to solve to optimality. For solution purposes, introductory textbooks demonstrate how Excel's Evolutionary Solver (ES) add-in produces a routing. The ES utilizes a genetic algorithm with a heuristic stopping rule to produce a routing that is not guaranteed to be optimal. Beyond pointing out that search controls, such as maximum execution time, may be extended and followed by restart(s) of ES, textbook treatments do not offer alternative ways to continue the search for a possibly better routing. In this paper, a suite of ways is presented in which students may investigate beyond what ES produces or any other optimality-uncertain VRP solution method. The suite includes perturbation methods and other ways that function within an Excel spreadsheet environment that is popular with students and textbook writers. Because there is no demonstrable feature that confirms optimality, the student problem Solver must settle for a 'best found' result as unsettling as it may be. The incertitude is addressed.

Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected. [ABSTRACT FROM AUTHOR]

Objective: Antiseizure medications (ASMs) are the first-line treatment for epilepsy. Many ASMs are available; this offers the opportunity to improve therapy by tailoring it to individual characteristics, but also increases the possibility of healthcare professionals making inappropriate treatment choices. To assist healthcare professionals, we developed a pragmatic algorithm aimed at facilitating medication selection for individuals whose epilepsy begins at age 10 years and older., Methods: Utilizing available evidence and a Delphi panel-based consensus process, a group of epilepsy experts developed an algorithm for selection of ASMs, depending on the seizure type(s) and the presence of relevant clinical variables (age, gender, comorbidities, and comedications). The algorithm was implemented into a web-based application that was tested and improved in an iterative process., Results: The algorithm categorizes ASMs deemed to be appropriate for each seizure type or combination of seizure types into three groups, with group 1 ASMs considered preferred, group 2 considered second line, and group 3 considered third line. Depending on the presence of relevant clinical variables, the ranking of individual ASMs is adjusted in the prioritization scheme to tailor recommendations to the characteristics of the individual. The algorithm is available on a web-based application at: https://epipick.org/#/., Significance: The proposed algorithm is user-friendly, requires less than 2 minutes to complete, and provides the user with a range of appropriate treatment options from which to choose. This should facilitate its broad utilization and contribute to improve epilepsy management for healthcare providers who desire advice, particularly those who lack special expertise in the field., (© 2020 International League Against Epilepsy.)

Tumor programmed cell death ligand-1 (PD-L1) expression is a key biomarker to identify patients with non-small cell lung cancer who may have an enhanced response to anti-programmed cell death-1 (PD-1)/PD-L1 treatment. Such treatments are used in conjunction with PD-L1 diagnostic immunohistochemistry assays. We developed a computer-aided automated image analysis with customized PD-L1 scoring algorithm that was evaluated via correlation with manual pathologist scores and used to determine comparability across PD-L1 immunohistochemistry assays. The image analysis scoring algorithm was developed to quantify the percentage of PD-L1 positive tumor cells on scans of whole-slide images of archival tumor samples from commercially available non-small cell lung cancer cases, stained with four immunohistochemistry PD-L1 assays (Ventana SP263 and SP142 and Dako 22C3 and 28-8). The scans were co-registered and tumor and exclusion annotations aligned to ensure that analysis of each case was restricted to comparable tissue areas. Reference pathologist scores were available from previous studies. F1, a statistical measure of precision and recall, and overall percentage agreement scores were used to assess concordance between pathologist and image analysis scores and between immunohistochemistry assays. In total, 471 PD-L1-evalulable samples were amenable to image analysis scoring. Image analysis and pathologist scores were highly concordant, with F1 scores ranging from 0.8 to 0.9 across varying matched PD-L1 cutoffs. Based on F1 and overall percentage agreement scores (both manual and image analysis scoring), the Ventana SP263 and Dako 28-8 and 22C3 assays were concordant across a broad range of cutoffs; however, the Ventana SP142 assay showed very different characteristics. In summary, a novel automated image analysis scoring algorithm was developed that was highly correlated with pathologist scores. The algorithm permitted quantitative comparison of existing PD-L1 diagnostic assays, confirming previous findings that indicate a high concordance between the Ventana SP263 and Dako 22C3 and 28-8 PD-L1 immunohistochemistry assays.

Seizure prediction is feasible, but greater accuracy is needed to make seizure prediction clinically viable across a large group of patients. Recent work crowdsourced state-of-the-art prediction algorithms in a worldwide competition, yielding improvements in seizure prediction performance for patients whose seizures were previously found hard to anticipate. The aim of the current analysis was to explore potential performance improvements using an ensemble of the top competition algorithms. The results suggest that minor increments in performance may be possible; however, the outcomes of statistical testing limit the confidence in these increments. Our results suggest that for the specific algorithms, evaluation framework, and data considered here, incremental improvements are achievable but there may be upper bounds on machine learning-based seizure prediction performance for some patients whose seizures are challenging to predict. Other more tailored approaches that, for example, take into account a deeper understanding of preictal mechanisms, patient-specific sleep-wake rhythms, or novel measurement approaches, may still offer further gains for these types of patients., (Wiley Periodicals, Inc. © 2019 International League Against Epilepsy.)

A method with high detection rate, low false-alarm rate, and low computational cost is presented for removing stars and noise and detecting space debris with signal-to-noise ratio (SNR>3) in consecutive raw frames. The top-hat transformation is implemented firstly to remove background, then a masking technique is proposed to remove stars, and finally, a weighted algorithm is used to detect the pieces of space debris. The simulation samples are images taken by 600 mm ground-based telescope. And a series of simulation targets are added to the image in order to test the detection rate and false-alarm rate of different SNRs. The telescope in this paper is worked in "staring target mode." The experimental results show that the proposed method can detect space debris effectively with low false-alarm by only three frames. When the SNR is higher than 3, the detection probability can reach 94%, and the false-alarm rate is below 2%. The running time of this algorithm is within 1 second. Additionally, algorithm performance tests in different regions are also carried out. A large set of image sequences are tested, which proves the stableness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The article presents a list of working papers related to management science. Some of the papers include "A Primal-Dual Surrogate Simplex Algorithm: Comparison with the Criss Cross Method," by R. S. Garfinkel and P. L. Yu, "Optimal Impulsive Trajectory Rendezvous Using Projection-Restoration," by W. A. Gruver, and "Asymptotic Normality of the Extremum of Certain Sample Functions," by P. K. Sen.

With the rapid development of adversarial example technologies, the concept of adversarial patches has been proposed, which can successfully transfer adversarial attacks to the real world and fool intelligent object detection systems. However, the real-world environment is complex and changeable, and the adversarial patch attack technology is susceptible to real-world factors, resulting in a decrease in the success rate of attack. Existing adversarial-patch-generation algorithms have a single direction of patch initialization and do not fully consider the impact of initial diversification on its upper limit of adversarial patch attack. Therefore, this paper proposes an initial diversified adversarial patch generation technology to improve the effect of adversarial patch attacks on the underlying algorithms in the real world. The method uses YOLOv4 as the attack model, and the experimental results show that the attack effect of the adversarial-patch-attack method proposed in this paper is higher than the baseline 8.46%, and it also has a stronger attack effect and fewer training rounds. [ABSTRACT FROM AUTHOR]

The problem of information security and copyright protection of video is becoming increasingly prominent. The current video watermarking algorithm does not have strong anti-compression, which has a significant impact on the visual effect of video. To solve this problem, this paper proposes a video watermarking algorithm based on H.264/AVC. The algorithm combines the non-zero quantization coefficient and the energy factor to select the appropriate chroma subblock, and then an optimized modulation is designed to embed the watermark into its DCT quantization coefficients in order to minimize the number of modifications of the subblocks. The invisibility and robustness experiments of the algorithm are conducted in the paper, and the Structural Similarity Indexes are above 0.99, and the False Bit Rates are all below 0.03. The results show that the algorithm has good invisibility, anti-compression performance and obvious advantages compared with other similar methods. [ABSTRACT FROM AUTHOR]

In recent years, highway accidents occur frequently, the main reason is that there is always foreign body invasion on the highway, which makes people unable to respond to emergencies in time. In order to reduce the occurrence of highway incidents, an object detection algorithm for highway intrusion was proposed in this paper. Firstly, a new feature extraction module was proposed to better preserve the main information. Secondly, a new feature fusion method was proposed to improve the accuracy of object detection. Finally, a lightweight method was proposed to reduce the computational complexity. We compare the algorithm in this paper with existing algorithms, the experimental results showed that: On the Visdrone dataset (small size targets), (a) the CS-YOLO was 3.6% more accurate than the YOLO v8. (b) The CS-YOLO was 1.2% more accurate than the YOLO v8 on the Tinypersons dataset (minimal size targets). (c) CS-YOLO was 1.4% more accurate than YOLO v8 on VOC2007 data set (normal size). [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]

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: With the installation of advanced metering infrastructures, the operation data of EVs in the distribution networks can be obtained with time intervals of seconds and minutes. Based on these operation data, the impacts of integrating EVs into the distribution networks can be calculated and discussed. Methods: In this paper, an improved clustering algorithm with a new distance index for the daily curves of different types of EVs was proposed. The different types of EVs can be classified into several typical groups and the required number of operation scenarios can be reduced. After reducing the large-scale database to typical clusters, research can be conducted on the characteristics of EVs specific to certain scenarios. Results and discussion: In this way, the capability of integrating different types of EVs into the distribution network, such as fast EV charging stations, slow EV charging stations, and EV bus charging stations, is assessed from the perspective of load capacity size. The proposed clustering algorithm was verified with practical operation data. [ABSTRACT FROM AUTHOR]

This paper considers the confrontation problem for two tank swarms of equal size and capability in a complex urban scenario. Based on the Unity platform (2022.3.20f1c1), the confrontation scenario is constructed featuring multiple crossing roads. Through the analysis of a substantial amount of biological data and wildlife videos regarding animal behavioral strategies during confrontations for hunting or food competition, two strategies are been utilized to design a novel bio-inspired intelligent swarm confrontation algorithm. The first one is the "fire concentration" strategy, which assigns a target for each tank in a way that the isolated opponent will be preferentially attacked with concentrated firepower. The second one is the "back and forth maneuver" strategy, which makes the tank tactically retreat after firing in order to avoid being hit when the shell is reloading. Two state-of-the-art swarm confrontation algorithms, namely the reinforcement learning algorithm and the assign nearest algorithm, are chosen as the opponents for the bio-inspired swarm confrontation algorithm proposed in this paper. Data of comprehensive confrontation tests show that the bio-inspired swarm confrontation algorithm has significant advantages over its opponents from the aspects of both win rate and efficiency. Moreover, we discuss how vital algorithm parameters would influence the performance indices. [ABSTRACT FROM AUTHOR]

Loop closure detection is an important component of the Simultaneous Localization and Mapping (SLAM) algorithm, which is utilized in environmental sensing. It helps to reduce drift errors during long-term operation, improving the accuracy and robustness of localization. Such improvements are sorely needed, as conventional visual-based loop detection algorithms are greatly affected by significant changes in viewpoint and lighting conditions. In this paper, we present a semantic spatial structure-based loop detection algorithm. In place of feature points, robust semantic features are used to cope with the variation in the viewpoint. In consideration of the semantic features, which are region-based, we provide a corresponding matching algorithm. Constraints on semantic information and spatial structure are used to determine the existence of loop-back. A multi-stage pipeline framework is proposed to systematically leverage semantic information at different levels, enabling efficient filtering of potential loop closure candidates. To validate the effectiveness of our algorithm, we conducted experiments using the uHumans2 dataset. Our results demonstrate that, even when there are significant changes in viewpoint, the algorithm exhibits superior robustness compared to that of traditional loop detection methods. [ABSTRACT FROM AUTHOR]

In this paper, an improved hybrid regularized grounded network imaging algorithm (ITR-Lp) combining Tikhonov regularization and Lp regularization is proposed; through the improvement of the filtering function, the correction of small magnitude for large singular values and increasing magnitude of correction with decreasing singular values for small singular values is implemented for the improvement of the convergence of the solution. The proposed algorithm constructs a regularization matrix to achieve selective correction of singular values and improve the convergence of the solution, while Lp regularization is used to enhance the sparsity of the solution and improve the boundary contrast. the effect of node distribution on convergence is investigated, and finally the ITR-Lp algorithm is validated by simulation and experiment. The results show that the ITR-Lp algorithm proposed in this paper achieves the lowest resistivity relative errors of 0.1695 and 0.1089 for resistive networks with 1 corrosion and 2 corrosions, respectively. The method has good convergence and boundary contrast, which effectively improves the pathology of the inverse problem of imaging the electrical impedance tomography of grounding grid. [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]

In recent years, there has been a significant development in unmanned platform technologies, specifically unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). As a result, their application scenarios have expanded considerably. Unmanned platforms are considered integral components of the Internet of Things system. However, certain challenges arise when dealing with specialized tasks, such as navigating complex urban low-altitude terrain with multiple obstacles and limited communication capabilities. These challenges can greatly impact the efficiency of the system due to information isolation. To address this issue, a messenger drone mechanism is introduced in this paper, which utilizes air superiority to facilitate indirect communication between unmanned platforms. Additionally, a task sequence planning algorithm based on sampling transformation is designed. This algorithm efficiently assigns the drone to mobile UGVs by discretely sampling their paths and considering the UAV-UGV motion relationship. By transforming the problem into an asymmetric traveler problem, it allows for a fast solution. Finally, the effectiveness of the algorithm is verified through comparative analysis in different scenarios. [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]

When wireless sensor networks are used in smart homes, the connection state will be unstable due to signal masking attenuation. This will cause low packet rate, high time delay and high cost in the network. In this paper, a network routing algorithm for wireless sensing based on connection conditions is designed. Secondly, the expected number of sends is proposed to evaluate the stability of links. Based on this, the following network signal delivery situation is forecasted in real time and quickly. According to the estimated expected number of transmissions, the path is dynamically corrected to effectively avoid attenuation in the channel and achieve optimal system performance. Experimental results show that the method proposed in this paper can improve the efficiency of message sending and reduce the routing cost under the condition of masking effect. [ABSTRACT FROM AUTHOR]

To reduce safety accidents caused by distracted driving and address issues such as low recognition accuracy and deployment difficulties in current algorithms for distracted behavior detection, this paper proposes an algorithm that utilizes an improved KNN for classifying driver posture features to predict distracted driving behavior. Firstly, the number of channels in the Lightweight OpenPose network is pruned to predict and output the coordinates of key points in the upper body of the driver. Secondly, based on the principles of ergonomics, driving behavior features are modeled, and a set of five-dimensional feature values are obtained through geometric calculations. Finally, considering the relationship between the distance between samples and the number of samples, this paper proposes an adjustable distance-weighted KNN algorithm (ADW-KNN), which is used for classification and prediction. The experimental results show that the proposed algorithm achieved a recognition rate of 94.04% for distracted driving behavior on the public dataset SFD3, with a speed of up to 50FPS, superior to mainstream deep learning algorithms in terms of accuracy and speed. The superiority of ADW-KNN was further verified through experiments on other public datasets. [ABSTRACT FROM AUTHOR]

Generalized target detection algorithms perform well for large- and medium-sized targets but struggle with small ones. However, with the growing importance of aerial images in urban transportation and environmental monitoring, detecting small targets in such imagery has been a promising research hotspot. The challenge in small object detection lies in the limited pixel proportion and the complexity of feature extraction. Moreover, current mainstream detection algorithms tend to be overly complex, leading to structural redundancy for small objects. To cope with these challenges, this paper recommends the PCSG model based on yolov5, which optimizes both the detection head and backbone networks. (1) An enhanced detection header is introduced, featuring a new structure that enhances the feature pyramid network and the path aggregation network. This enhancement bolsters the model's shallow feature reuse capability and introduces a dedicated detection layer for smaller objects. Additionally, redundant structures in the network are pruned, and the lightweight and versatile upsampling operator CARAFE is used to optimize the upsampling algorithm. (2) The paper proposes the module named SPD-Conv to replace the strided convolution operation and pooling structures in yolov5, thereby enhancing the backbone's feature extraction capability. Furthermore, Ghost convolution is utilized to optimize the parameter count, ensuring that the backbone meets the real-time needs of aerial image detection. The experimental results from the RSOD dataset show that the PCSG model exhibits superior detection performance. The value of mAP increases from 97.1% to 97.8%, while the number of model parameters decreases by 22.3%, from 1,761,871 to 1,368,823. These findings unequivocally highlight the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

INTRODUCTION: In present days, Automation of everything has become essential. Internet of things (IoT) play an important role among all medical advances of IT. In this paper, feasible solutions are discussed to compare and design better healthcare systems. A thorough investigation and survey of suitable approaches were done to select IoT based systems in hospitals consisting of various high precision sensors. OBJECTIVES: The challenge healthcare system face is to manage the real time patient’s data with high accuracy. Second challenge is at fog devices level to manage the load distribution to all sensors with limited availability of bandwidth. METHODS: This paper summarizes the selection criterions of suitable load balancing algorithms to reduce energy consumption and computational cost of fog devices and increase the network usage that are supposed to be used in IoT based healthcare systems. According to the survey BNBKnapack algorithm has been selected as best suitable approach to analyze the overall performance of fog devices and results are also verify the same. RESULTS: Comparative analysis of Overall performance of fog devices has been proposed with using SJF algorithm and Adaptive BNBKnapsack algorithm. It has been observed by analysing system performance, which is found as best among other load balancing algorithm Adaptive BNBKnapsack is successfully reduce the energy consumption by (99.29%), computational cost by (98.34%) and increase the network usage by (99.95%) of system CONCLUSION: It has been observed by analysing system performance, Adaptive BNBKnapsack Load balancing is successfully able to reduce the computational cost and energy consumption also increase the network usage of the fog network. The performance of the system is found best among other load balancing algorithm. [ABSTRACT FROM AUTHOR]

A deep learning-based Visual Inertial SLAM technique is proposed in this paper to ensure accurate autonomous localization of mobile robots in environments with dynamic objects. Addressing the limitations of real-time performance in deep learning algorithms and the poor robustness of pure visual geometry algorithms, this paper presents a deep learning-based Visual Inertial SLAM technique. Firstly, a non-blocking model is designed to extract semantic information from images. Then, a motion probability hierarchy model is proposed to obtain prior motion probabilities of feature points. For image frames without semantic information, a motion probability propagation model is designed to determine the prior motion probabilities of feature points. Furthermore, considering that the output of inertial measurements is unaffected by dynamic objects, this paper integrates inertial measurement information to improve the estimation accuracy of feature point motion probabilities. An adaptive threshold-based motion probability estimation method is proposed, and finally, the positioning accuracy is enhanced by eliminating feature points with excessively high motion probabilities. Experimental results demonstrate that the proposed algorithm achieves accurate localization in dynamic environments while maintaining real-time performance. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) energy trading has attracted a lot of attention and the number of electric vehicles (EVs) has increased in the past couple of years. Toward sustainable mobility, EVs meet the standard development goals (SDGs) for attaining a sustainable future in the transport sector. This development and increasing number of EVs creates an opportunity for prosumers to trade electricity. Considering this opportunity, this review article aims to provide an in-depth analysis of P2P energy trading of EVs using blockchain in centralized and decentralized networks, which enables prosumers to exchange energy directly with one another. The paper is aimed to provide the reader with a state-of-the-art review on the P2P energy trading for EVs, considering different blockchain algorithms that are practically implemented or still in the research phase. Moreover, the paper presents blockchain applications, current trends, and future challenges of EVs' energy trading. P2P energy trading for EVs using blockchain algorithms can be successfully implemented considering real-time scenarios and economically benefits smart sustainable societies. [ABSTRACT FROM AUTHOR]

Power shift tractors have been widely used in agricultural tractors in recent years because of their advantages of uninterrupted power during shifting, high transmission efficiency and high stability. As one of the indispensable driving states of the power shift tractor, the starting process requires a small impact and a starting speed that meets the driver's requirements. In this paper, aiming at such contradictory requirements, the starting control strategy of a power shift tractor is formulated with the goal of starting quality and the driver's intention. Firstly, the identification characteristics of the driver under three starting intentions are obtained by a real vehicle test. An extreme learning machine with fast identification speed and short training time is used to establish the basic driver's intention identification model. For the instability of the identification results of the Extreme Learning Machine (ELM), the particle swarm optimization algorithm (PSO) is used to optimize the ELM. The optimized extreme learning machine model has an accuracy of 96.891% for driver's intention identification. The wet clutch is an important part of the power shift gearbox. In this paper, the starting control strategy knowledge base of the starting clutch is established by a combination of bench tests and simulation tests. Through the fuzzy algorithm, the driver's intention is combined with the starting control strategy. Different drivers' intentions will affect the comprehensive evaluation model of the clutch (the single evaluation index of the clutch is: the maximum sliding power, the sliding power, the speed stability time, the impact degree), thus affecting the final choice of the starting clutch control strategy considering the driver's intention. On this basis, this paper studies and establishes the MPC starting controller for the power shift gearbox. Compared with the linear control strategy, the PSO-ELM-fuzzy weight starting strategy proposed in this paper can reduce the maximum sliding friction power by 45%, the sliding friction power by 69.45%, and the speed stabilization time by 0.11 s. The effectiveness of the starting control strategy considering the driver's intention proposed in this paper to improve the starting quality of the power shift tractor is verified. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is widely recognized as a highly effective evolutionary algorithm for global optimization. It has proven its efficacy in tackling diverse problems across various fields and real-world applications. DE boasts several advantages, such as ease of implementation, reliability, speed, and adaptability. However, DE does have certain limitations, such as suboptimal solution exploitation and challenging parameter tuning. To address these challenges, this research paper introduces a novel algorithm called Enhanced Binary JADE (EBJADE), which combines differential evolution with multi-population and elites regeneration. The primary innovation of this paper lies in the introduction of strategy with enhanced exploitation capabilities. This strategy is based on utilizing the sorting of three vectors from the current generation to perturb the target vector. By introducing directional differences, guiding the search towards improved solutions. Additionally, this study adopts a multi-population method with a rewarding subpopulation to dynamically adjust the allocation of two different mutation strategies. Finally, the paper incorporates the sampling concept of elite individuals from the Estimation of Distribution Algorithm (EDA) to regenerate new solutions through the selection process in DE. Experimental results, using the CEC2014 benchmark tests, demonstrate the strong competitiveness and superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

As graph models become increasingly prevalent in the processing of scientific data, the exploration of effective methods for the mining of meaningful patterns from large-scale graphs has garnered significant research attention. This paper delves into the complexity of frequent subgraph mining and proposes a frequent subgraph mining (FSM) algorithm. This FSM algorithm is developed within a distributed graph iterative system, designed for the Big Cloud (BC) environment of the China Mobile Corp., and is based on the bulk synchronous parallel (BSP) model, named FSM-BC-BSP. Its aim is to address the challenge of mining frequent subgraphs within a single, large graph. This study advocates for the incorporation of a message sending and receiving mechanism to facilitate data sharing across various stages of the frequent subgraph mining algorithm. Additionally, it suggests employing a standard coded subgraph and sending it to the same node for global support calculation on the large graph. The adoption of the rightmost path expansion strategy in generating candidate subgraphs helps to mitigate the occurrence of redundant subgraphs. The use of standard coding ensures the unique identification of subgraphs, thus eliminating the need for isomorphism calculations. Support calculation is executed using the Minimum Image (MNI) measurement method, aligning with the downward closure attribute. The experimental results demonstrate the robust performance of the FSM-BC-BSP algorithm across diverse input datasets and parameter configurations. Notably, the algorithm exhibits exceptional efficacy, particularly in scenarios with low support requirements, showcasing its superior performance under such conditions. [ABSTRACT FROM AUTHOR]

In the quality inspection process of high-voltage cables, several commonly used indicators include cable length, insulation thickness, and the number of conductors within the core. Among these factors, the count of conductors holds particular significance as a key determinant of cable quality. Machine vision technology has found extensive application in automatically detecting the number of conductors in cross-sectional images of high-voltage cables. However, the presence of scratch-type defects in cut high-voltage cable cross-sections can significantly compromise the precision of conductor count detection. To address this problem, this paper introduces a novel improved total variation (TV) algorithm, marking the first-ever application of the TV algorithm in this domain. Considering the staircase effect, the direct use of the TV algorithm is prone to cause serious loss of image edge information. The proposed algorithm firstly introduces multimodal features to effectively mitigate the staircase effect. While eliminating scratch-type defects, the algorithm endeavors to preserve the original image's edge information, consequently yielding a noteworthy enhancement in detection accuracy. Furthermore, a dataset was curated, comprising images of cross-sections of high-voltage cables of varying sizes, each displaying an assortment of scratch-type defects. Experimental findings conclusively demonstrate the algorithm's exceptional efficiency in eradicating diverse scratch-type defects within high-voltage cable cross-sections. The average scratch elimination rate surpasses 90%, with an impressive 96.15% achieved on cable sample 4. A series of conducted ablation experiments in this paper substantiate a significant enhancement in cable image quality. Notably, the Edge Preservation Index (EPI) exhibits an improvement of approximately 20%, resulting in a substantial boost to conductor count detection accuracy, thus effectively enhancing the quality of high-voltage cable production. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to propose a new implementation of the PID (proportional–integral–derivative) algorithm in digital hardware. The proposed structure is optimized for cost. It follows a serialized, rather than parallel, scheme. It uses only one arithmetic block, performing the multiply-and-add operation. The calculations are carried out in a sequentially cyclic manner. The proposed circuit operates on standard single-precision (32-bit) floating-point numbers. It implements an extended PID formula, containing a non-ideal derivative component, and weighting coefficients, which enable reducing the influence of setpoint changes in the proportional and derivative components. The circuit was implemented in a Cyclone V FPGA (Field-Programmable Gate Array) device from Intel, Santa Clara, CA, USA. The proper operation of the circuit was verified in a simulation. For the specific implementation, which is reported in the paper, the sampling period of 516 ns was obtained, which means that the proposed solution is comparable in terms of speed with other hardware implementations of the PID algorithm operating on single-precision floating-point numbers. However, the presented solution is much more efficient in terms of cost. It uses 1173 LUT (Look-up Table) blocks, 1026 registers, and 1 DSP (Digital Signal Processing) block, i.e., about 30% of logic resources required by comparable solutions. [ABSTRACT FROM AUTHOR]

In recent years, microservices, as an emerging technology in software development, have been favored by developers due to their lightweight and low-coupling features, and have been rapidly applied to the Internet of Things (IoT) and Internet of Vehicles (IoV), etc. Microservices deployed in each unit of the IoV use wireless links to transmit data, which exposes a larger attack surface, and it is precisely because of these features that the secure and efficient placement of microservices in the environment poses a serious challenge. Improving the security of all nodes in an IoV can significantly increase the service provider's operational costs and can create security resource redundancy issues. As the application of reinforcement learning matures, it is enabling faster convergence of algorithms by designing agents, and it performs well in large-scale data environments. Inspired by this, this paper firstly models the placement network and placement behavior abstractly and sets security constraints. The environment information is fully extracted, and an asynchronous reinforcement-learning-based algorithm is designed to improve the effect of microservice placement and reduce the security redundancy based on ensuring the security requirements of microservices. The experimental results show that the algorithm proposed in this paper has good results in terms of the fit of the security index with user requirements and request acceptance rate. [ABSTRACT FROM AUTHOR]

To ensure the long-term stable and uninterrupted service of satellite navigation systems, the robustness and reliability of time–frequency systems are crucial. Integrity monitoring is an effective method to enhance the robustness and reliability of time–frequency systems. Time–frequency signals are fundamental for integrity monitoring, with their time differences and frequency biases serving as essential indicators. These indicators are influenced by the inherent characteristics of the time–frequency signals, as well as the links and equipment they traverse. Meanwhile, existing research primarily focuses on only monitoring the integrity of the time–frequency signals' output by the atomic clock group, neglecting the integrity monitoring of the time–frequency signals generated and distributed by the time–frequency signal generation and distribution subsystem. This paper introduces a time–frequency signal integrity monitoring algorithm based on the temperature compensation frequency bias combination model. By analyzing the characteristics of time difference measurements, constructing the temperature compensation frequency bias combination model, and extracting and monitoring noise and frequency bias features from the time difference measurements, the algorithm achieves comprehensive time–frequency signal integrity monitoring. Experimental results demonstrate that the algorithm can effectively detect, identify, and alert users to time–frequency signal faults. Additionally, the model and the integrity monitoring parameters developed in this paper exhibit high adaptability, making them directly applicable to the integrity monitoring of time–frequency signals across various links. Compared with traditional monitoring algorithms, the algorithm proposed in this paper greatly improves the effectiveness, adaptability, and real-time performance of time–frequency signal integrity monitoring. [ABSTRACT FROM AUTHOR]

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) is a primary factor leading to the deterioration of insulation in electrical equipment. However, it is hard for traditional methods to precisely extract PD signals in increasingly complex engineering environments. This paper proposes a new PD signal denoising method combining Aquila Optimizer–Variational Mode Decomposition (AO-VMD) and K-Singular Value Decomposition (K-SVD) algorithms. Firstly, the AO algorithm optimizes critical parameters of the VMD algorithm. For the PD signal overwhelmed by noise, the AO-VMD algorithm can decompose it and reconstruct it by using kurtosis. In this process, the majority of the noise is removed, and the characteristics of the original signal are shown. Subsequently, the K-SVD algorithm performs sparse decomposition on the signal after OA-VMD, constructs a learned dictionary, and captures the characteristics of the signal for continuous learning and updating. After the dictionary learning is completed, the best matching atoms from the dictionary are selected to precisely reconstruct the original noiseless signal. Finally, the proposed method is compared with three traditional algorithms, Adaptive Ensemble Empirical Mode Decomposition (AEEMD), SVD-VMD, and the Adaptive Wavelet Multilevel Soft Threshold algorithm, on the simulated signal and the actual engineering signal. The results both demonstrate that the algorithm proposed by this paper has superior noise reduction and signal extraction performance. [ABSTRACT FROM AUTHOR]

As urbanization accelerates, traffic congestion and frequent accidents have become prominent issues, prompting the development of intelligent transportation systems. This paper focuses on the research of vehicle and pedestrian detection algorithms to improve detection accuracy in complex traffic environments. Considering the limitations of traditional object detection algorithms in complex situations, this study adopts the deep learning-based YOLOv8 algorithm and introduces the Coordinate Attention (CA) module to enhance the model's feature extraction and localization capabilities. Experimental results show that the improved YOLOv8 network achieves a 1.1% increase in detection accuracy while maintaining its original speed. Furthermore, this paper constructs a vehicle and pedestrian dataset suitable for Chinese traffic scenes, providing an effective solution for autonomous driving assistance systems. Overall, this study holds significant reference value for vehicle and pedestrian detection in the field of intelligent transportation. [ABSTRACT FROM AUTHOR]

In order to address the challenges of identifying, detecting, and tracking moving objects in video surveillance, this paper emphasizes image-based dynamic entity detection. It delves into the complexities of numerous moving objects, dense targets, and intricate backgrounds. Leveraging the You Only Look Once (YOLOv3) algorithm framework, this paper proposes improvements in image segmentation and data filtering to address these challenges. These enhancements form a novel multi-object detection algorithm based on an improved YOLOv3 framework, specifically designed for video applications. Experimental validation demonstrates the feasibility of this algorithm, with success rates exceeding 60% for videos such as "jogging", "subway", "video 1", and "video 2". Notably, the detection success rates for "jogging" and "video 1" consistently surpass 80%, indicating outstanding detection performance. Although the accuracy slightly decreases for "Bolt" and "Walking2", success rates still hover around 70%. Comparative analysis with other algorithms reveals that this method's tracking accuracy surpasses that of particle filters, Discriminative Scale Space Tracker (DSST), and Scale Adaptive Multiple Features (SAMF) algorithms, with an accuracy of 0.822. This indicates superior overall performance in target tracking. Therefore, the improved YOLOv3-based multi-object detection and tracking algorithm demonstrates robust filtering and detection capabilities in noise-resistant experiments, making it highly suitable for various detection tasks in practical applications. It can address inherent limitations such as missed detections, false positives, and imprecise localization. These improvements significantly enhance the efficiency and accuracy of target detection, providing valuable insights for researchers in the field of object detection, tracking, and recognition in video surveillance. [ABSTRACT FROM AUTHOR]

With the advancement of network technology, multimedia videos have emerged as a crucial channel for individuals to access external information, owing to their realistic and intuitive effects. In the presence of high frame rate and high dynamic range videos, the coding efficiency of high-efficiency video coding (HEVC) falls short of meeting the storage and transmission demands of the video content. Therefore, versatile video coding (VVC) introduces a nested quadtree plus multi-type tree (QTMT) segmentation structure based on the HEVC standard, while also expanding the intra-prediction modes from 35 to 67. While the new technology introduced by VVC has enhanced compression performance, it concurrently introduces a higher level of computational complexity. To enhance coding efficiency and diminish computational complexity, this paper explores two key aspects: coding unit (CU) partition decision-making and intra-frame mode selection. Firstly, to address the flexible partitioning structure of QTMT, we propose a decision-tree-based series partitioning decision algorithm for partitioning decisions. Through concatenating the quadtree (QT) partition division decision with the multi-type tree (MT) division decision, a strategy is implemented to determine whether to skip the MT division decision based on texture characteristics. If the MT partition decision is used, four decision tree classifiers are used to judge different partition types. Secondly, for intra-frame mode selection, this paper proposes an ensemble-learning-based algorithm for mode prediction termination. Through the reordering of complete candidate modes and the assessment of prediction accuracy, the termination of redundant candidate modes is accomplished. Experimental results show that compared with the VVC test model (VTM), the algorithm proposed in this paper achieves an average time saving of 54.74%, while the BDBR only increases by 1.61%. [ABSTRACT FROM AUTHOR]

In this paper, a method based on an improved intelligent bat algorithm (IIBA) in cooperation with a voltage and current sensor was applied in maximum power point tracking (MPPT) for a photovoltaic module array (PVMA), where the power generation performance of a PVMA was enhanced. Due to the partial shading of the PVMA from climate changes or the surrounding environment, multiple peak values were generated on the power–voltage (P-V) curve, where the conventional MPPT technology could only track the local maximum power point (LMPP), hence the reduction in output power of PVMAs. Therefore, the IIBA-based MPPT was proposed in this paper to solve such issues and to ensure the capability of a PVMA in tracking the global maximum power point (GMPP) and utilization for enhancing the output power of a PVMA. Firstly, the Matlab/Simulink software was used to establish a boost converter model that simulated the actual 4-series–3-parallel PVMA under different shaded conditions, where the P-V curve with 1-peak, 2-peak, 3-peak and 4-peak values were generated. Subsequently, the tracking paces of the conventional bat algorithm (BA) were adjusted according to the gradient of the P-V curve for a PVMA. At the same time, 0.8 times the maximum power point (MPP) voltage Vmp under standard test conditions (STCs) for a PVMA was set as the initial tracking voltage. Lastly, the simulation results proved that under different environmental impacts, the proposed IIBA led to better performances in tracking both dynamic and steady responses. [ABSTRACT FROM AUTHOR]

Over the past 20 years, there has been a significant increase in publication in complex network analysis research, especially in community detection. Many methods were proposed to identify community structure. Each community identification algorithm has strengths and weaknesses due to the complexity of information. Among them, the optimisation methods are widely focused on. This paper focuses on an empirical study of two community detection algorithms based on agglomerative techniques using modularity metric: Louvain and Leiden. In this regard, the Louvain algorithm has been shown to produce a bad connection in the community and disconnected when executed iteratively. Therefore, the Leiden algorithm is designed to successively resolve the weaknesses. Performance comparisons between the two and their concept were summarised in detail, as well as the step-by-step learning process of the state-of-the-art algorithms. This study is important and beneficial to the future study of interdisciplinary data sciences of network analysis. First, it demonstrates that the Leiden method outperformed the Louvain algorithm in terms of modularity metric and running time. Second, the paper displays the use of these two algorithms on synthetic and real networks. The experiment was successful as it identified better performance, and future work is required to confirm and validate these findings. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is the underlying technology that has enabled connecting daily apparatus to the Internet and enjoying the facilities of smart services. IoT marketing is experiencing an impressive 16.7% growth rate and is a nearly USD 300.3 billion market. These eye-catching figures have made it an attractive playground for cybercriminals. IoT devices are built using resource-constrained architecture to offer compact sizes and competitive prices. As a result, integrating sophisticated cybersecurity features is beyond the scope of the computational capabilities of IoT. All of these have contributed to a surge in IoT intrusion. This paper presents an LSTM-based Intrusion Detection System (IDS) with a Dynamic Access Control (DAC) algorithm that not only detects but also defends against intrusion. This novel approach has achieved an impressive 97.16% validation accuracy. Unlike most of the IDSs, the model of the proposed IDS has been selected and optimized through mathematical analysis. Additionally, it boasts the ability to identify a wider range of threats (14 to be exact) compared to other IDS solutions, translating to enhanced security. Furthermore, it has been fine-tuned to strike a balance between accurately flagging threats and minimizing false alarms. Its impressive performance metrics (precision, recall, and F1 score all hovering around 97%) showcase the potential of this innovative IDS to elevate IoT security. The proposed IDS boasts an impressive detection rate, exceeding 98%. This high accuracy instills confidence in its reliability. Furthermore, its lightning-fast response time, averaging under 1.2 s, positions it among the fastest intrusion detection systems available. [ABSTRACT FROM AUTHOR]

Flocking cohesion is critical for maintaining a group's aggregation and integrity. Designing a potential function to maintain flocking cohesion unaffected by social distance is challenging due to the uncertainty of real-world conditions and environments that cause changes in agents' social distance. Previous flocking research based on potential functions has primarily focused on agents' same social distance and the attraction–repulsion of the potential function, ignoring another property affecting flocking cohesion: well depth, as well as the effect of changes in agents' social distance on well depth. This paper investigates the effect of potential function well depths and agent's social distances on the multi-agent flocking cohesion. Through the analysis, proofs, and classification of these potential functions, we have found that the potential function well depth is proportional to the flocking cohesion. Moreover, we observe that the potential function well depth varies with the agents' social distance changes. Therefore, we design a segmentation potential function and combine it with the flocking control algorithm in this paper. It enhances flocking cohesion significantly and has good robustness to ensure the flocking cohesion is unaffected by variations in the agents' social distance. Meanwhile, it reduces the time required for flocking formation. Subsequently, the Lyapunov theorem and the LaSalle invariance principle prove the stability and convergence of the proposed control algorithm. Finally, this paper adopts two subgroups with different potential function well depths and social distances to encounter for simulation verification. The corresponding simulation results demonstrate and verify the effectiveness of the flocking control algorithm. [ABSTRACT FROM AUTHOR]

The National Forestland "One Map" applies the boundaries and attributes of sub-elements to mountain plots by means of spatial data to achieve digital management of forest resources. The change detection and analysis of forest space and property is the key to determining the change characteristics, evolution trend and management effectiveness of forest land. The existing spatial overlay method, rasterization method, object matching method, etc., cannot meet the requirements of high efficiency and high precision at the same time. In this paper, we investigate a fast algorithm for the detection of changes in "One Map", taking Sichuan Province as an example. The key spatial characteristic extraction method is used to uniquely determine the sub-compartments. We construct an unbalanced quadtree based on the number of maximum leaf node elements (NLNE Quad-Tree) to narrow down the query range of the target sub-compartments and quickly locate the sub-compartments. Based on NLNE Quad-Tree, we establish a change detection model for "One Map" (NQT-FCDM). The results show that the spatial feature combination of barycentric coordinates and area can ensure the spatial uniqueness of 44.45 million sub-compartments in Sichuan Province with 1 m~0.000001 m precision. The NQT-FCDM constructed with 1000–6000 as the maximum number of leaf nodes has the best retrieval efficiency in the range of 100,000–500,000 sub-compartments. The NQT-FCDM shortens the time by about 75% compared with the traditional spatial union analysis method, shortens the time by about 50% compared with the normal quadtree and effectively solves the problem of generating a large amount of intermediate data in the spatial union analysis method. The NQT-FCDM proposed in this paper improves the efficiency of change detection in "One Map" and can be generalized to other industries applying geographic information systems to carry out change detection, providing a basis for the detection of changes in vector spatial data. [ABSTRACT FROM AUTHOR]

Recently, a signal sorting algorithm based on the congruence transform has been proposed, which is effective in dealing with the staggered Pulse Repetition Interval (PRI) signals. It can effectively sort the staggered PRI signals and obtain the sub-PRI sequence directly without sub-PRI ranking, and it is less affected by interfered pulses and pulse loss. Nevertheless, we find that the algorithm causes pseudo-peaks in the remainder histogram when sorting signals such as sliding PRI, sinusoidal PRI, etc. (collectively referred to as periodic PRI signal in this paper) and pseudo-peaks will cause errors in signal sorting. To solve the issue of pseudo-peaks when sorting periodic PRI signals, an improved sorting algorithm based on congruence transform is proposed. According to the analysis of the congruence characteristics of the periodic PRI signal, a novel method is proposed to identify pseudo-peaks based on the histogram peak amplitude and symmetric difference set. The signal sorting algorithm based on congruence transform is improved to achieve a good sorting effect on periodic PRI signals. Simulation experiments demonstrate that the novel algorithm can effectively sort periodic PRI signals and improve Precall, Pd, and Pf by 6.9%, 5.1%, and 3.2%, respectively, compared to the typical similar algorithms. [ABSTRACT FROM AUTHOR]

This paper thoroughly explores the role of object detection in smart cities, specifically focusing on advancements in deep learning-based methods. Deep learning models gain popularity for their autonomous feature learning, surpassing traditional approaches. Despite progress, challenges remain, such as achieving high accuracy in urban scenes and meeting real-time requirements. The study aims to contribute by analyzing state-of-the-art deep learning algorithms, identifying accurate models for smart cities, and evaluating real-time performance using the Average Precision at Medium Intersection over Union (IoU) metric. The reported results showcase various algorithms' performance, with Dynamic Head (DyHead) emerging as the top scorer, excelling in accurately localizing and classifying objects. Its high precision and recall at medium IoU thresholds signify robustness. The paper suggests considering the mean Average Precision (mAP) metric for a comprehensive evaluation across IoU thresholds, if available. Despite this, DyHead stands out as the superior algorithm, particularly at medium IoU thresholds, making it suitable for precise object detection in smart city applications. The performance analysis using Average Precision at Medium IoU is reinforced by the Average Precision at Low IoU (APL), consistently depicting DyHead's superiority. These findings provide valuable insights for researchers and practitioners, guiding them toward employing DyHead for tasks prioritizing accurate object localization and classification in smart cities. Overall, the paper navigates through the complexities of object detection in urban environments, presenting DyHead as a leading solution with robust performance metrics. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for filling holes in Level of Detail 2 (LoD2) building mesh models, addressing the challenges posed by geometric inaccuracies and topological errors. Unlike traditional methods that often alter the original geometric structure or impose stringent input requirements, our approach preserves the integrity of the original model while effectively managing a range of topological errors. The algorithm operates in three distinct phases: (1) pre-processing, which addresses topological errors and identifies pseudo-holes; (2) detecting and extracting complete border rings of holes; and (3) remeshing, aimed at reconstructing the complete geometric surface. Our method demonstrates superior performance compared to related work in filling holes in building mesh models, achieving both uniform local geometry around the holes and structural completeness. Comparative experiments with established methods demonstrate our algorithm's effectiveness in delivering more complete and geometrically consistent hole-filling results, albeit with a slight trade-off in efficiency. The paper also identifies challenges in handling certain complex scenarios and outlines future directions for research, including the pursuit of a comprehensive repair goal for LoD2 models to achieve watertight 2-manifold models with correctly oriented normals. Our source code is available at https://github.com/tudelft3d/Automatic-Repair-of-LoD2-Building-Models.git. [ABSTRACT FROM AUTHOR]

Evolutionary multi-objective clustering (EMOC) algorithms have gained popularity recently, as they can obtain a set of clustering solutions in a single run by optimizing multiple objectives. Particularly, in one type of EMOC algorithm, the number of clusters k is taken as one of the multiple objectives to obtain a set of clustering solutions with different k. However, the numbers of clusters k and other objectives are not always in conflict, so it is impossible to obtain the clustering solutions with all different k in a single run. Therefore, evolutionary multi-objective k-clustering (EMO-KC) has recently been proposed to ensure this conflict. However, EMO-KC could not obtain good clustering accuracy on high-dimensional datasets. Moreover, EMO-KC's validity is not ensured as one of its objectives (SSDexp, which is transformed from the sum of squared distances (SSD)) could not be effectively optimized and it could not avoid invalid solutions in its initialization. In this paper, an improved evolutionary multi-objective clustering algorithm based on autoencoder (AE-IEMOKC) is proposed to improve the accuracy and ensure the validity of EMO-KC. The proposed AE-IEMOKC is established by combining an autoencoder with an improved version of EMO-KC (IEMO-KC) for better accuracy, where IEMO-KC is improved based on EMO-KC by proposing a scaling factor to help effectively optimize the objective of SSDexp and introducing a valid initialization to avoid the invalid solutions. Experimental results on several datasets demonstrate the accuracy and validity of AE-IEMOKC. The results of this paper may provide some useful information for other EMOC algorithms to improve accuracy and convergence. [ABSTRACT FROM AUTHOR]