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Compared to the conventional boost power factor correction (PFC) converter, a totem-pole bridgeless PFC has high efficiency because it does not have an input diode rectifier stage, but a current spike may occur when the polarity of the grid voltage changes. This paper proposes a digital control algorithm for bridgeless totem-pole PFC with a simple control structure based on the phase angle of grid voltage. The proposed algorithm has a PI-based double-loop control structure and performs DC-link voltage and input inductor current control. Rectifying switches operate based on the proposed rectification algorithm using phase angle information calculated through a single-phase phase-locked loop (PLL) to prevent current spikes. The feed-forward duty ratio value is calculated according to the polarity of the grid voltage and added to the double-loop controller to perform appropriate power factor control. The performance and feasibility of the proposed control algorithm are verified through a 3 kW hardware prototype. [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]

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]

Fast factorized back-projection (FFBP) is a classical fast time-domain technique that has garnered significant success in spotlight synthetic aperture radar (SAR) signal processing. The algorithm's efficiency has been extended to stripmap SAR through integral aperture determination and full-aperture data block processing while retaining its computational efficiency. However, the above method is only operated in the azimuth direction, and the computing efficiency needs to be urgently improved in the actual processing process. This paper proposes a fast factorized back-projection algorithm for stripmap SAR imaging based on range block division. The echo data are divided into multiple subblocks in the range direction, and FFBP processing is applied separately to each full-aperture subblock, further enhancing computational efficiency. The paper analyzes the algorithm's principles, underscores the necessity of integral aperture determination and full-aperture data block processing, provides specific implementation steps, and applies the algorithm to point target simulation and experimental data from a vehicle-mounted ice radar. The experiments validate the algorithm's efficiency in stripmap SAR imaging. [ABSTRACT FROM AUTHOR]

To address the issue of low accuracy in detecting malicious program behaviors in new power system edge-side applications, we present a detection model based on API call sequences that combines rule matching and deep learning techniques in this paper. We first use the PrefixSpan algorithm to mine frequent API call sequences in different threads of the same program within a malicious program dataset to create a rule base for malicious behavior sequences. The API call sequences to be examined are then matched using the malicious behavior sequence matching model, and those that do not match are fed into the TextCNN deep learning detection model for additional detection. The two models collaborate to accomplish program behavior detection. Experimental results demonstrate that the proposed detection model can effectively identify malicious samples and discern malicious program behaviors. [ABSTRACT FROM AUTHOR]

This paper proposes an all-digital calibration algorithm that utilizes a reference channel to suppress the timing mismatch in the Time-Interleaved Analog-to-Digital Converter (TIADC). The output of the reference channel is aligned with each sub-channel in turn, therefore enabling the simultaneous sampling and conversion of the same input signal. First, the statistical characteristics across the channels are employed for estimating the timing mismatch; then, by comparing the output difference between the reference channel and the sub-channels that are sampled simultaneously, the deviation of the derivator can be calibrated. Finally, combining both calibration results yields an accurate final output. This proposed algorithm provides an effective solution to improve TIADC performance in high-speed data acquisition systems. The proposed architecture is applied to a 12-bit 2.4 GS/s four-channel TIADC model, and then its effectiveness is verified. The simulation results exhibit that the Effective Number Of Bits (ENOB) at an input signal frequency of 984 MHz shows a remarkable improvement from 6.88 bits to 11.92 bits. The effectiveness of this technique is also demonstrated through the off-chip calibration of a commercial 12-bit four-channel 2 GS/s TIADC using a 680 MHz input signal that is based on the actual chip results. [ABSTRACT FROM AUTHOR]

It is well known that the recursive least squares (RLS) algorithm is renowned for its rapid convergence and excellent tracking capability. However, its performance is significantly compromised when the system is sparse or when the input signals are contaminated by impulse noise. Therefore, in this paper, the minimum error entropy (MEE) criterion is introduced into the cost function of the RLS algorithm in this paper, with the aim of counteracting the interference from impulse noise. To address the sparse characteristics of the system, we employ a universally applicable convex function to regularize the cost function. The resulting new algorithm is named the convex regularization recursive minimum error entropy (CR-RMEE) algorithm. Simulation results indicate that the performance of the CR-RMEE algorithm surpasses that of other similar algorithms, and the new algorithm excels not only in scenarios with sparse systems but also demonstrates strong robustness against pulse noise. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoT), ensuring secure communication between devices has become a crucial challenge. This paper proposes a novel secure communication solution by extracting wireless channel state information (CSI) features from IoT devices to generate a device identity. Due to the instability of the wireless channel, the CSI features are fuzzy and time-varying; thus, we a employ locally sensitive hashing (LSH) algorithm to ensure the stability of the generated identity in a dynamically changing wireless channel environment. Furthermore, zero-knowledge proofs are utilized to guarantee the authenticity and effectiveness of the generated identity. Finally, the identity generated using the aforementioned approach is integrated into an IBE communication scheme, which involves the fuzzy extraction of channel state information from IoT devices, stable identity extraction for fuzzy IoT devices using LSH, and the use of zero-knowledge proofs to ensure the authenticity of the generated identity. This identity is then employed as the identity information in identity-based encryption (IBE), constructing the device's public key for achieving confidential communication between devices. [ABSTRACT FROM AUTHOR]

High-altitude work poses significant safety risks, and wearing safety belts is crucial to prevent falls and ensure worker safety. However, manual monitoring of safety belt usage is time consuming and prone to errors. In this paper, we propose an improved high-altitude safety belt detection algorithm based on the YOLOv8 model to address these challenges. Our paper introduces several improvements to enhance its performance in detecting safety belts. First, to enhance the feature extraction capability, we introduce a BiFormer attention mechanism. Moreover, we used a lightweight upsampling operator instead of the original upsampling layer to better preserve and recover detailed information without adding an excessive computational burden. Meanwhile, Slim-neck was introduced into the neck layer. Additionally, extra auxiliary training heads were incorporated into the head layer to enhance the detection capability. Lastly, to optimize the prediction of bounding box position and size, we replaced the original loss function with MPDIOU. We evaluated our algorithm using a dataset collected from high-altitude work scenarios and demonstrated its effectiveness in detecting safety belts with high accuracy. Compared to the original YOLOv8 model, the improved model achieves P (precision), R (recall), and mAP (mean average precision) values of 98%, 91.4%, and 97.3%, respectively. These values represent an improvement of 5.1%, 0.5%, and 1.2%, respectively, compared to the original model. The proposed algorithm has the potential to improve workplace safety and reduce the risk of accidents in high-altitude work environments. [ABSTRACT FROM AUTHOR]

Underwater acoustic channels often have to face the interference of impulsive noise, which is usually modeled by α-stable distribution in simulation experiments. To solve the problem of underwater acoustic channel estimation under impulsive noise, this paper proposes a convex combination–variable-step-size least mean p-norm algorithm. The algorithm incorporates a convex combination into the variable-step-size least mean p-norm algorithm and uses the convex combination of different convergence domains provided by changing the parameters of the Gaussian function to further improve the effect after convergence. The simulation results of channel estimation show that the convex combination–variable-step-size least mean p-norm algorithm provides a more stable, robust, and universal solution than the variable-step-size least mean p-norm algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Grid-based Non-uniform Probability Density Sampling Probabilistic Roadmap algorithm (GN-PRM) in response to the challenges of difficult sampling in narrow passages and low-probability map generation in traditional Probabilistic Roadmap algorithms (PRM). The improved algorithm incorporates grid-based processing for map segmentation, employing non-uniform probability density sampling based on the different attributes of each block to enhance sampling probability in narrow passages. Additionally, considering the computational cost and frequent ineffective searches in traditional PRM algorithms during pathfinding, this paper optimizes the time required for query route planning by altering connection strategies to improve the algorithm's runtime. Finally, the simulation results indicate that, with a reduction of over 50% in undirected line segments and a reduction of over 85% in runtime, the GN-PRM algorithm achieves a 100% success rate in complex planning scenarios with a sampling point value of K = 500. In comparison, the traditional PRM algorithm has a success rate of no more than 10%, with a sampling point value of K = 500. [ABSTRACT FROM AUTHOR]

Link prediction is a crucial area of study within complex networks research. Mapping nodes to low-dimensional vectors through network embeddings is a vital technique for link prediction. Most of the existing methods employ "node–edge"-structured networks to model the data and learn node embeddings. In this paper, we initially introduce the Clique structure to enhance the existing model and investigate the impact of introducing two Clique structures (LECON: Learning Embedding based on Clique Of the Network) and nine motifs (LEMON: Learning Embedding based on Motif Of the Network), respectively, on experimental performance. Subsequently, we introduce a hypergraph to model the network and reconfigure the network by mapping hypermotifs to two structures: open hypermotif and closed hypermotif, respectively. Then, we introduce hypermotifs as supernodes to capture the structural similarity between nodes in the network (HMRLH: HyperMotif Representation Learning on Hypergraph). After that, taking into account the connectivity and structural similarity of the involved nodes, we propose the Depth and Breadth Motif Random Walk method to acquire node sequences. We then apply this method to the LEMON (LEMON-DB: LEMON-Depth and Breadth Motif Random Walk) and HMRLH (HMRLH-DB: HMRLH-Depth and Breadth Motif Random Walk) algorithms. The experimental results on four different datasets indicate that, compared with the LEMON method, the LECON method improves experimental performance while reducing time complexity. The HMRLH method, utilizing hypernetwork modeling, proves more effective in extracting node features. The LEMON-DB and HMRLH-DB methods, incorporating new random walk approaches, outperform the original methods in the field of link prediction. Compared with state-of-the-art baselines, the proposed approach in this paper effectively enhances link prediction accuracy, demonstrating a certain level of superiority. [ABSTRACT FROM AUTHOR]

Smart communication has significantly advanced with the integration of the Internet of Things (IoT). Many devices and online services are utilized in the network system to cope with data gathering and forwarding. Recently, many traffic-aware solutions have explored autonomous systems to attain the intelligent routing and flowing of internet traffic with the support of artificial intelligence. However, the inefficient usage of nodes' batteries and long-range communication degrades the connectivity time for the deployed sensors with the end devices. Moreover, trustworthy route identification is another significant research challenge for formulating a smart system. Therefore, this paper presents a smart Random walk Distributed Secured Edge algorithm (RDSE), using a multi-regression model for IoT networks, which aims to enhance the stability of the chosen IoT network with the support of an optimal system. In addition, by using secured computing, the proposed architecture increases the trustworthiness of smart devices with the least node complexity. The proposed algorithm differs from other works in terms of the following factors. Firstly, it uses the random walk to form the initial routes with certain probabilities, and later, by exploring a multi-variant function, it attains long-lasting communication with a high degree of network stability. This helps to improve the optimization criteria for the nodes' communication, and efficiently utilizes energy with the combination of mobile edges. Secondly, the trusted factors successfully identify the normal nodes even when the system is compromised. Therefore, the proposed algorithm reduces data risks and offers a more reliable and private system. In addition, the simulations-based testing reveals the significant performance of the proposed algorithm in comparison to the existing work. [ABSTRACT FROM AUTHOR]

Emotion recognition commonly relies on single-modal recognition methods, such as voice and video signals, which demonstrate a good practicability and universality in some scenarios. Nevertheless, as emotion-recognition application scenarios continue to expand and the data volume surges, single-modal emotion recognition proves insufficient to meet people's needs for accuracy and comprehensiveness when the amount of data reaches a certain scale. Thus, this paper proposes the application of multimodal thought to enhance emotion-recognition accuracy and conducts corresponding data preprocessing on the selected dataset. Appropriate models are constructed for both audio and video modalities: for the audio-modality emotion-recognition task, this paper adopts the "time-distributed CNNs + LSTMs" model construction scheme; for the video-modality emotion-recognition task, the "DeepID V3 + Xception architecture" model construction scheme is selected. Furthermore, each model construction scheme undergoes experimental verification and comparison with existing emotion-recognition algorithms. Finally, this paper attempts late fusion and proposes and implements a late-fusion method based on the idea of weight adaptation. The experimental results demonstrate the superiority of the multimodal fusion algorithm proposed in this paper. When compared to the single-modal emotion-recognition algorithm, the accuracy of recognition is increased by almost 4%, reaching 84.33%. [ABSTRACT FROM AUTHOR]

Stereophonic acoustic echo cancellation (SAEC) requires the identification of four unknown impulse responses corresponding to four loudspeaker-to-microphone pairs. Recent developments in the field of adaptive filters for SAEC setups have allowed for the handling of a single complex-valued adaptive impulse response, instead of the four classical real-valued adaptive filters. With the simplified framework provided by the widely linear (WL) model, more advanced versions of recursive least-squares (RLS) were employed in order to take advantage of their superior tracking speeds when working with highly correlated input signals (such as speech). Despite the performances and numerical stability provided by using exponentially weighted versions of the RLS family in combination with line search methods (LSMs), the SAEC configurations have limited capabilities in mitigating the negative effects caused by high-level disturbances affecting the two microphone signals. Such is the case of double-talk scenarios, which considerably reduce the update accuracy of the adaptive system. This paper analyzes a regularization technique for the named WL-RLS-LSM adaptive filters by adjusting the correlation matrix associated with the input signals and creating a reaction in the update process. The proposed method is designed to considerably slow (or even freeze) the adaptation process while the disturbance is manifested. Simulation results are discussed in order to validate the theoretical content. [ABSTRACT FROM AUTHOR]

In this paper, a swell neural network (SNN) algorithm was proposed for solving time-varying path query (TVPQ) problems with privacy protection with the following goals: (i) querying the K-nearest paths with time limitations in a time-varying scenario, and (ii) protecting private information from neighborhood attacks. The proposed SNN is a network in which the optimal paths can be calculated at the same time with no need for training. For TVPQ, a node is considered a neuron, and time-varying means that an edge has different costs in different time windows. For SNN, the query paths are swell sets from the start to the target within an upper limit. An encrypted index is designed for privacy protection. The evaluation of the efficiency and accuracy of the SNN was carried out based on New York road instances. [ABSTRACT FROM AUTHOR]

In the industrial field, the 3D target detection algorithm PointPillars has gained popularity. Improving target detection accuracy while maintaining high efficiency has been a significant challenge. To address the issue of low target detection accuracy in the PointPillars 3D target detection algorithm, this paper proposes an algorithm based on feature enhancement to improve the backbone network. The algorithm enhances preliminary feature information of the backbone network by modifying it based on PointPillars with the aid of channel attention and spatial attention mechanisms. To address the inefficiency caused by the excessive number of subsampled parameters in PointPillars, FasterNet (a lightweight and efficient feature extraction network) is utilized for down-sampling and forming different scale feature maps. To prevent the loss and blurring of extracted features resulting from the use of inverse convolution, we utilize the lightweight and efficient up-sampling modules Carafe and Dysample for adjusting resolution. Experimental results indicate improved accuracy under all difficulties of the KITTI dataset, demonstrating the superiority of the algorithm over PointPillars. [ABSTRACT FROM AUTHOR]

Due to the shortage of wireless resources and the emergence of a large number of users, determining how to guarantee the quality-of-service (QoS) requirements of users and make more users work in the same spectrum has become an urgent research topic. In this paper, we study a multi-user MIMO (MU-MIMO) cellular network system model in which cellular users (CUs) share the same spectrum resource with multiple device-to-device (D2D) pairs. To maximize the number of admitted D2D pairs sharing the same spectrum with the CUs, a joint power allocation and channel gain (JPACG) algorithm is proposed. The optimization problem is divided into two steps to be solved. First, the power allocation of CUs without D2D pairs admitted is solved. Then, the optimization problem is transformed into minimizing the interference to CUs when CUs are treated as primary users. The admittance order of D2D pairs is determined by the transmission power and channel gain. The proposed algorithm uses a convex optimization algorithm to solve the problem of power allocation joint interference channel gain in order to maximize the number of admitted D2D pairs under the constraints of the signal-to-interference-plus-noise ratio (SINR) threshold and maximum transmission power. In addition, the effect of the number of admitted D2D pairs on the total sum rate of all users is also analyzed. The simulation results show that the proposed JPACG algorithm can achieve better performance in admitting D2D pairs. [ABSTRACT FROM AUTHOR]

To address the challenge of distinguishing the health status of bearings, in this paper, a health index (HI) is developed through utilization of the multiple target time-varying black widow optimization–bidirectional gating recurrent unit (MTBWO-BiGRU) model and the Bray–Curtis distance. This index offers a visual representation of the health status of bearings, enabling more intuitive monitoring and prediction. The first step involves utilizing L1 regularization to extract effective features as degradation elements from the current bearing vibration data. Additionally, the characteristics of the initial time window of the vibration data serve as the health features. Next, the HI of the bearing is constructed by computing the Bray–Curtis distance between the bearing's degradation characteristics and health features. The cloud monitoring platform constantly tracks the health of the bearing and employs the MTBWO-BiGRU model to anticipate the forthcoming state of health. The platform generates an immediate alert when the HI of the bearing overtakes the alteration rate threshold and foresees the condition of the bearing. We compare the MTBWO-BiGRU model with the bidirectional long short-term memory (BiLSTM) and BiGRU models. The results indicate an accuracy level of 92.57%, which is evidently higher than that obtained when using the other two models. Moreover, the MTBWO-BiGRU model is lighter, demonstrating the practicality of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper explores the concept of digital identity in the evolving landscape of Web 3.0, focusing on the development and implications of a novel authentication scheme using verifiable credentials. The background sets the stage by placing digital identity within the broad context of Web 3.0′s decentralized, blockchain-based internet, highlighting the transition from earlier web paradigms. The methods section outlines the theoretical framework and technologies employed, such as blockchain, smart contracts, and cryptographic algorithms. The results summarize the main findings, including the proposed authentication scheme's ability to enhance user control, security, and privacy in digital interactions. Finally, the conclusions discuss the broader implications of this scheme for future online transactions and digital identity management, emphasizing the shift towards self-sovereignty and reduced reliance on centralized authorities. [ABSTRACT FROM AUTHOR]

Community detection has been a subject of extensive research due to its broad applications across social media, computer science, biology, and complex systems. Modularity stands out as a predominant metric guiding community detection, with numerous algorithms aimed at maximizing modularity. However, modularity encounters a resolution limit problem when identifying small community structures. To tackle this challenge, this paper presents a novel approach by defining community structure information from the perspective of encoding edge information. This pioneering definition lays the foundation for the proposed fast community detection algorithm CSIM, boasting an average time complexity of only O (n log n) . Experimental results showcase that communities identified via the CSIM algorithm across various graph data types closely resemble ground truth community structures compared to those revealed via modularity-based algorithms. Furthermore, CSIM not only boasts lower time complexity than greedy algorithms optimizing community structure information but also achieves superior optimization results. Notably, in cyclic network graphs, CSIM surpasses modularity-based algorithms in effectively addressing the resolution limit problem. [ABSTRACT FROM AUTHOR]

Programmable networks comprise heterogeneous network devices based on both hardware and software. Hardware devices provide superior bandwidth and low latency but encounter challenges in managing large table entries. Conversely, software devices offer abundant flow tables but have a limited forwarding capacity. To overcome this limitation, some commercial switches offer implementations that combine both hardware and software devices. In this context, this paper presents the Composite Pipeline (ComPipe), an algorithm for high-performance and high-precision flow placement and measurement. ComPipe utilizes a multi-level hashing algorithm for the real-time identification of heavy hitters, incorporates a unique flow eviction strategy, and is implemented on commercial programmable hardware. For non-heavy flows, ComPipe employs sketch structures to accomplish a high-performance flow synopsis within limited memory constraints. This design allows to replace flow rules entirely in the data plane, ensuring the timely detection and offloading of heavy-hitter flows, and offering a unified interface to the controller. The ComPipe prototype has been implemented in both testbed and simulation environments. The results indicate that ComPipe is an effective solution for dynamic flow placement in programmable networks, distinguished by its low cost, high performance, and high accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the problem of difficult obstacle avoidance for unmanned ground vehicles (UGVs) in complex dynamic environments, an improved A*-APF algorithm (BA*-MAPF algorithm) is proposed in this paper. Addressing the A* algorithm's challenges of lengthy paths, excess nodes, and lack of smoothness, the BA*-MAPF algorithm integrates a bidirectional search strategy, applies interpolation to remove redundant nodes, and uses cubic B-spline curves for path smoothing. To rectify the traditional APF algorithm's issues with local optimization and ineffective dynamic obstacle avoidance, the BA*-MAPF algorithm revises the gravitational field function by incorporating a distance factor, and fine-tunes the repulsive field function to vary with distance. This adjustment ensures a reduction in gravitational force as distance increases and moderates the repulsive force near obstacles, facilitating more effective local path planning and dynamic obstacle navigation. Through our experimental analysis, the BA*-MAPF algorithm has been validated to significantly outperform existing methods in achieving optimal path planning and dynamic obstacle avoidance, thereby markedly boosting path planning efficiency in varied scenarios. [ABSTRACT FROM AUTHOR]

Insider threats are one of the most costly and difficult types of attacks to detect due to the fact that insiders have the right to access an organization's network systems and understand its structure and security procedures, making it difficult to detect this type of behavior through traditional behavioral auditing. This paper proposes a method to leverage unsupervised outlier scores to enhance supervised insider threat detection by integrating the advantages of supervised and unsupervised learning methods and using multiple unsupervised outlier mining algorithms to extract from the underlying data useful representations, thereby enhancing the predictive power of supervised classifiers on the enhanced feature space. This novel approach provides superior performance, and our method provides better predictive power compared to other excellent abnormal detection methods. Using only 20% of the computing budget, our method achieved an accuracy of 86.12%. Compared with other anomaly detection methods, the accuracy increased by up to 12.5% under the same computing budget. [ABSTRACT FROM AUTHOR]

Currently, cryptographic hash functions are widely used in various applications, including message authentication codes, cryptographic random generators, digital signatures, key derivation functions, and post-quantum algorithms. Notably, they play a vital role in establishing secure communication between servers and clients. Specifically, servers often need to compute a large number of hash functions simultaneously to provide smooth services to connected clients. In this paper, we present highly optimized parallel implementations of Lightweight Secure Hash (LSH), a hash algorithm developed in Korea, on server sides. To optimize LSH performance, we leverage two parallel architectures: AVX-512 on high-end CPUs and NVIDIA GPUs. In essence, we introduce a word-level parallel processing design suitable for AVX-512 instruction sets and a data parallel processing design appropriate for the NVIDIA CUDA platform. In the former approach, we parallelize the core functions of LSH using AVX-512 registers and instructions. As a result, our first implementation achieves a performance improvement of up to 50.37% compared to the latest LSH AVX-2 implementation. In the latter approach, we optimize the core operation of LSH with CUDA PTX assembly and apply a coalesced memory access pattern. Furthermore, we determine the optimal number of blocks/threads configuration and CUDA streams for RTX 2080Ti and RTX 3090. Consequently, in the RTX 3090 architecture, our optimized CUDA implementation achieves about a 180.62% performance improvement compared with the initially ported LSH implementation to the CUDA platform. As far as we know, this is the first work on optimizing LSH with AVX-512 and NVIDIA GPU. The proposed implementation methodologies can be used alone or together in a server environment to achieve the maximum throughput of LSH computation. [ABSTRACT FROM AUTHOR]

The evolving landscape of network systems necessitates automated tools for streamlined management and configuration. Intent-driven networking (IDN) has emerged as a promising solution for autonomous network management by prioritizing declaratively defined desired outcomes over traditional manual configurations without specifying the implementation details. This paradigm shift towards flexibility, agility, and simplification in network management is particularly crucial in addressing inefficiencies and high costs linked to manual management, notably in the radio access part. This paper explores the concurrent operation of multiple intents, acknowledging the potential for conflicts, and proposes an innovative reformulation of these conflicts to enhance network administration effectiveness. Following the initial detection of conflicts among intents using a gradient-based approach, our work employs the Multiple Gradient Descent Algorithm (MGDA) to minimize all loss functions assigned to each intent simultaneously. In response to the challenge posed by the absence of a closed-form representation for each key performance indicator in a dynamic environment for computing gradient descent, the Stochastic Perturbation Stochastic Approximation (SPSA) is integrated into the MGDA algorithm. The proposed method undergoes initial testing using a commonly employed toy example in the literature before being simulated for conflict scenarios within a mobile network using the ns3 network simulator. [ABSTRACT FROM AUTHOR]

Ground segmentation is a crucial task in the field of 3D LiDAR perception for autonomous driving. It is commonly used as a preprocessing step for tasks such as object detection and road extraction. However, the existing ground segmentation algorithms often struggle to meet the requirements of robustness and real-time performance due to significant variations in ground slopes and flatness across different scenes, as well as the influence of objects such as grass, flowerbeds, and trees in the environment. To address these challenges, this paper proposes a staged real-time ground segmentation algorithm. The proposed algorithm not only achieves high real-time performance but also exhibits improved robustness. Based on a concentric zone model, the algorithm filters out reflected noise points and vertical non-ground points in the first stage, improving the validity of the fitted ground plane. In the second stage, the algorithm effectively addresses the issue of undersegmentation of ground points through three steps: ground plane fitting, ground plane validity judgment, and ground plane repair. The experimental results on the SemanticKITTI dataset demonstrate that the proposed algorithm outperforms the existing methods in terms of segmentation results. [ABSTRACT FROM AUTHOR]

The problem of multiple DAGs sharing satellite constellation resources has gradually attracted widespread attention. Due to the limited computing resources and energy consumption of satellite networks, it is necessary to formulate a reasonable multi-DAG task scheduling scheme to ensure the fairness of each workflow under the premise of considering latency and energy consumption. Therefore, in this paper, we propose a multi-DAG satellite network task scheduling algorithm based on cache-composite priority under the Software-Defined Networking satellite network architecture. The basic idea of this algorithm lies in the DAG selection phase, where not only are task priorities computed but also the concept of fair scheduling is introduced, so as to prevent the excessively delayed scheduling of low-priority DAG tasks. In addition, the concept of public subtasks is introduced to reduce the system overhead caused by repetitive tasks. The experimental results show that the hybrid scheduling strategy proposed in this paper can meet the demand of DAG scheduling and improve the degree of task completion while effectively reducing the task latency and energy consumption. [ABSTRACT FROM AUTHOR]

Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies, as well as increasing the effectiveness of the training process. In this context, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in medical image scan segmentation and intelligent visual content generation, i.e., computer-generated three-dimensional (3D) images in the context of extended reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. It concludes with possible developments and open challenges in AI applications in extended reality-based solutions. Finally, future lines of research and development directions of artificial intelligence applications, both in medical image segmentation and extended reality-based medical solutions, are discussed. [ABSTRACT FROM AUTHOR]

The optimization and allocation of transport cost savings among stakeholders are two important issues that influence the satisfaction of information providers, drivers and passengers in ridesharing recommendation systems. For optimization issues, finding optimal solutions for nonconvex constrained discrete ridesharing optimization problems poses a challenge due to computational complexity. For the allocation of transport cost savings issues, the development of an effective method to allocate cost savings in ridesharing recommendation systems is an urgent need to improve the acceptability of ridesharing. The hybridization of different metaheuristic approaches has demonstrated its advantages in tackling the complexity of optimization problems. The principle of the hybridization of metaheuristic approaches is similar to a marriage of two people with the goal of having a happy ending. However, the effectiveness of hybrid metaheuristic algorithms is unknown a priori and depends on the problem to be solved. This is similar to a situation where no one knows whether a marriage will have a happy ending a priori. Whether the hybridization of the Firefly Algorithm (FA) with Particle Swarm Optimization (PSO) or Differential Evolution (DE) can work effectively in solving ridesharing optimization problems needs further study. Motivated by deficiencies in existing studies, this paper focuses on the effectiveness of hybrid metaheuristic algorithms for solving ridesharing problems based on the hybridization of FA with PSO or the hybridization of FA with DE. Another focus of this paper is to propose and study the effectiveness of a new method to allocate ridesharing cost savings to the stakeholders in ridesharing systems. The developed hybrid metaheuristic algorithms and the allocation method have been compared with examples of several application scenarios to illustrate their effectiveness. The results indicate that hybridizing FA with PSO creates a more efficient algorithm, whereas hybridizing FA with DE does not lead to a more efficient algorithm for the ridesharing recommendation problem. An interesting finding of this study is very similar to what happens in the real world: "Not all marriages have happy endings". [ABSTRACT FROM AUTHOR]

With the development of UAV automatic cruising along power transmission lines, intelligent defect detection in aerial images has become increasingly important. In the process of target detection for aerial photography of transmission lines, insulator defects often pose challenges due to complex backgrounds, resulting in noisy images and issues such as slow detection speed, leakage, and the misidentification of small-sized targets. To address these challenges, this paper proposes an insulator defect detection algorithm called DFCG_YOLOv5, which focuses on improving both the accuracy and speed by enhancing the network structure and optimizing the loss function. Firstly, the input part is optimized, and a High-Speed Adaptive Median Filtering (HSMF) algorithm is introduced to preprocess the images captured by the UAV system, effectively reducing the noise interference in target detection. Secondly, the original Ghost backbone structure is further optimized, and the DFC attention mechanism is incorporated to strike a balance between the target detection accuracy and speed. Additionally, the original CIOU loss function is replaced with the Poly Loss, which addresses the issue of imbalanced positive and negative samples for small targets. By adjusting the parameters for different datasets, this modification effectively suppresses background positive samples and enhances the detection accuracy. To align with real-world engineering applications, the dataset utilized in this study consists of unmanned aircraft system machine patrol images from the Yunnan Power Supply Bureau Company. The experimental results demonstrate a 9.2% improvement in the algorithm accuracy and a 26.2% increase in the inference speed compared to YOLOv5s. These findings hold significant implications for the practical implementation of target detection in engineering scenarios. [ABSTRACT FROM AUTHOR]

Chip contour detection aims to detect damaged chips in chip slots during IC packaging and testing using vision facilities. However, the operation speed of the new chip transportation machine is too fast, and the current chip contour detection models, such as Yolov5, M3-Yolov5, FGHSE-Yolov5, and GSEH-Yolov5, running on the embedded platform, Jetson Nano, cannot detect chip contours in a timely manner. Therefore, there must be a rapid response for chip contour detection. This paper introduces the DSGSE-Yolov5s algorithm, which can accelerate object detection and image recognition to resolve this problem. Additionally, this study makes a performance comparison between the different models. Compared with the traditional model Yolov5, the proposed DSGSE-Yolov5s algorithm can significantly promote the speed of object detection by 132.17% and slightly increase the precision by 0.85%. As a result, the proposed approach can outperform the other methods. [ABSTRACT FROM AUTHOR]

This paper proposes a reconfiguration algorithm for electricity grids, based on Model Predictive Control (MPC). Reconfiguration is dynamically performed to reduce losses, and in reaction to adverse events, such as faults or attacks. Most of the previous works in the literature (including a previous paper from the authors) focus on the reconfiguration of grids while ensuring they are always radially operated and connected (i.e., islands are not allowed to form). At present, including the possibility of performing a dynamic islanding of the network (i.e., where portions of the grid dynamically detach and reconnect to the main grid) is seen as a way to improve the flexibility and resiliency of the grid, especially in the present context, with the increased penetration of digital technologies and renewables. Therefore, by extending the previous work, the algorithm proposed in the present paper also allows for the formation of islands, while still constraining them to radial islands, in line with the operational practice adopted by most electric companies. The mathematical formulation of the grid reconfiguration problem is discussed, and simulation results are presented, showing the effectiveness of the proposed algorithm in dynamically managing the grid reconfiguration. [ABSTRACT FROM AUTHOR]

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]

Based on the complex structural characteristics of permanent magnet-assisted synchronous reluctance motors (PMA-SynRMs), this paper proposes a multi-objective optimization design method for the motor using a composite algorithm. Firstly, the power density, electromagnetic torque, cogging torque, and torque fluctuation coefficient were used as optimization targets based on parametric analysis data of 14 motor structure variables, where parametric sensitivity analysis helped select eight optimization variables. Secondly, the motor prediction model was fitted using the genetic algorithm–back propagation (GA-BP) neural network. Finally, non-dominated sorting genetic algorithm-III (NSGA-III), based on the reference points, was used to find the optimization of the prediction model and complete the multi-objective optimization design of the external rotor PMA-SynRM with eight inputs and four outputs. A comparative analysis of the electromagnetic performance of the motor before and after optimization verifies the feasibility of optimizing the motor using the composite algorithm. This paper provides an analytical tool for the multi-parameter and multi-objective PMA-SynRM optimization design. [ABSTRACT FROM AUTHOR]

Today's intruders usually send attacking commands to a target system through several stepping-stone hosts, for the purpose of decreasing the probability of being caught. Using stepping-stone intrusion (SSI), the intruder's identity is very difficult to discover as it is concealed by a long interactive connection chain of hosts. An effective approach for SSI detection (SSID) is to determine how many connections are contained in a connection chain. This type of method is called network-based SSID. Most existing network-based SSID only worked for network traffic without intruders' session manipulation. These known SSID algorithms are either weak to resist intruders' chaff-perturbation manipulation or have very limited capability in resisting attacker's session manipulation. This paper develops a novel network-based SSID algorithm resistant to intruders' chaff-perturbation by using packet crossover. The SSID approach proposed in this paper is simple and easy to implement as the number of packet crossovers can be easily computed. Our proposed algorithm is verified by rigorous technical proofs as well as well-designed network experiments. Our experimental results show that the proposed SSID algorithm works effectively and perfectly in resisting intruders' chaff-perturbation up to a chaff rate of 50%. [ABSTRACT FROM AUTHOR]

Aiming at the problems of the difficult extraction of small target feature information, complex background, and variable target scale in unmanned aerial vehicle (UAV) aerial photography images. In this paper, an anchor-free target detection algorithm based on fully convolutional one-stage object detection (FCOS) for UAV aerial photography images is proposed. For the problem of complex backgrounds, the global context module is introduced in the ResNet50 network, which is combined with feature pyramid networks (FPN) as the backbone feature extraction network to enhance the feature representation of targets in complex backgrounds. To address the problem of the difficult detection of small targets, an adaptive feature balancing sub-network is designed to filter the invalid information generated at all levels of feature fusion, strengthen multi-layer features, and improve the recognition capability of the model for small targets. To address the problem of variable target scales, complete intersection over union (CIOU) Loss is used to optimize the regression loss and strengthen the model's ability to locate multi-scale targets. The algorithm of this paper is compared quantitatively and qualitatively on the VisDrone dataset. The experiments show that the proposed algorithm improves 4.96% on average precision (AP) compared with the baseline algorithm FCOS, and the detection speed is 35 frames per second (FPS), confirming that the algorithm has satisfactory detection performance, real-time inference speed, and has effectively improved the problem of missed detection and false detection of targets in UAV aerial images. [ABSTRACT FROM AUTHOR]

In positioning systems in wireless sensor networks, the accuracy of localization is often affected by signal distortion or attenuation caused by environmental factors, especially in indoor environments. Although using a combination of K-Nearest Neighbor (KNN) algorithm and fingerprinting matching can reduce positioning errors due to poor signal quality, the improvement in accuracy by increasing the number of reference points and K values is not significant. This paper proposes a Differential Evolution-based KNN (DE-KNN) method to overcome the performance limitations of the KNN algorithm and enhance indoor area positioning accuracy in WSNs. The DE-KNN method aims to improve the accuracy and stability of indoor positioning in wireless sensor networks. According to the simulation results, in a simple indoor environment with four reference points, when the sensors are deployed in both fixed and random arrangements, the positioning accuracy was improved by 29.09% and 30.20%, respectively, compared to using the KNN algorithm alone. In a complex indoor environment with four reference points, the positioning accuracy was increased by 32.24% and 33.72%, respectively. When the number of reference points increased to five, in a simple environment, the accuracy improvement for both fixed and random deployment was 20.70% and 26.01%, respectively. In a complex environment, the accuracy improvement was 23.88% and 27.99% for fixed and random deployment, respectively. [ABSTRACT FROM AUTHOR]

Orthogonal frequency division multiplexing (OFDM) inherits multi-carrier systems' inevitable high peak-to-average power ratio (PAPR) problem. In this paper, a novel alternating companding technique is proposed to combat the harassment of high PAPR. The sequential μ -law companding (SULC) and a tone with a lower PAPR result in only partial tones needing companding. The SULC scheme's PAPR and bit error rate (BER) performance has been balanced and improved. However, the computational complexity is still too high to be implemented. Therefore, this study sorted the transmission signals according to their amplitudes. Then, all the tones are divided into two groups by estimating the rough companding amount (around 54% of the subcarriers), using traditional parallel companding for the first group and the other group only by partial μ -law companding. This alternating μ -law companding (AULC) is proposed to improve the PAPR performance and simultaneously reduce complexity. Simulation results show that the proposed AULC method appreciably reduces the PAPR by about 5 dB (around 45%) compared with the original μ -law at complementary cumulative distribution function (CCDF) equal to 10 − 4 . Moreover, it only requires a moderate complexity to outperform the other companding schemes without sacrificing the BER performance in the OFDM systems. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) distributes computing and storage resources to the edge of the network closer to the user and significantly reduces user task completion latency and system energy consumption. This paper investigates the problem of computation offloading in a three-tier mobile edge computing network composed of multiple users, multiple edge servers, and a cloud server. In this network, each user's task can be divided into multiple subtasks with serial and parallel priority relationships existing among these subtasks. An optimization model is established with the objective of minimizing the total user delay and processor cost under constraints such as the available resources of users and servers and the interrelationships among the subtasks. An improved gravitational search algorithm (IGSA) is proposed to solve this optimization model. In contrast with the other gravitational search algorithm, the convergence factor is introduced in the calculation of the resultant force and the crossover operation in a genetic algorithm is performed when generating the new particles during each iteration. The simulation results show that the proposed IGSA greatly improves the system performance compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

Active front-end (AFE) rectifiers have become widely employed in power systems to achieve unity power factor and harmonic mitigations. The typical modeling approaches applied for AFE rectifiers in the literature mostly relied on two baselines: the detailed model and the time-average model. The former approach deals with the switching element model (SEM), which leads to significant harmonics in currents with distorted waveforms. The latter approach uses the average-value model (AVM) to overcome the currents' harmonics as well as provide fast responses. However, even the AVM baseline has shown problems during the starting stage (lack of control signals) and over the dead-time periods, which causes serious issues in the implementation process. This paper presents an improved dynamic AVM for AFE rectifiers by precisely considering the issues mentioned above, along with the practical starting procedure and desirable initialization. The studied AFE rectifier is developed using the voltage-oriented control (VOC) technique based on the different modeling methodologies, including SEM, Conventional AVM, and the proposed AVM. The performance of all models is analyzed and compared using simulation results with MATLAB/Simulink R2023a Function blocks for all the algorithm parts and SimScape elements for the electrical circuit model. The simulation results illustrate that the performance of the proposed AVM approach can closely resemble the behavior of the SEM baseline with low harmonic distortion. To evaluate the performance of the proposed model, several case studies are investigated to verify the AFE rectifier operation, regarding mostly the total harmonic distortion (THD) wherein the THD percentages are improved to 4.78 and 2.5 from 5.14 and 2.78 for low- and high-power loads, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a fast phase-only beamforming algorithm for frequency diverse array multiple-input multiple-output radar systems. Specifically, we use the Kronecker decomposition to decompose the desired phase-only weight vector into phase-only transmit and receive weight vectors and to decompose the target steering vector into transmit and receive steering vectors. By using the properties of the Kronecker product, the transmit and receive steering vectors and the transmit and receive weight vectors with the Vandermonde structure are decomposed into Kronecker factors with uni-modulus vectors, respectively. On this basis, in order to maintain the mainlobe gain and form a deep null at the desired position, the Kronecker factors are divided into two parts.The first component, referred to as the interference suppression factors, is responsible for creating deep nulls. The second component, known as the signal enhancement factor, maintains the mainlobe gain. We provide an analytical solution with low complexity for the Kronecker factors. This strategy can obtain the phase-only weights while effectively forming a deep null at the desired position. Numerical experiments are conducted to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Recently, the field of vehicle-mounted visual intelligence technology has witnessed a surge of interest in pedestrian detection. Existing algorithms for dense pedestrian detection at intersections face challenges such as high computational weight, complex models that are difficult to deploy, and suboptimal detection accuracy for small targets and highly occluded pedestrians. To address these issues, this paper proposes an improved lightweight multi-scale pedestrian detection algorithm, YOLOv8-CB. The algorithm introduces a lightweight cascade fusion network, CFNet (cascade fusion network), and a CBAM attention module to improve the characterization of multi-scale feature semantics and location information, and it superimposes a bidirectional weighted feature fusion path BIFPN structure to fuse more effective features and improve pedestrian detection performance. It is experimentally verified that compared with the YOLOv8n algorithm, the accuracy of the improved model is increased by 2.4%, the number of model parameters is reduced by 6.45%, and the computational load is reduced by 6.74%. The inference time for a single image is 10.8 ms. The cascade fusion algorithm YOLOv8-CB has higher detection accuracy and is a lighter model for multi-scale pedestrian detection in complex scenes such as streets or intersections. This proposed algorithm presents a valuable approach for device-side pedestrian detection with limited computational resources. [ABSTRACT FROM AUTHOR]

This paper presents an optimal weighted modulus (OWM) algorithm able to conceal secret messages in a high dynamic range image encoded via the RGBE format, consisting of the red, green, blue, and exponent channels. In contrast to current state-of-the-art schemes, which mainly employ limited and vulnerable homogeneous representations, our OWM scheme exploits four channels and an embedding weight to conceal secret messages, thereby offering more embedding capacities and undetectability against steganalytic tools. To reduce the impact on the luminance variation, we confine the maximal change incurred in the exponent channel when embedding secret messages. In addition, we propose an SEC scheme to eliminate the pixel saturation problem, even though a pixel contains values close to the boundary extreme. As a result, the stego images produced not only exhibit high quality but also comply with the RGBE encoding format, making them able to resist malicious steganalytic detection. The experimental results show that our scheme offers larger embedding rates, between 2.8074 and 5.7549 bits per pixel, and the average PSNR value for twelve tone-mapped images is over 48 dB. In addition, the HDR VDP 3.0 metric demonstrates the high fidelity of stego HDR images, where the average Q value is close to the upper bound of 10.0. Our scheme can defeat RS steganalytic attacks and resist image compatibility attacks. A comparison result confirms that our scheme outperforms six current state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

In this paper, a control strategy of a cell-based multi-agent system is proposed to solve the problem of inconsistency of series lithium-ion battery packs. The bidirectional Cuk converter is utilized as an equalizing circuit serving for balancing adjacent cells in a pack. A SOC-based consensus control with a time-triggered mechanism (TTM) is proposed. In order to reduce the actuator updates, the control method is ameliorated by altering TTM to an event-triggered mechanism (ETM). Adjustable balancing currents are designed in both TTM and ETM methods for the acceleration of the equalization process. The cases in dynamic environments under externally imposed charging/discharging currents by adopting TTM and ETM methods are investigated in detail. By comparison, the simulations and hardware-in-the-loop (HIL) experiments with a Typhoon real-time simulator are illustrated to show that, both in standby or external charging/discharging conditions, the proposed ETM algorithms are superior to TTM's in terms of equalization time and adaptability to the external environment. [ABSTRACT FROM AUTHOR]

Multi-object tracking (MOT) is an important problem in computer vision that has a wide range of applications. Currently, object occlusion detecting is still a serious challenge in multi-object tracking tasks. In this paper, we propose a method to simultaneously improve occluded object detection and occluded object tracking, as well as propose a tracking method for when the object is completely occluded. First, motion track prediction is utilized to improve the upper limit of occluded object detection. Then, the spatio-temporal feature information between the object and the surrounding environment is used for multi-object tracking. Finally, we use the hypothesis frame to continuously track the completely occluded object. Our study shows that we achieve competitive performances compared to the current state-of-the-art methods on popular multi-object tracking benchmarks such as MOT16, MOT17, and MOT20. [ABSTRACT FROM AUTHOR]

On the basis of analyzing the problems concerning hotel accommodation recommendation (HAR), this paper constructs a tourism HAR algorithm based on the CS-IDIANA clustering model (cellular space-improved divisive analysis). The algorithm integrates the cellular space model with DIANA, and takes the tourist attractions and the travel route costs as the research background and constraint conditions. Considering the feature attributes and spatial attributes of the tourist attractions, the tourist attraction recommendation algorithm based on the CS-IDIANA clustering model is established, then the HAR algorithm based on the spatial accessibility and route cost is constructed, with the constraints of the spatial accessibility field strength (SAFS) between the hotels and attractions and the travel route costs between the hotels and attractions. The experiment selects the tourism city Zhengzhou as the research object, and the experimental results are analyzed in four dimensions: the clustering results, the recommendation field strength of the tourist attractions, the hotel SAFS and the HAR results. The experiment proves that the proposed algorithm can find the best matched tourist attractions for tourists and the hotels with the lowest tour route cost based on the constraint conditions. Compared to the suboptimal hotels, the route costs are reduced by 5.67% and 9.63%, respectively. Compared to the hotel with the highest route cost, it reduces the travel costs by 29.23%. Compared with the two commonly used recommendation methods, the UCFR (user-based collaborative filtering recommendation) and ICFR (item-based collaborative filtering recommendation), the proposed CSIDR (CS-IDIANA recommendation) has a higher accuracy and recall rate. [ABSTRACT FROM AUTHOR]

The inductance model used in traditional sensorless control methods for switched reluctance machines (SRMs) exhibits high-order harmonics. The precision of the motor may be impacted by the buildup of rotor estimate errors caused by these harmonics. To address this issue, this paper proposes a novel method for SRMs that employs a hybrid algorithm combining an enhanced second-order generalized integrator (SOGI)-based frequency-locked loop (FLL) and an active disturbance rejection control (ADRC)-based phase-locked loop (PLL). This approach involves coordinate transformation and parameter identification to reconstruct the motor inductance model. Rotor position errors are calculated using the unsaturated inductance difference method. In order to enhance the accuracy of motor position estimation, a hybrid algorithm is employed to efficiently filter out harmonic errors and mitigate the tremor effect caused by the rotor position differential algorithm. This hybrid algorithm enables the estimate of the motor's speed and rotor position. A sensorless control simulation model was developed using a 12/8 pole SRM to assess the motor's performance under varying load conditions. Based on the results obtained, it is established that the application of this method can accurately estimate the rotor's position and rotational speed and thus improve the performance of position sensorless control. Ultimately, a prototype system for a switched reluctance motor was created, and the effectiveness and feasibility of the proposed control technique were confirmed through experimental validation. This presents an innovative approach to engineering practice. [ABSTRACT FROM AUTHOR]