Journal of Computer Science and Electrical Engineering
Journal of Computer Science and Electrical Engineering (JCSEE) is an international peer-reviewed, open access journal.
OPTIMIZATION OF ESPRIT ALGORITHM AND FAST DOA ESTIMATION FOR UNIFORM LINEAR ARRAYS
To tackle the problems of high computational complexity and cumbersome engineering deployment posed by traditional subspace-based direction-of-arrival (DOA) estimation algorithms, this study presents a low-complexity DOA estimation approach designed specifically for uniform linear arrays (ULA). The key innovation of the proposed method is the elimination of eigenvalue decomposition or singular value decomposition (EVD/SVD)—two core components of classical subspace-based techniques. Instead, it employs a strategy of array segmentation and matrix reconstruction. In detail, the ULA is first split into two separate subarrays, and their cross-covariance matrix is used to build a joint cross-covariance matrix. An equivalent signal subspace is then directly generated via a linear transformation, which greatly simplifies the computational procedure. Finally, by exploiting the rotation invariance principle of ESPRIT, the proposed approach realizes fast and precise DOA estimation. Theoretical analyses and simulation results indicate that, in comparison with traditional subspace-based algorithms, the proposed method reduces computational complexity significantly while retaining comparable estimation accuracy, thus improving the overall efficiency of DOA estimation.
Copyright (c) 2026 XinYu Weng https://creativecommons.org/licenses/by/4.0
DUAL-FAIRNESS DRIVEN 3D TRAJECTORY DESIGN AND RESOURCE ALLOCATION FOR UAV-ASSISTED ISAC SYSTEMS
Unmanned Aerial Vehicles (UAVs), recognized for their high flexibility and rapid deployment, are increasingly utilized as key platforms for Integrated Sensing and Communication (ISAC). However, balancing on-demand service quality for users with robust sensing coverage for targets remains a critical challenge, especially in emergency scenarios where blind spots must be avoided. This paper proposes a dual-fairness framework for a UAV-assisted ISCC system. We formulate a joint optimization problem of sensing, computation, communication scheduling, and UAV 3D trajectory. To ensure service fairness among users, we adopt a logarithmic utility function to maximize the sum of the logarithm of user-received data volume. Simultaneously, to guarantee sensing fairness, we enforce a Minimum Sensing Rate Ratio (MSRR) constraint, ensuring that no target is persistently neglected. The formulated non-convex problem is solved via a proposed four-stage Alternating Optimization (AO) algorithm. Simulation results demonstrate that the proposed scheme effectively balances user service fairness with sensing robustness, achieving high-quality data delivery while strictly satisfying the sensing fairness requirements.
Copyright (c) 2026 LuLu Jing; Hai Wang https://creativecommons.org/licenses/by/4.0
ST-GNAD: MULTI-LEVEL ANOMALY DETECTION FOR 5G CORE NETWORKS BASED ON SPATIAL-TEMPORAL GRAPH NEURAL NETWORK
The rapid expansion of 5G infrastructure has intensified the need for robust security within the Service-Based Architecture (SBA). Critical signaling interfaces, specifically N2 (connecting the Radio Access Network to the Control Plane) and N4 (linking the Control and User Planes), are increasingly targeted by exploits such as signaling storms, session hijacking, and unauthorized access. Traditional security measures often fail to account for the complex, non-Euclidean relationships between decentralized Network Functions. This research addresses these vulnerabilities by proposing a multi-level anomaly detection framework grounded in Graph Neural Networks (GNN). By modeling the 5G core network as a dynamic graph, the proposed ST-GNAD model effectively aggregates spatial dependencies across N2 and N4 interfaces while capturing temporal signaling evolutions.The performance of this framework was rigorously evaluated through a series of experiments on a high-fidelity simulation platform utilizing Open5gs and UERANSIM. The experimental campaign involved simulating diverse attack vectors targeting the NGAP and PFCP protocols to reflect authentic network perturbations. Quantitative results demonstrate that the model excels in identifying multi-stage anomalies, achieving superior detection precision and lower false-alarm rates compared to traditional sequential models. This approach provides a scalable and resilient solution for securing the signaling backbone of modern 5G architectures.
Copyright (c) 2026 HuiYuan Ke https://creativecommons.org/licenses/by/4.0
LINK QUALITY EVALUATION AND FAULT PREDICTION OF COMMUNICATION NETWORKS BASED ON MACHINE LEARNING
As a core infrastructure of modern society, the stability of communication networks directly affects the operation of key areas. Traditional passive maintenance methods suffer from drawbacks such as delayed fault detection and difficulty in real-time monitoring. Machine learning's strong data processing and pattern recognition capabilities offer a new path to solve this problem. This study aims to construct a machine learning-based communication network link quality evaluation and fault prediction system to achieve accurate link quality assessment and early fault warning. The research designs a complete research framework including data preprocessing, feature engineering, model building, and prediction evaluation. Key performance indicators such as link bandwidth utilization and packet loss rate are collected, and multi-dimensional data quality control is implemented. A deep learning model combining traditional machine learning algorithms such as support vector machines and random forests with CNN-LSTM is used to conduct link quality evaluation, and a fault prediction model is constructed based on continuous-time Markov chains. Experimental results show that the fault detection accuracy of the proposed machine learning method reaches 92.3%, while the deep learning method further improves it to 95.8%. Compared with traditional methods, this significantly shortens the average fault location time, significantly increases the fault prediction lead time, and reduces the false alarm rate. The link quality assessment and fault prediction framework constructed in this study provides an efficient technical solution for communication network fault prevention and control, promoting the intelligent development of communication network operation and maintenance.
Copyright (c) 2026 ZhiYi Tan https://creativecommons.org/licenses/by/4.0
INTELLIGENT TRANSPORTATION SYSTEM OF MEDICAL WASTE BASED ON UWB POSITIONING AND CLOUD COLLABORATION
To address the pain points of traditional medical waste transportation, such as high reliance on manual labor, irrational routes, prominent dumping risks, and difficulties in traceability, this paper designs an intelligent transportation system integrating multiple technologies. The core innovation lies in constructing an integrated intelligent architecture of "perception-localization-scheduling-traceability-risk prevention." This system breaks the traditional human-dominated model of medical waste transportation and achieves intelligent control of the entire transportation process through deep integration of software and hardware. The system primarily uses ESP32C3 as the main controller, innovatively integrating UWB high-precision positioning, K230 visual recognition, and anti-dumping mechanical structures to form a triple innovation system: First, the linkage between UWB and K230 visual recognition enables precise positioning and classification identification of waste bins; Second, the integration of improved genetic algorithms with A* algorithms achieves dynamic route scheduling and obstacle avoidance; Third, a cloud-based traceability system is constructed based on the MQTT protocol, complemented by anti-dumping structures to mitigate risks. After debugging, the system's positioning accuracy reaches the centimeter level. The classification recognition accuracy of the medical waste trash bins is relatively high. The single transportation efficiency has been improved, and the transportation process can be traced, meeting the digital supervision requirements of the "Medical Waste Management Regulations". This provides a reliable solution for the intelligent upgrade of medical waste transportation.
Copyright (c) 2026 YuHan Chen; XinYuan Gao; BoYu Shan; XianXing Luo; ShangXuan Li; Yi Zhang https://creativecommons.org/licenses/by/4.0
BIOINFORMATICS-BASED PREDICTION OF THE IMPACT OF ASCL2 ON THE PROGNOSIS OF ESOPHAGEAL CANCER
Objective: To investigate the expression characteristics, clinical significance, molecular mechanisms, and drug sensitivity of Achaete-scute family BHLH transcription factor 2 (ASCL2) in esophageal cancer, and to clarify its value as a prognostic marker and potential therapeutic target for esophageal cancer. Methods: RNA sequencing data and clinical information of esophageal cancer were downloaded from The Cancer Genome Atlas (TCGA) database. After standardization, 163 samples were included. Bioinformatics methods were used to analyze the expression difference of ASCL2 and its correlation with clinicopathological features and prognosis. Maftools package was employed for gene mutation characteristic analysis, immunedeconv package for immune scoring and immune checkpoint-related gene analysis, Limma package and ClusterProfiler package for differential gene screening and GO/KEGG enrichment analysis, and Genomics of Drug Sensitivity in Cancer (GDSC) database for drug sensitivity prediction. Meanwhile, 20 pairs of esophageal cancer tissue samples were collected, and the expression level of ASCL2 was verified by quantitative real-time PCR (qRT-PCR). Results: Both bioinformatics analysis and clinical sample verification showed that the expression of ASCL2 in esophageal cancer tissues was significantly higher than that in adjacent normal tissues, and its expression was partially correlated with tumor differentiation grade and clinical stage. Survival analysis revealed that the median overall survival (OS) of patients in the ASCL2 high-expression subgroup was significantly shorter than that in the low-expression subgroup (1.9 years vs 3.2 years), and the area under the curve (AUC) of ASCL2 for predicting 5-year survival rate was 0.732, indicating that ASCL2 could serve as an independent prognostic factor for the overall survival of esophageal cancer patients. Differential gene and enrichment analysis showed that ASCL2-related differential genes were mainly enriched in lipid metabolism reprogramming, DNA damage repair, cell cycle regulation, as well as Wnt and TGF-beta signaling pathways. Drug sensitivity prediction indicated that the ASCL2 high-expression subgroup had significantly reduced sensitivity to paclitaxel and cisplatin. Conclusion: ASCL2 is highly expressed in esophageal cancer tissues and is closely associated with lymph node metastasis and poor prognosis of patients, which can be used as a potential biomarker for prognostic evaluation of esophageal cancer. ASCL2 may promote the malignant progression of esophageal cancer by interacting with TP53, constructing an inhibitory tumor immune microenvironment, regulating tumor-related signaling pathways, and reducing chemotherapeutic drug sensitivity, thus providing a new target for the personalized treatment of esophageal cancer.
Copyright (c) 2026 ZhiGang Zhai; Yuan Fang; ZhenYu Xue; Jian Guo; Xiang Tang https://creativecommons.org/licenses/by/4.0
UNDERWATER VISIBLE LIGHT COMMUNICATION AND PATH SELECTION ALGORITHM DESIGN
With the increasing demand for marine development and intelligent monitoring, underwater information transmission technologies characterized by high bandwidth, low latency, and strong anti-interference capabilities are becoming increasingly important. Underwater visible light communication (UVLC), with its advantages of high bandwidth, strong directionality, low power consumption, and absence of electromagnetic interference, is considered a highly promising underwater communication method. However, it still faces many challenges in real marine environments, such as signal absorption and scattering, fluctuations caused by turbulence and particle disturbances, and link mismatch due to environmental changes. Therefore, achieving intelligent communication and monitoring early warning in complex and dynamic waters has become a key issue. This paper, targeting dynamic underwater monitoring environments prone to link degradation, designs and implements an integrated intelligent underwater visible light communication monitoring system and path selection scheme. The core innovation lies in introducing chaotic systems and multi-physics coupling into channel modeling and path optimization, significantly improving the system's robustness and adaptability under multi-disturbance monitoring conditions, and enhancing path reliability in harsh environments. Experimental results demonstrate that the proposed monitoring channel model exhibits significantly superior performance to traditional models in predicting power attenuation and pulse broadening. Under the condition of a specific bit error rate (BER), the system can stably maintain a high link switching success rate with the aid of the chaotic optimization mechanism, while keeping the response time below the set threshold. This fully verifies that the system possesses excellent reliability and stability in complex underwater monitoring environments.
Copyright (c) 2026 ZhenHan Xu; Lei Guo; XiangYu Liu https://creativecommons.org/licenses/by/4.0
LOW-CARBON OPTIMIZATION SCHEDULING STRATEGY FOR INTEGRATED ENERGY SYSTEM CONSIDERING GAS AND THERMAL INERTIA
With the increasing global emphasis on low-carbon development, integrated energy systems (IES) plays a pivotal role in optimizing energy utilization and reducing carbon emissions. Gas-thermal inertia refers to the energy storage and release characteristics exhibited by gas and thermal energy during transmission and conversion processes. This not only affects the dynamic balance of energy but also poses higher requirements for scheduling optimization.Meanwhile, carbon trading and green certificate trading mechanisms serve as crucial tools for balancing environmental protection and economic benefits. This paper establishes an IES low-carbon economic dispatch model that considers gas-heat inertia, and employs an improved particle swarm optimization algorithm for solving it.Finally, taking a power system in a certain region with a high proportion of renewable energy as a case study, the results show that operating the power system considering gas-thermal inertia can significantly improve the operational economy of the system, optimize the electricity prices in the power market, and enhance the earnings from green certificates and carbon trading.
Copyright (c) 2026 WenJin Zhang; Geigeicao Qiao; Yu Li; Qi Chen; Jian Yan https://creativecommons.org/licenses/by/4.0
A FAULT DIAGNOSIS METHOD OF ACTIVE BRAKING SYSTEMS BASED ON 1D-CNN AND MHAM
To overcome insufficient feature representation and limited status quantification in active braking systems (ABS), an end-to-end fault diagnosis method of ABS is constructed to combine a one-dimensional convolutional neural network (1D-CNN) and a multi-head attention mechanism (MHAM) under conditions of stronger noise interference and multi-source nonlinear coupling. Based on a 1D-CNN front-end encoder to directly decouple local high-frequency transient features from the original time-series waveform, the long-distance dependency topology within the sequence is deeply reconstructed to quantify the contribution weights of features at different time steps to the evolution of the system's health status. according to the joint constraints of the Adam adaptive optimization algorithm and regularization penalty term, the model effectively avoids the overfitting risk of deep networks and significantly enhances its generalization robustness under unknown and complex conditions. Empirical results strongly demonstrate the superiority of this method. The regression prediction determination coefficient R2 for the degree of failure reaches 0.9472, and the root mean square error (RMSE) is reduced by 1.56%, achieving high-precision quantitative perception of the system's health status.
Copyright (c) 2026 Hui Du https://creativecommons.org/licenses/by/4.0
NONLINEAR CHANNEL EQUALIZATION AND ADAPTIVE LEARNING METHODS FOR LOW-POWER VLC SYSTEMS
To address the challenges of limited modulation bandwidth and nonlinear channel impairments in low-power visible light communication (VLC) systems, this paper proposes a hardware-software co-designed adaptive learning equalization scheme. At the physical layer, a hardware pre-equalization circuit is designed to compensate for Light Emitting Diode (LED) high-frequency attenuation, thereby expanding the system's physical bandwidth. At the algorithmic layer, an adaptive gradient descent learning algorithm arctan-softsign variable-step least mean square (A-SVSLMS) based on the softsign activation function is proposed. This algorithm leverages the nonlinear mapping mechanism between the step size factor and the error gradient to achieve dynamic optimization of weight updates, effectively resolving the challenge faced by traditional algorithms in balancing convergence speed and steady-state accuracy. Experimental results demonstrate that under a 0.06 W light source, the system's -3 dB bandwidth increases from 1.6 MHz to 13.5 MHz. Compared to traditional LMS algorithms, the proposed algorithm exhibits faster learning rates and enhanced robustness, successfully achieving 2 Mbps error-free transmission at a 0.55 m distance. This validates the application potential of lightweight intelligent algorithms in resource-constrained devices.
Copyright (c) 2026 LiuYang Niu; Lei Guo; XiangYu Liu; GuoDong Zhao https://creativecommons.org/licenses/by/4.0
ANTI-INTERFERENCE TRAJECTORY TRACKING CONTROL OF QUADROTOR UAV BASED ON REINFORCEMENT LEARNING
This study presents a quadrotor UAV tracking control algorithm that addresses the issues of parameter uncertainty and external disturbances during trajectory tracking. The algorithm combines the Q-Learning reinforcement learning algorithm with a nonsingular terminal sliding mode controller. Firstly, a four-rotor UAV model based on tracking error is defined, and the coupling and external interference between channels are converted into lumped interference. Extended state observers are designed for estimation and compensation of lumped interference by the outer loop position subsystem and the inner loop attitude subsystem. At the same time, a fast non-singular terminal sliding mode UAV controller is constructed, which includes an outer loop position controller and an inner loop attitude controller. Then, a Q-learning algorithm based on fuzzy strategy is proposed to realize the adaptive adjustment of the key parameters of the controller and the observer. By designing the reward function, the Q values of the UAV under different flight states are iteratively optimized and the Q table is constantly updated. Finally, the trained Q table is used for drone control. The algorithm can not only save the complicated process of manual parameter adjustment, but also realize the adaptive adjustment of key parameters in the face of different flight environments and flight states. The simulation and comparison experiments show that the proposed algorithm has a higher degree of fit with the reference trajectory in the trajectory tracking control process and has good robustness.
Copyright (c) 2026 PingAn Ren; EnHui Ren; YiFan Qu https://creativecommons.org/licenses/by/4.0
TASK-ORIENTED SEMANTIC COMMUNICATION FOR SMART CLASSROOM IMAGE ANALYTICS
Smart classrooms continuously generate large volumes of visual data that enable educational big data analytics, including learner-state assessment and classroom activity understanding. In practice, however, bandwidth-constrained and noise-impaired wireless links render conventional compress-then-transmit pipelines inefficient: pixel-fidelity-oriented compression typically requires high transmission rates to sustain downstream recognition performance. This paper studies a task-oriented semantic communication framework for image analytics over additive white Gaussian noise (AWGN) channels. We develop a unified multi-SNR, multi-rate training and evaluation protocol and benchmark a learned semantic link against a conventional DCT-based link under matched rate budgets. The proposed semantic link integrates (i) an encoder that extracts task-relevant latent representations, (ii) a rate-controllable channel-selection bottleneck that regulates the transmitted feature budget, and (iii) a decoder that reconstructs images for a fixed downstream classifier. Using CIFAR-10 as a reproducible testbed, we report task performance alongside perceptual quality metrics across a grid of SNR and rate settings. Experimental results indicate that the semantic link consistently sustains higher classification accuracy in low-to-medium SNR and low-rate regimes. In addition, PSNR/SSIM do not necessarily exceed the DCT baseline, revealing a task-perception mismatch that favors task-driven transmission. Overall, the proposed framework offers a practical methodology for designing communication pipelines that better support educational big data image analytics.
Copyright (c) 2026 Xue Lu; JiZhe Zhang https://creativecommons.org/licenses/by/4.0
VISUALIZATION PLATFORM FOR OCEAN ACOUSTIC SIMULATION BASED ON MAYAVI
Meteorological navigation data are critical to navigational safety and route planning, but their massive volume imposes a heavy burden on data storage and communication in intelligent shipping systems. Considering the spatiotemporal characteristics of such data and the fact that they allow lossy compression within an acceptable error range, this paper proposes an efficient error-guaranteed lossy compression method for floating-point data. The method modifies the binary representation of mantissa bits within a preset error bound to increase the number of trailing zeros in the XOR results between adjacent values, thereby reducing the meaningful bits that need to be encoded, and selects corresponding encoding strategies according to the resulting XOR patterns. Experimental results on public datasets and real meteorological navigation datasets show that, compared with existing mainstream compression algorithms, the proposed method incurs slightly higher compression time but achieves the highest compression efficiency under the preset error constraints, demonstrating its suitability for scenarios with strict error control requirements in large-scale meteorological navigation data storage and transmission.
Copyright (c) 2025 Yan Zhang https://creativecommons.org/licenses/by/4.0
GRAPH-RAG ENHANCED RETRIEVAL AND MULTI-AGENT COLLABORATIVE METHOD FOR INTELLIGENT NUCLEAR POWER ELECTRICAL DESIGN
The design of nuclear power plant electrical systems is characterized by an extensive and intricate body of standards, strong cross-disciplinary coupling, and stringent safety and compliance requirements. Traditional digital design workflows face significant limitations in knowledge reuse, intelligent decision support, and quality assurance. At the same time, general-purpose large language models (LLMs) exhibit hallucinations and lack reliability in safety-critical engineering domains. To address these challenges, this paper proposes an intelligent nuclear power electrical design methodology that integrates knowledge-graph-enhanced retrieval-augmented generation (Graph-RAG) with a multi-agent system (MAS). First, a domain ontology covering IEC/GB standards, equipment parameter libraries, and design rules is constructed to provide a structured knowledge backbone. Second, a graph-guided hybrid retrieval strategy that combines semantic retrieval with graph path reasoning is designed to enhance retrieval accuracy and contextual relevance. Third, a multi-agent collaboration architecture—comprising requirement analysis, intelligent recommendation, automatic document generation, consistency checking, format review, and interactive question answering agents—is developed. Task orchestration is implemented through a directed acyclic graph (DAG), enabling parallel and coordinated execution of complex design workflows. Theoretical analysis and scenario-based validation indicate that the proposed approach substantially reduces hallucination rates, improves design efficiency, and ensures the standard compliance of design outputs. Each design decision becomes traceable to explicit standard clauses and equipment parameters, demonstrating the feasibility of the method for the intelligent transformation of nuclear power electrical design and its potential transferability to other complex engineering systems.
Copyright (c) 2025 Yuan Zhang; YanKun Li; Chao Si https://creativecommons.org/licenses/by/4.0
ADVANCES IN INTELLIGENT ROCK IMAGE RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORKS
Lithology identification is a fundamental task in resource exploration and engineering geology, yet traditional methods face bottlenecks such as low efficiency and high subjectivity. In recent years, intelligent rock image recognition techniques based on convolutional neural network (CNN) have demonstrated remarkable advantages. This paper systematically reviews the research progress of CNN in intelligent rock image recognition and explores their application potential and technical challenges in this field. First, the basic architecture and working principles of CNN are introduced, including the synergistic interactions among convolutional layers, pooling layers, and fully connected layers. Subsequently, the criteria for model selection and optimization pathways in rock image recognition are analyzed, covering task-specific model adaptation strategies and multi-model comparative evaluation and selection strategies. Additionally, the roles of data augmentation strategies, resolution enhancement techniques, and model architecture innovations in improving model performance are discussed. Finally, this paper summarizes the limitations of current research and proposes future research directions, aiming to provide theoretical support and practical guidance for overcoming existing technical bottlenecks.
Copyright (c) 2025 He Ma https://creativecommons.org/licenses/by/4.0
SMOKE DECOY DEPLOYMENT STRATEGIES BASED ON HYBRID INTELLIGENT OPTIMIZATION MODELS
This paper systematically investigates optimal deployment strategies for smoke decoys launched by unmanned aerial vehicles (UAVs) to defend against high-speed incoming missiles in complex dynamic scenarios. The research is of significant importance for enhancing the survivability of high-value assets by providing a theoretical foundation and practical optimization tools for intelligent and adaptive smoke countermeasure systems. The research spans from high-precision calculation of effective duration to continuous multi-decoys shielding by a single UAV. First, for calculating effective shielding duration under fixed parameters, a three-dimensional kinematic model was established to characterize the spatial relationships among the missile, the drone's parabolic motion, and the uniform descent of the smoke cloud. Using three-dimensional geometric shielding criteria and a high-resolution time-stepping method, the effective shielding duration achieved by the FY1 drone deploying a single decoy against the M1 missile was precisely calculated to be approximately 1.412 seconds. Subsequently, the problem was elevated to single-deployable parameter optimization. A nonlinear constrained optimization model was constructed with the objective of maximizing masking duration, incorporating decision variables such as the UAV's heading angle, flight speed, deployment timing, and detonation delay. To address the non-differentiable nature of this objective function, a hybrid genetic algorithm and particle swarm optimization method was employed for global search and local refinement. This approach ultimately maximized effective masking duration to 4.690 seconds, with analysis indicating deployment timing and detonation delay as the most critical parameters. Finally, for a single-UAV multi-munition continuous shielding strategy, this paper designed a two-stage analytical-numerical hybrid model: "inverse solution for optimal detonation points followed by forward inversion of UAV strategy." Through the synergistic evolution of the particle swarm optimization framework and GA-PSO, the deployment interval constraints between multiple decoys were successfully resolved, achieving seamless relay between two decoys. The total effective masking duration reached 9.020 s, demonstrating that the proposed model can obtain high-quality solutions with both accuracy and robustness under complex constraints.
Copyright (c) 2025 TianJian Zhong https://creativecommons.org/licenses/by/4.0
SEMANTIC PRIVACY RISKS IN SOCIAL TRAJECTORY PUBLICATION
The publication of trajectory data in mobile applications raises significant privacy concerns for users. When combined with behavioral pattern analysis, the semantic similarity of published trajectories can be exploited by attackers to infer users' travel motivations, posing substantial risks to personal privacy. In this paper, we simulate such attacks by proposing an observation-based algorithm to infer user travel behavior and develop a corresponding privacy risk quantification mechanism. Extensive experiments on real-world datasets validate the effectiveness of the proposed risk quantification approach, providing a foundation for the further development of semantic privacy protection schemes.
Copyright (c) 2025 ZhenZhen Wu; YanFei Yuan https://creativecommons.org/licenses/by/4.0
DISTRIBUTION NETWORK OPTIMIZATION STRATEGY AND RELIABILITY ANALYSIS CONSIDERING LARGE-CAPACITY ENERGY STORAGE SYSTEM
As an important distributed power source, energy storage systems are gradually being used in distribution networks. This article is based on the 10kV distribution network system taking into account the large-capacity energy storage system, which introduce the principle of the energy storage system access to the distribution network and analyze the control strategy of the energy storage system under different working modes and propose an optimization modeling method for energy storage system based on genetic algorithm. Finally, using the failure mode consequence analysis method to calculate the reliability analysis index of the distribution system, and then verifing that the application of the energy storage system can improve power supply reliability of distribution network.
Copyright (c) 2025 WenXiang Liao https://creativecommons.org/licenses/by/4.0
RECOGNITION OF CITRUS PLANTING AREA BY INTEGRATING PYRAMID BOTTLENECK RESIDUAL NETWORK AND DECISION TREE ALGORITHM
As the agricultural cornerstone of Ganzhou, Jiangxi Province, China, citrus production necessitates rapid and precise mapping of orchard spatial distribution for agricultural management, resource assessment, ecological conservation, and science-driven industry development. Remote sensing has emerged as a vital agricultural informatics tool in China due to its non-contact, large-scale data acquisition capabilities. Recent advances in deep neural networks enable state-of-the-art solutions for image processing and computer vision tasks. Capitalizing on this, we propose a multispectral remote sensing framework integrating a Pyramid-Shaped Residual Network with a decision tree algorithm customized for citrus cultivation patterns. The results indicate that the new method demonstrates extremely high accuracy, with an estimation precision exceeding 80% when compared to the statistical yearbook. This approach demonstrates significant potential for citrus planting area identification, providing a valuable reference for precision agriculture applications.
Copyright (c) 2025 Yue Deng; KaiMing Zeng https://creativecommons.org/licenses/by/4.0
A TEMPERATURE CONTROL SYSTEM FOR RURAL FREE-RANGE PIG FARMING USING ARTIFICIAL INTELLIGENCE AND THE INTERNET OF THINGS
Traditional temperature regulation methods in rural Large White pig farming in China suffer from low efficiency and insufficient precision, alongside a lack of intelligent solutions adapted to rural environments. To address these issues, this paper presents the design and implementation of a temperature control system integrating Artificial Intelligence (AI) and the Internet of Things (IoT). The system employs the YOLOv8 object detection algorithm as its core, combined with keypoint detection, Region of Interest (ROI) filtering, and abnormal posture detection to achieve contactless and precise collection of the pigs' body dimension parameters. A mapping model correlating "body dimensions - weight - age - optimal temperature" is established using a fuzzy algorithm, and a Kalman filter is introduced for dynamic optimization and regulation of the ambient temperature. The system utilizes an IoT cloud platform for real-time data transmission and intelligent analysis, while solar power is adopted to suit rural energy scenarios. This system effectively fills a technical gap in contactless detection and integrated temperature control within the field of smart rural farming. It offers a low-cost, highly adaptable intelligent solution for small and medium-sized farms, holding significant value for advancing livestock industry modernization and promoting rural development.
Copyright (c) 2025 AnQi Zhang https://creativecommons.org/licenses/by/4.0
KEY NODE IDENTIFICATION ALGORITHM BASED ON LOCAL SEMI GLOBAL TRIANGLE CALCULATION
Aiming at the challenges of low identification accuracy and slow computation time in existing key node identification algorithms for complex networks, the paper proposes a key node identification algorithm based on local semi global triangular computation (LSTC). First, inspired by the structural stability of triangles in the physical world, the triangular patterns of nodes in complex networks and their importance are defined. Second, drawing on the third-order partition theory which highlights strong connections between a node and its third-order neighbors, the algorithm incorporates the influence of a node's local third-order neighbors when evaluating its importance. To validate the experimental performance of the proposed algorithm, the LSTC algorithm is compared with eight other algorithms of the same type using both the Susceptible–Infected–Recovered (SIR) model and the Linear Threshold (LT) model. Experimental results demonstrate that the proposed algorithm achieves the highest overall performance.
Copyright (c) 2025 HanYi Yang; YiJia He https://creativecommons.org/licenses/by/4.0
SIMULTANEOUS PLANNING AND DYNAMIC STABILITY SIMULATION VERIFICATION OF HUMANOID ROBOT DANCE MOVEMENTS
This paper addresses the problem of multi-joint trajectory planning and cooperative control for humanoid robots in dance performances. Taking the dance task of the Unitree G1 robot developed by Hangzhou Unitree Robotics at the opening ceremony of a technology exhibition as the background, a hierarchical motion-planning method is proposed that decomposes the overall movement into three sub-tasks: torso rotation, coordinated arm motion, and leg balance adjustment. Torso rotation is planned with a fifth-order polynomial for smooth trajectories; the two arms perform counter-rotating circular motions and their joint angles are computed via inverse kinematics; the legs provide real-time balance compensation based on centroidal dynamics and the Zero-Moment Point (ZMP) stability criterion. Simulation analysis yields coordinated motion trajectories for all 12 degrees of freedom of the torso, arms and legs, achieving fluent dance motions while guaranteeing overall stability and safety during dynamic movement. The study offers a viable theoretical and methodological reference for motion planning and control of humanoid robots in complex scenarios such as artistic performances.
Copyright (c) 2025 JiaXin Huang; JunPeng Yuan; Yang Feng; XiaoLu Zou; YiTing Qiu; JingMin Lan; Dang Gan; LinYing Jiang; HaoMin Liang; SiNan Ge; YuJia Jiang https://creativecommons.org/licenses/by/4.0
UAV SMOKE BOMB DELIVERY STRATEGY BASED ON IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM
The cooperative application of smoke jamming bomb and UAV is an important means to protect the target in combat, and the effective shielding time is the key index to measure its jamming effect. In this paper, aiming at the combat scene with one fixed real target, one false target, three incoming missiles and five UAVs, by analyzing the multi-object motion law and the smoke shielding judgment conditions, the multi-object motion model and the smoke shielding judgment model are established, and the improved particle swarm optimization algorithm is used to study the optimal strategy of smoke delivery under different constraints. We found that the total effective shielding time is 13.79s.
UAV smoke bomb cooperative jamming model
Copyright (c) 2025 ShunYu Li; YuXin Wang; Yang Rong https://creativecommons.org/licenses/by/4.0
A BI-DIRECTIONAL CASCADED RELATION EXTRACTION MODEL BASED ON DYNAMIC GATING MECHANISM: AN ENHANCED APPROACH TO OVERLAPPING TRIPLES
Relation extraction is a core task in natural language processing, aiming to identify semantic relationships between entities in unstructured text. Existing relation extraction methods face significant challenges when dealing with overlapping triples, especially in Entity Pair Overlap (EPO) and Single Entity Overlap (SEO) scenarios. This paper proposes a Bi-directional Dynamic Gating Cascaded (B-DGC) relation extraction model, which is improved based on the CasRel model. The model first uses a BERT encoder to obtain text embeddings, then employs a fully connected neural network to simultaneously identify head and tail entities. It fuses text embeddings with head entity features from the head entity sequence to identify corresponding tail entities under specific relationships. Finally, a dynamic gating mechanism is used to fuse the tail entity recognition results from two stages. Experimental results on NYT and WebNLG datasets show that the B-DGC model significantly outperforms the baseline model CasRel in precision, recall, and F1-score, achieving 90.4%, 91.1%, and 90.7% on NYT, and 92.6%, 92.2%, and 92.4% on WebNLG. Additionally, the B-DGC model demonstrates superior performance across various overlapping triple types, confirming the effectiveness of the bi-directional verification and dynamic gating mechanism.
Copyright (c) 2025 JiaBao Wang; ZhiBin Guo https://creativecommons.org/licenses/by/4.0
MQPF: A MULTI-DIMENSIONAL QUALITY-AWARE PATH FUSION FRAMEWORK FOR QUESTION ANSWERING
In multi-hop question answering (MHQA) tasks, existing methods typically integrate multiple reasoning paths from knowledge graphs (KGs) and chains of thought (CoTs). Early KG-enhanced methods primarily focus on obtaining relevant knowledge but fail to consider the multi-dimensional quality of reasoning paths. Subsequent works filter paths but treat all retained paths as equally important without further differentiation. Although some recent works attempt to rank paths by quality, they only provide a relative order without quantifying the actual quality differences between paths. To address these limitations, we propose a Multi-dimensional Quality-aware Path Fusion (MQPF) framework. MQPF introduces a multi-dimensional evaluation mechanism that quantifies path quality from semantic, structural, and outcome-based dimensions. Based on the overall scores, MQPF first filters out low-quality paths to reduce noise and then assigns adaptive weights to the remaining paths according to their scores. This method effectively removes unreliable information and enhances the utilization of trustworthy information during reasoning. Experiments show that MQPF performs comparably to baselines on multiple datasets. Moreover, as a model-agnostic module, it can be used as a plug-and-play module to enhance the performance of existing multi-path reasoning methods.
Copyright (c) 2025 XinYi Wang; Bo Liu https://creativecommons.org/licenses/by/4.0
SEMANTIC SEGMENTATION OF SHIP HULLS FOR UNDERWATER CLEANING ROBOTS
Underwater barnacle cleaning robots require a semantic segmentation algorithm deployable onboard to effectively segment barnacles, obstacles, and background on ship hulls. This study independently constructed a dedicated ship hull semantic segmentation dataset and implemented an improved BiSeNet network. The approach integrates features from both a spatial path and a context path. Building upon the cross-entropy loss function, class weights were strategically assigned during training according to the distribution of different target categories within the dataset. This method achieved significantly high Intersection over Union (IOU) and F1 scores.
Copyright (c) 2025 Lu Li; Wei Fang; YiGe Shang; Zhi Zhang; MingFu Li; DaoYi Chen https://creativecommons.org/licenses/by/4.0
DESIGN OF A PHOTOVOLTAIC TRACKING SYSTEM CONTROL SYSTEM
With the continuous advancement of the "dual carbon" goals, improving the efficiency of solar energy utilization and reducing the cost of power generation have become key directions for technological innovation in the photovoltaic industry. This paper, starting from practical application needs, designs a photovoltaic tracking support system based on the CH31. The system is built on the Alibaba Cloud IoT platform, combined with 4G wireless communication technology, and uses the MQTT communication protocol to complete data upload and command download. The system integrates tilt sensors, light sensors, wind speed and direction sensors, and rain and snow sensors, and uses GPS to obtain longitude and latitude and RTC time. Using astronomical algorithms, the system achieves forward and reverse tracking of the photovoltaic system. To facilitate user management, the system includes a manual mode, which allows for the adjustment of the photovoltaic panel tilt angle through buttons or the cloud, at which point the tracking function is disabled. Combined with meteorological sensors, when extreme weather is detected, the photovoltaic panel is set to a fixed tilt angle mode to achieve self-protection.
Copyright (c) 2025 MingMao Gong; XingHui Liu https://creativecommons.org/licenses/by/4.0
AHP-TOPSIS-BASED ASSESSMENT FRAMEWORK FOR CYBERSECURITY POLICY EFFECTIVENESS: EMPIRICAL EVIDENCE FROM GCI INDICATORS
With the acceleration of global digitalization, cybercrime has exhibited transnational and covert characteristics, posing severe challenges to national cybersecurity. To scientifically evaluate the cybersecurity level of various countries and identify effective policy pathways, this study proposes a comprehensive evaluation model integrating the Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution. Based on the five pillars of the International Telecommunication Union Global Cybersecurity Index, an evaluation system comprising 15 sub-indicators is constructed. Indicator weights are determined through expert scoring, and the TOPSIS algorithm is employed to conduct quantitative ranking and grading of the cybersecurity levels of 27 countries. The results show that there is a significant positive correlation between cybersecurity level and comprehensive national strength. Technologically advanced countries such as Finland and the United States are rated as "Excellent", while less developed countries such as Zimbabwe fall into the "Weak" category. Furthermore, Pearson correlation analysis on U.S. cybercrime data and demographic characteristics reveals that internet usage, employment to population ratio of men aged 15-24 and GDP exhibit significant statistical associations with cybercrime incidence. This study provides a data-driven decision-making framework for policymakers to optimize cybersecurity strategies and verifies the core role of synergistic governance of law and technology.
Copyright (c) 2025 JiaWei Wen; Sai Yang; ChenGxin Zhang https://creativecommons.org/licenses/by/4.0
DEEP LEARNING DRIVEN ANALYSIS OF THE FOOD-RELATED VIDEO COMMUNICATION EFFECT: INVESTIGATING VISUAL FEATURE IMPACTS
To extend the scope of computational communication in an era dominated by visual media, this study explores effective methods for analyzing video content at scale, moving beyond a traditional focus on textual data. Using a large dataset of food-related videos from the popular platform Bilibili, we developed and trained a custom deep learning model to automatically identify, extract, and quantify the presence of core visual elements within each video frame. A systematic correlation analysis was conducted to examine the relationship between these extracted visual features—specifically the face appearance rate, food appearance rate, and overall image brightness—and composite measures of the videos' communication effects. Our statistical analysis reveals that both a higher face appearance rate and greater image brightness are significant positive predictors of communication effectiveness. In contrast, and counter to common assumptions, a higher frequency of shots featuring only food was found to negatively impact the video's overall performance. These findings suggest that effective video communication relies on emotional connection rather than mere content display; facial presence significantly drives deep engagement, likely through social relationships, while the visibility of food itself negatively impact audience response, highlighting a preference for cultural context and human narratives. Furthermore, metadata such as expressive titles and channel fan count, along with release time and duration, also critically shape dissemination success. This research not only offers valuable empirical guidance for content creators but also demonstrates a replicable and cost-effective computational paradigm for large-scale video content analysis.
Copyright (c) 2025 YaWei He; ZiJing Pan; LinYi Bao; YanXi Zhu; ChiYue Zhang; ZhouChu Zhang https://creativecommons.org/licenses/by/4.0
THE INTERACTIVE EFFECTS OF TITLE SENTIMENT AND LENGTH ON VIDEO DISSEMINATION: OPTIMAL LENGTH AND THE DIMINISHING RETURNS OF EMOTION
In the highly competitive ecosystem of online video platforms, metadata elements such as titles are critical determinants of user engagement. This study uses computational communication methods to explore the interactive effects of title sentiment intensity and title length on video view counts for automotive videos on the Bilibili platform. Through hierarchical regression analysis on a large-scale dataset of 984 videos, and supported by non-parametric Generalized Additive Models (GAM) and bootstrap analysis for robustness, this research reveals two key findings: First, there is a significant inverted U-shaped relationship between title length and video views, meaning views increase with length up to a peak at approximately 26-30 characters, and then decline. Second, title length plays a negative moderating role in the relationship between sentiment intensity and views. This finding suggests that emotional language is more effective in attracting user attention in short titles, but its appeal diminishes in longer, more information-dense titles, and may even have a negative impact if perceived as "clickbait." These findings offer nuanced, data–driven strategies for content creators and digital marketers for optimizing video metadata to maximize audience engagement on competitive digital platforms.
Copyright (c) 2025 YaWei He; ZiYi Guan; Wen Liu https://creativecommons.org/licenses/by/4.0