Journal of Computer Science and Electrical Engineering
Journal of Computer Science and Electrical Engineering (JCSEE) is an international peer-reviewed, open access journal.
AI-ENABLED DESIGN AND OPTIMIZATION OF AEROSPACE ELECTRIC MOTORS: A TASK-ORIENTED REVIEW
Artificial intelligence (AI) becomes an effective tool in electric motor design. It can speed up design updates, improve optimization in complex conditions, and support physical modeling. However, most existing review papers discuss AI in motor design in a general way. These studies usually do not focus on the special needs of aerospace systems. This paper presents a task-based review of AI methods for aerospace electric motor design under extreme constraints which mainly covers data-efficient learning, surrogate models, multi-objective optimization, constraint-aware optimization, and physics-informed methods. These methods help designers deal with difficult trade-offs among power density, thermal behavior, mechanical strength, and fault tolerance, even when training data are limited. This paper also discusses the main problems in current studies. For example, many methods still lack validation under real flight conditions. In addition, this paper points out several future directions, such as the use of digital twins and AI workflows that can support certification. This review can provide a clear reference for researchers and engineers who apply AI to next-generation aerospace electric propulsion and actuation systems.
Copyright (c) 2026 SaiBo Wang; HaoZheng Yu; JinXiao Wang; SiYu Wang; Chao Gong; Ke Tang https://creativecommons.org/licenses/by/4.0
SIGNAL DENOISING BASED ON QUANTUM ADAPTIVE TRANSFORMATION AND SINGULAR VALUE DECOMPOSITION
The denoising algorithm plays an important role in biomedical signal processing such as spectrophotometry, and is an important means to improve signal quality and detection accuracy. This article combines singular value decomposition denoising and quantum adaptive transformation to process Gaussian spectral data. The results indicate that denoising evaluation indicators such as signal-to-noise ratio, mean square error, and normalized cross-correlation coefficient have all improved. Moreover, the stronger the noise, the more obvious the advantages of the proposed algorithm compared to other denoising algorithms. And this method can be extended to pre-processing biomedical signals, such as electrocardiograms and electroencephalograms.
Copyright (c) 2026 YingYing Sun; MengYao Lv; ChenLin Wu; WeiXian Xie; ZiYuan Fang; RuiTong Zhao https://creativecommons.org/licenses/by/4.0
DEEP REINFORCEMENT LEARNING FOR TEST CASE GENERATION AND PRIORITIZATION
With the increasing scale and complexity of software systems, improving testing efficiency and fault detection capability under limited testing resources has become a critical issue in software testing research. To address the limitations of traditional approaches, which are often static, separately designed, and unable to make full use of dynamic execution feedback in test case generation and prioritization, this paper proposes a unified testing optimization framework based on deep reinforcement learning. The proposed method formulates both test case generation and test case prioritization as a sequential decision-making problem aimed at maximizing testing utility, and develops a feedback-driven strategy learning mechanism through state representation, action selection, and reward design, enabling the model to dynamically adjust testing behavior according to coverage information, fault detection outcomes, and budget constraints. Experimental results show that the proposed method effectively improves statement coverage, branch coverage, and the number of detected faults in test case generation, while also achieving higher APFD and earlier fault detection under limited budgets in test case prioritization. Further ablation analysis demonstrates that coverage reward, fault-related reward, and budget constraint play complementary roles in optimizing the testing strategy. Overall, this study shows that deep reinforcement learning provides an adaptive and unified solution for automated decision-making in software testing and offers a promising direction for the development of intelligent software testing methods.
Copyright (c) 2026 JingXuan Guo; JingJing Liu https://creativecommons.org/licenses/by/4.0
THE ALGORITHM FOR DETECTING OIL STAINS AND FOREIGN OBJECTS ON THE BOTTOM OF EMU CARS BASED ON DEEP LEARNING
The operation safety of EMU (Electric Multiple Unit) trains is the core guarantee of the high-speed railway transportation system. Currently, the widely deployed EMU operation fault dynamic image detection system (TEDS) in China mainly relies on manual image interpretation, resulting in low detection efficiency, prone to missed detections and false detections. To address the challenges in detecting oil stains under EMU car bottoms, such as scarce samples, insufficient accuracy of a single model, and difficulty in identifying reflective oil stains, this paper proposes an oil stain detection algorithm based on the YOLOv12 and DeepLabV3+ dual-model collaboration. In terms of data augmentation, to address the deficiency of only 356 original oil stain samples, this paper designs a three-stage data augmentation strategy. This strategy expands the training set to 6786 images through basic geometric transformations, noise addition and blurring processing, as well as a composite enhancement pipeline based on the Albumentations library, effectively enhancing the generalization ability of the model. This paper uses the YOLOv12 as the target detection model and trains an oil stain detector on the expanded dataset. Experimental results show that the YOLOv12 model achieves an accuracy of0.88 on the validation set, a recall rate of 0.70, and a recall rate of 0.79 for the oil stain category, effectively identifying most oil stain targets. The oil stain candidate regions detected by YOLOv12 are input into the DeepLabV3+ network, using MobileNetV2 as a lightweight backbone network, and training a pixel-level oil stain segmentation model. Experimental results show that the model achieves an average IoU of 0.4535 and an average Dice coefficient of 0.4872 on the validation set. The test results show that the model can effectively identify reflective oil stains that are difficult for humans to distinguish and reduces false alarms for dried traces. The joint detection framework combining target detection and semantic segmentation proposed in this paper integrates the characteristics of YOLOv12’s rapid localization and DeepLabV3+’s fine segmentation. This model can adapt to the oil stain defect detection requirements in complex EMU operation environments and provide a reference path for research on railway image intelligent detection technologies.
Copyright (c) 2026 ZhiJian Wei; SongTao Zhang; ZiYi Xu; Hang Zhou https://creativecommons.org/licenses/by/4.0
A MULTI-LABEL IMAGE RECOGNITION NETWORK DRIVEN BY LABEL-IMAGE SEMANTIC ALIGNMENT
Multi-label image recognition aims to predict a set of semantic labels for an image and has wide applications in other fields. Existing methods have two main problems: the attention regions generated by attention-based methods are insufficiently correlated with label semantics; methods based on label correlation modeling lack dynamic interaction with visual image content, making it difficult to achieve precise alignment between label semantics and image regions. To address these issues, this paper proposes a novel multi-label image recognition algorithm. First, we construct a label graph and leverage a graph convolutional network to learn label semantic priors, modeling the dependencies among labels. Second, we design a semantic decoupling module that adaptively focuses on relevant image regions under the guidance of label semantics to generate label semantic representations. Finally, we introduce a semantic association reasoning module that employs a multi-head self-attention mechanism to dynamically capture semantic correlations among labels, thereby enhancing the discriminative ability of features. Experimental results on the PASCAL VOC 2007 dataset show that our method achieves 95.0% mAP, surpassing existing state-of-the-art methods and improving by 1.6 percentage points over the baseline SSGRL. Ablation studies further validate the effectiveness of each module.
Copyright (c) 2026 YuYu Chen; LinJun Wu; LianZhi Chu https://creativecommons.org/licenses/by/4.0
APPLICATION OF LARGE LANGUAGE MODELS IN SMART CONTRACT VULNERABILITY DETECTION
With the rapid iteration of blockchain technology, smart contracts, as core components of decentralized applications, directly impact the stability of on-chain assets and ecosystems through their security. Traditional vulnerability detection methods primarily rely on expert rules and static analysis, facing bottlenecks such as high false positive rates and poor adaptability to complex logical vulnerabilities. In recent years, Large Language Models (LLMs), with their exceptional code understanding and reasoning capabilities, have provided new technical pathways for smart contract security auditing. This paper focuses on LLM-driven smart contract vulnerability detection technologies, systematically reviewing mainstream application paradigms from prompt engineering to model fine-tuning. The paper first reviews the current state of smart contract security and the limitations of traditional methods; subsequently, it provides in-depth analysis of the architectural design and core mechanisms of representative frameworks such as GPTLens and SmartVD, evaluating their performance in detection accuracy and recall rate; finally, addressing current challenges including data scarcity, model hallucinations, and computational overhead, it proposes future evolution directions such as multimodal fusion and human-in-the-loop auditing, providing reference for research and practice in related fields.
Copyright (c) 2026 Yun Li; YanLing Liu; ChangEr Liu; XinMan Luo https://creativecommons.org/licenses/by/4.0
SMART CONTRACTS IN INDUSTRY APPLICATIONS AND ECONOMIC ECOSYSTEMS: CURRENT STATUS AND TRENDS
As a core technology of blockchain ecosystems, smart contracts are fundamentally reshaping the operational logic and commercial landscape of the digital economy. This paper systematically analyzes the current economic ecosystem of smart contracts from four dimensions: business value creation, multi-dimensional industry applications, legal and regulatory challenges, and frontier technological evolution. Research indicates that smart contracts, by reducing transaction costs and enhancing trust mechanisms, have achieved maturity in financial applications (DeFi) and are demonstrating significant enabling effects in real-economy sectors such as supply chains, healthcare, and energy internet. However, their widespread implementation still faces institutional obstacles including ambiguous jurisdictional boundaries, compatibility between code and law, and liability attribution. Looking ahead, Layer-2 scaling solutions have significantly improved cost-effectiveness, while the integration of cross-chain interoperability and AI-driven intelligent decision-making will become key trends driving the expansion of "contractability" boundaries. Smart contracts are not merely technical tools but rather institutional infrastructure driving the transformation of business models from intermediary-dominated to algorithm-autonomous paradigms.
Copyright (c) 2026 YiWen Gao; ChangEr Liu https://creativecommons.org/licenses/by/4.0
A SPATIOTEMPORAL DUAL-BRANCH NETWORK BASED ON CHANNEL ATTENTION AND RESIDUAL CONNECTION IN MOTOR IMAGERY EEG CLASSIFICATION
Accurate decoding of motor imagery (MI) electroencephalogram (EEG) signals is a core bottleneck for the practical application of brain-computer interface (BCI) technology. Due to the characteristics of low signal-to-noise ratio, non-stationarity, and multi-dimensional feature coupling of these signals, a single feature extraction method is difficult to fully mine discriminative information. To address this problem, this study proposes a spatiotemporal dual-branch MI-EEG decoding method based on hierarchical channel attention and learnable residual connections. Taking a 3D EEG tensor as input, the method captures scalp electrode topological features and temporal dynamic dependencies through parallel spatial and temporal branches, respectively. It also introduces a channel attention mechanism with learnable residual connections to adaptively enhance the representation ability of key feature channels. Experimental results on the public BCI Competition IV 2A dataset show that the proposed method achieves an average classification accuracy of 79.63% and a Kappa coefficient of 0.7284 in four types of motor imagery tasks, significantly outperforming mainstream models such as EEGNet and FBCNet. Research indicates that the organic combination of the spatiotemporal dual-branch structure and the hierarchical channel attention mechanism can effectively improve the ability to extract spatiotemporal features and channel dependency relationships of EEG signals. The method exhibits excellent classification performance and cross-subject generalization in motor imagery EEG decoding tasks, providing strong support for feature construction and model design in related fields.
Copyright (c) 2026 HuiXi Mo https://creativecommons.org/licenses/by/4.0
OPTIMIZATION OF SCATTERING CHARACTERISTICS OF SUPPORT ROD BASED ON CST SOFTWARE
This paper addresses the problem of strong electromagnetic scattering from traditional diamond-shaped/cylindrical support rods in electromagnetic testing. Low-scattering optimization research is carried out based on finite integration software CST (Computer Simulation Technology) simulation. The scattering mechanism is analyzed by combining geometric diffraction and traveling wave theories, the "convex" water-drop-shaped cross-section is optimized, and a segmented coarse-fine alternating cylindrical structure is designed to disperse the scattering field. Simulations show that the designed water-drop-shaped cross-section segmented support rod has lower scattering intensity in the target frequency band, with significantly reduced scattering in high-frequency bands compared to traditional structures.
Copyright (c) 2026 ZhongXin Yang; MengMeng Zhang; QiPeng Xu https://creativecommons.org/licenses/by/4.0
MODELING THE EVOLUTIONARY DYNAMICS OF SMARTPHONE BATTERY TIMING
Addressing the complexity of smartphone battery life prediction, this study constructs a continuous-time dynamic model based on the law of energy conservation. The model defines the rate of change in battery state of charge as the ratio of total power consumption to effective capacity, which is influenced by temperature and aging. A first-order ordinary differential equation describes its evolution over time. Through physical modular modeling of core components—including the display, processor, network communication, and background applications—the study achieves a refined decomposition of total transient power. Parameters are calibrated using high-precision datasets, and numerical solutions are obtained via the fourth-order Runge-Kutta method. The resulting state-of-charge evolution curves exhibit smoothness and stable convergence, with locally truncated errors controlled at extremely low levels. Empirical analysis demonstrates the model's ability to accurately capture instantaneous power fluctuations triggered by user behavior transitions, revealing the dominant role of processor power consumption across different usage phases. This research establishes a robust physical foundation for achieving high-precision remaining power prediction and energy efficiency attribution analysis.
Copyright (c) 2026 MingGuang Jia https://creativecommons.org/licenses/by/4.0
DESIGN OF A DYNAMIC MONITORING AND TIERED EARLY WARNING SYSTEM FOR CULVERT WATERLOGGING BASED ON A CLOUD-EDGE COLLABORATIVE ARCHITECTURE
In response to the frequent waterlogging of urban culverts induced by extreme rainfall, the vulnerability of traditional monitoring methods, and the lack of targeted early warnings, this paper designs and implements an Internet of Things (IoT)-based dynamic monitoring and tiered early warning system utilizing a cloud-edge collaborative architecture. At the perception layer, the system employs a non-contact computer vision approach. By deploying dual lightweight YOLOv8 object detection models on edge computing nodes (Raspberry Pi 4B), it synchronously extracts on-site water depth and the chassis height of passing vehicles in real time. At the decision layer, a tiered early warning logic based on dynamic "water level-vehicle type" matching is proposed. This approach overcomes the limitations of traditional uniform static thresholding strategies and enables sub-second local audio-visual traffic control directly at the edge. Furthermore, the edge nodes transmit lightweight structured data to the Huawei Cloud IoT platform for aggregation, ultimately delivering public services via a WeChat Mini Program. Users can leverage the embedded map component to monitor the real-time waterlogging status across various monitoring sites and receive vehicle-specific routing recommendations. Prototype testing results demonstrate that the system's visual perception error is maintained within a reasonable margin, and the overall response latency at the edge is less than 500 ms. Crucially, the system retains offline resilience and local warning capabilities even in extreme scenarios involving network interruptions. This research provides a low-cost, robust engineering solution for smart urban flood control and emergency traffic dispatching.
Copyright (c) 2026 YiTao Li https://creativecommons.org/licenses/by/4.0
MOTION COMPENSATION FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY BASED ON MULTI-DIMENSIONAL MATRIX PENCIL METHOD AND ADAPTIVE EN-FACE IMAGE ENHANCEMENT
This paper proposes a two-stage algorithm combining the multi-dimensional matrix pencil method (MDMP) with En-face image enhancement to address the problem of eye motion artifacts severely degrading image quality in optical coherence tomography angiography (OCTA). In the first stage, a two-dimensional Hankel matrix pencil in the depth-time domain is constructed to jointly estimate Doppler frequency shifts and motion parameters of blood flow signals. The Centro-Hermitian property is exploited via unitary transformation to reduce computational complexity by approximately 75%, and an automatic parameter pairing mechanism is introduced to avoid mispairing problems inherent in conventional approaches. In the second stage, En-face image enhancement is applied to the MDMP-compensated retinal volume data, sequentially performing FFT difference calculation, weighted layered projection, CLAHE adaptive enhancement, column-wise median normalization for stripe removal, and FAZ region cosine gradient darkening to generate high-quality En-face vascular images. Experimental validation on a retinal OCTA dataset demonstrates that compared with multiple existing methods, the proposed method achieves a 65.1% improvement in signal-to-noise ratio (SNR), an 80.2% improvement in contrast-to-noise ratio (CNR), a structural similarity index (SSIM) of 0.94, and a 34.0% reduction in motion artifact index (MAI). The results confirm that the proposed two-stage algorithm effectively suppresses eye motion artifacts and significantly enhances OCTA En-face vascular imaging quality.
Optical coherence tomography angiography
Copyright (c) 2026 LinKang Du; Lei Liu; HaiYi Bian https://creativecommons.org/licenses/by/4.0
INNOVATIVE APPLICATION OF AI AGENT BASED ON MARKOV DECISION PROCESS IN DYNAMIC OPTIMIZATION DECISION-MAKING FOR ENTERPRISE SERVICES
Against the backdrop of the booming digital economy, the enterprise service environment is increasingly characterized by dynamics, high uncertainty, and multi-objective collaboration. Facing core challenges such as fluctuating service demands, changing resource constraints, and rapidly iterating customer preferences, traditional static decision-making methods struggle to adapt to actual operational needs, gradually revealing their limitations. This paper focuses on key issues in dynamic enterprise service decision-making, including resource allocation, service priority setting, and precise matching of customer needs. Targeting the regional characteristics and operational pain points of enterprise service scenarios in South China, we innovatively improve the traditional Markov Decision Process (MDP) model. Leveraging the intelligent computing power and data simulation support provided by Shenzhen Qicheng Zhiyuan Network Technology Co., Ltd. through the "LiQiCloud" AI-powered Sci-Tech Innovation Policy Platform, we construct an AI agent decision-making framework that integrates Deep Reinforcement Learning (DRL) and Digital Twin technology. Furthermore, we propose an improved value iteration algorithm based on function approximation, which effectively overcomes the limitations of traditional models and significantly enhances decision-making efficiency and adaptability. Through rigorous mathematical derivation, we elucidate the theoretical foundations for state representation, action output, reward function design, and optimal policy solutions. Simulation and empirical analysis were conducted using real-world service scenarios from a small-to-medium-sized clothing e-commerce enterprise in South China (daily order volume of 1,500–2,200; peak order volume of 4,000 during major promotions in 2025; customer service team of 25). Experimental results demonstrate that the proposed model significantly outperforms traditional MDP models and manual decision-making methods in core metrics, including service cost control (reduced by 22.8%), customer waiting time (shortened by 56.1%), resource utilization (improved by 14.7%), and effective problem resolution rate (increased by 5.9 percentage points). This study provides an implementable and replicable solution for optimizing service decision-making in the digital transformation of SMEs in South China.
Copyright (c) 2026 XueYong Li; WeiBin Zhao; Xing Xu; JinHan Cai https://creativecommons.org/licenses/by/4.0
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
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
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
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
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
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
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
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
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
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
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