Journal of Computer Science and Electrical Engineering
type: channel
description: Journal of Computer Science and Electrical Engineering (JCSEE) is an international peer-reviewed, open access journal.
publisher: UPub Science
language: en
publication name: Journal of Computer Science and Electrical Engineering
issn: 2663-1938
items: CLASS-CONDITIONAL INTERMEDIATE-DOMAIN ADVERSARIAL ADAPTATION FOR CROSS-DEVICE ACOUSTIC SCENE CLASSIFICATION
items: AN EXPLAINABLE TEMPORAL GRAPH NEURAL NETWORK FOR DISRUPTION PREDICTION AND RISK PROPAGATION IN MULTI-TIER SUPPLY CHAINS
items: CAPA: A PREDICTION-DRIVEN AUTOSCALING FRAMEWORK FOR SLO-AWARE CLOUD-NATIVE MACHINE LEARNING INFERENCE
items: THE EVOLUTION OF LARGE MODEL INFERENCE ARCHITECTURES: FROM CENTRALIZED CLOUDS TO DECENTRALIZED ON-DEVICE INTELLIGENCE
items: INTELLIGENT DETECTION METHOD FOR FOREIGN OBJECTS ON OVERHEAD LINES OF URBAN BUILDINGS
items: DYNAMIC TRUST EVALUATION AND RISK CONTROL TECHNOLOGY FOR BOUNDARIES OF NOVEL POWER SYSTEMS
items: TIME-SERIES PREDICTION AND PERFORMANCE EVALUATION OF OPTICAL COMMUNICATION CHANNEL STATES BASED ON AN IMPROVED LSTM MODEL
items: DESIGN AND IMPLEMENTATION OF A PYTHON-BASED SYSTEM FOR NANYANG HAN DYNASTY STONE RELIEF RESOURCES RESTORATION AND VISUALIZATION
items: BUILDING AN ACCOUNTING EDUCATION ECOSYSTEM FOR LIFELONG LEARNING SUPPORTED BY VIRTUAL SIMULATION TECHNOLOGY: A TRIADIC INTERACTIONIST FRAMEWORK
items: CROP PEST AND DISEASE IDENTIFICATION SYSTEM BASED ON MUTUAL LEARNING
items: DESIGN AND REALIZATION OF MONITORING AND CONTROL SYSTEM FOR SMART FARMS USING ESP32-P4 AND LVGL
items: PROPAGATION CHARACTERISTICS AND SYSTEM PERFORMANCE OF TERAHERTZ–OPTICAL HYBRID COMMUNICATION CHANNELS
items: SMART HOME SPACE DESIGN INTEGRATING XR AND AIGC
items: DEEP LEARNING-BASED MALICIOUS TRAFFIC ANALYSIS: A COMPREHENSIVE SURVEY
items: AI-ENABLED DESIGN AND OPTIMIZATION OF AEROSPACE ELECTRIC MOTORS: A TASK-ORIENTED REVIEW
items: SIGNAL DENOISING BASED ON QUANTUM ADAPTIVE TRANSFORMATION AND SINGULAR VALUE DECOMPOSITION
items: DEEP REINFORCEMENT LEARNING FOR TEST CASE GENERATION AND PRIORITIZATION
items: THE ALGORITHM FOR DETECTING OIL STAINS AND FOREIGN OBJECTS ON THE BOTTOM OF EMU CARS BASED ON DEEP LEARNING
items: A MULTI-LABEL IMAGE RECOGNITION NETWORK DRIVEN BY LABEL-IMAGE SEMANTIC ALIGNMENT
items: APPLICATION OF LARGE LANGUAGE MODELS IN SMART CONTRACT VULNERABILITY DETECTION
items: SMART CONTRACTS IN INDUSTRY APPLICATIONS AND ECONOMIC ECOSYSTEMS: CURRENT STATUS AND TRENDS
items: A SPATIOTEMPORAL DUAL-BRANCH NETWORK BASED ON CHANNEL ATTENTION AND RESIDUAL CONNECTION IN MOTOR IMAGERY EEG CLASSIFICATION
items: OPTIMIZATION OF SCATTERING CHARACTERISTICS OF SUPPORT ROD BASED ON CST SOFTWARE
items: MODELING THE EVOLUTIONARY DYNAMICS OF SMARTPHONE BATTERY TIMING
items: DESIGN OF A DYNAMIC MONITORING AND TIERED EARLY WARNING SYSTEM FOR CULVERT WATERLOGGING BASED ON A CLOUD-EDGE COLLABORATIVE ARCHITECTURE
items: MOTION COMPENSATION FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY BASED ON MULTI-DIMENSIONAL MATRIX PENCIL METHOD AND ADAPTIVE EN-FACE IMAGE ENHANCEMENT
items: INNOVATIVE APPLICATION OF AI AGENT BASED ON MARKOV DECISION PROCESS IN DYNAMIC OPTIMIZATION DECISION-MAKING FOR ENTERPRISE SERVICES
items: OPTIMIZATION OF ESPRIT ALGORITHM AND FAST DOA ESTIMATION FOR UNIFORM LINEAR ARRAYS
items: DUAL-FAIRNESS DRIVEN 3D TRAJECTORY DESIGN AND RESOURCE ALLOCATION FOR UAV-ASSISTED ISAC SYSTEMS
items: ST-GNAD: MULTI-LEVEL ANOMALY DETECTION FOR 5G CORE NETWORKS BASED ON SPATIAL-TEMPORAL GRAPH NEURAL NETWORK

CLASS-CONDITIONAL INTERMEDIATE-DOMAIN ADVERSARIAL ADAPTATION FOR CROSS-DEVICE ACOUSTIC SCENE CLASSIFICATION
type: item
description: Cross-device acoustic scene classification (ASC) is difficult because microphone frequency response, noise floor, nonlinear compression, and device–scene interactions can move recordings across class boundaries. Global adversarial alignment is especially fragile when the domain gap is large: it can reduce marginal discrepancy while mixing semantically different modes. This paper proposes class-conditional intermediate-domain adversarial adaptation (CC-IDA), a lightweight unsupervised adaptation framework that decomposes a large device shift into a physically interpretable source-to-bridge-to-target path. Two label-preserving bridge devices are generated from source recordings through progressively stronger transfer functions. A shared convolutional encoder is trained with source and bridge classification, a global maximum mean discrepancy anchor, a four-domain conditional discriminator acting on the multilinear feature–prediction representation, a class-centroid bridge chain, confident target prototype alignment, and target entropy regularization. To ensure complete reproducibility and avoid unsupported benchmark claims, experiments use a controlled procedural proxy benchmark rather than reporting results on TAU/DCASE: six acoustic-scene waveforms are rendered through two source, two intermediate, and one unseen target device; target latent recordings are independent of source/bridge recordings. Across three deterministic seeds, CC-IDA achieves 89.78% ± 8.95% target accuracy and 87.18% ± 11.32% macro-F1, compared with 42.44% ± 15.64% and 31.78% ± 16.04% for source-only training. It also improves over unconditional intermediate-domain adaptation by 1.78 percentage points in accuracy and 2.87 points in macro-F1, with no additional inference parameters. The results support gradual, class-aware alignment while also exposing limitations: only three seeds and a controlled proxy are evaluated, so real-corpus validation remains necessary.
subject: Research Article
  topic: section
subject: Acoustic scene classification
  topic: keywords
subject: Unsupervised domain adaptation
  topic: keywords
subject: Cross-device generalization
  topic: keywords
subject: Intermediate domains
  topic: keywords
subject: Conditional adversarial learning
  topic: keywords
subject: Maximum mean discrepancy
  topic: keywords
creator: ZiXuan Guo
creator: TingJie Chen
creator: WenBin Shang
rights: Copyright (c) 2022 ZiXuan Guo; TingJie Chen; WenBin Shang https://creativecommons.org/licenses/by/4.0
date: 2022-11-02
publication date: 2022-11-02
starting page: 26
ending page: 35
doi: 10.61784/jcsee4152

AN EXPLAINABLE TEMPORAL GRAPH NEURAL NETWORK FOR DISRUPTION PREDICTION AND RISK PROPAGATION IN MULTI-TIER SUPPLY CHAINS
type: item
description: Global supply chains are increasingly multi-tier, interconnected, and vulnerable to disruptions that do not stay local but cascade upstream-to-downstream through material-flow dependencies—the so-called ripple effect. Effective risk management requires not only predicting which entities will be disrupted, but also explaining why, by tracing a predicted disruption back to its root-cause suppliers. We present X-TGNN, an explainable temporal graph neural network that couples a per-node recurrent temporal encoder with a directed, tier-respecting, edge-weighted multi-head graph-attention mechanism aligned with the direction of risk propagation. The learned attention doubles as an intrinsic, single-forward-pass explanation that attributes each predicted disruption to specific upstream suppliers. Because real multi-tier disruption datasets with ground-truth propagation paths are proprietary and unavailable, we further contribute a high-fidelity simulation testbed whose cascade dynamics are grounded in the supply-chain risk-diffusion literature and which records the true responsible edges for every propagation event—enabling, for the first time in this setting, a quantitative evaluation of explanation faithfulness and correctness. Across a testbed in which 61% of disruptions are propagation-driven, graph models improve new-onset prediction by 2.5× in average precision over non-graph baselines. X-TGNN matches the strongest baseline in prediction while its attention explanations identify true root-cause suppliers with precision@1 of 0.77 and NDCG of 0.97—matching, and in rank correlation exceeding, far more expensive post-hoc gradient and perturbation methods, and vastly better than a random baseline (0.26). The result is a model that is at once accurate, propagation-aware, and inherently explainable.
subject: Research Article
  topic: section
subject: Supply chain disruption
  topic: keywords
subject: Risk propagation
  topic: keywords
subject: Ripple effect
  topic: keywords
subject: Temporal graph neural networks
  topic: keywords
subject: Explainable artificial intelligence
  topic: keywords
subject: Attention mechanism
  topic: keywords
creator: Ying Wang
creator: MengLin Bian
creator: Yizhen Lin
rights: Copyright (c) 2022 Ying Wang; MengLin Bian; Yizhen Lin https://creativecommons.org/licenses/by/4.0
date: 2022-10-31
publication date: 2022-10-31
starting page: 17
ending page: 25
doi: 10.61784/jcsee4151

CAPA: A PREDICTION-DRIVEN AUTOSCALING FRAMEWORK FOR SLO-AWARE CLOUD-NATIVE MACHINE LEARNING INFERENCE
type: item
description: Cloud-native machine-learning inference must reconcile sub-second demand changes with non-negligible replica startup delay. Reactive horizontal scaling therefore tends to provision after a burst has already accumulated a queue, whereas aggressive prediction wastes resources when a workload is stable. This paper presents CAPA, a confidence-aware predictive autoscaler that couples a lightweight rolling ridge forecaster with an online one-sided residual-quantile bound and an SLO-debt feedback signal. The upper demand estimate covers startup delay; the debt signal lowers the utilization target when recent requests approach or exceed a latency objective; and an explicit queue-drain term converts backlog into temporary capacity. A reproducible evaluation combines 250 measured CPU inference latencies and seven measured process cold starts for MobileNetV3-Small with three fixed-seed, trace-calibrated stress profiles. Across eight paired runs, CAPA reduces the estimated SLO violation rate relative to a reactive HPA-style baseline by 17.83% on bursty traffic and 11.37% under regime shifts, while increasing mean replicas by 2.59% and 1.79%, respectively. The improvement is statistically significant in both nonstationary profiles. On a regular periodic workload, CAPA uses 3.82% fewer replicas but raises violations by 14.55%, exposing a clear boundary condition rather than hiding a negative result. The code, raw measurements, generated traces, and statistical outputs are packaged with the paper.
subject: Research Article
  topic: section
subject: Cloud-native systems
  topic: keywords
subject: Autoscaling
  topic: keywords
subject: Machine-learning inference
  topic: keywords
subject: Service-level objectives
  topic: keywords
subject: Workload forecasting
  topic: keywords
subject: Uncertainty calibration
  topic: keywords
creator: WenYu Zhao
creator: BoYuan Wang
rights: Copyright (c) 2022 WenYu Zhao; BoYuan Wang https://creativecommons.org/licenses/by/4.0
date: 2022-10-12
publication date: 2022-10-12
starting page: 8
ending page: 16
doi: 10.61784/jcsee4150

THE EVOLUTION OF LARGE MODEL INFERENCE ARCHITECTURES: FROM CENTRALIZED CLOUDS TO DECENTRALIZED ON-DEVICE INTELLIGENCE
type: item
description: The proliferation of large-scale AI models, particularly Large Language Models (LLMs), has made inference a critical and resource-intensive workload. This survey provides a comprehensive review of the historical evolution of inference architectures, charting a distinct trajectory from centralized, cloud-native paradigms to fully decentralized, on-device intelligence. We systematically analyze four key architectural epochs: (1) Device-Cloud, (2) Device-Edge-Cloud, (3) Device-Edge, and (4) pure On-Device inference. For each paradigm, we conduct an in-depth examination of its dominant systems, key enabling technologies, and the inherent advantages and limitations that catalyzed the transition to the subsequent stage. Our analysis reveals that this evolution is driven by a persistent set of trade-offs between computational power, latency, data privacy, cost, and energy efficiency. This paper concludes that the future of AI inference lies not in a single monolithic architecture but in a heterogeneous "compute continuum," where workloads are dynamically orchestrated across a spectrum of resources to meet diverse application demands.
subject: Research Article
  topic: section
subject: Large Language Models (LLMs)
  topic: keywords
subject: AI inference
  topic: keywords
subject: Cloud computing
  topic: keywords
subject: Edge computing
  topic: keywords
subject: On-device AI
  topic: keywords
subject: Distributed systems
  topic: keywords
subject: Model optimization
  topic: keywords
subject: Multi-Access Edge Computing (MEC)
  topic: keywords
creator: ZeYi Luo
rights: Copyright (c) 2026 ZeYi Luo https://creativecommons.org/licenses/by/4.0
date: 2026-07-03
publication date: 2026-07-03
starting page: 1
ending page: 5
doi: 10.61784/jcsee3146

INTELLIGENT DETECTION METHOD FOR FOREIGN OBJECTS ON OVERHEAD LINES OF URBAN BUILDINGS
type: item
description: To address practical engineering challenges of overhead power lines near urban residential buildings, including low erection height, close proximity to building facades, lightweight litter such as plastic bags easily tangled on cables, complex background interference from building walls, vegetation and outdoor air-conditioning units, as well as variable shapes of tangled foreign objects, tiny target sizes and imbalanced scarce fault samples, this paper proposes an intelligent foreign object detection method for overhead lines combining improved EfficientDet and improved Vision Transformer (ViT). Firstly, the improved EfficientDet is adopted to locate regions containing bird nests and tangled plastic bags. Dilated convolutions are embedded into shallow backbone layers P1 and P2 to enlarge the receptive field, and the CBAM attention mechanism replaces the original SE module to strengthen the extraction of contour features of foreign objects under cluttered backgrounds. Secondly, the improved Vision Transformer performs refined discrimination on the presence or absence of foreign objects based on cropped local images. The rigid hard patch embedding of raw images is substituted by multi-layer small convolutions, the number of multi-head attention heads is optimized, and Focal Loss is introduced to alleviate sample imbalance. Actual test results demonstrate that the mean average precision (mAP) of foreign object detection reaches 96.41%, and the classification accuracy of foreign objects is 96.73%. The proposed method exhibits outstanding stability in typical urban scenarios including backlight, building occlusion and dense shrub interference.
subject: Research Article
  topic: section
subject: Urban overhead lines
  topic: keywords
subject: Fault detection
  topic: keywords
subject: Improved EfficientDet
  topic: keywords
subject: Improved Vision Transformer (ViT)
  topic: keywords
creator: ZhiPeng Li
creator: ZhiYong Liu
creator: ZhaoYun Liu
creator: JiaXing Hao
creator: ShuJun Yang
rights: Copyright (c) 2026 ZhiPeng Li; ZhiYong Liu; ZhaoYun Liu; JiaXing Hao; ShuJun Yang https://creativecommons.org/licenses/by/4.0
date: 2026-07-02
publication date: 2026-07-02
starting page: 72
ending page: 81
doi: 10.61784/jcsee3145

DYNAMIC TRUST EVALUATION AND RISK CONTROL TECHNOLOGY FOR BOUNDARIES OF NOVEL POWER SYSTEMS
type: item
description: Against the backdrop of the full advancement of the "dual carbon" strategy, the new power system has gradually formed an operation pattern featuring multi-stakeholder collaboration across source, grid, load and storage. With the ubiquitous access of massive distributed terminals and the open, interconnected network architecture, the traditional static security protection mode can no longer cope with the increasingly complex cybersecurity risks. Targeting practical problems such as blurred power protection boundaries, rapid risk propagation, lagging static rule-based defense and single disposal methods, this paper proposes an integrated security protection scheme combining dynamic trust evaluation and grayscale risk control. Based on multi-dimensional features, a quantitative trust evaluation system is constructed, and a layered grayscale disposal strategy is designed tailored to business scenarios. The full-link system covering data collection, transmission, intelligent analysis, decision issuance and closed-loop optimization is built, and core modules including subject-object identification, trust assessment and intelligent risk control are deeply designed and engineered. Verified by multiple sets of simulation experiments, the system achieves millisecond-level risk identification and policy response, and significantly improves boundary security protection capabilities while ensuring continuous operation of power businesses. It can provide feasible technical ideas and engineering references for the construction of cybersecurity systems for new power systems.
subject: Research Article
  topic: section
subject: New power system
  topic: keywords
subject: Boundary protection
  topic: keywords
subject: Dynamic trust
  topic: keywords
subject: Risk control
  topic: keywords
subject: Grayscale strategy
  topic: keywords
subject: Stream computing
  topic: keywords
creator: Yang Cao
creator: Yang Su
creator: Peng Zhou
creator: XueFei Tian
creator: ShuXiang Wen
creator: KaiMin Zheng
creator: JinYu Wu
rights: Copyright (c) 2026 Yang Cao; Yang Su; Peng Zhou; XueFei Tian; ShuXiang Wen; KaiMin Zheng; JinYu Wu https://creativecommons.org/licenses/by/4.0
date: 2026-07-02
publication date: 2026-07-02
starting page: 63
ending page: 71
doi: 10.61784/jcsee3144

TIME-SERIES PREDICTION AND PERFORMANCE EVALUATION OF OPTICAL COMMUNICATION CHANNEL STATES BASED ON AN IMPROVED LSTM MODEL
type: item
description: Optical communication channel states are highly dynamic and are easily affected by noise, nonlinear impairments, received power fluctuations, atmospheric turbulence, and link instability. Accurate time-series prediction of channel states is therefore important for link quality assessment, performance optimization, and intelligent communication control. To improve prediction accuracy and stability, this study proposes an improved CNN-BiLSTM-Attention model for time-series prediction and performance evaluation of optical communication channel states. In the proposed framework, convolutional neural networks extract local fluctuation features from multidimensional channel state sequences, bidirectional long short-term memory networks capture temporal dependencies, and an attention mechanism emphasizes critical time steps related to future channel degradation. Channel state indicators, including received optical power, OSNR, SNR, Q-factor, BER, and EVM, are constructed as model inputs or evaluation variables. Experimental results show that the proposed model outperforms ARIMA, SVR, GRU, LSTM, and BiLSTM in terms of MAE, RMSE, MAPE, and R2. In addition, the prediction-assisted evaluation results indicate that the proposed method can reduce BER and outage probability while improving Q-factor, demonstrating its potential for intelligent optical link monitoring and adaptive communication control.
subject: Research Article
  topic: section
subject: Optical communication
  topic: keywords
subject: Channel state prediction
  topic: keywords
subject: CNN-BiLSTM-Attention
  topic: keywords
subject: Performance evaluation
  topic: keywords
creator: HaoYu Tian
rights: Copyright (c) 2026 HaoYu Tian https://creativecommons.org/licenses/by/4.0
date: 2026-06-29
publication date: 2026-06-29
starting page: 56
ending page: 62
doi: 10.61784/jcsee3143

DESIGN AND IMPLEMENTATION OF A PYTHON-BASED SYSTEM FOR NANYANG HAN DYNASTY STONE RELIEF RESOURCES RESTORATION AND VISUALIZATION
type: item
description: The Han dynasty stone reliefs in Nanyang are an important part of China's historical and cultural heritage, with high historical, artistic, and archaeological value. However, due to long-term natural weathering, environmental erosion, and human damage, many of these stone reliefs have problems like cracks, missing parts, dirt, and blurred patterns, which seriously affect their digital preservation and academic research. Currently, the management of Han painting resources generally faces issues such as scattered data, low search efficiency, and a lack of intelligent analysis tools. To address this, we designed and implemented a Python-based system for restoring and visually analyzing Nanyang Han paintings. The system uses a B/S structure, is developed with the Django framework, uses a MySQL database as a central data storage, combines Bootstrap for front-end design, and employs ECharts for multi-dimensional visual analysis. For image restoration, the system introduces a deep learning image repair model that can automatically fill in damaged areas of the stone reliefs and reconstruct patterns. For resource management, it allows adding, deleting, updating, and retrieving Han painting resource information along with categorized management. For visual analysis, it supports multi-dimensional statistical analysis like distribution by era, theme classification, excavation sites, and preservation status, and also provides word cloud analysis, combined searches, and Excel data export. Test results show that the system is stable, user-friendly, and can effectively improve the digital preservation, resource management, and academic research efficiency of the Nanyang Han dynasty stone reliefs.
subject: Research Article
  topic: section
subject: Han stone reliefs
  topic: keywords
subject: Image restoration
  topic: keywords
subject: Python
  topic: keywords
subject: Django
  topic: keywords
subject: Data visualization
  topic: keywords
subject: Digitalization of cultural heritage
  topic: keywords
creator: Rui Li
creator: Xiang Li
rights: Copyright (c) 2026 Rui Li; Xiang Li https://creativecommons.org/licenses/by/4.0
date: 2026-06-27
publication date: 2026-06-27
starting page: 51
ending page: 55
doi: 10.61784/jcsee3142

BUILDING AN ACCOUNTING EDUCATION ECOSYSTEM FOR LIFELONG LEARNING SUPPORTED BY VIRTUAL SIMULATION TECHNOLOGY: A TRIADIC INTERACTIONIST FRAMEWORK
type: item
description: The rapid digital transformation of the accounting profession, driven by artificial intelligence, blockchain, and continuous regulatory changes, has created an urgent demand for lifelong learning ecosystems that transcend traditional educational boundaries. Virtual simulation technology offers unprecedented opportunities to bridge the gap between theoretical accounting knowledge and practical professional competencies within a lifelong learning paradigm. This study proposes and empirically validates a novel Triadic Interactionist Framework for Accounting Education Ecosystems, integrating Bandura's social cognitive theory with ecosystem theory and technology-mediated learning paradigms. Using a mixed-methods design combining structural equation modeling with longitudinal experimental data from one thousand two hundred forty-eight participants across eighteen Chinese universities and forty-two accounting firms, we demonstrate that virtual simulation technology-enabled accounting education ecosystems significantly enhance learners' self-efficacy, professional competence transfer, and lifelong learning orientation. The ecosystem's modular architecture, comprising virtual simulation laboratories, AI-adaptive learning pathways, blockchain-verified credentialing, and industry-university collaborative platforms, explains a substantial portion of the variance in lifelong learning outcomes. Our findings reveal that the mediating role of immersive experiential learning and the moderating effect of digital readiness are critical mechanisms through which virtual simulation technology transforms accounting education. This research contributes to educational technology theory by extending ecosystem thinking to discipline-specific lifelong learning and provides actionable design principles for institutions seeking to build future-ready accounting education infrastructures.
subject: Research Article
  topic: section
subject: Virtual simulation technology
  topic: keywords
subject: Accounting education ecosystem
  topic: keywords
subject: Lifelong learning
  topic: keywords
subject: Triadic interactionism
  topic: keywords
subject: Educational technology
  topic: keywords
subject: Professional competence
  topic: keywords
creator: YongHui Li
creator: HaiBo Zhang
rights: Copyright (c) 2026 YongHui Li; HaiBo Zhang https://creativecommons.org/licenses/by/4.0
date: 2026-06-17
publication date: 2026-06-17
starting page: 42
ending page: 50
doi: 10.61784/jcsee3141

CROP PEST AND DISEASE IDENTIFICATION SYSTEM BASED ON MUTUAL LEARNING
type: item
description: As the fundamental industry supporting the national economy, agricultural production is seriously restricted by frequent occurrences of crop pests and diseases, which have been proven to cause enormous economic losses worldwide every year. Therefore, the rapid and accurate identification of crop pests and diseases is widely regarded as a crucial prerequisite for the sustainable development of green agriculture and high-quality agricultural production. Traditional pattern classification algorithms and representative deep learning models including VGG and ResNet have been gradually applied in the field of pest and disease identification, yet most deep networks are confronted with great challenges in real-world deployment on mobile and edge devices due to their excessively large number of parameters and high computational complexity. To effectively tackle this application bottleneck, a lightweight BMV3 network based on the mutual learning framework is proposed in this paper. After heterogeneous knowledge interaction and mutual learning with ResNet50, the identification accuracy of the proposed model is significantly improved by 2.6%, and the dual optimization of high identification accuracy and flexible terminal deployment is successfully realized.
subject: Research Article
  topic: section
subject: Crop disease
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Mutual learning
  topic: keywords
creator: HanQiu Shen
creator: Zhuo Xie
creator: XiaoYu Guo
creator: XinYing Liu
rights: Copyright (c) 2026 HanQiu Shen; Zhuo Xie; XiaoYu Guo; XinYing Liu https://creativecommons.org/licenses/by/4.0
date: 2026-06-08
publication date: 2026-06-08
starting page: 37
ending page: 41
doi: 10.61784/jcsee3140

DESIGN AND REALIZATION OF MONITORING AND CONTROL SYSTEM FOR SMART FARMS USING ESP32-P4 AND LVGL
type: item
description: Aiming at the problems of poor real-time performance, weak local interaction and limited data processing capacity of traditional agricultural monitoring systems, this paper presents an intelligent farm monitoring and control system based on ESP32-P4 and a 7-inch IPS touch screen. Taking the dual-core RISC-V ESP32-P4 with a main frequency of 400 MHz as the main controller, the system drives the display screen via the MIPI-DSI interface and adopts the LVGL graphics library to realize a high-frame-rate local human-machine interaction interface. It integrates five types of sensors for temperature, humidity, soil moisture, light intensity and carbon dioxide concentration, and supports Wi-Fi cloud data transmission as well as closed-loop control functions including automatic irrigation and environmental regulation. The test results show that the interface refresh rate is no less than 28 FPS, and the control response time is less than 100 ms. The system can satisfy the requirements of real-time monitoring and precise control for small and medium-sized intelligent farms.
subject: Research Article
  topic: section
subject: Smart agriculture
  topic: keywords
subject: ESP32-P4
  topic: keywords
subject: LVGL
  topic: keywords
subject: Embedded GUI
  topic: keywords
creator: YuanJu Zhou
rights: Copyright (c) 2026 YuanJu Zhou https://creativecommons.org/licenses/by/4.0
date: 2026-06-08
publication date: 2026-06-08
starting page: 29
ending page: 36
doi: 10.61784/jcsee3139

PROPAGATION CHARACTERISTICS AND SYSTEM PERFORMANCE OF TERAHERTZ–OPTICAL HYBRID COMMUNICATION CHANNELS
type: item
description: This paper investigates the propagation characteristics and system performance of terahertz–optical hybrid communication channels. To meet the increasing demand for high capacity and high reliability in future high-speed wireless communication systems, a hybrid communication model consisting of a terahertz link and an optical wireless link is established. First, the dominant propagation impairments are analyzed, including free-space path loss, molecular absorption, and pointing errors in the terahertz link, as well as atmospheric attenuation, turbulence effects, and pointing errors in the optical wireless link. Then, a hybrid channel model is developed based on an instantaneous signal-to-noise-ratio-based link selection mechanism, and outage probability, bit error rate, and channel capacity are selected as the main performance metrics. Numerical results show that the terahertz and optical wireless links exhibit clear complementarity in terms of propagation impairments. Compared with single-link transmission, the terahertz–optical hybrid communication system can effectively reduce the outage probability and maintain more stable system capacity under different transmission distances and propagation conditions. The results of this work may provide useful guidance for the design of future 6G high-speed wireless backhaul and reliable communication systems in complex environments.
subject: Research Article
  topic: section
subject: Terahertz communication
  topic: keywords
subject: Optical wireless communication
  topic: keywords
subject: Hybrid communication channel
  topic: keywords
subject: System performance
  topic: keywords
creator: HaoYu Tian
rights: Copyright (c) 2026 HaoYu Tian https://creativecommons.org/licenses/by/4.0
date: 2026-06-06
publication date: 2026-06-06
starting page: 23
ending page: 28
doi: 10.61784/jcsee3138

SMART HOME SPACE DESIGN INTEGRATING XR AND AIGC
type: item
description: With the rapid advancement of extended reality (XR), artificial intelligence-driven generation (AIGC), and digital twin technologies, home space design is transitioning from "digital assistance" to a new phase of "intelligent symbiosis." Traditional virtual reality (VR) design faces limitations such as lengthy modeling cycles, conflicts between realism and real-time performance, and insufficient user engagement, making it inadequate for contemporary personalized and immersive design demands. This paper first analyzes the technological evolution of current home space design and highlights the necessity of integrating XR with AIGC; secondly, it establishes an intelligent design framework based on 3D Gaussian Spatter (3DGS) and generative spatial models (e.g., PanoWorld), enabling end-to-end generation of high-fidelity virtual scenes from user intent comprehension; thirdly, it employs an improved collaborative filtering algorithm to quantitatively evaluate spatial design preferences and similarity; finally, it compares simulation experiments with traditional design methods. Results demonstrate that the novel design approach achieves approximately 11% improvement in comprehensive metrics for spatial practicality, intuitiveness, and functional diversity compared to conventional methods, enhances user satisfaction by nearly 12%, and reduces the solution generation cycle from days to minutes. This study provides a novel technical pathway for advancing theoretical frameworks and engineering applications in smart home space design.
subject: Research Article
  topic: section
subject: Smart home space design
  topic: keywords
subject: Extended Reality (XR)
  topic: keywords
subject: Generative Artificial Intelligence (AIGC)
  topic: keywords
subject: 3D gaussian spleen
  topic: keywords
subject: Collaborative filtering algorithm
  topic: keywords
subject: Virtual simulation
  topic: keywords
creator: LingJuan Wang
rights: Copyright (c) 2026 LingJuan Wang https://creativecommons.org/licenses/by/4.0
date: 2026-05-29
publication date: 2026-05-29
starting page: 17
ending page: 22
doi: 10.61784/jcsee3137

DEEP LEARNING-BASED MALICIOUS TRAFFIC ANALYSIS: A COMPREHENSIVE SURVEY
type: item
description: Malicious traffic analysis has become increasingly challenging due to encryption-by-default communication, evolving attack behaviors, and distribution shifts across heterogeneous environments. At present, many studies still do not share consistent pipeline designs, traffic representations, benchmark datasets, or assessment methods. Based on this problem, this paper systematically examines deep-learning-based malicious-traffic-analysis technologies. Existing works are organized into four main parts: data acquisition and preprocessing, traffic representation, learning models, and performance evaluation methods. In particular, we compare representative methods, including traditional machine learning, deep learning, and hybrid forms, in terms of detection accuracy, computational cost, cross-scenario generalization ability, and suitable metrics. We also identify key problems in practical applications, such as encrypted traffic monitoring capability, biased datasets, and adversarial attacks, and summarize related research paths. This paper introduces the research area and provides a reference for reproducible literature-based evaluation design.
subject: Research Article
  topic: section
subject: Malicious traffic analysis
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Encrypted traffic detection
  topic: keywords
subject: Traffic representation learning
  topic: keywords
subject: Network security
  topic: keywords
creator: WenCai He
creator: ZhiJie Peng
creator: MingShen Zhang
creator: He Zhu
rights: Copyright (c) 2026 WenCai He; ZhiJie Peng; MingShen Zhang; He Zhu https://creativecommons.org/licenses/by/4.0
date: 2026-05-22
publication date: 2026-05-22
starting page: 1
ending page: 16
doi: 10.61784/jcsee3136

AI-ENABLED DESIGN AND OPTIMIZATION OF AEROSPACE ELECTRIC MOTORS: A TASK-ORIENTED REVIEW
type: item
description: 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.
subject: Research Article
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Task-oriented review
  topic: keywords
subject: Motor design
  topic: keywords
subject: Aerospace electric motors
  topic: keywords
subject: Extreme constraints
  topic: keywords
creator: SaiBo Wang
creator: HaoZheng Yu
creator: JinXiao Wang
creator: SiYu Wang
creator: Chao Gong
creator: Ke Tang
rights: Copyright (c) 2026 SaiBo Wang; HaoZheng Yu; JinXiao Wang; SiYu Wang; Chao Gong; Ke Tang https://creativecommons.org/licenses/by/4.0
date: 2026-05-11
publication date: 2026-05-11
starting page: 34
ending page: 54
doi: 10.61784/jcsee3135

SIGNAL DENOISING BASED ON QUANTUM ADAPTIVE TRANSFORMATION AND SINGULAR VALUE DECOMPOSITION
type: item
description: 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.
subject: Research Article
  topic: section
subject: Denoising algorithm
  topic: keywords
subject: Quantum adaptive transformation
  topic: keywords
subject: Singular value decomposition
  topic: keywords
subject: Spectral data
  topic: keywords
creator: YingYing Sun
creator: MengYao Lv
creator: ChenLin Wu
creator: WeiXian Xie
creator: ZiYuan Fang
creator: RuiTong Zhao
rights: Copyright (c) 2026 YingYing Sun; MengYao Lv; ChenLin Wu; WeiXian Xie; ZiYuan Fang; RuiTong Zhao https://creativecommons.org/licenses/by/4.0
date: 2026-05-08
publication date: 2026-05-08
starting page: 27
ending page: 33
doi: 10.61784/jcsee3134

DEEP REINFORCEMENT LEARNING FOR TEST CASE GENERATION AND PRIORITIZATION
type: item
description: 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.
subject: Research Article
  topic: section
subject: Software testing
  topic: keywords
subject: Deep reinforcement learning
  topic: keywords
subject: Test case generation
  topic: keywords
subject: Test case prioritization
  topic: keywords
creator: JingXuan Guo
creator: JingJing Liu
rights: Copyright (c) 2026 JingXuan Guo; JingJing Liu https://creativecommons.org/licenses/by/4.0
date: 2026-04-28
publication date: 2026-04-28
starting page: 21
ending page: 26
doi: 10.61784/jcsee3133

THE ALGORITHM FOR DETECTING OIL STAINS AND FOREIGN OBJECTS ON THE BOTTOM OF EMU CARS BASED ON DEEP LEARNING
type: item
description: 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 …
subject: Research Article
  topic: section
subject: Multiple-unit train
  topic: keywords
subject: Oil contamination detection
  topic: keywords
subject: YOLOv12
  topic: keywords
subject: DeepLabV3+
  topic: keywords
subject: Data augmentation
  topic: keywords
subject: Semantic segmentation
  topic: keywords
creator: ZhiJian Wei
creator: SongTao Zhang
creator: ZiYi Xu
creator: Hang Zhou
rights: Copyright (c) 2026 ZhiJian Wei; SongTao Zhang; ZiYi Xu; Hang Zhou https://creativecommons.org/licenses/by/4.0
date: 2026-04-24
publication date: 2026-04-24
starting page: 6
ending page: 20
doi: 10.61784/jcsee3132

A MULTI-LABEL IMAGE RECOGNITION NETWORK DRIVEN BY LABEL-IMAGE SEMANTIC ALIGNMENT
type: item
description: 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.
subject: Research Article
  topic: section
subject: Multi-label image recognition
  topic: keywords
subject: Graph convolutional network
  topic: keywords
subject: Semantic decoupling
  topic: keywords
subject: Multi-head self-attention
  topic: keywords
subject: Label correlation
  topic: keywords
creator: YuYu Chen
creator: LinJun Wu
creator: LianZhi Chu
rights: Copyright (c) 2026 YuYu Chen; LinJun Wu; LianZhi Chu https://creativecommons.org/licenses/by/4.0
date: 2026-04-14
publication date: 2026-04-14
starting page: 1
ending page: 5
doi: 10.61784/jcsee3131

APPLICATION OF LARGE LANGUAGE MODELS IN SMART CONTRACT VULNERABILITY DETECTION
type: item
description: 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.
subject: Research Article
  topic: section
subject: Large language models
  topic: keywords
subject: Smart contracts
  topic: keywords
subject: Vulnerability detection
  topic: keywords
subject: Blockchain security
  topic: keywords
subject: Program analysis
  topic: keywords
creator: Yun Li
creator: YanLing Liu
creator: ChangEr Liu
creator: XinMan Luo
rights: Copyright (c) 2026 Yun Li; YanLing Liu; ChangEr Liu; XinMan Luo https://creativecommons.org/licenses/by/4.0
date: 2026-04-10
publication date: 2026-04-10
starting page: 67
ending page: 70
doi: 10.61784/jcsee3130

SMART CONTRACTS IN INDUSTRY APPLICATIONS AND ECONOMIC ECOSYSTEMS: CURRENT STATUS AND TRENDS
type: item
description: 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.
subject: Research Article
  topic: section
subject: Smart contracts
  topic: keywords
subject: Business applications
  topic: keywords
subject: Economic ecosystem
  topic: keywords
subject: Decentralized finance
  topic: keywords
creator: YiWen Gao
creator: ChangEr Liu
rights: Copyright (c) 2026 YiWen Gao; ChangEr Liu https://creativecommons.org/licenses/by/4.0
date: 2026-04-10
publication date: 2026-04-10
starting page: 63
ending page: 66
doi: 10.61784/jcsee3129

A SPATIOTEMPORAL DUAL-BRANCH NETWORK BASED ON CHANNEL ATTENTION AND RESIDUAL CONNECTION IN MOTOR IMAGERY EEG CLASSIFICATION
type: item
description: 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.
subject: Research Article
  topic: section
subject: Motor imagery
  topic: keywords
subject: Electroencephalogram
  topic: keywords
subject: Channel attention
  topic: keywords
subject: Residual connection
  topic: keywords
creator: HuiXi Mo
rights: Copyright (c) 2026 HuiXi Mo https://creativecommons.org/licenses/by/4.0
date: 2026-04-09
publication date: 2026-04-09
starting page: 55
ending page: 62
doi: 10.61784/jcsee3128

OPTIMIZATION OF SCATTERING CHARACTERISTICS OF SUPPORT ROD BASED ON CST SOFTWARE
type: item
description: 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.
subject: Research Article
  topic: section
subject: Support rod
  topic: keywords
subject: Low scattering
  topic: keywords
subject: CST simulation
  topic: keywords
creator: ZhongXin Yang
creator: MengMeng Zhang
creator: QiPeng Xu
rights: Copyright (c) 2026 ZhongXin Yang; MengMeng Zhang; QiPeng Xu https://creativecommons.org/licenses/by/4.0
date: 2026-04-08
publication date: 2026-04-08
starting page: 49
ending page: 54
doi: 10.61784/jcsee3127

MODELING THE EVOLUTIONARY DYNAMICS OF SMARTPHONE BATTERY TIMING
type: item
description: 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.
subject: Research Article
  topic: section
subject: State of charge
  topic: keywords
subject: Continuous-time modeling
  topic: keywords
subject: Temporal evolution
  topic: keywords
creator: MingGuang Jia
rights: Copyright (c) 2026 MingGuang Jia https://creativecommons.org/licenses/by/4.0
date: 2026-04-02
publication date: 2026-04-02
starting page: 38
ending page: 48
doi: 10.61784/jcsee3126

DESIGN OF A DYNAMIC MONITORING AND TIERED EARLY WARNING SYSTEM FOR CULVERT WATERLOGGING BASED ON A CLOUD-EDGE COLLABORATIVE ARCHITECTURE
type: item
description: 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.
subject: Research Article
  topic: section
subject: Culvert waterlogging
  topic: keywords
subject: Cloud-edge collaboration
  topic: keywords
subject: Edge computing
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: Tiered early warning
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
creator: YiTao Li
rights: Copyright (c) 2026 YiTao Li https://creativecommons.org/licenses/by/4.0
date: 2026-04-02
publication date: 2026-04-02
starting page: 33
ending page: 37
doi: 10.61784/jcsee3125

MOTION COMPENSATION FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY BASED ON MULTI-DIMENSIONAL MATRIX PENCIL METHOD AND ADAPTIVE EN-FACE IMAGE ENHANCEMENT
type: item
description: 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.
subject: Research Article
  topic: section
subject: Optical coherence tomography angiography
  topic: keywords
subject: Multi-dimensional matrix pencil method
  topic: keywords
subject: En-face image enhancement
  topic: keywords
subject: Weighted layered projection
  topic: keywords
subject: CLAHE
  topic: keywords
subject: FAZ processing
  topic: keywords
subject: Motion compensation
  topic: keywords
subject: Retinal imaging
  topic: keywords
creator: LinKang Du
creator: Lei Liu
creator: HaiYi Bian
rights: Copyright (c) 2026 LinKang Du; Lei Liu; HaiYi Bian https://creativecommons.org/licenses/by/4.0
date: 2026-03-27
publication date: 2026-03-27
starting page: 27
ending page: 32
doi: 10.61784/jcsee3124

INNOVATIVE APPLICATION OF AI AGENT BASED ON MARKOV DECISION PROCESS IN DYNAMIC OPTIMIZATION DECISION-MAKING FOR ENTERPRISE SERVICES
type: item
description: 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…
subject: Research Article
  topic: section
subject: Markov decision process
  topic: keywords
subject: AI agent
  topic: keywords
subject: Dynamic service decision-making
  topic: keywords
subject: Deep reinforcement learning
  topic: keywords
subject: LiQiCloud
  topic: keywords
creator: XueYong Li
creator: WeiBin Zhao
creator: Xing Xu
creator: JinHan Cai
rights: Copyright (c) 2026 XueYong Li; WeiBin Zhao; Xing Xu; JinHan Cai https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 19
ending page: 26
doi: 10.61784/jcsee3123

OPTIMIZATION OF ESPRIT ALGORITHM AND FAST DOA ESTIMATION FOR UNIFORM LINEAR ARRAYS
type: item
description: 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.
subject: Research Article
  topic: section
subject: DOA estimation
  topic: keywords
subject: Joint cross-covariance matrix
  topic: keywords
subject: Signal subspace
  topic: keywords
subject: ESPRIT algorithm
  topic: keywords
creator: XinYu Weng
rights: Copyright (c) 2026 XinYu Weng https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 12
ending page: 18
doi: 10.61784/jcsee3122

DUAL-FAIRNESS DRIVEN 3D TRAJECTORY DESIGN AND RESOURCE ALLOCATION FOR UAV-ASSISTED ISAC SYSTEMS
type: item
description: 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.
subject: Research Article
  topic: section
subject: ISCC
  topic: keywords
subject: UAV
  topic: keywords
subject: Trajectory design
  topic: keywords
subject: Logarithmic utility function
  topic: keywords
subject: Dual-fairness
  topic: keywords
creator: LuLu Jing
creator: Hai Wang
rights: Copyright (c) 2026 LuLu Jing; Hai Wang https://creativecommons.org/licenses/by/4.0
date: 2026-03-12
publication date: 2026-03-12
starting page: 1
ending page: 11
doi: 10.61784/jcsee3121

ST-GNAD: MULTI-LEVEL ANOMALY DETECTION FOR 5G CORE NETWORKS BASED ON SPATIAL-TEMPORAL GRAPH NEURAL NETWORK
type: item
description: 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.
subject: Research Article
  topic: section
subject: 5G core network
  topic: keywords
subject: N2/N4 interfaces
  topic: keywords
subject: Anomaly detection
  topic: keywords
subject: Graph neural network
  topic: keywords
subject: Spatial-temporal correlation
  topic: keywords
subject: Signaling security
  topic: keywords
creator: HuiYuan Ke
rights: Copyright (c) 2026 HuiYuan Ke https://creativecommons.org/licenses/by/4.0
date: 2026-03-12
publication date: 2026-03-12
starting page: 69
ending page: 73
doi: 10.61784/jcsee3120