Engineering Science and Technology-An International Journal-Jestech最新文献

A robust control strategy for two-stage single-phase grid-connected proton-exchange membrane fuel cell system with an LCL filter 带LCL滤波器的两级单相并网质子交换膜燃料电池系统鲁棒控制策略

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-31 DOI: 10.1016/j.jestch.2026.102289

Hamedalneel BA Hamid , Ahmed Mohamed Ishag , Jamal Hassan , Gomaa Haroun Ali , Tianjun Ma , Adeel Abbas

Proton exchange membrane fuel cell (PEMFC) system is a promising renewable energy source for power system grid integration due to their high energy efficiency. Nevertheless, PEMFC system is highly sensitive to the operating conditions, which could degrade their output performance over time during operation. This article proposes a robust control strategy for a two-stage single-phase grid-connected PEMFC system with an LCL filter to ensure that a sinusoidal current is injected into the utility grid. A robust control strategy includes a reinforcement learning-based maximum power point tracking (RL-MPPT) algorithm and an adaptive current predictive control (ACPC) scheme. The synthesis of RL into an MPPT algorithm simplifies the control problem, eliminates the need for the system model, and prevents deviations in the PEMFC’s maximum power point (MPP) during dynamic variations in temperature and membrane water content (MWC) by simultaneously tuning the boost converter duty cycle. Furthermore, an (ACPC scheme comprises an outer-loop dc-link voltage controller using a PI controller augmented with a notch filter (NF) to prevent double-line frequency dc-link voltage ripple from affecting the grid current reference amplitude and an inner-loop current controller to generate the predicted grid current. To achieve high-accuracy current predictions, a real-time parameter estimator based on the Kalman filter (KF) is integrated into the controller framework. Lastly, findings show that the RL-MPPT algorithm achieves faster settling time and 95.5% MPP average tracking efficiency compared to INC and FLC MPPT algorithms. Additionally, an ACPC scheme shows good sinusoidal reference tracking and minimum THD in the presences of the large LCL filter parameter variations and model uncertainties.

质子交换膜燃料电池（PEMFC）系统因其高能效而成为一种很有前途的可再生能源并网系统。然而，PEMFC系统对运行条件非常敏感，随着运行时间的推移，其输出性能可能会下降。本文提出了一种具有LCL滤波器的两级单相并网PEMFC系统的鲁棒控制策略，以确保向公用电网注入正弦电流。鲁棒控制策略包括基于强化学习的最大功率点跟踪（RL-MPPT）算法和自适应电流预测控制（ACPC）方案。将RL合成为MPPT算法简化了控制问题，消除了对系统模型的需要，并通过同时调整升压转换器占空比，防止了在温度和膜含水量（MWC）动态变化期间PEMFC最大功率点（MPP）的偏差。此外，ACPC方案包括一个外环直流电压控制器，该控制器使用带陷波滤波器（NF）的PI控制器来防止双线频率直流电压纹波影响电网电流参考幅值，以及一个内环电流控制器来产生预测的电网电流。为了实现高精度的电流预测，在控制器框架中集成了基于卡尔曼滤波的实时参数估计器。最后，研究结果表明，与INC和FLC MPPT算法相比，RL-MPPT算法实现了更快的沉降时间和95.5%的MPP平均跟踪效率。此外，ACPC方案在存在较大LCL滤波器参数变化和模型不确定性的情况下具有良好的正弦参考跟踪和最小的THD。

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引用次数: 0

A lightweight, GPU-accelerated batch image encryption framework with integrated ECC and multi-attack resilience 一个轻量级的，gpu加速批处理图像加密框架，集成了ECC和多攻击弹性

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-24 DOI: 10.1016/j.jestch.2026.102287

Shaima Safa Aldin Baha Aldin , Noor Baha Aldin , Mahmut Aykaç

The secure delivery of visual content over noisy or lossy communication networks requires strong cryptographic schemes that combine security with error control and resilience. Despite the security being available for most chaos-based encryption schemes, they are in general sensitive to transmission errors. This paper presents a simple but efficient Graphics Processing Unit (GPU) based image-encryption which combines chaotic encryption and integrated Error Correction Codes (ECC). It consists of a 3D logistic-map for producing different keystreams of rearranged pixels and mixup values using XOR operations. In order to make the cipher more robust to transmission issues, we have integrated a Combined ReedSolomon (RS) and Low-Density ParityCheck (LDPC) ECC layer. All packed in an interactive MATLAB framework for easy test, visualization, and realtime analysis. The experimental results on the USC-SIPI dataset show that the proposed framework has a high entropy of 7.9993, NPCR = 99.63%, and UACI = 33.52%. The systems got a 39 Mbps on a standard GPU with 5 times overall speed compared to the CPU. Thus, this design gives a practical, efficient, and robust approach for secure image communication, as well as a good educational tool for exploring multimedia security concepts.

在嘈杂或有损的通信网络上安全地传输视觉内容需要强大的加密方案，该方案将安全性与错误控制和弹性相结合。尽管大多数基于混沌的加密方案都具有安全性，但它们通常对传输错误很敏感。本文提出了一种简单而高效的基于图形处理器（GPU）的图像加密算法，该算法将混沌加密和集成纠错码（ECC）相结合。它由一个3D逻辑图组成，用于使用异或操作产生不同的重排像素和混合值的键流。为了使密码对传输问题更具鲁棒性，我们集成了一个组合的ReedSolomon （RS）和低密度ParityCheck (LDPC) ECC层。所有包装在一个交互式的MATLAB框架，便于测试，可视化和实时分析。在USC-SIPI数据集上的实验结果表明，该框架具有较高的熵值7.9993,NPCR = 99.63%, UACI = 33.52%。该系统在标准GPU上的速度为39 Mbps，总体速度是CPU的5倍。因此，本设计为安全图像通信提供了一种实用、高效和健壮的方法，也是探索多媒体安全概念的良好教育工具。

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引用次数: 0

Algorithm-oriented benchmarking of deep learning and hybrid architectures for robust SOC estimation in electric vehicle batteries 面向算法的基于深度学习和混合架构的电动汽车电池稳健SOC评估

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-23 DOI: 10.1016/j.jestch.2026.102286

Osman Demirci , Sezai Taskin

Accurate state-of-charge (SOC) estimation is a key requirement for the safe and efficient management of lithium-ion batteries in electric vehicles, especially under varying thermal and dynamic operating conditions. This study presents a comprehensive, algorithm-oriented assessment of several deep learning and hybrid SOC estimation architectures—including feedforward neural networks (FNN), gated recurrent networks (GRU), long short-term memory networks (LSTM), temporal convolutional networks (TCN), and their hybrid combinations—using a multi-temperature dataset collected at 10 °C, 25 °C, and 40 °C under diverse dynamic load profiles and standardized drive cycles such as UDDS, HWFET, US06, and LA92. All architectures were trained and evaluated under a unified preprocessing and training configuration to ensure methodological consistency and a fair basis for comparison.

The evaluation highlights how different recurrent, convolutional, and hybrid architectures respond to thermal variations and dynamic load transitions, revealing model-specific strengths and limitations under realistic operating conditions. Among the evaluated models, the hybrid FNN + GRU architecture demonstrated the most reliable overall performance, achieving an RMSE of 1.11 % and reducing peak estimation errors to 3.6 % under nominal temperature conditions. SOC-zone analysis further showed characteristic error amplification at low and high SOC levels, emphasizing the importance of architectures capable of capturing nonlinear boundary dynamics. Computational benchmarking indicated that hybrid structures—particularly FNN + GRU—also provide an advantageous balance between estimation accuracy and inference speed, supporting their suitability for embedded Battery Management Systems (BMSs) with real-time constraints.

Overall, this study contributes a unified evaluation framework that simultaneously addresses thermal robustness, dynamic load variability, SOC-dependent behavior, and computational efficiency, offering practical guidance for selecting reliable and deployable SOC estimation models for next-generation electric vehicle BMSs.

准确的荷电状态（SOC）估算是电动汽车锂离子电池安全高效管理的关键要求，特别是在不同的热动态运行条件下。本研究对几种深度学习和混合SOC估计架构进行了全面的、面向算法的评估，包括前馈神经网络（FNN）、门控循环网络（GRU）、长短期记忆网络（LSTM）、时间卷积网络（TCN）及其混合组合，使用在10°C、25°C和40°C下收集的多温度数据集，在不同的动态负载配置和标准化驱动循环（如UDDS、HWFET、US06和LA92）下进行。所有架构都在统一的预处理和训练配置下进行训练和评估，以确保方法的一致性和公平的比较基础。评估强调了不同的循环、卷积和混合架构如何响应热变化和动态负载转换，揭示了模型在实际操作条件下的特定优势和局限性。在评估的模型中，混合FNN + GRU架构表现出最可靠的整体性能，在标称温度条件下实现了1.11%的RMSE，并将峰值估计误差降低到3.6%。SOC-zone分析进一步显示了低SOC和高SOC水平下的特征误差放大，强调了能够捕获非线性边界动力学的架构的重要性。计算基准测试表明，混合结构-特别是FNN + gru -还在估计精度和推理速度之间提供了有利的平衡，支持它们适用于具有实时约束的嵌入式电池管理系统（bms）。总体而言，该研究提供了一个统一的评估框架，同时解决了热鲁棒性、动态负载可变性、SOC依赖行为和计算效率问题，为下一代电动汽车bms选择可靠和可部署的SOC评估模型提供了实用指导。

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引用次数: 0

State-dependent efficiency estimation in electric vehicles using an artificial neural network approach 基于人工神经网络的电动汽车状态相关效率估计

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-22 DOI: 10.1016/j.jestch.2025.102270

Ahmet Burak Kaydeci , Salih Baris Ozturk

Accurate modeling of powertrain efficiency is essential for optimizing energy management and range prediction in electric vehicles. This is particularly important under varying real-world driving conditions. To address the limitations of fixed efficiency assumptions in conventional models, this study proposes a hybrid approach combining experimental data with physics-based simulation. A feedforward artificial neural network (ANN) is trained to predict powertrain efficiency dynamically using real-world data collected from a prototype electric vehicle. The ANN utilizes four input variables—motor torque, motor speed, battery temperature, and state of charge—selected through a combined physical and experimental data-driven relevance analysis. The trained model is integrated into a longitudinal vehicle simulation framework, enabling dynamic efficiency estimation and energy consumption analysis. The validation was performed by comparing the ANN predictions against a separate set of experimental measurements. Compared to a baseline linear regression model, the ANN demonstrated a 95.2% lower mean squared error (MSE) and 80.4% lower mean absolute error (MAE) during efficiency interpolation, with a coefficient of determination (

R^{2}

) of 0.995. Simulations were conducted on both long-haul and city drive cycles, validating the model’s adaptability in diverse scenarios. These results support its application in predictive energy control, route-specific planning, and on-board performance evaluation.

准确的动力系统效率建模对于优化电动汽车的能量管理和里程预测至关重要。在多变的真实驾驶条件下，这一点尤为重要。为了解决传统模型中固定效率假设的局限性，本研究提出了一种将实验数据与基于物理的模拟相结合的混合方法。通过对前馈人工神经网络（ANN）的训练，利用从原型车收集的真实数据动态预测动力总成效率。人工神经网络利用四个输入变量——电机扭矩、电机速度、电池温度和充电状态——通过物理和实验数据驱动的相关性分析进行选择。将训练后的模型集成到纵向车辆仿真框架中，实现动态效率估计和能耗分析。验证是通过将人工神经网络预测与一组单独的实验测量进行比较来完成的。与基线线性回归模型相比，人工神经网络在效率插值时的均方误差（MSE）降低95.2%，平均绝对误差（MAE）降低80.4%，决定系数（R2）为0.995。在长途和城市驾驶工况下进行了仿真，验证了该模型在不同工况下的适应性。这些结果支持其在预测能量控制、路线特定规划和车载性能评估中的应用。

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引用次数: 0

Reliability enhancement method for distribution system using a network cooperation recovery optimization technique 基于网络协同恢复优化技术的配电系统可靠性增强方法

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-21 DOI: 10.1016/j.jestch.2026.102285

Hejun Yang , Yangxu Yue , Jing Ma , Dabo Zhang , Xianjun Qi

The distributed generation (DG) and soft open point (SOP) have been connected to the distribution network, so distribution network fault recovery has changed from the single tie line recovery to collaborated recovery of DG and SOP, resulting in the reliability of distribution network is seriously underestimated under the traditional reliability assessment mode. Therefore, in order to overcome this shortcoming, this paper presents reliability assessment methodology for enhancing reliability of electrical distribution system using a network collaboration recovery technique. The paper employs a highly flexible model to fully exploit the synergistic restoration potential of flexible resources, enabling precise reliability evaluation through the formulation of optimal fault recovery strategies. Firstly, the restoration strategy for SOP and tie line reconfiguration in coordination with DG islanding is proposed in order to consider the mutual influence between SOP and DG in fault recovery and fully explore the collaborative recovery ability of DG and SOP; Secondly, this paper proposes a radial network constraint method that allows island recovery and load shedding operations. The method ensures to obtain the optimal solution for the restoration strategy while constraining the radial operation of the distribution network; Thirdly, in order to improve the computational accuracy of the proposed model, this paper uses the big M method and second-order cone relaxation to transform the model into a mixed-integer second-order cone programming problem and solves the model using a solver; Finally, the effectiveness and superiority of the proposed method is investigated through the case study on IEEE 33 and 54-node distribution systems, and the SAIDI index can be reduced by 5.98% for IEEE 33 system and 3.07% for 54-node system.

分布式发电（DG）和软开路点（SOP）已接入配电网，配电网故障恢复由单一并线恢复向DG和软开路点协同恢复转变，导致传统可靠性评估模式下配电网可靠性严重低估。因此，为了克服这一缺点，本文提出了利用网络协同恢复技术提高配电系统可靠性的可靠性评估方法。本文采用高度灵活的模型，充分挖掘柔性资源的协同恢复潜力，通过制定最优的故障恢复策略，实现精确的可靠性评估。首先，为了考虑SOP与DG在故障恢复中的相互影响，充分挖掘DG与SOP的协同恢复能力，提出了SOP与DG孤岛协调的配线重构恢复策略；其次，提出了一种允许孤岛恢复和减载的径向网络约束方法。该方法在约束配电网径向运行的情况下保证了恢复策略的最优解；第三，为了提高所提模型的计算精度，采用大M法和二阶锥松弛法将模型转化为混合整数二阶锥规划问题，并用求解器对模型进行求解；最后，通过对IEEE 33节点和54节点配电系统的实例分析，验证了所提方法的有效性和优越性，结果表明，IEEE 33节点和54节点配电系统的SAIDI指数分别降低了5.98%和3.07%。

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引用次数: 0

Integrated quantum-classical hybrid architectures for robust lung lesion segmentation in volumetric CT video data samples 基于集成量子经典混合架构的体积CT视频数据样本鲁棒肺病变分割

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 DOI: 10.1016/j.jestch.2025.102272

Sai Babu Veesam , Lalitha Kumari Pappala , Aravapalli Rama Satish , Sravan Kumar Chirumamilla , Vunnava Dinesh Babu , Shonak Bansal , Krishna Prakash , Mohamad A. Alawad , Mohammad Tariqul Islam

Segmentation of lung lesions in volumetric CT data is crucial for the clinical aspects of diagnosis, therapy planning, and monitoring disease progression. Currently, deep learning applications are unable to model spatiotemporal coherency alongside anatomical consistency and uncertainty-aware refinement across sequential slices. In this study, we propose a hybrid quantum–classical framework that would accommodate multiple innovative modules. The architecture features a Quantum Latent Entanglement Consistency validator to establish spatiotemporal coherence across slices by maximizing von Neumann entropy. A Quantum-Classical Interventional Gradient Alignment ensures the harmony of gradients between classical CNN encoders and quantum discriminators. Further, the Temporal Quantum Attention for Boundary Stabilization captures the temporal context in the boundary refinement using controlled quantum gates. Alongside these, a Quantum-Enhanced Structural Similarity Feedback mechanism is proposed that exploits anatomical priors for retrofitting spatial lesion structures, as well as a Hybrid Quantum Adversarial Ensemble Validation, which provides confidence-aware validity through disagreement modeling. Collection and experimental evaluations over LIDC IDRI, NSCLC-Radiomics, and MosMedData datasets depict that the entirety of the systems significantly increases the Dice Similarity Coefficient by 5–7%, holds Hausdorff Distance lower at 10–12%, narrows down the over-segmentation errors by 8–10%, while reducing overall false positives near lung boundaries by 15% or even less. This represents a significant advancement toward fusing quantum learning with clinical-grade imaging pipelines, demonstrating clear improvements in segmentation stability, precision, and trustworthiness in real-world settings.

体积CT数据中肺病变的分割对于临床诊断、治疗计划和监测疾病进展至关重要。目前，深度学习应用程序无法模拟时空一致性以及跨序列切片的解剖一致性和不确定性感知细化。在这项研究中，我们提出了一个混合量子-经典框架，将容纳多个创新模块。该架构具有量子潜在纠缠一致性验证器，通过最大化冯·诺伊曼熵来建立跨片的时空相干性。量子-经典干涉梯度对准保证了经典CNN编码器和量子鉴别器之间梯度的和谐。此外，用于边界稳定的时间量子注意在使用受控量子门的边界细化中捕获时间上下文。除此之外，还提出了一种量子增强结构相似性反馈机制，该机制利用解剖先验来改造空间病变结构，以及一种混合量子对抗集成验证，该验证通过分歧建模提供信心感知有效性。对LIDC IDRI、NSCLC-Radiomics和MosMedData数据集的收集和实验评估表明，整个系统显着将Dice Similarity Coefficient提高了5-7%，将Hausdorff Distance降低了10-12%，将过度分割错误降低了8-10%，同时将肺边界附近的总体假阳性降低了15%甚至更少。这代表了将量子学习与临床级成像管道融合的重大进步，在现实世界的分割稳定性、精度和可信度方面有了明显的提高。

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引用次数: 0

Constrained optimal formation control for nonlinear multi-agent systems using data-driven adaptive neural networks 基于数据驱动自适应神经网络的非线性多智能体系统约束最优编队控制

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 DOI: 10.1016/j.jestch.2025.102269

Saleh Mobayen , Mai The Vu , Reza Rahmani , Hamid Toshani , Wudhichai Assawinchaichote , Paweł Skruch

This paper presents a constrained optimal adaptive control strategy for formation control in nonlinear multi-agent systems (MASs) using a data-driven approach. In contrast to traditional methods that require detailed system models, the proposed method employs Locally Linearized Dynamic Models (LLDMs), in which key parameters as Pseudo-Partial Derivatives (PPDs) are estimated adaptively from input–output data. This removes the need for explicit mathematical modeling and broadens the method’s applicability to uncertain systems. To address actuator limitations and reduce control effort, a performance criterion incorporating control constraints is defined, and the problem is reformulated as a Constrained Quadratic Program (CQP) with control increments as optimization variables. A Projection Recurrent Neural Network (PRNN) is developed to solve this CQP in real time, which ensures convergence of the numerical optimizer and guarantees closed-loop stability using Lyapunov analysis and singular value approach. The proposed algorithm achieves robust, model-free formation control, explicitly manages input constraints, and enables fast convergence. Simulation results show that this approach outperforms existing data-driven methods under uncertainty, which demonstrates its potential for applications in multi-agent system applications.

提出了一种基于数据驱动的非线性多智能体系统约束最优自适应控制策略。与传统方法需要详细的系统模型相比，该方法采用局部线性化动态模型（lldm），其中关键参数作为伪偏导数（PPDs）自适应地从输入输出数据中估计。这消除了对显式数学建模的需要，并扩大了该方法对不确定系统的适用性。为了解决执行器的限制和减少控制工作量，定义了包含控制约束的性能标准，并将问题重新表述为以控制增量为优化变量的约束二次规划（CQP）。利用李雅普诺夫分析和奇异值法，建立了一种投影递归神经网络（PRNN）来实时求解该CQP，保证了数值优化器的收敛性和闭环稳定性。该算法实现了鲁棒性、无模型的编队控制，明确地管理输入约束，并实现了快速收敛。仿真结果表明，该方法在不确定条件下优于现有的数据驱动方法，证明了其在多智能体系统中的应用潜力。

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引用次数: 0

DGait: Robust gait recognition using dynamic ST-GCN with global aware attention 步态：基于全局感知注意力的动态ST-GCN鲁棒步态识别

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 DOI: 10.1016/j.jestch.2025.102267

Md. Khaliluzzaman , Kaushik Deb

Gait recognition, a promising behavioral soft biometric technology, has a significant research area in security and computer vision. Nowadays, joint position-based approaches are of significant interest in gait recognition. ST-GCN, the spatio-temporal graph convolutional network, is employed on the joint stream to identify the gait feature from the spatial–temporal graph, prone to provide attention to dynamic information. Many methods utilize multi-scale operations to integrate long-range relationships among joints. However, these approaches fail to assign equal significance to all joints, leading to an incomplete perception of long-range joint connections. Furthermore, considering the joint stream solely may fail to extract the discriminative features produced by motion and bone structures. This paper presents a multi-stream dynamic spatio-temporal graph convolution (DSTGCN) approach with attention, denoted as DGait. It leverages bone and joint data from the spatial frames and joint-motion data from successive frames to early fusion of informative skeleton features. An improved HOP-extraction approach is introduced to provide equal importance to the relationship between further and closer joints while avoiding redundant dependencies. To address the limitations of ST-GCN, Global Aware Attention (GAA) is incorporated into the ST-GCN units, enhancing the capability for dynamically correlating the spatial–temporal joints. The suggested model exhibits remarkable accuracy on widely used CASIA-B, OUMVLP-Pose, and GREW datasets. The CASIA-B reveals an average accuracy of 96.94 %, 93.56 %, and 90.78 % for the normal walking, carrying-bag, and clothing conditions, respectively. The OUMVLP-Pose and GREW exhibit an average and rank-1 accuracy of 92.7 % and 72.6 %, respectively.

步态识别是一种很有发展前景的行为软生物识别技术，在安全和计算机视觉领域有着重要的研究领域。目前，基于关节位置的方法是步态识别的重要研究方向。在关节流上采用时空图卷积网络ST-GCN，从时空图中识别步态特征，易于关注动态信息。许多方法利用多尺度操作来整合关节之间的远程关系。然而，这些方法不能对所有关节赋予同等的重要性，导致对远距离关节连接的不完整感知。此外，仅考虑关节流可能无法提取由运动和骨结构产生的区别特征。本文提出了一种带注意的多流动态时空图卷积（DSTGCN）方法，记为DGait。它利用来自空间框架的骨骼和关节数据以及来自连续框架的关节运动数据来早期融合信息骨骼特征。引入了一种改进的hop提取方法，在避免冗余依赖的同时，对更远和更近的关节之间的关系提供同等的重视。为了解决ST-GCN的局限性，在ST-GCN单元中加入了全局感知注意（Global Aware Attention， GAA），增强了动态关联时空节点的能力。该模型在广泛使用的CASIA-B、OUMVLP-Pose和grow数据集上显示出显著的准确性。CASIA-B在正常行走、携带包和穿衣服条件下的平均准确率分别为96.94%、93.56%和90.78%。OUMVLP-Pose和grow的平均准确率和rank-1准确率分别为92.7%和72.6%。

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引用次数: 0

Characterizing the FRA curves of transformer tertiary helical windings by deriving transfer functions from FRA data 利用铁磁数据推导传递函数，对变压器三级螺旋绕组铁磁曲线进行了表征

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 DOI: 10.1016/j.jestch.2025.102268

Zhi Zhang

Interpreting frequency response analysis (FRA) data presents a formidable challenge in transformer fault diagnosis. Previous attempts to derive transfer functions (TF) for characterizing FRA curves have been both desirable and unsuccessful. The collected FRA data aims to represent the mechanical conditions of the transformer windings under examination. Nonetheless, the techniques applied to FRA results for assessing mechanical integrity face inherent uncertainty due to the lack of a direct link between the measured data and the electrical characteristics of an equivalent circuit (EC) consisting of resistance, inductance, and capacitance (RLC) components. As such, a rigorous analysis of the FRA data becomes crucial for a comprehensive assessment and interpretation of the mechanical state of these windings. The proposed investigation into TF is designed to offer a detailed mathematical interpretation of FRA characteristics, potentially enabling the early detection of potential faults through the derived TF and relevant parameters. This research paper revolves around the computation of TFs for power transformer helical windings. Consequently, a strong correlation emerges between the recorded FRA curves and the computed TF curves, affirming the precision of TF estimation and its significant contribution to advance FRA technology.

在变压器故障诊断中，频响分析（FRA）数据的解释是一个巨大的挑战。以前试图推导传递函数（TF）来表征FRA曲线的尝试既有可取的，也有失败的。收集的FRA数据旨在表示被检查的变压器绕组的机械状况。尽管如此，由于测量数据与等效电路（EC）（由电阻、电感和电容（RLC）组成）的电气特性之间缺乏直接联系，应用于评估机械完整性的FRA结果的技术面临固有的不确定性。因此，对FRA数据的严格分析对于全面评估和解释这些绕组的机械状态至关重要。该研究旨在为FRA特征提供详细的数学解释，从而通过推导出的TF和相关参数及早发现潜在故障。本研究围绕电力变压器螺旋绕组的热载荷计算展开。结果表明，实测的FRA曲线与计算的TF曲线之间存在很强的相关性，证实了TF估计的精度及其对FRA技术进步的重要贡献。

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引用次数: 0

A comprehensive review of noise-cancellation antenna sensors in ultra-high frequency: techniques, challenges, and future directions 超高频消噪天线传感器：技术、挑战和未来方向综述

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 DOI: 10.1016/j.jestch.2025.102271

Zongxing Wei , Mohamadariff Othman , Tarik Abdul Latef , Hazlee Azil Illias , S. M. Kayser Azam , Tengku Faiz Tengku Mohmed Noor Izam , Muhammad Ubaid Ullah , Mohamed Alkhatib , Mousa I. Hussein

This paper provides a comprehensive review of ultra-high frequency (UHF) noise-cancellation antenna (NCA) sensors. It identifies the critical challenges posed by noise interference in UHF bands and their impact on signal quality, particularly in partial discharge (PD) detection applications. The paper summarises the various types of noise present in the UHF range and highlights the importance of advanced design methods to enhance signal integrity. A significant contribution of this work is the detailed analysis of several noise-cancellation (NC) techniques, including the integrated feedline approach, embedded filter antenna technique, slot design modification, parasitic element incorporation, and shorting pin integration. These are systematically evaluated for their effectiveness in reducing interference. The review also provides a comparative analysis using tabular data, covering performance metrics such as NC implementation, radiation nulls (RN) frequency, bandwidth, gain, and other parameters. In addition, the paper identifies the most suitable techniques for PD detection and discusses their practical limitations. By highlighting potential directions for future research, this study offers valuable insights for advancing UHF antenna sensor design and its application in industrial PD monitoring systems.

本文综述了超高频（UHF）噪声消除天线（NCA）传感器的研究进展。它确定了UHF频段噪声干扰带来的关键挑战及其对信号质量的影响，特别是在局部放电（PD）检测应用中。本文总结了超高频范围内存在的各种类型的噪声，并强调了采用先进的设计方法来提高信号完整性的重要性。这项工作的一个重要贡献是详细分析了几种噪声消除（NC）技术，包括集成馈线方法、嵌入式滤波器天线技术、槽设计修改、寄生元件集成和短引脚集成。系统地评估它们在减少干扰方面的有效性。该综述还提供了使用表格数据的比较分析，包括性能指标，如NC实现、辐射零值（RN）频率、带宽、增益和其他参数。此外，本文确定了最适合PD检测的技术，并讨论了它们的实际局限性。通过强调未来研究的潜在方向，本研究为推进UHF天线传感器的设计及其在工业PD监测系统中的应用提供了有价值的见解。

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