Engineering Science and Technology-An International Journal-Jestech最新文献_第3页

Graph-based EEG analysis for seizure prediction enhanced with Kolmogorov–Arnold Networks and Self-Supervised Learning 基于图的脑电图分析与Kolmogorov-Arnold网络和自监督学习的癫痫发作预测

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 Epub Date: 2025-12-11 DOI: 10.1016/j.jestch.2025.102245

Safa Ben Atitallah , Maha Driss , Wadii Boulila , Anis Koubaa

Epilepsy is a common neurological disorder in which seizures pose major health problems, including sudden death. Despite advances in medicine, many patients do not react positively to treatments; therefore, early prediction of seizures is essential. However, precise detection remains challenging due to the complexity and variability of brain signals, as well as the scarcity of labeled clinical data to train conventional learning models. In this work, we propose SeizureNet-KAN, a novel fusion-based approach to predict seizures by transforming EEG data into graphs and integrating a custom-designed Kolmogorov–Arnold Network (KAN) layer into the Graph Convolutional Network (GCN). Furthermore, the proposed approach incorporates Self-Supervised Learning (SSL) to capture latent features from the EEG graph data effectively. To the best of our knowledge, this is the first time that the idea of KAN has been used in graph SSL. We demonstrated that the proposed SeizureNet-KAN model, implemented within an SSL framework, effectively enhances seizure prediction. Unlike traditional methods, our model captures complex non-linear EEG dynamics and reduces dependence on labeled data. The hybrid SSL pretraining strategy effectively extracts meaningful representations, improving generalization across patients. The experimental findings on the CHB-MIT dataset demonstrated that our proposed approach achieves excellent accuracy and resilience in seizure prediction, with a mean accuracy of 97.68%, precision of 97.72%, recall of 97.53%, F1-score of 97.59%, and AUC of 99.28%. These results highlight the potential of SSL-driven graph models for real-time seizure prediction in personalized healthcare applications.

癫痫是一种常见的神经系统疾病，癫痫发作会造成严重的健康问题，包括猝死。尽管医学取得了进步，但许多患者对治疗的反应并不积极；因此，早期预测癫痫发作是至关重要的。然而，由于大脑信号的复杂性和可变性，以及用于训练传统学习模型的标记临床数据的稀缺性，精确检测仍然具有挑战性。在这项工作中，我们提出了一种新的基于融合的方法，通过将EEG数据转换为图形并将定制设计的Kolmogorov-Arnold网络（KAN）层集成到图卷积网络（GCN）中来预测癫痫发作。此外，该方法还结合了自监督学习（Self-Supervised Learning， SSL）来有效地捕获脑电信号数据中的潜在特征。据我们所知，这是第一次在图形SSL中使用KAN的思想。我们证明了提出的在SSL框架内实现的SeizureNet-KAN模型有效地增强了癫痫发作的预测。与传统方法不同，我们的模型捕获了复杂的非线性脑电图动态，减少了对标记数据的依赖。混合SSL预训练策略有效地提取有意义的表征，提高了患者的泛化程度。在CHB-MIT数据集上的实验结果表明，我们提出的方法在癫痫发作预测中具有良好的准确性和弹性，平均准确率为97.68%，精密度为97.72%，召回率为97.53%，f1得分为97.59%，AUC为99.28%。这些结果突出了ssl驱动的图形模型在个性化医疗保健应用中用于实时癫痫预测的潜力。

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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 Epub Date: 2025-12-29 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 Epub Date: 2026-01-02 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

State-of-the-art soft robotic systems for unstructured and real-world environments: A systematic review 用于非结构化和现实世界环境的最先进的软机器人系统：系统综述

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 Epub Date: 2025-12-22 DOI: 10.1016/j.jestch.2025.102264

Arameh Eyvazian , Yooseob Song , Chibukhchyan Hovhannes , Ardeshir Savari , Narinderjit Singh Sawaran Singh

Soft robotics has emerged as a transformative paradigm in automation, offering unprecedented compliance, adaptability, and safety for operation in unstructured and dynamic environments. This study systematically reviews the latest advances in soft robotic systems, spanning novel material innovations, intelligent hybrid architectures, and cutting-edge actuation and control strategies. Key developments in integration with artificial intelligence, computer vision, and machine learning are highlighted, enabling enhanced perception, autonomy, and adaptive behavior. Application-driven case studies in healthcare, exploration, and search-and-rescue showcase the evolving capabilities of soft robots in challenging real-world settings. Persistent challenges, such as untethered operation, robust sensorimotor integration, scalable fabrication, and interpretable AI, are discussed alongside emerging multidisciplinary solutions. The review concludes by outlining future research directions, emphasizing the need for unified codesign approaches and collaboration across robotics, materials science, AI, and biology. This synthesis provides a roadmap for advancing next-generation soft robotic systems, aiming to bridge the gap between laboratory innovations and impactful deployment in complex, unpredictable environments, in support of industrial and innovation progress.

软机器人已经成为自动化的变革范例，为非结构化和动态环境中的操作提供前所未有的合规性、适应性和安全性。本研究系统地回顾了软机器人系统的最新进展，包括新型材料创新、智能混合架构以及前沿驱动和控制策略。重点介绍了与人工智能、计算机视觉和机器学习集成的关键发展，从而增强了感知、自主性和自适应行为。医疗保健、勘探和搜救领域的应用程序驱动案例研究展示了软机器人在具有挑战性的现实环境中不断发展的能力。持续的挑战，如不受约束的操作，强大的感觉运动集成，可扩展的制造和可解释的人工智能，与新兴的多学科解决方案一起讨论。该综述总结了未来的研究方向，强调需要统一的协同设计方法和机器人、材料科学、人工智能和生物学之间的协作。这一综合为推进下一代软机器人系统提供了路线图，旨在弥合实验室创新与复杂、不可预测环境中有影响力的部署之间的差距，以支持工业和创新进展。

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引用次数: 0

Design and implementation of PIλDμ controller for ROVs: Thruster modeling, controller parameter optimization, and FPGA realization rov pi - λ dμ控制器的设计与实现：推力器建模、控制器参数优化及FPGA实现

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 Epub Date: 2025-12-22 DOI: 10.1016/j.jestch.2025.102261

Hakan Ersoy, Berke Akgül, Emin Akpınar, Aslihan Kartci, Umut Engin Ayten

Remotely operated and autonomous underwater vehicles (ROVs/AUVs) operate in a harsh environment dominated by nonlinear hydrodynamics, strong coupling, and wave–current disturbances. In most of the existing literature, surge-axis motion is still regulated by integer-order PID controllers that are tuned either heuristically or via single-scenario optimization. Such designs often exhibit limited robustness: their performance degrades significantly under severe noise, targeted wave excitation, or time-varying operational profiles. These limitations motivate the use of fractional-order control and more systematic tuning procedures. This paper investigates fractional-order

{PI}^{λ} D^{μ}

(FOPID) controllers for surge control and compares two popular meta-heuristics, Particle Swarm Optimization (PSO) and Differential Evolution Algorithm (DEA), in comparison with classical PID. A fourth-order surge plant model is first obtained via system identification of experimental data from a BlueRobotics T200 thruster. Then, PSO and DEA are used to tune both PID and

{PI}^{λ} D^{μ}

parameters over a multi-scenario cost function that combines step-response quality, disturbance rejection, and control effort. The resulting controllers are evaluated under four increasingly demanding tests: noiseless step tracking, severe white-noise excitation, sinusoidal “storm” disturbance, and a final scenario with time-varying set-points under the same storm condition. Across all 16 scalar performance metrics (IAE, ISE, and, ITAE over four tests), the DEA-tuned

{PI}^{λ} D^{μ}

achieves the best value in 12 cases, consistently outperforming both PID designs and the PSO-based

{PI}^{λ} D^{μ}

. In the most demanding final test (multi-level reference + storm), it reduces the integral time-weighted absolute error ITAE from 0.1065 (best PID) to 0.0893, i.e., by approximately 16%, while preserving competitive control effort. These results provide quantitative evidence that DEA-tuned

{PI}^{λ} D^{μ}

offers a more robust and energy-aware solution for single-axis surge control in ROV/AUV applications.

遥控和自主水下航行器（rov / auv）在非线性流体动力学、强耦合和波流干扰的恶劣环境中工作。在大多数现有文献中，浪涌轴运动仍然由启发式或单场景优化调谐的整阶PID控制器调节。这种设计通常表现出有限的鲁棒性：在严重噪声、目标波激励或时变操作剖面下，它们的性能显著下降。这些限制促使使用分数阶控制和更系统的调优过程。本文研究了分数阶pi - λ dμ (FOPID)控制器在喘振控制中的应用，并将两种常用的元启发式算法粒子群优化（PSO）和差分进化算法（DEA）与经典PID进行了比较。首先通过对BlueRobotics T200推进器实验数据的系统识别，得到了一个四阶调压装置模型。然后，PSO和DEA用于在结合阶跃响应质量，干扰抑制和控制努力的多场景成本函数上调整PID和PIλDμ参数。所得到的控制器在四种要求越来越高的测试中进行评估：无噪声步进跟踪、严重白噪声激励、正弦“风暴”干扰，以及在相同风暴条件下具有时变设值的最终场景。在所有16个标量性能指标（IAE， ISE和ITAE超过四次测试）中，dea调谐的PIλDμ在12种情况下达到最佳值，始终优于PID设计和基于pso的PIλDμ。在最苛刻的最终测试（多级参考+风暴）中，它将积分时间加权绝对误差ITAE从0.1065（最佳PID）降低到0.0893，即大约16%，同时保持竞争性控制努力。这些结果提供了定量证据，表明dea调谐PIλDμ为ROV/AUV应用中的单轴浪涌控制提供了更强大和能量感知的解决方案。

{"title":"Design and implementation of PIλDμ controller for ROVs: Thruster modeling, controller parameter optimization, and FPGA realization","authors":"Hakan Ersoy, Berke Akgül, Emin Akpınar, Aslihan Kartci, Umut Engin Ayten","doi":"10.1016/j.jestch.2025.102261","DOIUrl":"10.1016/j.jestch.2025.102261","url":null,"abstract":"<div><div>Remotely operated and autonomous underwater vehicles (ROVs/AUVs) operate in a harsh environment dominated by nonlinear hydrodynamics, strong coupling, and wave–current disturbances. In most of the existing literature, surge-axis motion is still regulated by integer-order PID controllers that are tuned either heuristically or via single-scenario optimization. Such designs often exhibit limited robustness: their performance degrades significantly under severe noise, targeted wave excitation, or time-varying operational profiles. These limitations motivate the use of fractional-order control and more systematic tuning procedures. This paper investigates fractional-order <span><math><mrow><msup><mrow><mi>PI</mi></mrow><mrow><mi>λ</mi></mrow></msup><msup><mrow><mi>D</mi></mrow><mrow><mi>μ</mi></mrow></msup></mrow></math></span> (FOPID) controllers for surge control and compares two popular meta-heuristics, Particle Swarm Optimization (PSO) and Differential Evolution Algorithm (DEA), in comparison with classical PID. A fourth-order surge plant model is first obtained via system identification of experimental data from a BlueRobotics T200 thruster. Then, PSO and DEA are used to tune both PID and <span><math><mrow><msup><mrow><mi>PI</mi></mrow><mrow><mi>λ</mi></mrow></msup><msup><mrow><mi>D</mi></mrow><mrow><mi>μ</mi></mrow></msup></mrow></math></span> parameters over a multi-scenario cost function that combines step-response quality, disturbance rejection, and control effort. The resulting controllers are evaluated under four increasingly demanding tests: noiseless step tracking, severe white-noise excitation, sinusoidal “storm” disturbance, and a final scenario with time-varying set-points under the same storm condition. Across all 16 scalar performance metrics (IAE, ISE, and, ITAE over four tests), the DEA-tuned <span><math><mrow><msup><mrow><mi>PI</mi></mrow><mrow><mi>λ</mi></mrow></msup><msup><mrow><mi>D</mi></mrow><mrow><mi>μ</mi></mrow></msup></mrow></math></span> achieves the best value in 12 cases, consistently outperforming both PID designs and the PSO-based <span><math><mrow><msup><mrow><mi>PI</mi></mrow><mrow><mi>λ</mi></mrow></msup><msup><mrow><mi>D</mi></mrow><mrow><mi>μ</mi></mrow></msup></mrow></math></span>. In the most demanding final test (multi-level reference + storm), it reduces the integral time-weighted absolute error ITAE from 0.1065 (best PID) to 0.0893, i.e., by approximately 16%, while preserving competitive control effort. These results provide quantitative evidence that DEA-tuned <span><math><mrow><msup><mrow><mi>PI</mi></mrow><mrow><mi>λ</mi></mrow></msup><msup><mrow><mi>D</mi></mrow><mrow><mi>μ</mi></mrow></msup></mrow></math></span> offers a more robust and energy-aware solution for single-axis surge control in ROV/AUV applications.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102261"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neurocomputing capability evaluation of FHN model-based MSNN architectures with different window functions through the XOR problem 通过异或问题评估基于FHN模型的不同窗函数MSNN体系结构的神经计算能力

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 Epub Date: 2025-12-19 DOI: 10.1016/j.jestch.2025.102259

Ahmet Yasin Baran , Obeid Abdalla , İsmail Öztürk , Nimet Korkmaz , Recai Kiliç

This study focuses on utilizing Memristive Spiking Neural Network (MSNN) structures based on the Fitzhugh-Nagumo (FHN) neuron model coupled with the Voltage ThrEshold Adaptive Memristor (VTEAM), and constrained by five different window functions, to solve the Exclusive OR (XOR) problem through both numerical simulations and real-time hardware implementations. Specifically, two input and two output neurons have been constructed as the 2

\times

2 MSNN architectures, and the Joglekar, Biolek, Strukov, Prodromakis, and Blackman window functions define their memristor models. The optimal memductance values for synaptic connections in these five network configurations are set using the Spike-Timing-Dependent Plasticity (STDP) learning rule combined with the Winner-Takes-All (WTA) algorithm. The performance of these networks is evaluated based on their efficiency in solving the XOR problem. In this context, the XOR problem has been conducted as an image formed by a 2-pixel array. These pixels are transmitted as noisy signals from the MSNN input layer, where input neurons convert them into spike activities. These spike activities are then integrated through the memristive synapse layer and forwarded to the output layer as excitatory current signals, enabling the output layer to classify the input correctly. The simulation and Field-Programmable Gate Array (FPGA) hardware implementation results exhibit strong consistency. This work demonstrates, for the first time, the capacity of MSNNs to solve nonlinear problems by providing both simulation-based and hardware-based solutions to the XOR problem using various MSNN architectures with different window functions.

本研究主要利用基于Fitzhugh-Nagumo （FHN）神经元模型和电压阈值自适应记忆电阻器（VTEAM）的记忆尖峰神经网络（MSNN）结构，在五种不同窗口函数的约束下，通过数值模拟和实时硬件实现来解决异或（XOR）问题。具体来说，两个输入和两个输出神经元被构建为2 × 2 MSNN架构，Joglekar、Biolek、Strukov、Prodromakis和Blackman窗口函数定义了它们的忆阻器模型。在这五种网络配置中，使用峰值时间依赖的可塑性（STDP）学习规则结合赢者通吃（WTA）算法来确定突触连接的最佳电导值。这些网络的性能是根据它们解决异或问题的效率来评估的。在这种情况下，异或问题已作为由2像素阵列形成的图像进行处理。这些像素作为噪声信号从MSNN输入层传输，其中输入神经元将其转换为尖峰活动。然后，这些尖峰活动通过记忆突触层被整合，并作为兴奋性电流信号转发到输出层，使输出层能够正确地分类输入。仿真结果与现场可编程门阵列（FPGA）硬件实现结果具有较强的一致性。这项工作首次展示了MSNN解决非线性问题的能力，通过使用具有不同窗口函数的各种MSNN架构，为异或问题提供基于仿真和基于硬件的解决方案。

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引用次数: 0

Tracking with attention: A review of transformer-based object tracking 关注跟踪：基于变压器的目标跟踪综述

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 Epub Date: 2025-12-20 DOI: 10.1016/j.jestch.2025.102263

Seyed Alireza Khoshnevis , Abdollah Amirkhani

Traditional object tracking methods are often based on convolutional neural networks and handcrafted feature extraction techniques where they have seen remarkable success. However, these methods still face limitations in capturing global dependencies and contextual relationships in complex scenarios. Transformers, which were initially introduced to the field of natural language processing, have transfigured vision tasks by leveraging the self-attention mechanisms and global feature modeling capabilities. One of the tasks that has been most affected by the use of transformers is the object tracking task. This review explores the transformative impact of attention-based architectures in object tracking, and provides a comprehensive analysis of the current frameworks and their core principles. The ability of the attention mechanism to capture local and global dependencies and to associate queries between frames, has helped transformer-based models to achieve state-of-the-art performance. The utilization of transformers in object tracking has drastically increased over the past few years, initiating the new “tracking-by-attention” paradigm. This work focuses on different applications of transformer architecture in both single and multi-object tracking where each task is divided further by methodology. End-to-end approaches and hybrid fusion models that leverage additional data for tracking are also discussed. The models that are discussed, are categorized by their main approaches and transformer usage, and challenges such as computational cost and scalability are outlined, along with future research opportunities informed by successful methods. By examining recent advancements, this review is intended to advance understanding of transformer-based tracking capabilities and to promote continued innovation in this rapidly evolving field.

传统的目标跟踪方法通常基于卷积神经网络和手工特征提取技术，在这些技术上取得了显著的成功。然而，这些方法在捕获复杂场景中的全局依赖关系和上下文关系方面仍然面临限制。变形金刚最初被引入到自然语言处理领域，通过利用自注意机制和全局特征建模能力来改变视觉任务。受变压器使用影响最大的任务之一是目标跟踪任务。本文探讨了基于注意力的结构在目标跟踪中的变革性影响，并对当前的框架及其核心原理进行了全面分析。注意机制捕获本地和全局依赖关系以及在帧之间关联查询的能力，帮助基于转换器的模型实现了最先进的性能。在过去几年中，变形器在目标跟踪中的应用急剧增加，开创了新的“注意跟踪”范式。这项工作着重于变压器体系结构在单目标和多目标跟踪中的不同应用，其中每个任务通过方法进一步划分。还讨论了利用附加数据进行跟踪的端到端方法和混合融合模型。所讨论的模型根据其主要方法和变压器使用情况进行了分类，并概述了计算成本和可扩展性等挑战，以及成功方法所提供的未来研究机会。通过检查最近的进展，本综述旨在促进对基于变压器的跟踪能力的理解，并促进这个快速发展领域的持续创新。

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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 Epub Date: 2026-01-02 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 Epub Date: 2026-01-02 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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Front Matter 1 - Full Title Page (regular issues)/Special Issue Title page (special issues) 封面1 -完整的扉页（每期）/特刊扉页（每期）

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 Epub Date: 2026-01-18 DOI: 10.1016/S2215-0986(26)00009-1

引用次数: 0