Engineering Science and Technology-An International Journal-Jestech最新文献_第4页

Risk assessment method for stator transposition bar of AC generator based on fault dataset and Markov chain 基于故障数据集和马尔可夫链的交流发电机定子换位杆风险评估方法

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 Epub Date: 2025-12-10 DOI: 10.1016/j.jestch.2025.102249

Chenguang Wang, Hecheng Yang, Lei Ni, Xu Bian, Dongmei Wang, Yanping Liang

The transposition bar is an important component in large generators, the insulation fault in transposition bars is extremely dangerous for the daily operation of generators. The circulating current losses of the stator windings are usually considered in traditional transposition designs. The risk and hazard of the transposition bar inter-strands short-circuit fault are neglected. Therefore, it is necessary to evaluate the risk of the transposition bars. This paper proposes a new risk assessment method for transposition bars. This method enables the comprehensive risk assessment of the design, circulating current losses, and fault risk of transposition bars. Firstly, the strand current of all short-circuit fault schemes of the transposition bar is calculated by the leakage reactance electromotive potential method and the fault dataset is established. Further, based on the fault dataset, risk indexes: severity and probability are proposed. The probability of short-circuit fault for each category is calculated by Markov chain. Finally, the risk indexes of three transposition types are calculated and compared with the traditional index. The risk of 360° and 360°+25 mm transposition is 7.5 times larger than that of 308° transposition. A risk assessment method for the transposition bar is proposed, which considers both operation efficiency and reliability. This method provides a new basis for the transposition designs.

换位排是大型发电机的重要部件，换位排的绝缘故障对发电机的日常运行是极其危险的。传统的换位设计通常考虑定子绕组的循环电流损耗。忽略了换位杆股间短路故障的危险性和危害。因此，有必要对换位杆的风险进行评估。提出了一种新的换位杆风险评估方法。该方法能够对换位杆的设计、环流损耗和故障风险进行综合风险评估。首先，采用漏抗电势法计算换位杆各短路故障方案的股电流，建立故障数据集；进一步，基于故障数据集，提出了严重程度和概率风险指标。利用马尔可夫链计算各类别短路故障的概率。最后，计算了三种换位类型的风险指数，并与传统指数进行了比较。360°和360°+ 25mm转位的风险是308°转位的7.5倍。提出了一种兼顾运行效率和可靠性的换位杆风险评估方法。该方法为换位设计提供了新的依据。

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引用次数: 0

Optimization of modification parameters of face gear pair based on TCA-NRBPNN-HYPE hybrid drive model 基于TCA-NRBPNN-HYPE混合驱动模型的面齿轮副修形参数优化

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: 2026-01-10 DOI: 10.1016/j.jestch.2025.102274

Kun He , Ronghao Li , Yongquan Chen , Yachao Jia , Guolong Li

To enhance the meshing characteristics of the face gear pair and determine the optimal modification parameters, an optimization model of modification parameters, based on the TCA-NRBPNN-HYPE (Tooth Contact Analysis-Newton Raphson Back Propagation Neural Network-Hyperparameter Optimization) hybrid drive model is proposed. Firstly, a dual weight modification curve is introduced to modify the tooth surface of face gears, and the TCA model is employed to accurately obtain the meshing characteristics parameters of the modified gear pair, including contact position, transmission error, and contact stress. based on the modification parameters and TCA results, an NRBPNN prediction model is established to achieve mapping from modification parameters to meshing characteristics. Finally, the HYPE optimization model is applied to globally optimize the prediction results and obtain the optimal modification parameter combination. The results show that the optimal design reduces the contact position parameter from 4.15 to 1.50, the transmission error from 2.98″ to 0.314″, and the contact stress from 566.30 MPa to 292.33 MPa. These results indicate that the proposed method effectively improves the meshing characteristics and reliability of face gear pair.

为了提高面齿轮副的啮合特性，确定最优修形参数，提出了一种基于TCA-NRBPNN-HYPE（齿接触分析- newton Raphson反向传播神经网络-超参数优化）混合驱动模型的修形参数优化模型。首先，引入双权值修形曲线对面齿轮齿面进行修形，利用TCA模型精确获取修形后齿轮副的啮合特性参数，包括接触位置、传动误差和接触应力。基于修正参数和TCA结果，建立了NRBPNN预测模型，实现了修正参数与网格特征的映射。最后，应用HYPE优化模型对预测结果进行全局优化，得到最优修正参数组合。结果表明：优化设计后，接触位置参数由4.15降至1.50，传动误差由2.98″降至0.314″，接触应力由566.30 MPa降至292.33 MPa。结果表明，该方法有效地改善了面齿轮副的啮合特性和可靠性。

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引用次数: 0

Experimental investigation of hydration temperature variation in reinforced concrete beam construction under winter conditions 冬季条件下钢筋混凝土梁结构水化温度变化试验研究

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: 2025-12-13 DOI: 10.1016/j.jestch.2025.102243

Burak Gedik , Dilek Okuyucu , İlker Kazaz , Ahmet Ferhat Bingöl , Muhammet Şahin , Burak Şahin

This study investigates the early-age thermal behaviour and hydration process of large-scale reinforced concrete beams cast under both winter and summer conditions, with a focus on the influence of fresh concrete temperature, admixture use, and environmental factors. Temperature monitoring was conducted for a minimum of 72 h using embedded sensors, and special attention was given to winter concreting under natural atmospheric conditions. Two winter casting scenarios were defined: Winter-1, with ambient temperatures below –15 °C, and Winter-2, ranging between –15 °C and +5 °C. Results show that fresh concrete temperature is the dominant factor in initiating hydration, with admixtures alone proving insufficient at low temperatures. Solar exposure and daytime casting significantly improved hydration behaviour, especially when specimens were covered with greenhouse plastic. In contrast, night-time casting under sub-zero temperatures delayed hydration by several hours, particularly in unheated conditions. Summer specimens exhibited consistent hydration aligned with reference laboratory behaviour. The temperature–time factor development of the without admixture winter group specimens average decreased by 39 % after 48 h. The findings highlight the importance of controlling fresh concrete temperature and utilizing environmental aids in cold weather concreting.

本研究研究了冬季和夏季条件下大型钢筋混凝土梁的早期热行为和水化过程，重点研究了新混凝土温度、外加剂使用和环境因素的影响。使用嵌入式传感器进行了至少72小时的温度监测，并特别注意自然大气条件下的冬季混凝土。定义了两种冬季铸造场景：冬季-1，环境温度低于-15°C，冬季-2，环境温度介于-15°C至+5°C之间。结果表明，初拌混凝土的温度是水化的主导因素，单独使用外加剂在低温条件下是不够的。日晒和日间浇铸显著改善了水化行为，特别是当标本被温室塑料覆盖时。相比之下，在零度以下的夜间浇筑会使水合作用延迟几个小时，特别是在没有加热的条件下。夏季标本表现出与参考实验室行为一致的水化。无外加剂冬季组试件的温度-时间因子发展在48 h后平均下降39%。研究结果强调了在寒冷天气混凝土中控制新拌混凝土温度和利用环境助剂的重要性。

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引用次数: 0

Hybrid fragile image watermarking for tamper detection, localization and dual self-recovery 用于篡改检测、定位和双重自恢复的混合脆弱图像水印

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.2025.102266

Aditya Kumar Sahu , Monalisa Sahu

This paper presents a novel image watermarking framework that effectively addresses the issue of random block mapping. This phenomenon compromises tampered regions and their corresponding recovery blocks, resulting in irretrievable image data. To mitigate the random block mapping issue, a crisscross block mapping strategy (CrCsBMS) is proposed to enhance the robustness of block mapping by ensuring non-randomised reference allocation. The authentication bit generation leverages Gram-Schmidt Orthonormalization (GSO), extracting pivotal image characteristics, such as mean intensity, variance, and edge strength, thereby fortifying the integrity verification mechanism. The hybrid embedding strategy integrates discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) to maintain an optimal balance between imperceptibility and embedding capacity, while distortion compensated quantization index modulation (DC-QIM) is employed for recovery bit encoding. A dual self-recovery mechanism incorporating bilinear interpolation-based inpainting and an 8-neighborhood method with a 255-color range scaling (255-CRS) is introduced, significantly augmenting recovery efficiency and ensuring precise restoration of tampered pixels. Experimental analysis demonstrates superior imperceptibility, robustness against image processing attacks, and reduced computational complexity compared to contemporary techniques. The proposed scheme achieves an average PSNR of 52.22 dB, an SSIM of 0.9983, and a payload capacity of 1 bit per pixel, surpassing existing self-recovery watermarking frameworks in both accuracy and resilience.

本文提出了一种新的图像水印框架，有效地解决了随机块映射问题。这种现象危及篡改区域及其相应的恢复块，导致不可恢复的图像数据。为了缓解随机块映射问题，提出了一种交错块映射策略（CrCsBMS），通过确保非随机引用分配来增强块映射的鲁棒性。认证位的生成利用Gram-Schmidt正交规格化（GSO），提取关键图像特征，如平均强度、方差和边缘强度，从而加强完整性验证机制。混合嵌入策略将离散小波变换（DWT）、离散余弦变换（DCT）和奇异值分解（SVD）相结合，在隐密性和嵌入容量之间保持最佳平衡，同时采用失真补偿量化指标调制（DC-QIM）进行恢复位编码。采用双线性插值法和8邻域255色范围缩放法（255-CRS）的双重自恢复机制，大大提高了恢复效率，确保了篡改像素的精确恢复。实验分析表明，与当代技术相比，优越的不可感知性，对图像处理攻击的鲁棒性以及降低的计算复杂性。该方案的平均PSNR为52.22 dB， SSIM为0.9983，有效载荷容量为1比特/像素，在精度和弹性方面都优于现有的自恢复水印框架。

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引用次数: 0

Towards automated metaphase cell detection using foundation models: A SAM and DINO-based approach 使用基础模型实现中期细胞自动检测：基于SAM和dino的方法

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 Epub Date: 2025-12-19 DOI: 10.1016/j.jestch.2025.102262

M. Cihad Arslanoglu , Abdulkadir Albayrak , Huseyin Acar

Karyotyping is a widely used laboratory technique to detect abnormalities in chromosomes. Karyotyping consists of many steps, including collecting samples, cell culturing and metaphase cell detection. Cytogeneticists examine all the images manually and annotate metaphase cells that is tedious and time consuming after collecting the samples and creating cell cultures. Metaphase cells can be detected by using computer-aided techniques. In this study, Segment Anything Model (SAM) and Self-Distillation with No Labels (DINO) foundation models were employed for metaphase detection task to minimize training time and costs. Potential metaphase regions were detected using SAM foundation segmentation model and these regions were given to classification models to identify metaphase cells. ResNet-50, a convolutional neural network algorithm, Vision Transformer (ViT) and Cross-Covariance Image Transformer (XCIT) based backbones were used in metaphase cell identification step. The results show that foundation models can give promising results on metaphase cell detection as much as supervised deep learning-based models. As a result, while supervised XCIT model with small architecture exhibit 0.9966 True Positive Ratio (TPR) and self-supervised ViT base accomplish 0.9961 TPR.

染色体组型是一种广泛应用于检测染色体异常的实验室技术。核型分型包括许多步骤，包括收集样本、细胞培养和中期细胞检测。细胞遗传学家手动检查所有图像，并在收集样本和创建细胞培养后对中期细胞进行繁琐且耗时的注释。使用计算机辅助技术可以检测中期细胞。本研究采用分段任意模型（SAM）和无标签自蒸馏（DINO）基础模型进行中期检测任务，以最大限度地减少训练时间和成本。利用SAM基础分割模型检测到潜在的中期区域，并将这些区域提供给分类模型进行中期细胞的识别。中期细胞鉴定采用卷积神经网络算法ResNet-50，基于视觉变换（Vision Transformer, ViT）和交叉协方差图像变换（Cross-Covariance Image Transformer， XCIT）的主干。结果表明，基础模型与基于监督的深度学习模型一样，在中期细胞检测方面具有良好的效果。结果表明，具有小结构的监督式xit模型的真正比（TPR）为0.9966，自监督式ViT库的真正比为0.9961。

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引用次数: 0

A FEM-ANN framework to estimate the on-diagonal elements of the impedance matrix in a Cochlear Implant 一种估算人工耳蜗阻抗矩阵对角元素的FEM-ANN框架

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: 2026-01-08 DOI: 10.1016/j.jestch.2025.102273

M.J. Hernández-Gil , A. Ramos-de-Miguel , D. Greiner , D. Benítez , G. Montero , J.M. Escobar

Accurate estimation of the impedance matrix is essential for optimizing cochlear implant (CI) performance, yet the on-diagonal terms, that represent the contact impedances of electrodes, remain poorly characterized in existing models. In this work, we first analyze these on-diagonal terms and highlight their impact on electric field distribution. We then revisit the classic linear extrapolation approach and introduce two novel extrapolation methods to enhance prediction accuracy. To capture patient-specific variability, real impedance measurements are incorporated into a resistive–conductive finite-element method (FEM) model, whose matrices serve as the basis for a supervised neural network. The network is trained and validated on a diverse dataset of FEM-derived impedance matrices, enabling robust generalization across electrode configurations. Benchmarking against state-of-the-art techniques shows that our hybrid FEM-ANN framework reduces prediction error for diagonal terms. Moreover, when used in multipolar stimulation strategies, the ANN-based impedance matrices yield comparable focalization while requiring lower electrical power. Our results demonstrate that combining physical modeling with data-driven methods produces more reliable and efficient impedance estimates, paving the way for improved CI fitting and patient outcomes.

阻抗矩阵的准确估计对于优化人工耳蜗（CI）性能至关重要，然而，在现有模型中，代表电极接触阻抗的对角线项仍然很差。在这项工作中，我们首先分析了这些对角线项，并强调了它们对电场分布的影响。然后，我们回顾了经典的线性外推方法，并引入了两种新的外推方法来提高预测精度。为了捕获患者特异性的可变性，实际阻抗测量被纳入电阻-导电有限元方法（FEM）模型，其矩阵作为监督神经网络的基础。该网络在fem衍生阻抗矩阵的不同数据集上进行训练和验证，从而实现跨电极配置的鲁棒泛化。对最先进技术的基准测试表明，我们的混合FEM-ANN框架减少了对角项的预测误差。此外，当用于多极刺激策略时，基于人工神经网络的阻抗矩阵可以产生相当的聚焦，同时需要更低的电力。我们的研究结果表明，将物理建模与数据驱动方法相结合，可以产生更可靠、更有效的阻抗估计，为改善CI拟合和患者预后铺平了道路。

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引用次数: 0

Hybrid camera and LiDAR sensor for payload transfer control on RTGC using the Lyapunov-Kinetic energy approach under varying light levels 混合摄像机和激光雷达传感器在不同光照水平下使用李亚普诺夫-动能方法进行RTGC有效载荷转移控制

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2025-12-01 Epub Date: 2025-10-30 DOI: 10.1016/j.jestch.2025.102206

Steven Bandong , Cristian MP Napitupulu , Yul Yunazwin Nazaruddin , Endra Joelianto

This paper presents an advanced approach for automating Rubber-Tired Gantry Crane (RTGC) systems, integrating a hybrid sensor and optimized control methods to enhance container handling in diverse port environments. The proposed hybrid sensor combines LiDAR and camera data to create depth images, utilizing YOLOv11 for accurate container detection while reducing the computation demands typically associated with LiDAR data. This integration provides reliable sensing capabilities even in low-light or variable lighting conditions. Additionally, a Lyapunov-Kinetic Energy-based control system is introduced to optimize container positioning and sway angle reduction, with parameters refined using Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), and Bayesian based optimization, Gaussian Process (GP) and GP with Markov Chain Monte Carlo (GP-MCMC). To address system nonlinearities, particularly deadzone effects, a Type-2 Fuzzy Precompensator is employed, enhancing control stability and accuracy. Experimental results on a laboratory-scale RTGC prototype demonstrate that the combination of hybrid sensing, optimized Lyapunov control, and deadzone mitigation delivers strong automation performance under varying lighting conditions. The best optimization result was achieved using Gaussian Process optimization, with a mean squared error (MSE) of 0.007 for the steady-state position and 0.005 for the sway angle. Additional tests were conducted under variations in setpoint, rope length, and payload mass. The results show robust performance under these uncertainties, with MSE values for each state remaining close to those previously reported.

本文提出了一种先进的橡胶轮胎龙门起重机（RTGC）系统自动化方法，该方法集成了混合传感器和优化控制方法，以提高在不同港口环境下的集装箱处理能力。该混合传感器结合了激光雷达和相机数据来创建深度图像，利用YOLOv11进行精确的容器检测，同时减少了通常与激光雷达数据相关的计算需求。这种集成即使在低光或可变照明条件下也提供可靠的传感能力。此外，引入了一种基于Lyapunov-Kinetic energy的控制系统，通过粒子群优化（PSO）、授粉算法（FPA）和基于贝叶斯的优化、高斯过程（GP）和马尔科夫链蒙特卡罗（GP- mcmc）的GP优化参数，对集装箱定位和减少摆动角进行优化。为了解决系统的非线性，特别是死区效应，采用了2型模糊预补偿器，提高了控制的稳定性和精度。在实验室规模的RTGC样机上的实验结果表明，混合传感、优化李亚普诺夫控制和死区缓解的组合在不同光照条件下具有很强的自动化性能。采用高斯过程优化获得最佳优化结果，稳态位置的均方误差（MSE）为0.007，摇摆角的均方误差（MSE）为0.005。在设定值、绳索长度和有效载荷质量的变化下进行了额外的测试。在这些不确定性下，结果显示出稳健的性能，每个状态的MSE值都接近于先前报道的值。

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引用次数: 0

Opposition learning & PID-based grey wolf optimizer with swarm intelligence for improved load forecasting 基于群智能的基于对立学习和pid的灰狼优化算法改进负荷预测

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2025-12-01 Epub Date: 2025-11-17 DOI: 10.1016/j.jestch.2025.102237

Murat Akil , Ugur Yuzgec , Emrah Dokur

Electricity load forecasting helps grid operators to make informed decisions in terms of planning and managing demand response. Electric power companies utilize load forecasting to make optimal power management. Therefore, accurate forecasting of total electrical load in a region is of great importance. To overcome this problem, this paper proposes a multi-layer perceptron (MLP) hybrid model that contain Swarm Decomposition (SWD) aided Opposition Learning and proportional–integral–derivative based Grey Wolf Optimizer (OLPIDGWO) using historical electricity demand data in non-consecutive years. The dataset used for load forecasting includes loads with different characteristics. Empirical mode decomposition method and swarm decomposition are applied to the original data to decompose the data features. Then, MLP hybrid model is applied for each decomposed signal of the data as the load forecasting model. The advantages of the proposed hybrid model include a significant improvement in forecast accuracy and capture of local maxima. The advantage of the proposed hybrid model over other hybrid models and existing single forecasting models is also verified by error performance metrics. The result of the hybrid forecast model shows that the error performance metrics of MSE, RMSE, MAE and MAPE for the year 2020 are 35 MW, 0.591MW, 0.452MW and 1.47%, respectively, and the error performance metrics of MSE, RMSE, MAE and MAPE for the year 2022 are 22.6MW, 0.475MW, 0.367MW and 1.21%, respectively. The results reveal the SWD decomposition and GWO optimizer module of MLP improve the load prediction, and the proposed model outperforms other load prediction models.

电力负荷预测有助于电网运营商在规划和管理需求响应方面做出明智的决策。电力公司利用负荷预测进行电力优化管理。因此，准确预测某一地区的总电力负荷是十分重要的。为了克服这一问题，本文提出了一种多层感知器（MLP）混合模型，该模型包含群体分解（SWD）辅助的对立学习和基于比例-积分-导数的灰狼优化器（OLPIDGWO），使用非连续年的历史电力需求数据。用于负荷预测的数据集包括具有不同特征的负荷。对原始数据采用经验模态分解方法和群分解方法对数据特征进行分解。然后，对数据的每个分解信号采用MLP混合模型作为负荷预测模型。该混合模型的优点包括预测精度的显著提高和局部极大值的捕获。通过误差性能指标验证了混合预测模型相对于其他混合预测模型和现有单一预测模型的优越性。混合预测模型结果表明，2020年MSE、RMSE、MAE和MAPE的误差性能指标分别为35 MW、0.591MW、0.452MW和1.47%，2022年MSE、RMSE、MAE和MAPE的误差性能指标分别为22.6MW、0.475MW、0.367MW和1.21%。结果表明，MLP的SWD分解和GWO优化器模块改善了负荷预测，该模型优于其他负荷预测模型。

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引用次数: 0

A hybrid PatchNet-Attention based deep learning architecture for multi-type fabric defect classification in textile manufacturing and quality control 基于PatchNet-Attention的混合深度学习体系结构在纺织制造和质量控制中的多类型织物缺陷分类

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2025-12-01 Epub Date: 2025-11-17 DOI: 10.1016/j.jestch.2025.102231

Isil Karabey Aksakalli , Kubra Demir , Ozlem Sokmen

Accurate and timely detection of defects that may occur on fabric surfaces is a critical requirement for ensuring sustainable production quality in the textile industry. Due to human resource, time, and cost limitations, there is a growing interest in advanced image processing and deep learning-based automatic defect detection systems to improve the accuracy and efficiency of quality control in fabric manufacturing processes. In this study, we propose a novel hybrid PatchNet–Attention architecture that integrates patch-based feature extraction with an attention mechanism to improve defect localization and recognition. To evaluate the generalizability of the proposed architecture, its performance was tested on three public datasets using different class structures. Specifically, four classification scenarios were conducted: (i) classification with baseline models, (ii) patch-based classification, (iii) classification with a Convolutional Block Attention Module (CBAM)-enhanced model, and (iv) the proposed hybrid PatchNet–Attention architecture. Initially, 15 pre-trained Convolutional Neural Network (CNN) architectures were evaluated using transfer learning on the ZD001 dataset. The best-performing models, ResNet101V2 and Xception, were then selected as the foundation for constructing the hybrid PatchNet–Attention model. The experimental results demonstrate that configurations incorporating the attention mechanism consistently achieve the highest performance across all evaluated datasets. Specifically, the hybrid PatchNet–Attention model attained superior outcomes on the ZD001 dataset, with an F1-score of 99.15% and a Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) of 99.5% in the three-class setting, and an F1-score of 97.28% with a ROC–AUC of 99.74% in the nine-subclass configuration. In the TILDA data set, the proposed model produced an F1 score of 87.74% and an ROC-AUC of 98.09%, while in the FDD data set it achieved an F1 score of 98.95% and a ROC-AUC of 99.50%. The source code of the proposed method can be accessed from the Data Availability section.

准确和及时地检测织物表面可能出现的缺陷是确保纺织工业可持续生产质量的关键要求。由于人力资源、时间和成本的限制，人们对先进的图像处理和基于深度学习的自动缺陷检测系统越来越感兴趣，以提高织物制造过程中质量控制的准确性和效率。在这项研究中，我们提出了一种新的混合补丁-注意力架构，该架构将基于补丁的特征提取与注意力机制相结合，以提高缺陷的定位和识别。为了评估所提出的体系结构的泛化性，在使用不同类结构的三个公共数据集上测试了其性能。具体来说，进行了四种分类场景：(i)使用基线模型进行分类，（ii）基于补丁的分类，（iii）使用卷积块注意力模块（CBAM）增强模型进行分类，以及（iv）提出的混合PatchNet-Attention架构。首先，在ZD001数据集上使用迁移学习对15个预训练的卷积神经网络（CNN）架构进行评估。然后选择表现最好的模型ResNet101V2和Xception作为构建混合PatchNet-Attention模型的基础。实验结果表明，包含注意机制的配置在所有评估的数据集上都一致地获得了最高的性能。具体而言，混合PatchNet-Attention模型在ZD001数据集上获得了更好的结果，在3类设置下的f1得分为99.15%，受试者工作特征曲线下面积（ROC-AUC）为99.5%；在9类配置下的f1得分为97.28%，ROC-AUC为99.74%。在TILDA数据集中，该模型的F1得分为87.74%，ROC-AUC为98.09%，而在FDD数据集中，该模型的F1得分为98.95%，ROC-AUC为99.50%。建议的方法的源代码可以从Data Availability部分访问。

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引用次数: 0

Fuel-cell electric vehicle integrated shunt active power filter with advanced control algorithm for energy management and harmonic suppression 燃料电池汽车集成并联有源电力滤波器，采用先进的能量管理和谐波抑制控制算法

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2025-12-01 DOI: 10.1016/j.jestch.2025.102241

Mehmet Zahid Erel

Fuel Cell Electric Vehicles (FCEVs) offer high energy density, zero emissions, and fast refueling, making them ideal for sustainable transport. However, power converters and charging stations introduce nonlinear currents that distort waveforms and reduce power quality (PQ). Shunt Active Power Filters (SAPFs) have emerged as effective solutions to these challenges. This study presents a sliding mode control (SMC) strategy with phase-decoupled Kalman filtering for improved harmonic suppression, power factor correction, and DC-link voltage regulation under varying load and grid conditions. The Kalman filter, based on a simplified converter model, enhances noise immunity and enables independent SMCs with fixed switching frequency. A saturation-based SMC is used for robust DC-link voltage control. The proposed control reduces grid current THD to 1.53 % under 10 % voltage sag and maintains 4.23 % under distorted grid conditions, complying with IEEE-519 standards. Power factor improves to near unity (0.989–1). DC-link voltage ripple is limited to ΔV = 2 V, with fast response time under 50 ms and minimal overshoot. Additionally, the SAPF supports 3 kW of active power, easing the grid burden. Simulations confirm the strategy’s effectiveness in improving FCEV performance, addressing power quality and efficiency issues.

燃料电池电动汽车（fcev）具有高能量密度、零排放和快速加油的特点，是可持续交通的理想选择。然而，电源转换器和充电站引入的非线性电流会扭曲波形并降低电能质量（PQ）。并联有源电源滤波器（sapf）已成为应对这些挑战的有效解决方案。该研究提出了一种采用相位解耦卡尔曼滤波的滑模控制策略，以改善谐波抑制、功率因数校正以及在变负载和电网条件下的直流链路电压调节。卡尔曼滤波器基于简化的变换器模型，增强了噪声抗扰性，并使独立的smc具有固定的开关频率。基于饱和的SMC用于稳健的直流链路电压控制。该控制方案在电压跌落10%的情况下将电网电流THD降低至1.53%，在电网畸变条件下保持4.23%，符合IEEE-519标准。功率因数提高到接近统一（0.989-1）。直流链路电压纹波限制为ΔV = 2 V，响应时间在50 ms以下，超调最小。此外，SAPF支持3kw有功功率，减轻电网负担。仿真验证了该策略在提高FCEV性能、解决电能质量和效率问题方面的有效性。

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