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

{"title":"Risk assessment method for stator transposition bar of AC generator based on fault dataset and Markov chain","authors":"Chenguang Wang,&nbsp;Hecheng Yang,&nbsp;Lei Ni,&nbsp;Xu Bian,&nbsp;Dongmei Wang,&nbsp;Yanping Liang","doi":"10.1016/j.jestch.2025.102249","DOIUrl":"10.1016/j.jestch.2025.102249","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102249"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705385","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}

{"title":"Optimization of modification parameters of face gear pair based on TCA-NRBPNN-HYPE hybrid drive model","authors":"Kun He ,&nbsp;Ronghao Li ,&nbsp;Yongquan Chen ,&nbsp;Yachao Jia ,&nbsp;Guolong Li","doi":"10.1016/j.jestch.2025.102274","DOIUrl":"10.1016/j.jestch.2025.102274","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102274"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926314","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}

{"title":"Experimental investigation of hydration temperature variation in reinforced concrete beam construction under winter conditions","authors":"Burak Gedik ,&nbsp;Dilek Okuyucu ,&nbsp;İlker Kazaz ,&nbsp;Ahmet Ferhat Bingöl ,&nbsp;Muhammet Şahin ,&nbsp;Burak Şahin","doi":"10.1016/j.jestch.2025.102243","DOIUrl":"10.1016/j.jestch.2025.102243","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102243"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792037","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}

{"title":"Hybrid fragile image watermarking for tamper detection, localization and dual self-recovery","authors":"Aditya Kumar Sahu ,&nbsp;Monalisa Sahu","doi":"10.1016/j.jestch.2025.102266","DOIUrl":"10.1016/j.jestch.2025.102266","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102266"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841469","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}

{"title":"Towards automated metaphase cell detection using foundation models: A SAM and DINO-based approach","authors":"M. Cihad Arslanoglu ,&nbsp;Abdulkadir Albayrak ,&nbsp;Huseyin Acar","doi":"10.1016/j.jestch.2025.102262","DOIUrl":"10.1016/j.jestch.2025.102262","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102262"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792038","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}

{"title":"A FEM-ANN framework to estimate the on-diagonal elements of the impedance matrix in a Cochlear Implant","authors":"M.J. Hernández-Gil ,&nbsp;A. Ramos-de-Miguel ,&nbsp;D. Greiner ,&nbsp;D. Benítez ,&nbsp;G. Montero ,&nbsp;J.M. Escobar","doi":"10.1016/j.jestch.2025.102273","DOIUrl":"10.1016/j.jestch.2025.102273","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"73 ","pages":"Article 102273"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926315","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}

{"title":"Hybrid camera and LiDAR sensor for payload transfer control on RTGC using the Lyapunov-Kinetic energy approach under varying light levels","authors":"Steven Bandong ,&nbsp;Cristian MP Napitupulu ,&nbsp;Yul Yunazwin Nazaruddin ,&nbsp;Endra Joelianto","doi":"10.1016/j.jestch.2025.102206","DOIUrl":"10.1016/j.jestch.2025.102206","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"72 ","pages":"Article 102206"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145425130","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}

{"title":"Opposition learning & PID-based grey wolf optimizer with swarm intelligence for improved load forecasting","authors":"Murat Akil ,&nbsp;Ugur Yuzgec ,&nbsp;Emrah Dokur","doi":"10.1016/j.jestch.2025.102237","DOIUrl":"10.1016/j.jestch.2025.102237","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"72 ","pages":"Article 102237"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579400","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}

{"title":"A hybrid PatchNet-Attention based deep learning architecture for multi-type fabric defect classification in textile manufacturing and quality control","authors":"Isil Karabey Aksakalli ,&nbsp;Kubra Demir ,&nbsp;Ozlem Sokmen","doi":"10.1016/j.jestch.2025.102231","DOIUrl":"10.1016/j.jestch.2025.102231","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"72 ","pages":"Article 102231"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579478","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}

{"title":"Fuel-cell electric vehicle integrated shunt active power filter with advanced control algorithm for energy management and harmonic suppression","authors":"Mehmet Zahid Erel","doi":"10.1016/j.jestch.2025.102241","DOIUrl":"10.1016/j.jestch.2025.102241","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"72 ","pages":"Article 102241"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694182","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}