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

{"title":"Retraction notice to “Multi-objective scheduling and optimization for smart energy systems with energy hubs and microgrids” [Eng. Sci. Technol. Int. J. 51 (2024) 101649]","authors":"Yanliang Wang , Bo Wang , Hashem Farjam","doi":"10.1016/j.jestch.2025.101954","DOIUrl":"10.1016/j.jestch.2025.101954","url":null,"abstract":"","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"63 ","pages":"Article 101954"},"PeriodicalIF":5.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Increasing integration of wind energy through the joint operation of electric vehicles and the grid using the intelligent spider monkey algorithm","authors":"Han Xiaoming,&nbsp;Qian Jingjing","doi":"10.1016/j.jestch.2025.102017","DOIUrl":"10.1016/j.jestch.2025.102017","url":null,"abstract":"<div><div>This study introduces an advanced framework to enhance the integration of wind energy into power systems by utilizing electric vehicles (EVs) as dynamic energy storage units through vehicle-to-grid (V2G) technology. The proposed cooperative model integrates EV aggregators with wind power systems, operating under a single commitment approach with security constraints. Unlike existing studies, this framework incorporates detailed modeling of EV aggregation, accounting for diverse user travel patterns, time-varying energy storage capacities, and the complexities of charging and discharging behaviors. A significant innovation involves demonstrating that prohibitive constraints on simultaneous charging and discharging can, under specific conditions, be relaxed without compromising system stability, thereby simplifying the optimization process. The optimization problem is formulated as a single objective problem and solved using an improved spider monkey optimization algorithm. This algorithm delivers robust convergence, computational efficiency, and highly accurate solutions to complex energy dispatch problems. The framework is validated through extensive case studies on the IEEE system, incorporating realistic wind energy profiles and EV penetration scenarios. The research demonstrates a 28 % improvement in wind energy absorption, a 15 % reduction in thermal unit operating costs, and a substantial mitigation of storage violations under dynamic conditions. Furthermore, the study highlights the role of V2G in balancing renewable energy variability and reducing peak load pressures, showcasing its potential to optimize grid operations and improve reliability.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"64 ","pages":"Article 102017"},"PeriodicalIF":5.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing photovoltaic thermal systems with ternary hybrid nanofluids: Statistical and regression analysis","authors":"Ahmad Ayyad Alharbi ,&nbsp;Ali Rashash Alzahrani","doi":"10.1016/j.jestch.2025.102010","DOIUrl":"10.1016/j.jestch.2025.102010","url":null,"abstract":"<div><div>This study explores the intricate interplay between ternary hybrid nanofluids and their impact on the electrical and thermal efficiencies of Photovoltaic Thermal systems (PV/T), considering a diverse range of nanomaterial shapes. It meticulously scrutinizes these systems within a structured framework, incorporating phase change materials and strategically positioned cylinders, leveraging advanced numerical methodologies for detailed analyses. This comprehensive investigation systematically amalgamates multiple nanomaterials and crucial parameters, offering invaluable insights into the optimization of PV/T performance within complex fluid dynamics and configurations. Examining the electrical and thermal efficiencies of the PV/T system using ternary hybrid nanofluids with nanomaterials suspended in variously shaped base fluids, this research involves a three-dimensional domain housing a rectangular block with a square base. This block integrates a phase change material (PCM), and its interior features four equally spaced circular cylinders regulating the flow of ternary hybrid nanofluids. Atop this block sits a PV panel comprised of glass, a silicon layer, and a copper absorber. The study incorporates three nanomaterials—multi-walled carbon nanotubes (MWCNT), alumina, and copper—suspended in water, each with unique shapes. These four cylinders are strategically positioned along the block’s length at 20 %, 40 %, 60%, and 80%, assuming the block’s height constitutes 20 % of its length. Utilizing the finite element method with the incompressible Navier-Stokes equations and heat equation in three dimensions, the investigation employs the conjugate heat transfer interface within COMSOL Multiphysics 6.0. It encompasses a broad array of numerical, statistical, and regression analyses to delve into the electrical, thermal, and phase transition characteristics of paraffin wax. The study scrutinizes four fundamental parameters: Reynolds number (ranging from 100 to 1000), total volume fraction of nanomaterials (ranging from 1 % to 10 %), shape factors (ranging from 3 to 5.7), and an aspect ratio (with the cylinder’s diameter-to-length ratio between 0.3 and 0.5). The present study reports an average electrical efficiency of 8.14 % for the PV/T system while achieving a maximum thermal efficiency of 71.62 %. Regression analyses indicate enhancements of 0.000008675, 0.00775855, and 0.00014403 in electrical efficiency per unit increase in Reynolds number, total volume fraction, and aspect ratio, respectively. Likewise, thermal efficiency shows improvements of 0.00000738889, 0.00482906, and 0.000118775 for one-unit increments in Reynolds number, volume fraction, and shape factor, respectively.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"64 ","pages":"Article 102010"},"PeriodicalIF":5.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel model for higher performance object detection with deep channel attention super resolution","authors":"Ayse Berika Varol Malkocoglu,&nbsp;Ruya Samli","doi":"10.1016/j.jestch.2025.102003","DOIUrl":"10.1016/j.jestch.2025.102003","url":null,"abstract":"<div><div>With the introduction of deep learning methods, target object detection studies have gained momentum and started to be used in many areas. In recent years, various problems related to training the model with appropriate images and improving the current image quality to detect the target object more successfully have been discussed. In the light of these discussions, the identification of dangerous objects, which is one of the most important areas of object detection, is quite remarkable. For this reason, within the scope of study, a new dataset called DMGDATA-mini was prepared to detect dangerous objects and presented to the public. By using the data in the training of the You Only Look Once (YOLO) model of YOLOv5, it was ensured that the model successfully detected the object. In order to increase the object detection success and observe the results, popular Super Resolution (SR) algorithms and the Deep Channel Attention Super Resolution (DCASR) algorithm developed by us were integrated into the structure and the effect of SR algorithms on object detection was observed. It was determined with the Peak Signal-to-Noise Ratio (PSNR) metric that the developed DCASR algorithm performs better image enhancement than all SR algorithms in the study. Also, YOLOv5 and YOLOv5 + SR models were also compared. Thanks to the improved DCASR model, a 9.9 % increase in object detection success was observed. It was observed that SR algorithms have a positive effect on performance in general.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"64 ","pages":"Article 102003"},"PeriodicalIF":5.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human and environmental feature-driven neural network for path-constrained robot navigation using deep reinforcement learning","authors":"Nabih Pico ,&nbsp;Estrella Montero ,&nbsp;Alisher Amirbek ,&nbsp;Eugene Auh ,&nbsp;Jeongmin Jeon ,&nbsp;Manuel S. Alvarez-Alvarado ,&nbsp;Babar Jamil ,&nbsp;Redhwan Algabri ,&nbsp;Hyungpil Moon","doi":"10.1016/j.jestch.2025.101993","DOIUrl":"10.1016/j.jestch.2025.101993","url":null,"abstract":"<div><div>This paper introduces a neural network model designed for autonomous navigation in complex environments. It combines DRL methodologies to capture critical environmental features in the neural network. These features encompass data about the robot, humans, static obstacles, and path constraints. The representation, combined with weighted features from humans and environmental limitations, is processed through three multi-layer perceptrons (MLP) to calculate the value function and optimal policy, thereby enhancing navigation tasks. A novel reward function is proposed to accommodate path constraints and steer the robot’s navigation policies during neural network training. Additionally, common metrics like success rate, collision avoidance, time to reach the goal, and new comprehensive log information are included to provide an overview of the robot’s performance. The model’s efficacy is demonstrated through navigation in simulation scenarios involving curved and cross pathways, with the agents’ random position and velocity occasionally exceeding the maximum robot speed, as well as real experiments in limited spaces. The paper provides a GitHub repository that includes comparative performance videos with state-of-the-art models in path-constrained scenarios, along with strategies for reward functions. Link: <span><span>https://github.com/nabihandres/Wallproximity_DRL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"64 ","pages":"Article 101993"},"PeriodicalIF":5.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the effects of Gaussian noise on epileptic seizure detection: The role of spectral flatness, bandwidth, and entropy","authors":"Nuri Ikizler,&nbsp;Gunes Ekim","doi":"10.1016/j.jestch.2025.102005","DOIUrl":"10.1016/j.jestch.2025.102005","url":null,"abstract":"<div><div>This study investigates the effect of Gaussian noise on the classification of EEG signals from five classes in the Bonn University EEG dataset for epileptic seizure detection, using Power Spectral Density features. The EEG data are pre-processed with a low-pass filter at a cutoff frequency of 40 Hz, and a total of 11 features, including spectral flatness difference, spectral bandwidth difference, and entropy difference, are extracted. Feature vectors are generated for both original signals and signals with varying levels of injected Gaussian noise. The results demonstrate that noise injections significantly improve classification accuracy across all class combinations by enhancing feature separability and generalization. Notably, 100 % accuracy was achieved in classifications with different noise levels. Analyses performed using classifiers such as Random Forest, Multilayer Perceptron, and k-Nearest Neighbors show that the Random Forest classifier achieves high classification success across all noise levels. Additionally, it was found that incorporating spectral flatness difference, spectral bandwidth difference, and entropy difference features significantly contributes to classification accuracy when combined with noise injection. This study highlights the potential of noise injections to reduce overfitting and enhance the robustness of EEG classification, providing valuable insights for future biomedical signal analysis. Noise injection, traditionally viewed as a factor that could hinder performance, is utilized in this study as a novel approach to enhance classification accuracy, marking a significant innovation in the field.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"64 ","pages":"Article 102005"},"PeriodicalIF":5.1,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of high-risk diseases in poultry feces through transfer learning","authors":"Abdulkadir Tasdelen,&nbsp;Yenal Arslan","doi":"10.1016/j.jestch.2025.102002","DOIUrl":"10.1016/j.jestch.2025.102002","url":null,"abstract":"<div><div>Poultry farming industry constitutes a significant part of the global economy. Deep learning technology possesses the ability to autonomously analyze images, allowing constructed models to aid in the analysis and management of the poultry farming industry, particularly in early detection of sick poultry. Ensuring a sustainable industrial white meat production relies significantly on maintaining a high-quality living environment and early detection of diseases with prompt preventive measures. Early diagnosis of infectious and high-risk diseases such as Coccidiosis, Salmonellosis, and Newcastle disease, coupled with taking necessary precautions, will contribute to the efficient functioning of global economies supply chain. This study aims to detect high-risk Coccidiosis, Salmonellosis, and Newcastle diseases in poultry through transfer learning using poultry feces. Six different transfer learning architectures, namely DenseNet201, Resnet152V2, InceptionV3, InceptionResnetV2, MobileNetV2, and Xception, were employed in the study due to their widespread use and high accuracy rates.</div><div>The analysis revealed that MobileNetV2 achieved the highest accuracy rate of 97.1%. Considering the training times, it was observed that MobileNetV2 also exhibited the fastest training. The results of the analysis provide evidence that disease analysis from poultry feces can be carried out with high accuracy through transfer learning in the context of sustainable white meat production.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"64 ","pages":"Article 102002"},"PeriodicalIF":5.1,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GIS-based landslide susceptibility mapping using AHP, FMEA, and Pareto systematic analysis in central Yalova, Türkiye","authors":"Burak Demirel ,&nbsp;Eray Yildirim ,&nbsp;Eray Can","doi":"10.1016/j.jestch.2025.102013","DOIUrl":"10.1016/j.jestch.2025.102013","url":null,"abstract":"<div><div>Yalova is a region with high landslide risk due to its climate, terrain, geographical features, and geological structure. Landslides in the region are of critical importance to produce landslide susceptibility maps and risk management because of the material and moral damage they cause. This study aimed to produce a Geographic Information System (GIS)-based landslide susceptibility map (LSM) of the Yalova central region using the Analytic Hierarchy Process (AHP), Failure Mode and Effect Analysis (FMEA), and Pareto systematic analyses. Lithology, slope, aspect, distance from roads, NDVI (Normalized Difference Vegetation Index), distance from faults, land use, rainfall, distance from rivers, and elevation factors were used in the analyses. The FMEA, Pareto, and AHP methods were used in an integrated and sequential manner to determine the LCF weights. According to the analysis, lithology was identified as the most influential factor, with the highest weight at 29.5%, while appearance had the lowest weight at 1.5%. The LSM was generated by processing the weight values in the prepared LCFs. Based on the produced landslide susceptibility map, the study area was categorized as having 29.60% very low-risk, 29.51% low-risk, 24.04% moderate-risk, 13.18% high-risk, and 3.68% very high-risk. Regional planning should be undertaken according to the landslide risk categories, and appropriate measures should be determined for each level in advance. Upon comparing the produced LSM with the existing inventory, it was determined that 95.29% of the previously occurring landslides in the region were in risky areas on the LSM. The results demonstrate the integrated applicability of the AHP, FMEA, and Pareto methods and provide a more accurate weighting of LCFs compared to a single method. The approach used in this study can be easily adapted to different regions and can be used not only for landslide analysis but also for risk assessment studies in other disciplines.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"64 ","pages":"Article 102013"},"PeriodicalIF":5.1,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust robot localization with visually adaptive consensus filters in dynamic corridor environments","authors":"Suhyeon Kang,&nbsp;Heoncheol Lee","doi":"10.1016/j.jestch.2025.101998","DOIUrl":"10.1016/j.jestch.2025.101998","url":null,"abstract":"<div><div>This paper deals with the problem of robot localization in dynamic corridor environments. If a robot uses only a LiDAR (light detection and ranging) for its localization, the accuracy of robot localization degenerates as time goes due to the occlusions by moving people around a robot and the lack of scan features in corridors. This paper proposes a robust robot localization method with visually adaptive consensus filters (VACF) to solve the problem. The VACF consists of LiDAR odometry estimation, probabilistic localization, visual odometry estimation, optical flow recognition, object detection and adaptive consensus filters. To deal with long corridor environments, optical flow methods are used to correct the robot’s position. For robust localization in dynamic environments, object detection algorithm is used to detect dynamic objects, and localization algorithms are adaptively used as input to a consensus filter based on the number of dynamic objects detected. The VACF was tested in real-world experiments in dynamic corridor environments and showed better accuracy than other existing methods when compared to pre-determined ground truth points.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"64 ","pages":"Article 101998"},"PeriodicalIF":5.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trajectory planning and tracking control in autonomous driving system: Leveraging machine learning and advanced control algorithms","authors":"Md Hafizur Rahman ,&nbsp;Muhammad Majid Gulzar ,&nbsp;Tansu Sila Haque ,&nbsp;Salman Habib ,&nbsp;Adnan Shakoor ,&nbsp;Ali Faisal Murtaza","doi":"10.1016/j.jestch.2025.101950","DOIUrl":"10.1016/j.jestch.2025.101950","url":null,"abstract":"<div><div>Automated vehicles may soon be seen on our roads as automation is becoming more and more prominent in transportation research. This comprehensive analysis provides a detailed synopsis of the cutting-edge algorithms and technologies propelling the advancement and adoption of autonomous driving. It begins with assessing the fundamental system architectures needed to operate autonomous vehicles: Control over tracking and trajectory planning. Also, this review proceeds to cover in-depth discussions on the decision-making, and trajectory planning techniques that are essential for seamless autonomous vehicle navigation, with a focus on the function of State-of-the-art algorithms, optimization algorithms, machine learning (ML), and deep learning (DL). In addition, Trajectory tracking control methods are also represented in this review, which describes types of tracking control techniques aligned with trajectory planning. Moreover, this review paper also discussed the challenges and limitations of algorithms or techniques implemented in the reviewed paper and suggested some future perspectives. In conclusion, the survey provides an extensive evaluation of the concepts and technologies required to move towards a safe and successful autonomous future, while also documenting the swift advancements in autonomous driving.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"64 ","pages":"Article 101950"},"PeriodicalIF":5.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}