Engineering Research Express最新文献_第2页

A review of optimization of energy involved in rolling stock of a sub-urban rail transport system 城郊铁路运输系统机车车辆能源优化综述

Engineering Research Express Pub Date : 2024-07-26 DOI: 10.1088/2631-8695/ad6834

Mohammad Ishaq, Praveen Kumar Shukla, Haroon Ashfaq

{"title":"A review of optimization of energy involved in rolling stock of a sub-urban rail transport system","authors":"Mohammad Ishaq, Praveen Kumar Shukla, Haroon Ashfaq","doi":"10.1088/2631-8695/ad6834","DOIUrl":"https://doi.org/10.1088/2631-8695/ad6834","url":null,"abstract":"\u0000 Railway systems stand out as highly efficient modes of transportation compared to others, leading to a rising demand for the sake of research and development aimed at reducing their energy consumption. This pursuit not only enhances sustainability but also addresses the pressing issue of climate change. A multitude of studies delve into modeling, analyzing, and optimizing energy usage within railway systems, showcasing a diverse array of methodologies and techniques for formulating, and solving optimization problems. This review paper undertakes a comparative examination of approximately 33 relevant studies focusing on railway energy consumption encompassing both traction and auxiliary energy. The research emphasizes various modeling techniques employed in simulating train movement and energy consumption; alongside different optimization methods focused at improving operational efficiency on railway tracks. It meticulously scrutinizes the most prevalent optimization methods, techniques and variables are utilized. Through an extensive review of literature, it becomes apparent that deterministic approaches, particularly based on the Davis equations, dominate the modeling landscape, accounting for over 80% of the approaches. However, when it comes to optimization, meta-heuristic approaches take precedence, with Genetic Algorithms being a prominent choice. These findings underscore the significance of meta-heuristic approaches, crucial for enhancing both human activities and mitigating energy consumption, especially in a heavy energy-consuming sector like railway transportation.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":"12 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141801738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Underwater manipulator arm control based on Harris Hawk algorithm optimized RBF neural network 基于 Harris Hawk 算法优化 RBF 神经网络的水下机械臂控制

Engineering Research Express Pub Date : 2024-07-26 DOI: 10.1088/2631-8695/ad681a

Chuanzhe Zhao, Haibo Wang, Yadi Song, Ronglin Wang, Zhifeng Li, Pengtao Li

引用次数: 0

A novel SiC VD-MOSFET with optimized P-type shielding structure in JFET region for improved short circuit robustness 新型碳化硅 VD-MOSFET 在 JFET 区域采用优化的 P 型屏蔽结构，提高了短路稳健性

Engineering Research Express Pub Date : 2024-07-26 DOI: 10.1088/2631-8695/ad681b

Zhijia Guo, Dongyuan Zhai, Jiwu Lu, Chunming Tu

{"title":"A novel SiC VD-MOSFET with optimized P-type shielding structure in JFET region for improved short circuit robustness","authors":"Zhijia Guo, Dongyuan Zhai, Jiwu Lu, Chunming Tu","doi":"10.1088/2631-8695/ad681b","DOIUrl":"https://doi.org/10.1088/2631-8695/ad681b","url":null,"abstract":"\u0000 This paper investigates the short-circuit characteristics of Silicon Carbide (SiC) Vertical Double-Diffused Metal-Oxide-Semiconductor Field-Effect Transistor (VD-MOSFET) utilizing TCAD tools. Expanding upon the conventional VD-MOSFET framework, a novel 900V SiC VD-MOSFET with two P-type shielding layer introduced in JFET region, PW-MOSFET, is proposed and designed. In contrast to the traditional VD MOSFET, PW- -MOSFET not only significantly improves short-circuit (SC) reliability but also optimizes static performance. Simulation results reveal that PW-MOSFET demonstrates notably superior SC performance at a DC link voltage of 600V compared to the traditional VD-MOSFET, with a 63% increase in Short-Circuit Withstand Time (SCWT) and a 25% enhancement in Baliga Figure of Merit (FOM). The key factor contributing to this performance enhancement is attributed to the advantageous role of the P-type shielding layer, facilitating adjustments in the current flow path, thereby suppressing saturation current and enhancing the reliability of short-circuit events. Furthermore, the issue of increased characteristic on-state resistance (Ron, sp) resulting from the introduction of the P-type shielding layer is addressed by augmenting the doping concentration in the JFET region.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental study of the effect of the addition of Posidonia oceanica fiber on the thermal and acoustic insulation properties of plaster 关于添加 Posidonia oceanica 纤维对石膏隔热和隔音性能影响的实验研究

Engineering Research Express Pub Date : 2024-07-26 DOI: 10.1088/2631-8695/ad6818

Malek Jedidi

{"title":"Experimental study of the effect of the addition of Posidonia oceanica fiber on the thermal and acoustic insulation properties of plaster","authors":"Malek Jedidi","doi":"10.1088/2631-8695/ad6818","DOIUrl":"https://doi.org/10.1088/2631-8695/ad6818","url":null,"abstract":"\u0000 In this work, plaster and natural Posidonia Oceanica (PO) fibers are combined to create a composite material that was recently produced. This work's primary objective is to assess the mechanical and thermophysical performance of the composite material in order to determine whether or not it may be used as a thermal and acoustic insulation material in buildings. For this end, prismatic and parallelepipedic specimens of different dimensions were made with fiber percentages ranging from 0% to 20%. In addition, parallelepiped panels (600 mm x 600 mm x 40 mm3) were also prepared containing 10% of PO fibers. Mechanical properties (flexual strength, compressive strength), thermal properties and sound absorption coefficient were investigated. For each test specimen, the density was calculated for a proportion of fibers ranging from 0% to 20%. The results indicated a marked improvement in the compressive and flexural strength of the fiber-reinforced mixtures. This improvement is respectively 14.5% and 33.8% for mixtures containing 10% of PO fibers. Additionally, the addition of PO fibers significantly decreases density (by 40.5%), thermal conductivity (by 68.5%) and thermal diffusivity (by 36.9%) of the different mixtures. The ideal mechanical characteristics are attained when 5–10% of the volume is made up of Posidonia Oceanica fibers. The results of the sound absorption coefficient test show that the mixture of plaster with 10% fibers has a good sound absorption coefficient of 0.78 for high frequencies between 1000 Hz and 4000 Hz. This work has shown conclusively that the incorporation of PO fibers, up to a maximum of 10% with plaster, makes it possible to obtain a lightweight composite that can potentially be used as a new insulating construction material.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":"17 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141801686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anomaly detection combining bidirectional gated recurrent unit and autoencoder in the context of E-commerce 结合双向门控递归单元和自动编码器的电子商务异常检测

Engineering Research Express Pub Date : 2024-07-26 DOI: 10.1088/2631-8695/ad6819

Yue Lin

{"title":"Anomaly detection combining bidirectional gated recurrent unit and autoencoder in the context of E-commerce","authors":"Yue Lin","doi":"10.1088/2631-8695/ad6819","DOIUrl":"https://doi.org/10.1088/2631-8695/ad6819","url":null,"abstract":"\u0000 E-commerce platforms store a large amount of user personal information, transaction data, and financial information, which have extremely high value for hackers and criminals. Therefore, protecting the security of e-commerce platforms is particularly important, and intrusion detection is a technical means used to discover and respond to possible security threats and attacks. But with the development of Internet technology, there are more and more types of intrusion attacks and more sophisticated means. Traditional intrusion detection systems are difficult to cope with. This study proposes an anomaly detection model based on bidirectional gated loop units and autoencoders. The model learns HTTP text data, trains the model, and uses bidirectional gated loop units to convert text sequences from characters to numbers. The experimental results show that when the training set size is 1000, the false alarm rates of Analytic Hierarchy Process, Support Vector Machine, Long Short Term Recurrent Memory Network, and Improved end-to-end algorithm models are 0.30, 0.27, 0.23, and 0.10, respectively. The loss function values are 0.35, 0.28, 0.17, and 0.13, respectively. The F1 values are 0.78, 0.88, 0.91, and 0.99, and the accuracy rates are 0.88, 0.91, 0.95, and 0.99, respectively. The research results indicate that the proposed method model has excellent performance.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":"12 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-bandwidth FPGA based randomized voltage states for controlling optoelectronic devices in QKD systems. 基于高带宽 FPGA 的随机电压状态，用于控制 QKD 系统中的光电器件。

Engineering Research Express Pub Date : 2024-07-26 DOI: 10.1088/2631-8695/ad6833

Aman Satija, Dustin Cruise, Vaibhav Garg

引用次数: 0

AEDN-YOLO: an efficient one-stage detection network for strip steel surface defects AEDN-YOLO：高效的带钢表面缺陷单级检测网络

Engineering Research Express Pub Date : 2024-07-26 DOI: 10.1088/2631-8695/ad681d

Mingjun Wei, Beilong Chen, Jianuo Liu, Na Yuan, Jinyun Liu, Zhanlin Ji

{"title":"AEDN-YOLO: an efficient one-stage detection network for strip steel surface defects","authors":"Mingjun Wei, Beilong Chen, Jianuo Liu, Na Yuan, Jinyun Liu, Zhanlin Ji","doi":"10.1088/2631-8695/ad681d","DOIUrl":"https://doi.org/10.1088/2631-8695/ad681d","url":null,"abstract":"\u0000 Steel surface defect detection is one of the key tasks in industrial production and quality control. Research on defect detection using deep learning algorithms has shown promising results. However, due to the complex backgrounds, large differences in defect sizes, and diverse defect types present in steel strip surface defect images, existing deep learning algorithms struggle to achieve precise detection. To address these challenges, this paper proposes an efficient detection model named AEDN-YOLO. Firstly, an adaptive feature extraction (AFE) module is designed, embedded into C2f to better capture irregularly shaped objects. Secondly, the Triplet Attention module is incorporated into the bottom layer of the backbone network to enhance the model's ability to locate defect features accurately. Additionally, replace the standard convolution in the neck network with GSConv, which not only accelerates feature fusion to improve detection speed but also enlarges the model's receptive field to enhance detection accuracy. Finally, add a small target detection layer to enhance the detection capability for tiny defects. The model achieves mAP of 81.7% and 72.7% on the NEU-DET and GC10-DET datasets, respectively, with a detection speed of 72.1 FPS. Compared to mainstream defect detection algorithms, the proposed algorithm enables accurate and efficient detection of steel surface defects.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":"51 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SAMP rao algorithm based minimization of the roughness of milled surface of Ti-6Al-4V 基于 SAMP rao 算法的 Ti-6Al-4V 铣削表面粗糙度最小化算法

Engineering Research Express Pub Date : 2024-07-26 DOI: 10.1088/2631-8695/ad681f

N. K. Sahu, Ruchi Patel, Ashok Kumar Verma, Shailesh Khaparkar

{"title":"SAMP rao algorithm based minimization of the roughness of milled surface of Ti-6Al-4V","authors":"N. K. Sahu, Ruchi Patel, Ashok Kumar Verma, Shailesh Khaparkar","doi":"10.1088/2631-8695/ad681f","DOIUrl":"https://doi.org/10.1088/2631-8695/ad681f","url":null,"abstract":"\u0000 In order to solve optimization problems including machining responses as objectives, this study suggests a parameter-less method called the self-adaptive multi population (SAMP) Rao algorithm that does not rely on metaphors. When machining titanium alloys, achieving a good surface quality is a difficult process. In the current study, an effort has been made to reduce surface roughness during milling Ti-6Al-4V. Response surface methodology (RSM) was used in the experiment design to create a model for surface roughness using cutting parameters as variables. The developed model was tested in additional tests in addition to the primary experiments. It was shown that cutting speed and feed rate had the biggest effects on surface roughness, whereas depth of cut had very little of an impact. The model's quality is demonstrated by the correlation coefficient (R2) 98%, which indicates that the model can explain 98% of the data. Later, a response surface-based desirability technique was used to minimize surface roughness. The outcome of the proposed algorithm is compared with RSM optimizer. It has been noted that the outcomes achieved with the SAMP approach are more advantageous than RSM approach. SAMP Rao Algorithm provides cutting settings of 133.5 m/min, 0.13 mm/tooth feed rate, and 2.06 mm of milling depth along with a minimal roughness of milled surface of 0.37 µm.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":"31 51","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unveiling anomalies: harnessing machine learning for detection and insights 揭示异常：利用机器学习进行检测和洞察

Engineering Research Express Pub Date : 2024-07-23 DOI: 10.1088/2631-8695/ad66b2

Shubh Gupta, Sanoj Kumar, Karan Singh, Deepika Saini

{"title":"Unveiling anomalies: harnessing machine learning for detection and insights","authors":"Shubh Gupta, Sanoj Kumar, Karan Singh, Deepika Saini","doi":"10.1088/2631-8695/ad66b2","DOIUrl":"https://doi.org/10.1088/2631-8695/ad66b2","url":null,"abstract":"\u0000 The rise of Internet of Things (IoT) devices has brought about an increase in security risks, emphasizing the need for effective anomaly detection systems. Previous research introduced a dynamic voting classifier to overcome overfitting or inaccurate accuracies caused by dataset imbalance. This article introduces a new method for IoT anomaly detection that employs a hybrid voting classifier, which combines several machine learning models. To solve the overfitting and class weight issues, an adaptive voting classifier is used that adjusts weights according to the highest preference for accuracy. The developing voting system increases the effectiveness of more accurate classifiers, enhancing the group's overall capability. A proposed combined classifier combines Logistic Regression, AdaBoost, Gradient Boosting, and Multi-Layer Perceptron models using a soft voting method. To develop and assess this method, the CIC-IoT-2023 dataset is utilized, which contains 33 types of IoT attacks across 7 categories. This process includes thorough data preprocessing and feature selection from a pool of 42 available attributes. The performance of this approach is measured against individual classifiers across binary, 8-class, and 34-class classification tasks. The results highlight the effectiveness of the hybrid model. It achieves 98.95% accuracy, 76.72% recall, and 72.01% F1-score in the 34-class problem, surpassing the performance of all individual models. For the 8-class task, the hybrid classifier attains 99.39% accuracy, 90.89% recall, and an 83.01% F1-score. This demonstrates the high potential of the hybrid approach for IoT anomaly detection.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":"127 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141811639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machinability of gas metal arc based 3D printed Al-Mg 5356 alloy using wire-EDM 利用线切割加工基于气体金属弧的 3D 打印铝镁 5356 合金的可加工性

Engineering Research Express Pub Date : 2024-07-23 DOI: 10.1088/2631-8695/ad66b1

D. Doreswamy, Vijeesh Vijayan, Krrish Jain, S. K. Bhat

{"title":"Machinability of gas metal arc based 3D printed Al-Mg 5356 alloy using wire-EDM","authors":"D. Doreswamy, Vijeesh Vijayan, Krrish Jain, S. K. Bhat","doi":"10.1088/2631-8695/ad66b1","DOIUrl":"https://doi.org/10.1088/2631-8695/ad66b1","url":null,"abstract":"\u0000 Wire-Arc Additive Manufactured (WAAM) is relatively new method of metal 3D printing in which the raw material is heated by the gas metal arc and the molten metal pool is deposited layer-by-layer using a computer numerically controlled axis drive system. Since WAAM needs a finishing process for attaining the final dimensions of the components, there is a need to investigate the machinability aspects of WAAM fabricated materials. This work investigates the machinability of Al-Mg 5356 alloy test samples fabricated by WAAM process using wire-electric discharge machining (Wire-EDM). The test samples were subjected to Wire-EDM and the obtained material removal rate (MRR), kerf width (KW) and surface roughness (Ra) were investigated at different Wire-EDM process settings of voltage, current, pulse-on time (Ton), pulse-off time (Toff) and wire speed (Ws). Statistical analysis revealed that current had a significant influence on MRR. Ton had a strong influence on KW and Ra, whereas Toff exhibited a considerable impact on all these responses. Notably, Ws demonstrated a significant impact on Ra. However, voltage was found to have statistically negligible impact on all the machining responses. Microstructural investigations and compositional analysis were conducted providing valuable information on the cut surfaces. The results derived from the present investigation are useful for predicting the optimum process parameter settings for machining of WAAM-based 3D printed Al-Mg alloy in various manufacturing industries.","PeriodicalId":505725,"journal":{"name":"Engineering Research Express","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141813763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0