Engineering Research Express最新文献_第10页

Research on tracking and synchronization control of dual hydraulic cylinders based on improved active disturbance rejection control 基于改进型主动干扰抑制控制的双液压缸跟踪与同步控制研究

IF 1.7

Engineering Research Express Pub Date : 2024-07-15 DOI: 10.1088/2631-8695/ad5c2b

Yun Xiao, Chungeng Sun and Chaojie Lian

引用次数: 0

A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets 使用特征选择技术和多维数据集进行心律失常分类的异构机器学习算法比较研究

IF 1.7

Engineering Research Express Pub Date : 2024-07-15 DOI: 10.1088/2631-8695/ad5d51

Abhinav Sharma, Sanjay Dhanka, Ankur Kumar and Surita Maini

{"title":"A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets","authors":"Abhinav Sharma, Sanjay Dhanka, Ankur Kumar and Surita Maini","doi":"10.1088/2631-8695/ad5d51","DOIUrl":"https://doi.org/10.1088/2631-8695/ad5d51","url":null,"abstract":"Arrhythmia, a common cardiovascular disorder, refers to the abnormal electrical activity within the heart, leading to irregular heart rhythms. This condition affects millions of people worldwide, with severe implications on cardiac function and overall health. Arrhythmias can strike anyone at any age which is a significant cause of morbidity and mortality on a global scale. About 80% of deaths related to heart disease are caused by ventricular arrhythmias. This research investigated the application of an optimized multi-objectives supervised Machine Learning (ML) models for early arrhythmia diagnosis. The authors evaluated the model’s performance on the arrhythmia dataset from the UCI ML repository with varying train-test splits (70:30, 80:20, and 90:10). Standard preprocessing techniques such as handling missing values, formatting, balancing, and directory analysis were applied along with Pearson correlation for feature selection, all aimed at enhancing model performance. The proposed optimized RF model achieved impressive performance metrics, including accuracy (95.24%), precision (100%), sensitivity (89.47%), and specificity (100%). Furthermore, the study compared the proposed approach to existing models, demonstrating significant improvements across various performance measures.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"2022 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141718714","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

Design, computational analysis and experimental study of a high amplification piezoelectric actuated microgripper 高放大压电致动微型夹持器的设计、计算分析和实验研究

IF 1.7

Engineering Research Express Pub Date : 2024-07-11 DOI: 10.1088/2631-8695/ad5f19

Tilok Kumar Das and Bijan Shirinzadeh

{"title":"Design, computational analysis and experimental study of a high amplification piezoelectric actuated microgripper","authors":"Tilok Kumar Das and Bijan Shirinzadeh","doi":"10.1088/2631-8695/ad5f19","DOIUrl":"https://doi.org/10.1088/2631-8695/ad5f19","url":null,"abstract":"Increasing applications of compliant microgripper demands flexibility in working with a wide range of micro-objects which requires a large workspace, high precision motion, low parasitic motion, and satisfactory bandwidth control. To meet the requirement of pick and place manipulation tasks, a high amplification piezoelectric actuated microgripper is proposed and investigated in this paper. The high amplification of the microgripper is achieved using a compound amplifier. The compound amplifier is assisted to magnify the embedded piezoelectric actuator’s displacement. Two cascaded lever-type mechanisms are symmetrically connected with a bridge-type mechanism and form a three-stage amplification mechanism-based compound amplifier. Further, the four-bar parallelogram mechanisms are integrated with the third-stage displacement amplification mechanisms to linearize the output motion of the microgripper jaws. The characteristics of the microgripper were evaluated by computational analysis and validated using experimental investigations. Further, the design parameters are identified from the geometrical model of the individual displacement transmission mechanisms to perform a response surface optimization on the configured mechanism by the computational method. The design optimization of the microgripper resulted in a high displacement amplification ratio with a large workspace. The experimental investigations show that the designed microgripper is capable of achieving a high displacement amplification ratio of 34.5 and a total output displacement of 529.4 μm. Further, the characteristics of the microgripper such as motion resolution, and parasitic motion indicate that it will be able to perform high-precision micro-object grasping/releasing tasks.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"6 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613235","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

Failure evaluation on tailor made aerospace aluminum alloys via underwater friction stir welding employing predictive machine learning technologies 利用预测性机器学习技术，通过水下搅拌摩擦焊对定制航空航天铝合金进行失效评估

IF 1.7

Engineering Research Express Pub Date : 2024-07-11 DOI: 10.1088/2631-8695/ad5f05

Arun Prakash S and Gokul Kumar K

{"title":"Failure evaluation on tailor made aerospace aluminum alloys via underwater friction stir welding employing predictive machine learning technologies","authors":"Arun Prakash S and Gokul Kumar K","doi":"10.1088/2631-8695/ad5f05","DOIUrl":"https://doi.org/10.1088/2631-8695/ad5f05","url":null,"abstract":"Employing tailor-made alloys with uneven thickness achieves light weighting, a critical issue for reducing emissions, leading to lower aircraft pollutants and fuel costs. The research utilizes advanced machine learning techniques such as Gaussian process regression (GPR), artificial neural networks (ANN) linear regression (LR), and support vector machines (SVM) to predict the ultimate tensile strength of underwater friction stir welding of AA6082-T6 and A2219-T83 tailor-made joints. The models have been evaluated with an assortment of kernel functions, including the polynomial kernel (PK), the radial basis function (RBF), and the Pearson VII universal kernel (PUK). To acquire experimental data, we used a Central Composite Design (CCD) technique, incorporating various factors in the process encompassing tool tilt angle (TA), rotating speed (RS), and welding speed (WS). The SVM radial basis function model (SRBP) had a maximum correlation coefficient of 0.9995 and a minimum root mean square error value (RMSE) of 0.5433 in the training set and 0.6271 in the test set. The ANN model predicted the UTS with an error margin of 0.21%, while the SRBP model showed a 0.52% error, and the LR model exhibited a significantly higher error of 7.73%. A peak tensile strength of 252.98 MPa was recorded in the S20 specimen, accounting for 85.61% of the base metal’s (AA6082 T6) strength. A reduced acute tearing ridge indicates petite, shallow dimples due to the inherent cooling. Through the analysis of metrics and residuals, high accuracy rates were observed when employing the ANN and SRBP models to predict mechanical traits.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"39 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613233","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

Effect of aging treatment on microstructure and corrosion properties of Al-Cu-Mn-Si-Mg-Er-Zr alloy 时效处理对 Al-Cu-Mn-Si-Mg-Er-Zr 合金微观结构和腐蚀特性的影响

IF 1.7

Engineering Research Express Pub Date : 2024-07-10 DOI: 10.1088/2631-8695/ad5cd0

Chenxi Ma, Li Rong, Wu Wei, Jiongshen Chen, Meng Wang, Zezhong Wang, Li Zhou, Xiaobo Wang, Zhihao Zheng and Hui Huang

引用次数: 0

Advancing efficiency: comprehensive strategies for minimizing optical and electrical losses in group III-V compound tandem solar cells for future photovoltaic technology 提高效率：针对未来光伏技术的 III-V 族化合物串联太阳能电池中尽量减少光学和电学损耗的综合策略

IF 1.7

Engineering Research Express Pub Date : 2024-07-10 DOI: 10.1088/2631-8695/ad5c2d

Swati S Soley, Shrikant Verma, Narendra Khatri and Sumit Pokhriyal

{"title":"Advancing efficiency: comprehensive strategies for minimizing optical and electrical losses in group III-V compound tandem solar cells for future photovoltaic technology","authors":"Swati S Soley, Shrikant Verma, Narendra Khatri and Sumit Pokhriyal","doi":"10.1088/2631-8695/ad5c2d","DOIUrl":"https://doi.org/10.1088/2631-8695/ad5c2d","url":null,"abstract":"Global energy consumption is rising, and fossil resources are dwindling, driving demand for clean, affordable energy. Solar power is the most promising alternative energy source and can meet future energy needs. In terrestrial photovoltaics, low-cost Silicon solar cells dominate. However, as the single junction silicon solar cells are approaching their highest achievable efficiency of 30%, high-efficiency, ‘group III-V Compound’ semiconductor tandem solar cells are being considered as an alternative energy source. The absorption capacity of the wide range of solar radiation photons enables them to achieve high efficiency. However, further improvement in efficiency is constrained due to the various loss mechanisms that occur during the physical process of converting light to electrical energy in ‘group III-V compound’ tandem solar cells. Extensive research is being conducted to develop solution approaches to minimize the loss mechanisms in order to improve efficiency. Although many published review articles have studied the research progress of ‘group III-V compound’ solar cells based on fabrication techniques, applications, status, and challenges, there is no article mentioning a comprehensive and comparative study of strategies employed by researchers to enhance efficiency in ‘group III-V compounds’ tandem solar cells considering loss mechanisms. The present study focuses on discussing the fundamental losses in ‘group III-V compounds’ tandem solar cells and various strategies employed by researchers to reduce optical and electrical losses to improve the efficiency of these devices so that they may be employed in terrestrial applications.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"245 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613234","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

RNDDNet: A residual nested dilated DenseNet based deep-learning model for chilli plant disease classification RNDDNet：基于残差嵌套扩张密集网络的辣椒植物病害分类深度学习模型

IF 1.7

Engineering Research Express Pub Date : 2024-07-10 DOI: 10.1088/2631-8695/ad5f03

Maramreddy Srinivasulu and Sandipan Maiti

{"title":"RNDDNet: A residual nested dilated DenseNet based deep-learning model for chilli plant disease classification","authors":"Maramreddy Srinivasulu and Sandipan Maiti","doi":"10.1088/2631-8695/ad5f03","DOIUrl":"https://doi.org/10.1088/2631-8695/ad5f03","url":null,"abstract":"The most significant peril to food safety arises from plant diseases, capable of substantially diminishing both the quantity and quality of agricultural yields. Identifying these plant diseases stands out as the foremost challenge within the agricultural sector. Convolutional and deep neural networks prove effective in resolving image classification challenges within the realm of computer vision. Numerous Deep Neural Network(DNN)-based structures have been employed to diagnose plant diseases. Many DNN models in the field make use of various iterations of Dense and DenseNet layers in order to enhance the receptive field and capture intricate features within the data. However, it is important to note that such models often come with a significant computational burden and can introduce aliasing artifacts due to their complexity and resource-intensive nature. To overcome those limitations, we proposed a novel Residual Nested Dilated DenseNet based deep-learning (RNDDNet) model in this paper. Residual Nested Dilated DenseNet model residual connections are achieving the required receptive field, and their dilation factors are effective in extracting more features. The RNDDNet model exhibits the highest level of accuracy in identifying plant diseases. This research introduces a less computational cost and compact model to detect diseases in plant leaves. The proposed model functions to identify diseases, utilizing a dataset comprising 3,800 photographs of chilli leaves, categorized into six distinct classes: five disorder classes and one healthy chilli class. Through experimentation, the outcomes indicate that the suggested model achieves an accuracy of 98.09 %, along with a precision of 97 %, a recall of 97.25 %, and an F1 score of 97.25%. The presented approach demonstrates its superiority over existing methodologies.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"16 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613284","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

A novel design of collapsed supply and boosted bit-line swing write driver for fast write access 9T SRAM 用于快速写入访问 9T SRAM 的新型折叠式供电和升压位线摆动写入驱动器设计

IF 1.7

Engineering Research Express Pub Date : 2024-07-10 DOI: 10.1088/2631-8695/ad5e5c

Chokkakula Ganesh, Aruru Sai Kumar, Sk Shoukath Vali, Girija Sravani Kondaveeti, Girish Wadhwa and Srinivasa Rao Karumuri

{"title":"A novel design of collapsed supply and boosted bit-line swing write driver for fast write access 9T SRAM","authors":"Chokkakula Ganesh, Aruru Sai Kumar, Sk Shoukath Vali, Girija Sravani Kondaveeti, Girish Wadhwa and Srinivasa Rao Karumuri","doi":"10.1088/2631-8695/ad5e5c","DOIUrl":"https://doi.org/10.1088/2631-8695/ad5e5c","url":null,"abstract":"This work presents a collapsed supply and boosted bit-line swing (CSBBS) write driver circuit, with the specific goal of enhancing write performance. The write ability of SRAM cells is gravely affected by device parameter variations in deep sub-threshold region of operations. The collapsed supply and boosted bit-line swing are key features aimed at achieving improvements in speed and efficiency during the memory write process. In comparison to conventional, Ultra dynamic scaled supply write (UDSS), Negative charge-boosted bit line (NCBBL), and Reconfigurable negative bit line collapsed supply (RNBLCS) write driver circuits, Proposed collapsed supply and boosted bit-line swing (CSBBS) for 9T SRAM cell has optimized write access delays of 0.74X, 0.41X, 0.32X and 0.21X, improvement in write margin (WM) of 1.51X, 1.34X, 1.22X and 1.12X respectively. The CSBBS Write driver circuit is implemented using custom compiler (Synopsys) through a 28 nm BSIM4 model card for bulk CMOS. MC simulation results are monitored on Cosmoscope wave viewer (Synopsys).","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"56 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613282","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

Investigation of oil palm fiber reinforced polylactic acid composite extruded filament quality 油棕纤维增强聚乳酸复合材料挤出丝质量研究

IF 1.7

Engineering Research Express Pub Date : 2024-07-10 DOI: 10.1088/2631-8695/ad5e5d

Mohd Afiq Sharum, Thavinnesh Kumar Rajendran, Shajahan Maidin and Shafinaz Ismail

{"title":"Investigation of oil palm fiber reinforced polylactic acid composite extruded filament quality","authors":"Mohd Afiq Sharum, Thavinnesh Kumar Rajendran, Shajahan Maidin and Shafinaz Ismail","doi":"10.1088/2631-8695/ad5e5d","DOIUrl":"https://doi.org/10.1088/2631-8695/ad5e5d","url":null,"abstract":"This study examines the quality of Polylactic Acid (PLA) filament reinforced with Oil Palm Fiber (OPF) for additive manufacturing applications. The research aims to create a composite filament that leverages the advantages of PLA, a biodegradable polymer, and OPF, a natural fiber from the oil palm tree, to enhance mechanical strength, dimensional stability, and printability. The methodology involves crushing the PLA filament and OPF to the desired size using a crusher machine, blending them in different ratios (e.g., 90:10 and 80:20 PLA to OPF), and using a hot-pressing process to bond the components. The resulting pelletized composites are then extruded into filaments using an extruder machine. The quality of the produced filament is assessed based on diameter consistency, surface smoothness, and printability, considering compatibility with 3D printers. The study reveals that composition ratios and processing parameters impact filament quality, leading to challenges such as diameter variations, rapid hardening, breakage, and extruder die clogs. Future recommendations were suggested to optimize compositions, refine processing, explore advanced extrusion, and investigate fiber distribution and bonding for improved filament properties. This research offers valuable insights for creating high-quality OPF-reinforced PLA filaments for additive manufacturing, advancing understanding of filament quality factors, and proposing ways to enhance composite filament performance across applications.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"33 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614945","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

Research on tool remaining useful life prediction algorithm based on machine learning 基于机器学习的工具剩余使用寿命预测算法研究

IF 1.7

Engineering Research Express Pub Date : 2024-07-10 DOI: 10.1088/2631-8695/ad5f1a

Yong Ge, Hiu Hong Teo, Lip Kean Moey and Walisijiang Tayier

{"title":"Research on tool remaining useful life prediction algorithm based on machine learning","authors":"Yong Ge, Hiu Hong Teo, Lip Kean Moey and Walisijiang Tayier","doi":"10.1088/2631-8695/ad5f1a","DOIUrl":"https://doi.org/10.1088/2631-8695/ad5f1a","url":null,"abstract":"Tool wear during machining significantly impacts workpiece quality and productivity, making continuous monitoring and accurate prediction essential. In this context, the present study develops an efficient tool wear prediction system to enhance production reliability and reduce tool costs. It is worth noting that conventional methods, including support vector regression, autoencoders, attention mechanisms, CNNs, and RNNs, have limitations in feature extraction and efficiency. Aiming at resolving these limitations, a multiscale convolutional neural network (MDCNN)-based algorithm is proposed for predicting the remaining life of milling cutters. The algorithm uses preprocessing techniques like wavelet transform and principal component analysis for noise reduction and feature extraction. It then extracts temporal data features using convolutional layers of different scales and employs a self-attention mechanism for feature encoding. Validation on the PHM2010 milling cutter wear dataset with 10-fold cross-validation demonstrates that the MDCNN model achieves a wear prediction accuracy of 97%, a recall rate of 98%, and an F1 score of 97%. The MDCNN model effectively processes multi-band data and captures complex temporal features, confirming its efficiency and accuracy in predicting milling cutter wear and remaining service life.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614948","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