WSEAS TRANSACTIONS ON ELECTRONICS最新文献_第2页

Design and Implementation of a High-Speed D Flip Flop using CMOS Inverter Logic 基于CMOS逆变逻辑的高速D触发器的设计与实现

WSEAS TRANSACTIONS ON ELECTRONICS Pub Date : 2022-12-19 DOI: 10.37394/232017.2022.13.16

Jayadeva G. S., Nikhil Murali, Meghana S., Raksha K. Kumar, Nithin Anil Nair

引用次数: 0

An Anti-Noise Gearbox Fault Diagnosis Method based on Multi-Scale Transformer Convolution and Transfer Learning 基于多尺度变压器卷积和迁移学习的齿轮箱抗噪声故障诊断方法

WSEAS TRANSACTIONS ON ELECTRONICS Pub Date : 2022-11-29 DOI: 10.37394/232017.2022.13.15

Jinliang Wu, Xiaoyang Zheng, X. Pei

{"title":"An Anti-Noise Gearbox Fault Diagnosis Method based on Multi-Scale Transformer Convolution and Transfer Learning","authors":"Jinliang Wu, Xiaoyang Zheng, X. Pei","doi":"10.37394/232017.2022.13.15","DOIUrl":"https://doi.org/10.37394/232017.2022.13.15","url":null,"abstract":"Gearbox fault diagnosis methods based on deep learning usually require a large amount of sample data for training, and these data are usually ideal experimental data without noise. However, due to the influence of complex environmental factors, a large number of effective fault samples may not be available and the sample data can be interfered with by noise, which affects the identification accuracy of fault diagnosis methods and the stability of diagnosis results. To improve the resistance to noise while achieving high diagnosis accuracy, a multi-scale Transformer convolution network (MTCN) based on transfer learning is proposed in this paper. Concretely, a multi-scale coarse-grained procedure is incorporated to capture different and complementary features from multiple scales and filter random noises to some extent. Meanwhile, the Transformer composed of an attention mechanism is utilized to extract high-level and effective features and the transfer learning strategy is applied to overcome the limitation of insufficient fault samples for model training. Finally, the experiments are conducted to verify the effectiveness of the proposed method. The results show that the proposed method has higher accuracy and robustness under noisy environments compared with previous methods.","PeriodicalId":202814,"journal":{"name":"WSEAS TRANSACTIONS ON ELECTRONICS","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134620295","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

Adaptively Equalized Bandwidth Optimization Model using SCADA-DWWAN based Neural Network 基于SCADA-DWWAN的神经网络自适应均衡带宽优化模型

WSEAS TRANSACTIONS ON ELECTRONICS Pub Date : 2022-09-19 DOI: 10.37394/232017.2022.13.14

Priyanko Raj Mudiar, K. K. Sarma, N. Mastorakis

{"title":"Adaptively Equalized Bandwidth Optimization Model using SCADA-DWWAN based Neural Network","authors":"Priyanko Raj Mudiar, K. K. Sarma, N. Mastorakis","doi":"10.37394/232017.2022.13.14","DOIUrl":"https://doi.org/10.37394/232017.2022.13.14","url":null,"abstract":"Artificial training and learning algorithms, enhanced with semi-supervised or self-supervised feature extraction capacities, employ adaptive decision optimization models. These are often favored over complex deep learning algorithms for achieving better controllability and ease of observation, lower complexity in simulating, building or designing and virtual prototyping of automatic network resource management (ANRM) standards. An Adaptive Linear Neuron type Artificial Neural Network (ADALINE-ANN) which is based on multi-tapered machine learning approach has been simulated in a virtual Supervisory Control and Data Acquisition (SCADA) framework integrated with a Distributed Control System (SCADA/DCS-Net). The system has been virtually simulated considering an adaptively equalized learning and decision approach which utilizes Markov Trained-Steepest Gradient Descent (HMM-SGD) based machine learning model employing Kalman optimization. Affinity clustering is employed for spectrum sensing by extracting the Constellation Nyquist Bands from an M-Quadrature Amplitude Modulation (QAM) orthogonal signal undergoing AWGN and Rayleigh fading as well as co-channel interference (CCI), and ensemble analysis using Channel State-Space plots are used for optimal spectrum allocation in an Adaptive Orthogonal Frequency Division Multiple Access (Adaptive-OFDMA) layout. It has been done by implementing an adaptively equalized Automatic Repeat Request (ARQ) pipelining model which utilizes minimum least square error (MLSE) minimization model. The objective is to improve the bandwidth allocation and usage ensuring most minimum spectrum wastage or loss. Successive Interference Cancellation (SIC) has been implemented to minimize static buffer and interference loss. Thus, spectrum loss due to latency and jitter which occurs from bandwidth congestion is minimized by improving the network resource tracking and allocation. It results in improved and stable bandwidth equalization.","PeriodicalId":202814,"journal":{"name":"WSEAS TRANSACTIONS ON ELECTRONICS","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127015085","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

Metrics for Performance Improvement in Organisations Using Scrum, ITIL and CMMI 使用Scrum、ITIL和CMMI的组织中的绩效改进度量

WSEAS TRANSACTIONS ON ELECTRONICS Pub Date : 2022-09-14 DOI: 10.37394/232017.2022.13.12

Sussy Bayona-Oré, Marcos Hostos

引用次数: 0

A Novel Personal Medicine Record Scheme Based on Block Chain and Cryptographic 一种基于区块链和密码学的个人病历新方案

WSEAS TRANSACTIONS ON ELECTRONICS Pub Date : 2022-09-14 DOI: 10.37394/232017.2022.13.13

Chenglian Liu, Sonia C.-I. Chen

引用次数: 0

Machine Learning Methods for Predicting Mechanical Behavior of Aluminum Alloys 预测铝合金力学行为的机器学习方法

WSEAS TRANSACTIONS ON ELECTRONICS Pub Date : 2022-09-14 DOI: 10.37394/232017.2022.13.11

A. Dorbane, F. Harrou, Y. Sun

引用次数: 0

Study of Enterprise Internal Control Based on Virtual Team and Cryptology Technique 基于虚拟团队和密码学技术的企业内部控制研究

WSEAS TRANSACTIONS ON ELECTRONICS Pub Date : 2022-09-14 DOI: 10.37394/232017.2022.13.10

Chenglian Liu, Sonia C.-I. Chen

引用次数: 0

Using ePUB3 eBook-based Project-Inquired Quality Talk to Enhance Students’ Learning Effects in Flipped Classes 基于ePUB3电子书的项目询问式质量会话提高翻转课堂学生学习效果

WSEAS TRANSACTIONS ON ELECTRONICS Pub Date : 2022-09-14 DOI: 10.37394/232017.2022.13.9

Tina Pingting Tsai, Jyhjong Lin

引用次数: 0

Microstructure- Assisted Changes in the Magnetic Barkhausen Emission of a Mild Steel Under the Influence of Various Mechanical and Environmental Loading Factors; a Semiquantitative Analysis 不同力学和环境载荷因素影响下低碳钢磁巴克豪森发射的微观组织辅助变化半定量分析

WSEAS TRANSACTIONS ON ELECTRONICS Pub Date : 2022-09-14 DOI: 10.37394/232017.2022.13.8

A. Altzoumailis, V. Kytopoulos

{"title":"Microstructure- Assisted Changes in the Magnetic Barkhausen Emission of a Mild Steel Under the Influence of Various Mechanical and Environmental Loading Factors; a Semiquantitative Analysis","authors":"A. Altzoumailis, V. Kytopoulos","doi":"10.37394/232017.2022.13.8","DOIUrl":"https://doi.org/10.37394/232017.2022.13.8","url":null,"abstract":"The aim of the present work is to introduce some novel possible ways for the semiquantitative analytical characterization of ferromagnetic steels. The characterization attempt focuses on microstructural changes occurring under the combined influence of certain mechanical loading as well as physico – chemical degradation factors such as applied stressstrain, tempering processing and salt water – induced electrochemical corrosion. In this direction, various conveniently simple developed functional modelling approaches are used to be able to analyse and estimate related experimental results obtained by means of micromagnetic Barkhausen emission measurements. The approaches are correlated to macroscaling as well as microscaling parameters such as stress-strain, dislocation – controlled cell size and pinning spacing – strength as well as internal cell flow stress. The needed correlations and estimations are made on the basis of an adopted specific micromagnetic activity parameter by which the obtained experimental data are compared and analysed. In this manner the opposite effects of internal flow stress and cell size reduction on the micromagnetic activity can reasonably be demonstrated where the first tends to stimulate an increase while the second a decrease in the specific micromagnetic parameter with progressive plastic deformation. One can further demonstrate that various physico-chemical degradation factors may markedly influence the microstructural as well as micromagnetic behaviour. For instance, hydrogen produced by corrosion of steel in NaCl – water solution and tempering processing lead to appreciable mechanical embrittlement as well as magnetic hardening of steel, expressed by an associated decrease in the specific micromagnetic parameter. With respect to this the tempering processing leads to a higher magnetic hardening compared to the corrosion process of steel.","PeriodicalId":202814,"journal":{"name":"WSEAS TRANSACTIONS ON ELECTRONICS","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123747971","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

Transfer Learning Model Training Time Comparison for Osteoporosis Classification on Knee Radiograph of RGB and Grayscale Images RGB与灰度膝关节x线片骨质疏松分类的迁移学习模型训练时间比较

WSEAS TRANSACTIONS ON ELECTRONICS Pub Date : 2022-09-13 DOI: 10.37394/232017.2022.13.7

Usman Bello Abubakar, Moussa Mahamat Boukar, Steve A. Adeshina, S. Dane

{"title":"Transfer Learning Model Training Time Comparison for Osteoporosis Classification on Knee Radiograph of RGB and Grayscale Images","authors":"Usman Bello Abubakar, Moussa Mahamat Boukar, Steve A. Adeshina, S. Dane","doi":"10.37394/232017.2022.13.7","DOIUrl":"https://doi.org/10.37394/232017.2022.13.7","url":null,"abstract":"In terms of financial costs and human suffering, osteoporosis poses a serious public health burden. Reduced bone mass, degeneration of the microarchitecture of bone tissue, and an increased risk of fracture are its main skeletal symptoms. Osteoporosis is caused not just by low bone mineral density, but also by other factors such as age, weight, height, and lifestyle. Recent advancement in Artificial Intelligence (AI) has led to successful applications of expert systems that use Deep Learning techniques for osteoporosis diagnosis based on some modalities such as dental radiographs amongst others. This study uses a dataset of knee radiographs (i.e., knee-Xray images) to apply and compare the training time of two robust transfer learning model algorithms: GoogLeNet, VGG-16, and ResNet50 to classify osteoporosis. The dataset was split into two subcategories using python opencv library: Grayscale Images and Red Green Blue (RGB) images. From the scikit learn python analysis, the training time of the GoogLeNet model on grayscale images and RGB images was 42minutes and 50 minutes respectively. The VGG-16 model training time on grayscale images and RGB images was 37 minutes and 44 minutes respectively. In addition, to compare the diagnostic performance of the two models, several state-of-the-art neural networks metric was used.","PeriodicalId":202814,"journal":{"name":"WSEAS TRANSACTIONS ON ELECTRONICS","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132222005","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