2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET)最新文献_第4页

Adaptive Field-Oriented Control of Permanent Magnet Synchronous Motor Using Feedfoward Actions 永磁同步电机前馈自适应磁场定向控制

2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) Pub Date : 2023-04-29 DOI: 10.1109/IC_ASET58101.2023.10150634

Naithan Peter, Nitendran Maniam, H. Mudaliar, M. Cirrincione, Ravneel Prasad, S. Chand, A. Fagiolini

引用次数: 1

Design and Modeling of an Exoskeleton Robotic System for the Rehabilitation of Lower Limbs 下肢康复外骨骼机器人系统的设计与建模

2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) Pub Date : 2023-04-29 DOI: 10.1109/IC_ASET58101.2023.10151312

Oumayma Ben Hammouda, H. Gritli

引用次数: 0

Integration of Different Optimization Approaches for the Classification of Different Arabic and English Voice Commands 整合不同优化方法对不同阿拉伯语和英语语音命令进行分类

2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) Pub Date : 2023-04-29 DOI: 10.1109/IC_ASET58101.2023.10151158

Karim Dabbabi, Abdelkarim Mars

{"title":"Integration of Different Optimization Approaches for the Classification of Different Arabic and English Voice Commands","authors":"Karim Dabbabi, Abdelkarim Mars","doi":"10.1109/IC_ASET58101.2023.10151158","DOIUrl":"https://doi.org/10.1109/IC_ASET58101.2023.10151158","url":null,"abstract":"Several hyperparameters represent major sensitive factors for deep learning models. For this, different hyperparameter optimization approaches are proposed to accelerate the convergence towards the optimal configurations and to support the calculations during the long learning times, and thus to give improved performance. These approaches include Bayesian Optimization (BO), Hyperband and Tree Parzen Estimator (TPE), and they are proposed for optimization task in this paper. Also, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are suggested as classifiers and Mel frequency cepstrum coefficients (MFCC) and Mel as features. Experiments showed that the best results in terms of evaluated performances (Precision = 94.96%, Recall = 94.85%, F1 = 94.85%) were obtained with the combination of LSTM and MFCC (LSTM-MFCC) with BO on English voice command database compared to those obtained with other combinations of features and classifiers with different optimization approaches. Moreover, there is evidence that BO converged faster than TPE and HB, and converged to better configurations.","PeriodicalId":272261,"journal":{"name":"2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132721115","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

Enhancing Traffic Sign Recognition Through Daubechies Discret Wavelet Transform and Convolutional Neural Networks 利用离散小波变换和卷积神经网络增强交通标志识别

2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) Pub Date : 2023-04-29 DOI: 10.1109/IC_ASET58101.2023.10150874

Rim Trabelsi, K. Nouri, Imen Ammari

引用次数: 0

Fiber Optic Incidents Detection and Classification with Yolo Method 基于Yolo方法的光纤事件检测与分类

2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) Pub Date : 2023-04-29 DOI: 10.1109/IC_ASET58101.2023.10151155

A. Diop, I. Ngom, I. Diop

{"title":"Fiber Optic Incidents Detection and Classification with Yolo Method","authors":"A. Diop, I. Ngom, I. Diop","doi":"10.1109/IC_ASET58101.2023.10151155","DOIUrl":"https://doi.org/10.1109/IC_ASET58101.2023.10151155","url":null,"abstract":"in this paper we propose an automatic and real time management system of incidents in fiber optic telecommunications networks. The first step is to collect images of incidents in the fiber optic transmission networks of telecommunications operators in Senegal. Then, from these collected images, we develop a convolutional neural network architecture YOLO (You Only Look Once) to automatically detect in real time incidents in the fiber optic telecommunications networks as well as the impacts on the different services of the operator. This proposed system is an extension of two works on incident management systems in fiber optic telecommunications networks with deep learning algorithms based on convolutional neural networks. Indeed, the first work deals with the use of convolutional neural networks (CNN) and the second with the use of regional mask neural networks (Mask-RCNN). The reality is that in the current networks (Internet) in Senegal as everywhere in the world the infrastructure based on optical fiber represents the most important part of the transmission media used for the transport of information. And this infrastructure is growing more and more in parallel with the needs of users of telecommunications networks. So finding ways to detect incidents faster than Mask-RCNN and more efficiently than CNN is essential for better incident handling. Our system ensures better handling through faster, more efficient detection and analysis of downed or cut optical link incidents through the use of the YOLO algorithm. YOLO is designed to detect objects on a digital image, i.e. the status of a link impacted by an incident from the image for our system.","PeriodicalId":272261,"journal":{"name":"2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124042733","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

Millimeter-Wave Sparse Channel Estimator Based-on Twin Support Vector Regression in Deep Multipath Environments 深度多径环境下基于双支持向量回归的毫米波稀疏信道估计

2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) Pub Date : 2023-04-29 DOI: 10.1109/IC_ASET58101.2023.10151172

A. Charrada

引用次数: 0

On Bifurcation and Inputs Influence Studies of Buck Converters Buck变换器的分岔及输入影响研究

2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) Pub Date : 2023-04-29 DOI: 10.1109/IC_ASET58101.2023.10151282

S. Said, K. B. Saad, M. Benrejeb

引用次数: 0

A Review on Implementation of AES Algorithm Using Parallelized Architecture on FPGA Platform AES算法在FPGA平台上的并行化实现综述

2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) Pub Date : 2023-04-29 DOI: 10.1109/IC_ASET58101.2023.10150938

N. Q. Mohammed, A. Amir, R Badlishah Ahmad, M. H. Salih, Hana Arrfou, Nisrean Thalji, Rema Matem, Jamal Kamil K. Abbas, Qasim Mohammed Hussien, Maki Mahdi Abdulhassan

引用次数: 1

Expert System to Improve and Develop Mathematic Teachers Skills 提高和发展数学教师技能的专家系统

2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) Pub Date : 2023-04-29 DOI: 10.1109/IC_ASET58101.2023.10151424

Omer Jomah, Khuloud A.B. Zargoun

引用次数: 0

Quantitative Evidences of Electrical Charges Creation at the Metal/Liquid Interface in Power Transformer Insulation Systems 电力变压器绝缘系统中金属/液体界面电荷产生的定量证据

2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) Pub Date : 2023-04-29 DOI: 10.1109/IC_ASET58101.2023.10150894

A. Beldjilali

{"title":"Quantitative Evidences of Electrical Charges Creation at the Metal/Liquid Interface in Power Transformer Insulation Systems","authors":"A. Beldjilali","doi":"10.1109/IC_ASET58101.2023.10150894","DOIUrl":"https://doi.org/10.1109/IC_ASET58101.2023.10150894","url":null,"abstract":"The aims of this work is linked to the problematic of electrical charges formation in power transformer insulation systems. After being qualitatively evidenced, this paper focuses on giving quantitative indications of the space charge injection process from metallic materials into dielectrics when these two materials are put in contact under electric stress. The Polarization and Depolarization Currents technique (PDC) is used to assess, in point of view of conduction process, different metal/insulating liquid interfaces. Three electrode systems of diffirent metallic nature (stainless steel, aluminium and brass) are associated with a transformer mineral oil. The metal/liquid/metal structures were submitted to a DC voltages and the electrical current circulating through the dielectric thickness was recorded with high sensitive electrometer. By integration of the measured response, the three cases are compared through the charge quantity involved. The obtained results meet well with the inj ection energetic barrier height at the metal/liquid contact. This is an additional proof of the physical assumption of charge creation, into the insulation system, by the transformer metallic compounds (windings, walls …).","PeriodicalId":272261,"journal":{"name":"2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114751590","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