2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)最新文献_第8页

No-blocking Heliostat Field Layout in Tamanrasset Tamanrasset的无阻塞定日镜场布局

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9737773

Dhikra Derbal, A. Abderrezak, Seif Eddine Chehaidia

引用次数: 1

Characters Type Recognition In Moroccan Documents Using CNN 基于CNN的摩洛哥文文字类型识别

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9738376

Ali Benaissa, A. Bahri, Ahmad El Allaoui

引用次数: 0

Vibrational study of hydrogenated Single Wall Carbon Nanotubes 氢化单壁碳纳米管的振动研究

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9737776

O. Haidar, S. Abdelkader, B. Fakrach, A. Rahmani, H. Chadli, A. Rahmani

引用次数: 0

Simultaneous communication of relevant parameters 相关参数同步通信

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9738415

C. Seida, M. Ziane, A. El Allati

引用次数: 0

Evaluation of critical success factors (CSFs) to lean implementation in Moroccan SMEs: A survey study 评价摩洛哥中小企业精益实施的关键成功因素:一项调查研究

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9737950

Salma Arabi, M. S. Bajjou, A. Chafi, M. El hammoumi

{"title":"Evaluation of critical success factors (CSFs) to lean implementation in Moroccan SMEs: A survey study","authors":"Salma Arabi, M. S. Bajjou, A. Chafi, M. El hammoumi","doi":"10.1109/IRASET52964.2022.9737950","DOIUrl":"https://doi.org/10.1109/IRASET52964.2022.9737950","url":null,"abstract":"For several decades, many companies of different sizes have implemented the principles of lean production due to the elimination of all non-value added and unprofitable activities to the companies. Several improvements and benefits have been made and derived, but they have also faced several difficulties. This article aims to assess the status of the understanding of lean manufacturing, the reasons for implementation, and the key success factors necessary for long-term sustainable growth and any implementation of lean manufacturing in small and medium-sized enterprises. The research is structured in two phases. First, to identify and define these factors, we identified an initial list of 16 factors based on the analysis of previous studies and through an extensive literature review. Subsequently, a statistical analysis based on a questionnaire survey was used to assess lean perception, reasons for implementation, and the degree of importance of these factors for successful lean implementation. The results of this research indicate that 27% of Moroccan professionals defined lean production as a way to “eliminate waste” as a first classification. The main reason for implementation declared by the respondents is the improvement of customer performance (73,1 %), followed by business project (56,4%). Finally, the survey indicates that the success of the lean implementation depends mainly on human elements, such as involvement and commitment of the Top management which was ranked first with an average of 4.44, followed by ‘Involvement and participation of employees’ (4.40), ‘Communication within the company (Goals, Objectives, Vision, etc.)’ (4.38), etc.","PeriodicalId":377115,"journal":{"name":"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"67 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116468086","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 Comparison Study on Training Optimization Algorithms in the biLSTM Neural Network for Classification of PCG Signals 用于PCG信号分类的biLSTM神经网络训练优化算法比较研究

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9738309

M. Fakhry, Abeer FathAllah Brery

{"title":"A Comparison Study on Training Optimization Algorithms in the biLSTM Neural Network for Classification of PCG Signals","authors":"M. Fakhry, Abeer FathAllah Brery","doi":"10.1109/IRASET52964.2022.9738309","DOIUrl":"https://doi.org/10.1109/IRASET52964.2022.9738309","url":null,"abstract":"A trained neural network classifier is often used to detect cardiac problems by the classification of heart sound signals, also known as phonocardiogram (PCG) signals. The choice of an appropriate training optimization algorithm for such a classification problem, on the other hand, is still being debated. In this paper, we use the bidirectional long short-term memory (biLSTM) network for the classification of sequences of short-time features extracted from labelled PCG signals. The classification performance of four different trained biLSTM models is described in terms of three different optimization algorithms that are used to train the classifier. The elaborated results on testing PCG signals show that the biLSTM classifier performs better when trained with the stochastic gradient descent with momentum (SGDM) algorithm than when trained with the RMSprop (root mean squared propagation) optimizer or the adaptive moment (ADAM) optimization algorithm. Furthermore, this classification method outperforms a baseline method.","PeriodicalId":377115,"journal":{"name":"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128098801","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: 9

Damage Modeling of Composites Laminate Containing Stress Concentrations 含应力集中的复合材料层合板损伤建模

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9737799

Hamza El idrissi, A. Seddouki

引用次数: 3

Comparison between the effects of different training algorithms on the performance of MLP type statistical neural models for gas classification 不同训练算法对MLP型气体分类统计神经模型性能影响的比较

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9737881

Hicham El Badaoui, Said El Yamani, A. Roukhe

{"title":"Comparison between the effects of different training algorithms on the performance of MLP type statistical neural models for gas classification","authors":"Hicham El Badaoui, Said El Yamani, A. Roukhe","doi":"10.1109/IRASET52964.2022.9737881","DOIUrl":"https://doi.org/10.1109/IRASET52964.2022.9737881","url":null,"abstract":"The present work uses a classification approach based on an artificial neural network (ANN) of the Multilayer Perceptron type (MLP). This algorithm was used to better discriminate individuals by highlighting non-linear relationships that are impossible to obtain with classical ordination methods. This method consists of projecting the spectrum of a gas, taken from remote sensing data, onto a three-dimensional space, using a MLP type neural network model. The latter adopts, during the training process, the gradient back-propagation algorithm, during which the mean squared error (MSE) at the output is continuously calculated and fed back to the input until it reaches a fixed minimum threshold, in order to correct the synaptic weights of the network. In this context, the ANN will provide undeniably effective solutions for classification. We have shown in this study that for the classification of gases (H2S-NO2 mixture, H2S, NO2), the best performing model is the one that uses as transfer functions, the Tansig function in the hidden layer and the Purelin function in the output layer, while using a Scalar Conjugate Gradient (SCG) training algorithm.","PeriodicalId":377115,"journal":{"name":"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133919327","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

Maintenance 4.0 Employees' Competencies: Systematic Literature Review 维护4.0员工胜任力:系统文献综述

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9737840

Fadoua Benhamza Hlihel, Youness Chater, Abderrazak Boumane

引用次数: 1

Reverse Osmosis Process Optimization for Acetic Acid Rejection, by Coupling RSM to PSO RSM - PSO耦合法优化反渗透工艺处理醋酸

2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) Pub Date : 2022-03-03 DOI: 10.1109/IRASET52964.2022.9737958

Jbari Yousra, A. Souad

{"title":"Reverse Osmosis Process Optimization for Acetic Acid Rejection, by Coupling RSM to PSO","authors":"Jbari Yousra, A. Souad","doi":"10.1109/IRASET52964.2022.9737958","DOIUrl":"https://doi.org/10.1109/IRASET52964.2022.9737958","url":null,"abstract":"Acetic acid is one of the main pollutants in ethanol production plants. It appears in the residues of the distillation of fermented musts. Its presence in fermentation inhibits microbial biocatalysts and reduces ethanol production rates. In this study, the separation of acetic acid from vinasse by the reverse osmosis process is studied. This separation is influenced by operatory parameters that must be optimized. Three parameters of feed flow rate, acetic acid concentration, and temperature are considered to maximize the acetic acid rejection. The response surface methodology (RSM) coupled to the particle swarm optimization (PSO) was followed for this purpose, using numerical data obtained, by the reliable model developed in our previous work. The RSM allows satisfactory prediction of acetic acid rejection obtained with a Mean Absolute Percentage Error of about 1%. This model was exploited as an objective function by PSO developed on software Python, to maximize the acetic acid rejection at different feed concentration cases. The results showed that the optimal values obtained, relating to an initial pressure of 17.51 atm and a temperature of 38.65°C could eliminate 99.6% of acetic acid at an energy consumption of 5.67 kWh/m3.","PeriodicalId":377115,"journal":{"name":"2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116155039","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