2021 IEEE International Conference on Electro Information Technology (EIT)最新文献_第5页

Branch selection and data optimization for selecting machines for processes in semiconductor manufacturing using AI-based predictions 使用基于人工智能的预测为半导体制造过程选择机器的分支选择和数据优化

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491836

Peter Stich, Rebecca Busch, M. Wahl, Christian Weber, M. Fathi

引用次数: 1

Changing Problem Solving Methods in Higher Education to Meet the Challenges of Industry 4.0 改变高等教育解决问题的方法，迎接工业4.0的挑战

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491909

Benjamin Ritter

引用次数: 0

Sound event classification using neural networks and feature selection based methods 基于神经网络和特征选择的声音事件分类方法

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491869

Ammar Ahmed, Y. Serrestou, K. Raoof, J. Diouris

{"title":"Sound event classification using neural networks and feature selection based methods","authors":"Ammar Ahmed, Y. Serrestou, K. Raoof, J. Diouris","doi":"10.1109/EIT51626.2021.9491869","DOIUrl":"https://doi.org/10.1109/EIT51626.2021.9491869","url":null,"abstract":"Sound events, emanate from several sources, are ubiquitous and manifest them selves with different characteristics in different environments. With the advancement of deep learning models and existence of ever increasing training data, the automatic recognition and classification task of these events has improved significantly over the years . Traditionally, environmental sound event recognition systems are developed by keeping the generic database that is readily available, while sound events generated in a particular environment are not focused. Another issue of training of large neural networks requires huge amount of parameters and training them costs computational resources. To tackle this issue, we firstly built a custom database consisting of events occurring outside and around smart homes or building. The sound events such as rain, wind, human gait, and passing of vehicles. We propose the use of a sequential feature selection technique for for reduction of dimension of features extracted with MFCC. Selected features are used for training recurrent neural network (RNN) on aforementioned sound events. We compared the results of our proposed method with the same RNN trained with MFCC features and convolutional neural networks (CNN) trained with mel frequency band (MFB) features. Our proposed system performed with high accuracy in former case but slightly better compared to CNN in achieving higher classification accuracy and a significant reduction of parameters during training with the proposed system.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131367967","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: 2

Convolutional Neural Network Hand Gesture Recognition for American Sign Language 美国手语的卷积神经网络手势识别

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491897

Shruti Chavan, Xinrui Yu, J. Saniie

{"title":"Convolutional Neural Network Hand Gesture Recognition for American Sign Language","authors":"Shruti Chavan, Xinrui Yu, J. Saniie","doi":"10.1109/EIT51626.2021.9491897","DOIUrl":"https://doi.org/10.1109/EIT51626.2021.9491897","url":null,"abstract":"With the advancements in the computer vision technology, learning and using sign languages to communicate with deaf and mute people has become easier. Exciting research is ongoing for providing a global platform for communication in different sign languages. In this paper, we present a Deep Learning based approach to recognize a sign performed in American Sign Language by capturing an image as input. The system can predict the signs of 0 to 9 digits performed by the user. By utilizing image processing to convert RGB data to grayscale images, efficient reduction is achieved in the storage requirements and training time of the Convolutional Neural Network. The objective of the experiment is to find a mix of Image Processing and Deep Learning Architecture with lesser complexity to deploy the system in mobile applications or embedded single board computers. The database is trained from scratch using smaller networks as LeNet-5 and AlexNet as well as deeper network such as Vgg16 and MobileNet v2. The comparison of the recognition accuracies is discussed in the paper. The final selected architecture has only 10 layers including a dropout layer which boosted the training accuracy to 91.37% and testing accuracy to 87.5%.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125340761","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: 10

Tractor Automated Ground Leveling (AGL) Simulation using Artificial Neural Network 基于人工神经网络的拖拉机自动地面调平仿真

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491922

Tien-Chuong Lim, K. Cheok, S. Ganesan

{"title":"Tractor Automated Ground Leveling (AGL) Simulation using Artificial Neural Network","authors":"Tien-Chuong Lim, K. Cheok, S. Ganesan","doi":"10.1109/EIT51626.2021.9491922","DOIUrl":"https://doi.org/10.1109/EIT51626.2021.9491922","url":null,"abstract":"Traditional tractor ground leveling operation applies a manual process with no electronic assistance. Automated Ground Leveling (AGL) will increase quality of leveling and operator comfort. This paper outlines a machine learning approach using Artificial Neural Network (ANN). The proposed AGL uses tractor inclined angle and leveling error as inputs. The target output is the tractor scraper implement raise or lower command. The equations to run simulations are formulated and applied to the model and verified during simulations. The details can be found in section IV of this paper. John Deere StarFire 6000 GPS receiver is proposed to be the device to obtain latitude, longitude, altitude and an IMU device to obtain pitch/angling data of tractor. The proposed inputs and target output proved to be effective in producing a set of weights and biases that learns to control the scraper implement. Twenty (20) ANN trainings were conducted using the same set of training data. Out of the twenty trainings, three sets of trained weights and biases outperformed the training set. The best trained weights and biases produced an RMS error of 0.50449 compared to human training data RMS error of 0.593, which was about 14.9% improvement. The algorithm recognizes the goal of staying close to the ground reference line. This paper provides a brief review on ANN for clarity and applies it to the AGL.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123957646","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

Autonomous Aerial Vehicle Vision and Sensor Guided Landing 自主飞行器视觉与传感器制导着陆

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491843

Gabriel Bitencourt, E. Brown, Cedric Bleimnling, G. Lai, A. Molki, Tolga Kaya

引用次数: 0

A Consolidated Approach towards Application of Machine Learning Principles in Additive Manufacturing 机器学习原理在增材制造中的应用

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491833

A. Raza, A. Haider, W. Haider

{"title":"A Consolidated Approach towards Application of Machine Learning Principles in Additive Manufacturing","authors":"A. Raza, A. Haider, W. Haider","doi":"10.1109/EIT51626.2021.9491833","DOIUrl":"https://doi.org/10.1109/EIT51626.2021.9491833","url":null,"abstract":"In recent years, additive manufacturing (AM) has garnered significant attention all over the world due to the exemplary benefits attained during design to achieving superior part quality. Researchers have also started utilizing machine learning (ML) tools to aid the AM process. Emphasis has been laid on the availability of ample datasets and the ease of their acquisition. The need for establishment of feature libraries has been highlighted. Different ML techniques and associated models such as Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Trees (DT), Deep Convolution Network (DNN), and Convolutional Neural Network (CNN) are being used by researchers for optimization of parameters, defect detection, creation of online monitoring systems as well as predicting the powder spreading mechanism for AM. In fact, most ML tools are utilized either for classification or regression purposes. This paper focuses on the availability of the resources required to employ ML in AM, the applications of ML in AM, present limitations, and potential opportunities for extended use in future.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"12 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114104850","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

Combined Wind and Solar Power Offering Strategy with Virtual Bidding and Risk Management in Two-Settlement Electricity Markets 双结算电力市场中虚拟竞价和风险管理下的风电和太阳能发电联合发售策略

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491892

Josue Campos do Prado, W. Qiao, Dongliang Xiao

引用次数: 1

Smart Health Integrated Framework and Topology (SHIFT) for Smart and Connected Community 面向智能互联社区的智能健康集成框架和拓扑(SHIFT)

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491837

B. Morshed

引用次数: 1

Predicting Hourly Energy Consumption in Buildings 建筑物每小时能源消耗预测

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491876

Houda Bouderraoui, Soufiane Chami, P. Ranganathan

引用次数: 1