2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE)最新文献_第10页

Convolutional Neural Network Based Kannada-MNIST Classification 基于卷积神经网络的Kannada-MNIST分类

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342474

Emily Xiaoxuan Gu

{"title":"Convolutional Neural Network Based Kannada-MNIST Classification","authors":"Emily Xiaoxuan Gu","doi":"10.1109/ICCECE51280.2021.9342474","DOIUrl":"https://doi.org/10.1109/ICCECE51280.2021.9342474","url":null,"abstract":"Before machine learning emerged, tasks that involve the recognition of handwritten characters, which include postcode recognition, document digitalization, and ancient character recognition, all require extensive human effort. Nowadays, various methods have been developed so that such tasks can be handled efficiently and accurately by computers. In 2018, the School of Computer and Information Science of Southwest University and the Research Institute of Yi Nationality at Guizhou University of Engineering Science jointly took lead in the research of ancient Yi language classification by using artificial intelligence technology. A model based on Convolutional Neural Networks (CNN) was developed and achieved a rather high accuracy. This triggered my great curiosity about handwritten character classification using machine learning methods.In 2019, the Kannada-MNIST (K-MNIST) dataset, a well-processed dataset of numerals in the Kannada language, was disseminated. We decided to make use of this dataset for our research in handwritten character classification. A CNN model was primarily developed due to its special architecture that makes it well suited for image classification tasks. This paper focuses on the establishment and experimentation of this CNN model and makes a detailed analysis. We also considered other methodologies such as Logistic Regression and Support Vector Machine for making comparisons, so that we can gain an insight into the performance of our model when compared to other machine learning methods. Through our experiment, the model achieved an accuracy of 98.77% over the testing set of K-MNIST, surpassing all the baselines, and it is effective for the classification of all categories of the dataset (0-9). Thus, we eventually concluded the strong capability of CNN models when performing classification tasks of handwritten characters.","PeriodicalId":229425,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122471682","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

Research on the Logistics Demand Transfer Strategy of e-commerce enterprises Based on the Method of Visual Algorithm with MATLAB 基于MATLAB可视化算法的电子商务企业物流需求转移策略研究

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342429

Shengnan Niu

引用次数: 0

An Detection Method of Substance Concentration 一种物质浓度的检测方法

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342277

Qiming Feng, Suping Qian

引用次数: 0

ICCECE 2021 Table of Contents ICCECE 2021目录

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/iccece51280.2021.9342240

引用次数: 0

SLAM Self - Cruise Vehicle Based on ROS Platform 基于ROS平台的SLAM自巡航车辆

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342204

Longan Yang, Haifei Chi

引用次数: 2

A website design to support teaching of algorithms 一个支持算法教学的网站设计

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342224

Liang Wang, Hong Wei

引用次数: 0

Systematic Heartbeat Monitoring using a FMCW mm-Wave Radar 基于FMCW毫米波雷达的系统心跳监测

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342280

Shanling Ji, Haiying Wen, Jian-Kang Wu, Zhisheng Zhang, Kunkun Zhao

引用次数: 9

Design and verification of neural network sliding mode controller based on FPGA 基于FPGA的神经网络滑模控制器的设计与验证

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342548

Yi Zhang, Weili Dai

{"title":"Design and verification of neural network sliding mode controller based on FPGA","authors":"Yi Zhang, Weili Dai","doi":"10.1109/ICCECE51280.2021.9342548","DOIUrl":"https://doi.org/10.1109/ICCECE51280.2021.9342548","url":null,"abstract":"This paper presents a design and verification method for a neural network sliding mode controller based on Field Programmable Gate Array(FPGA). This method uses FPGA as the main method of designing neural network sliding mode controller. Design experiments to verify the feasibility of the analysis algorithm for the core control variable control law and PWM signal in the doubly salient electromagnetic generator system (DSEG system). By intercepting the corresponding experimental data of the state variables in the finite element simulation of the motor as input, the corresponding control law and PWM signal are output after calculation by the neural network sliding mode controller. At the same time, the image processing capability of LabVIEW is used to compare and analyze the control law obtained by finite element simulation with the PWM signal and the signal output by FPGA. The experimental results show that the FPGA core controller can follow the control law well whether the system is in steady state or dynamic state. Corresponding PWM signal change trend is also consistent, which can achieve a better restoration of the simulation control state.","PeriodicalId":229425,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133328043","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

Supply Fraud Forecasting using Decision Tree Algorithm 基于决策树算法的供应欺诈预测

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342556

Hongya Wang, Fengtian Yang, Shaomeng Shen

引用次数: 0

An optimization of im2col, an important method of CNNs, based on continuous address access 基于连续地址访问的cnn重要方法im2col优化

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342343

Haoyu Wang, Chengguang Ma

{"title":"An optimization of im2col, an important method of CNNs, based on continuous address access","authors":"Haoyu Wang, Chengguang Ma","doi":"10.1109/ICCECE51280.2021.9342343","DOIUrl":"https://doi.org/10.1109/ICCECE51280.2021.9342343","url":null,"abstract":"Convolutional neural networks (CNNs) are now widely used in various common tasks such as image classification, semantic segmentation, and face recognition. Convolution layers are the core layers of CNNs, the computing speed of the convolution layer will directly affect the computing speed of the entire network, thereby affecting the real-time performance. The current general convolutional layer acceleration method is to use the image to column (im2col) algorithm to split the input image into a column matrix, then use the general matrix multiplication (GEMM) to perform matrix multiplication on the column vector and the convolution kernel. This operation can greatly improve the computing speed of the convolutional layer because most computing platforms have more mature optimizations for GEMM. However, DSP is very fast for vector multiplication and addition. In the inference of the convolutional layer, the memory access of the im2col algorithm consumes far more time than the GEMM. This has become a bottleneck for further optimization of computing speed. In this article, I will present an im2col algorithm acceleration method in the case of a single stride based on continuous memory address read. With this method, the speed of the im2col algorithm can be increased by more than 10 times when processing a single-step convolutional layer. This is a portable method. In this article, I’11 show the optimization effects on Xtensa BBE64ep DSP cores and stm32f4 processors.","PeriodicalId":229425,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132579059","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