International Conference on Electronic Technology and Information Science最新文献_第5页

Research on image features extraction based on machine learning algorithms 基于机器学习算法的图像特征提取研究

International Conference on Electronic Technology and Information Science Pub Date : 2023-06-20 DOI: 10.1117/12.2682449

Xiao-Chuang Chang

{"title":"Research on image features extraction based on machine learning algorithms","authors":"Xiao-Chuang Chang","doi":"10.1117/12.2682449","DOIUrl":"https://doi.org/10.1117/12.2682449","url":null,"abstract":"Image features are essential components for physical detection, classification of objectives and downstream tasks. Specifically, the image features can be utilized to automatically detect the characteristics of images and realize physical information mapping into information domains. However, existing image features are concentrated on the decrease the contrast, which can reduce the influence of lights. Another extraction process converts images into digital images and utilize digital information techniques to obtain the features. In this paper, we utilize the machine learning model to extract the features of images with enough training iterations. Initially, we utilize the CIFAR-10 data set, which contains the 10 categories of physical objectives and simulate as the training set. Indeed, the establish machine learning model is utilize to train through inputting the 80% of total data set. After training process, the output of machine learning mode can obtain the features of any physical images. Finally, we compare our proposed model with existing image features extraction methods and utilize 20% data to evaluate our model. From our extensive experimental results, we can conclude that our established model can effectively achieve the image features extraction with higher extraction accuracy and acceptable computation time through comparing with traditional mathematical analysis methods.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126432213","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

Research progress of crop seed quality detection based on spectral imaging technology 基于光谱成像技术的作物种子品质检测研究进展

International Conference on Electronic Technology and Information Science Pub Date : 2023-06-20 DOI: 10.1117/12.2682450

Hui Li, Chengjun Zou, Xuliang Duan

{"title":"Research progress of crop seed quality detection based on spectral imaging technology","authors":"Hui Li, Chengjun Zou, Xuliang Duan","doi":"10.1117/12.2682450","DOIUrl":"https://doi.org/10.1117/12.2682450","url":null,"abstract":"Seed is the key link to ensure the yield and quality of crops, and seed quality plays a vital role in ensuring agricultural production. For seed quality testing, traditional methods have problems such as irreversible destruction, strong professionalism, complex process, long cycle, high cost, and inability to detect seeds online and real-time, which are difficult to meet the requirements of modern agriculture for non-destructive, rapid and efficient production of seeds. Spectral imaging technology has been widely used in the non-destructive and rapid detection of agricultural products, food, etc., with its advantages of fast, efficient, nondestructive and simple. The research progress of three spectral imaging technologies in seed quality detection was reviewed from the perspective of multispectral image recognition, hyperspectral image recognition and near-infrared spectral imaging. The basic principles, key technologies, implementation methods and application research results of the three spectral imaging technologies were compared, analyzed and summarized. The advantages and problems of spectral imaging technology detection and identification in crop seed quality detection were pointed out, In view of these problems, the future research directions are given to improve the understanding of agricultural information detection process using spectral imaging technology and promote the development of agricultural seed sources and other key agricultural core technologies.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"12715 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128862110","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

Sentiment analysis of online public opinion based on CNN-BiLSTM and attention mechanism 基于CNN-BiLSTM和关注机制的网络舆情情感分析

International Conference on Electronic Technology and Information Science Pub Date : 2023-06-20 DOI: 10.1117/12.2682437

Lingwei Wei, Lei Yang

{"title":"Sentiment analysis of online public opinion based on CNN-BiLSTM and attention mechanism","authors":"Lingwei Wei, Lei Yang","doi":"10.1117/12.2682437","DOIUrl":"https://doi.org/10.1117/12.2682437","url":null,"abstract":"Emotion recognition from social network texts aims to mine netizens’ subjective emotions, such as stances and emotional tendencies, over an event, which is imperative for monitoring Internet public opinion. Convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) are widely used in the field of text classification. The combination of the two can exploit the CNNs’ feature extraction ability and BiLSTM’s ability to extract context dependency. However, for emotional recognition, it is also necessary to consider some specific words in online social text. Therefore, we constructed a model for emotion recognition based on Internet public opinion in three steps. First, the CNN was used to extract local features of social network texts. Second, context-related global features were extracted using the BiLSTM. Finally, we introduced an attention mechanism to obtain important features. Experiments were conducted using netizens’ comments from a microblog during the COVID-19 epidemic as the dataset. Experimental results showed that the feature vector of the proposed model (i.e., the CNN-BiLSTM-Attention model) contains richer semantic information of texts, which can effectively improve the performance of emotion recognition from Internet public opinions.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117175085","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

Analysis and research on the accuracy of optical fiber two-way time synchronization signal based on spread spectrum code ranging 基于扩频码测距的光纤双向时间同步信号精度分析与研究

International Conference on Electronic Technology and Information Science Pub Date : 2023-06-20 DOI: 10.1117/12.2682432

H. Sun, H. Gong, Jing Peng, Wenchi Zang, Ming Ma

引用次数: 0

Research on encryption algorithms in terahertz communication systems 太赫兹通信系统中的加密算法研究

International Conference on Electronic Technology and Information Science Pub Date : 2023-06-20 DOI: 10.1117/12.2682373

Yuanming Ding, Jing Cui, Jianxin Jianxin

引用次数: 0

Video face recognition based on 3D chaotic attractor 基于三维混沌吸引子的视频人脸识别

International Conference on Electronic Technology and Information Science Pub Date : 2023-06-20 DOI: 10.1117/12.2682356

Xiang Li, Xiaoran Chen, Wanbo Yu

引用次数: 0

FPGA implementation of QPSK visible light communication system QPSK可见光通信系统的FPGA实现

International Conference on Electronic Technology and Information Science Pub Date : 2023-06-20 DOI: 10.1117/12.2682313

Haoshen Wei, Shanchun Zong, Xiaohong Sun

引用次数: 0

Study on satellite communication transmission of meteorological information between main stations based on state transfer 基于状态转移的主站气象信息卫星通信传输研究

International Conference on Electronic Technology and Information Science Pub Date : 2023-06-20 DOI: 10.1117/12.2682409

Xiaojuan Fu, Yinglang Huang, Jiabao Huang, Zhang Li

{"title":"Study on satellite communication transmission of meteorological information between main stations based on state transfer","authors":"Xiaojuan Fu, Yinglang Huang, Jiabao Huang, Zhang Li","doi":"10.1117/12.2682409","DOIUrl":"https://doi.org/10.1117/12.2682409","url":null,"abstract":"Based on the information provided in Problem D of the 2022 National College Mathematical Modeling Contest, only considering the transmission and reception of meteorological message between the main stations, the success rate of sending and receiving is 100% , set half-time information as one unit, then each main station can transmit up to three units、150 characters (less than the limit of 158) at a time.Using the method of sequential transmission in turn, the first round and the last round are studied separately, the other rounds meet the maximum transmission of cases, the relationship between the minimum value of K and N is modeled, as shown in the formula (1) . As result, it takes at least 4.67(rounded up to 5) minutes to transmit 6 rounds, the information sharing between 9 master stations can be realized. Then, according to the state characteristics of each main station after each round of transmission,the state transfer equations between the main stations are established, such as formulas (8) and (9), and the scheme of meteorological message transmission for each round of main station 1 is obtained.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129635080","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

Communication signal modulation recognition based on cyclic spectrum features and bagged decision tree 基于循环频谱特征和套袋决策树的通信信号调制识别

International Conference on Electronic Technology and Information Science Pub Date : 2023-06-20 DOI: 10.1117/12.2682404

Tianyi Huang, F. Xin, Jiachen Wang

引用次数: 0

UTSN-net: medical image semantic segmentation model based on skip non-local attention module UTSN-net:基于跳过非局部关注模块的医学图像语义分割模型

International Conference on Electronic Technology and Information Science Pub Date : 2023-06-20 DOI: 10.1117/12.2682365

Li Zhang, BinBing Zhu, Chunpeng Ma

{"title":"UTSN-net: medical image semantic segmentation model based on skip non-local attention module","authors":"Li Zhang, BinBing Zhu, Chunpeng Ma","doi":"10.1117/12.2682365","DOIUrl":"https://doi.org/10.1117/12.2682365","url":null,"abstract":"The semantic segmentation task of medical image is to segment the focus, organ or substructure of human body in medical image. It plays an important role in locating and identifying the diseased area and making medical plan. In various medical image segmentation tasks, the U-shaped architecture has achieved great success. Transunet introduces Transformer with global attention mechanism into the U-shaped architecture, which overcomes the inherent limitations of convolution, but because it still continues the original skip connections structure, it will bring the strong noise from features in the shallow network into the high semantic features of the deep network, thus affecting the segmentation accuracy. UTSN-net model based on the combination of Transformer and nonlocal attention mechanism is proposed. UTSN-net uses Transformer to overcome the inherent limitations of convolution, and introduces the skip connections module based on nonlocal attention mechanism into the U-shaped network, which can comprehensively consider the deep features with global context information and the shallow features with accurate high-resolution positioning information to improve the accuracy of segmentation results. Experiments on synapse multi-organ abdominal CT dataset verify that UTSN-net has better semantic segmentation performance.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125646230","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