Volume 31, Issue 1最新文献

Enhancing Automation with Label Defect Detection and Content Parsing Algorithms 利用标签缺陷检测和内容解析算法提高自动化水平

Volume 31, Issue 1 Pub Date : 2024-01-08 DOI: 10.20532/cit.2023.1005734

Min Zheng

{"title":"Enhancing Automation with Label Defect Detection and Content Parsing Algorithms","authors":"Min Zheng","doi":"10.20532/cit.2023.1005734","DOIUrl":"https://doi.org/10.20532/cit.2023.1005734","url":null,"abstract":"The stable operation of power transmission and distribution is closely related to the overall performance and construction quality of circuit breakers. Focusing on circuit breakers as the research subject, we propose a machine vision method for automated defect detection, which can be applied in intelligent robots to improve detection efficiency, reduce costs, and address the issues related to performance and assembly quality. Based on the LeNet-5 convolutional neural network, a method for the detection of character defects on labels is proposed. This method is then combined with squeezing and excitation networks to achieve more precise classification with a feature graph mechanism. The experimental results show the accuracy of the LeNet-CB model can reach up to 99.75%, while the average time for single character detection is 17.9 milliseconds. Although the LeNet-SE model demonstrates certain limitations in handling some easily confused characters, it maintains an average accuracy of 98.95%. Through further optimization, a label content detection method based on the LSTM framework is constructed, with an average accuracy of 99.57%, and an average detection time of 84 milliseconds. Overall, the system meets the detection accuracy requirements and delivers a rapid response. making the results of this research a meaningful contribution to the practical foundation for ongoing improvements in robot intelligence and machine vision.","PeriodicalId":516796,"journal":{"name":"Volume 31, Issue 1","volume":"152 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640557","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

N-gram Language Model for Chinese Function-word-centered Patterns 以中文功能词为中心的 N-gram 语言模型

Volume 31, Issue 1 Pub Date : 2024-01-08 DOI: 10.20532/cit.2023.1005719

Jie Song, Yixiao Liu, Yunhua Qu

{"title":"N-gram Language Model for Chinese Function-word-centered Patterns","authors":"Jie Song, Yixiao Liu, Yunhua Qu","doi":"10.20532/cit.2023.1005719","DOIUrl":"https://doi.org/10.20532/cit.2023.1005719","url":null,"abstract":"N-gram language modelling, a proven and effective method in NLP, is widely used to calculate the probability of a sentence in natural language. Language pattern is a linguistic level between word/character and sentence, which exists in pattern grammar. In this research, the approach of language model and language pattern are combined for the first time, and language patterns are studied by use of the N-gram model. Chinese function-word-centered patterns are extracted from the LCMC corpus, and aligned into pattern chains. The language model is trained from these chains to investigate the properties and distribution of Chinese function words, the interaction of content words and function words, and the interaction between patterns. The results indicate that there are approximately 10,000 function-word-centered patterns in the texts, which are distributed exponentially. This research summarizes the most common function-word-centered patterns and content-word-centered patterns, and discusses the interactions of patterns based on corpus data. The bigram language model of these patterns reflects the restrictions of function words. In addition, the research adopts an innovative method to visualize the interactions between patterns. This research fills the research gap between word/character and sentence and reveals basic Chinese pattern categories and the interactions between patterns, which makes a significant contribution to Chinese linguistic research, and improves the efficiency of NLP.","PeriodicalId":516796,"journal":{"name":"Volume 31, Issue 1","volume":"174 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640552","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

Evaluation Model of the Mental Health Education Effectiveness Based on Deep Neural Networks 基于深度神经网络的心理健康教育效果评估模型

Volume 31, Issue 1 Pub Date : 2024-01-08 DOI: 10.20532/cit.2023.1005727

Junmei Luo, Shuchao Deng

引用次数: 0

A Visual Cortex-Attentive Deep Convolutional Neural Network for Digital Image Design 用于数字图像设计的视觉皮层深度卷积神经网络

Volume 31, Issue 1 Pub Date : 2024-01-08 DOI: 10.20532/cit.2023.1005695

Lei Zheng

{"title":"A Visual Cortex-Attentive Deep Convolutional Neural Network for Digital Image Design","authors":"Lei Zheng","doi":"10.20532/cit.2023.1005695","DOIUrl":"https://doi.org/10.20532/cit.2023.1005695","url":null,"abstract":"With the proliferation of advanced visualization techniques in visual communication, enhancing digital image quality remains a persistent challenge. This study presents a sophisticated Convolutional Neural Network (CNN) model to optimize image processing. The model incorporates a multi-stage architecture attentive to biological visual pathways. Inter-subnetwork connections enable integrated feature learning, guided by adaptive weighting of luminance, color, orientation, and edge maps. Spatial and channel attention modules further enrich feature interplay. When evaluated on the LIVE 3D Phase dataset, the approach demonstrates marked improvements, with saliency maps closely mirroring human visual perception. Pearson Correlation Coefficient and Histogram Intersection metrics exceed conventional models, at 0.6486 and 0.7074 respectively. Testing across distortion types reveals strong agreement with subjective rankings, confirming the model's effectiveness. By combining automated feature extraction with insights from visual cortex mechanisms, this bio-inspired CNN framework significantly enhances image optimization and quality. The scalable approach provides a foundation for next-generation computer vision and machine learning applications.","PeriodicalId":516796,"journal":{"name":"Volume 31, Issue 1","volume":"40 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140512562","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

Editorial for Vol.31, No.1 第 31 卷第 1 期社论

Volume 31, Issue 1 Pub Date : 2024-01-08 DOI: 10.20532/cit.2023.1005780

引用次数: 0