2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)最新文献_第4页

CDAE-C: A Fully Convolutional Denoising Auto-Encoder with 2.5D Convolutional Classifier 基于2.5D卷积分类器的全卷积去噪自编码器

2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) Pub Date : 2022-12-11 DOI: 10.1109/TOCS56154.2022.10015922

Haolan Zuo

{"title":"CDAE-C: A Fully Convolutional Denoising Auto-Encoder with 2.5D Convolutional Classifier","authors":"Haolan Zuo","doi":"10.1109/TOCS56154.2022.10015922","DOIUrl":"https://doi.org/10.1109/TOCS56154.2022.10015922","url":null,"abstract":"Lung cancer, a major burden of disease causing most cancer related deaths worldwide, can be well treated with early diagnosis of malignant lung nodule using high resolution chest computed tomography. Low dose computed tomography, with lower radiation risk to patients than normal dose computed tomography, benefits the health of patients but degrades the image quality with interfering noise, which can compromise diagnostic performance. In this paper, a multi-function model is introduced that deals with both lung nodule classification and CT image noise deduction. The proposed model consists of a fully convolutional denoising auto-encoder and a 2.5D convolutional classifier and is referred as Convolutional Denoising Auto-Encoder and Classifier (CDAE-C). The training of the proposed model is conducted following a two-phase process in which CDAE is firstly trained to denoise and reconstruct low-dose CT images and then CDAE-C is trained on latent code from pretrained encoder and 3D spatial relationship of lung nodules to classify benign and malignant lung nodules. Fully convolutional structure of denoising auto-encoder ensures the model can accept and reconstruct a low-dose CT image independent of its size, which is practical and very beneficial to the 2.5D classifier as the classification work of benign and malignant lung nodules needs regions of interest cropped from whole low-dose CT images. Extracting lung nodule’s latent representation from the pretrained encoder and using 3D spatial relationship of cropped lung nodule slices, 3D embedded features of each lung nodule are constructed as input of the proposed 2.5D convolutional classifier. Experimental results indicate that CDAE’s denoising performance is of RMSEapprox0.0458 and PSNRapprox27.2004, and CDAE-C classification performance reaches recall rateapprox97.67%, AUC approx99.45% and FNRapprox2.17%. After ablation experiment, the proposed model is proved to have higher accuracy and convergence speed.","PeriodicalId":227449,"journal":{"name":"2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116049289","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 Robust and Efficient Path Planning System Based on the Global Euclidean Distance Fields 基于全局欧氏距离场的鲁棒高效路径规划系统

2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) Pub Date : 2022-12-11 DOI: 10.1109/TOCS56154.2022.10015998

Hanxiong Zhou, Xiaoli Zhang, Xiafu Peng

引用次数: 0

Application of improved generalized predictive control for frequency control in ultrasound therapy instruments 改进广义预测控制在超声治疗仪频率控制中的应用

2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) Pub Date : 2022-12-11 DOI: 10.1109/TOCS56154.2022.10016171

Fenghua Shu, Jingwen Yang

引用次数: 1

Systematic Analysis of Big Data Based Machine Learning Algorithms on Various Fields 基于大数据的机器学习算法在各个领域的系统分析

2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) Pub Date : 2022-12-11 DOI: 10.1109/TOCS56154.2022.10015981

Yang Wang, Tianding Zhou, Chenglin Li, Zhixin Liu, Shichuang Zheng, Qingqing Liu

{"title":"Systematic Analysis of Big Data Based Machine Learning Algorithms on Various Fields","authors":"Yang Wang, Tianding Zhou, Chenglin Li, Zhixin Liu, Shichuang Zheng, Qingqing Liu","doi":"10.1109/TOCS56154.2022.10015981","DOIUrl":"https://doi.org/10.1109/TOCS56154.2022.10015981","url":null,"abstract":"Machine Learning is a basic innovation in foreseeing results in view of data. Four famous Big Data machine learning algorithms are tended to in this paper: Bayesian Decision Theory Classification, and Linear Regression. The underlying two are overseen learning algorithms, the third an independent learning algorithm, and the fourth a relationship algorithm. Advantages of machine learning integrate flexibility and adaptability differentiated and customary biostatistical methods, which makes it deployable for certain tasks, similar to bet partition, finding and gathering, and perseverance assumptions. One more benefit of machine learning algorithms is the capacity to examine assorted data types. Every methodology is broadly explored and examined. Likewise, the accentuation is placed on how the four techniques transaction with one another to rouse investigation of more strong and data-proficient algorithms. At long last, the review portrays the impediments, talks about research difficulties, and recommends future chances to propel the examination on data-proficiency in machine learning.","PeriodicalId":227449,"journal":{"name":"2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129726722","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 on university fire equipment management system based on RFID technology 基于RFID技术的高校消防设备管理系统研究

2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) Pub Date : 2022-12-11 DOI: 10.1109/TOCS56154.2022.10015941

Zirou Liu, Lin Zhu, Ting Jiang, D. Qiu

{"title":"Research on university fire equipment management system based on RFID technology","authors":"Zirou Liu, Lin Zhu, Ting Jiang, D. Qiu","doi":"10.1109/TOCS56154.2022.10015941","DOIUrl":"https://doi.org/10.1109/TOCS56154.2022.10015941","url":null,"abstract":"With the rapid development of Radio Frequency Identification technology (RFID), more and more attention has been paid to its application. By now, colleges and universities still use the traditional manual inspection, manual records and paper archiving to carry out the inventory and inspection of fire equipment. This management model gradually reflects the shortcomings of low efficiency and ineffective supervision. To solve these shortcomings, the RFID technology is introduced into the inventory management of fire equipment and inspection management of fire equipment, and a comprehensive fire equipment management system is built. By putting RFID electronic tags on fire equipment, the equipment has the perception ability, and by scanning related equipment to achieve automatic collection and automatic identification; Through WIFI/GPRS technology to achieve wireless data transmission, remote data management based on Web technology; Combined with the characteristics of daily inspection, inventory, supervision and other tasks of fire equipment, this paper designed and developed a set of intelligent management system of fire equipment based on RFID, which is an innovative research and application of RFID technology in the field of fire protection.","PeriodicalId":227449,"journal":{"name":"2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128357742","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 on remote intelligent monitoring system based on MQTT and LoRa communication for the five-axis NC machine tool 基于MQTT和LoRa通信的五轴数控机床远程智能监控系统研究

2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) Pub Date : 2022-12-11 DOI: 10.1109/TOCS56154.2022.10016059

Lei Xie, Bing Xie, Yuming Qi

{"title":"Research on remote intelligent monitoring system based on MQTT and LoRa communication for the five-axis NC machine tool","authors":"Lei Xie, Bing Xie, Yuming Qi","doi":"10.1109/TOCS56154.2022.10016059","DOIUrl":"https://doi.org/10.1109/TOCS56154.2022.10016059","url":null,"abstract":"Intelligent manufacturing has become a research hotspot, as the development direction of the new generation manufacturing system. CNC machine tools are the core manufacturing equipment of discrete manufacturing enterprises, however the traditional remote monitoring method for five-axis CNC machine tools has some problems, such as access latency, short coverage distance, low utilization rate of equipment, and so on. To solve this problem, this study proposed a remote intelligent monitoring system based on MQTT and LoRa communication for five-axis CNC machine tool. And the system integrates multi-source sensor signals of CNC machine tools to conduct synchronous real-time monitoring, interface control and fault alarm for multiple five-axis CNC machine tools. The results show that the system can monitor the functions and production process status of five-axis CNC machine tools within 3km based on MQTT and LoRa communication, and remotely realize the linkage control and real-time monitoring of multiple NC machining equipment by publishing and subscribing the status information of machine tools in real time.","PeriodicalId":227449,"journal":{"name":"2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129600904","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 on Motor Drive Control Strategy of Electric Vehicle 电动汽车电机驱动控制策略研究

2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) Pub Date : 2022-12-11 DOI: 10.1109/TOCS56154.2022.10016123

Ziqi Shen, Zhenpeng Luo, Siqing Zhang, Shuyu Wang

引用次数: 1

A music recommendation system based on collaborative filtering and SVD 基于协同过滤和 SVD 的音乐推荐系统

2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) Pub Date : 2022-12-11 DOI: 10.1109/TOCS56154.2022.10016210

Yu-Chuan Chen

{"title":"A music recommendation system based on collaborative filtering and SVD","authors":"Yu-Chuan Chen","doi":"10.1109/TOCS56154.2022.10016210","DOIUrl":"https://doi.org/10.1109/TOCS56154.2022.10016210","url":null,"abstract":"With the development of the Internet and the advent of music-streaming platforms, a large amount of music data available for selection is now greater than ever on the Internet. In addition to searching expected music objects for users, it becomes necessary to develop a recommendation service. A music recommendation system (MRS) relieves users from sorting through the various options by automatically recommending music based on their historical behaviors like the play count of each song. Recommender systems have aroused a lot of awareness in the past decade. Although algorithms including content-based, collaborative, singular value decomposition, and other techniques are used in the recommendation system, there does not exist any perfect recommendation system that can give completely precise feedback on what users actually want. To figure out which algorithm does a better job, the paper proposes a music recommendation system based on two algorithms, item-based collaborative filtering, and singular value decomposition, that are used in the music recommendation system and compares the two methods to find out which one can make a more precise recommendation. Item similarity between the songs listened by the user and other users is used to predict which songs are preferred by the user. In this paper, D-Recall is regarded as an evaluation indicator between the two algorithms. And the performance of SVD is better than item-based collaborative filtering on the recommendation.","PeriodicalId":227449,"journal":{"name":"2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127158890","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

Design of Intelligent Lock Based on Face Recognition 基于人脸识别的智能锁设计

2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) Pub Date : 2022-12-11 DOI: 10.1109/TOCS56154.2022.10016188

Hui Li, Lei Zhu, Shijie Sun, Ke Xie, Jiabing Wang, Dong Liang

引用次数: 0

Security Measurement and Protection Technology Based on Power Internet of Things Terminal 基于电力物联网终端的安全检测与防护技术

2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) Pub Date : 2022-12-11 DOI: 10.1109/TOCS56154.2022.10016113

Caiwei Guo, Yuxiang Cai, Wen Ji, Qimin Xiao, ShuLin Wu, Weitao Zheng, Wei Zhou, Taining Huang, Jianxiong Huang

引用次数: 0