2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)最新文献_第7页

Multi-target localization using distributed MIMO radar based on spatial sparsity 基于空间稀疏度的分布式MIMO雷达多目标定位

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497914

Chenyang Zhao, W. Ke, Tingting Wang

引用次数: 1

RCS sequence reconstruction based on ADMM sparse ISAR imaging 基于ADMM稀疏ISAR成像的RCS序列重建

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497891

Yingjun Li, B. Tian, Ruize Li, Yongxiang Liu

引用次数: 0

Automatic Analysis of Basketball Shooting Based on Machine Learning 基于机器学习的篮球投篮自动分析

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498159

Qingyao Yang, Jiangnan Shao, H. Zuo

引用次数: 0

Research on Electrical Control System of Numerical Control Machine Tool Based on Vector Control 基于矢量控制的数控机床电气控制系统研究

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497981

S. Qu

引用次数: 0

Research on Real-time Improvement Technology of Linux Based on Multi-core ARM 基于多核ARM的Linux实时改进技术研究

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498165

Yaxin Wei

{"title":"Research on Real-time Improvement Technology of Linux Based on Multi-core ARM","authors":"Yaxin Wei","doi":"10.1109/ICAICA52286.2021.9498165","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9498165","url":null,"abstract":"With the development and progress of automobile industry, the automobile electronic control system has put forward higher and higher requirements to the controller. From the realization of the controller to vehicle test, all need hardware in loop test, and ensure the real test results of core is to run in real-time processor on the real-time operating system, at the same time with a single core processor in the operation of the real-time system bottlenecks, this paper USES the multi-core processor platform to build real-time problems of real-time system research. The Linux system is studied in this paper after the defects in real time, this paper proposes a by adding preemption patch solution to realize real- time Linux system, at the same time, using the advantages of multi-core processors, the CPU resources of real-time and non- real-time tasks are divided, and the number of interrupts on the CPU where the real-time task is located is reduced. Finally, a specific RTOS development case based on the PREEMPT_RT patch is given, and on this basis, the effectiveness of the proposed scheme in the real-time performance of the system is verified.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131182098","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: 3

Ontology and Reinforcement Learning Based Intelligent Agent Automatic Penetration Test 基于本体和强化学习的智能体自动渗透测试

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497911

Kexiang Qian, Daojuan Zhang, Peng Zhang, Zhihong Zhou, Xiuzhen Chen, Shengxiong Duan

引用次数: 4

Design of wearable and portable physiological parameter monitoring system for attentiveness evaluation 可穿戴便携式注意力评价生理参数监测系统的设计

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498201

Tianao Cao, Jinwei Sun, Huanhuan Guo, Jiaze Tang, Qisong Wang, Dan Liu

{"title":"Design of wearable and portable physiological parameter monitoring system for attentiveness evaluation","authors":"Tianao Cao, Jinwei Sun, Huanhuan Guo, Jiaze Tang, Qisong Wang, Dan Liu","doi":"10.1109/ICAICA52286.2021.9498201","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9498201","url":null,"abstract":"With the rapid development of information technology, the \"human-machine collaboration\" smart education model is emerging increasingly. Aiming at addressing the problems of poor portability of the devices, single sort of physiological signals, and excessively subjective evaluation of attentiveness in current monitoring systems, this paper designed an attentiveness evaluation system based on multiple physiological information. First of all, in view of the large volume of traditional acquisition devices, we designed the miniaturized, wearable multi-physiological signal acquisition node. Based on a single-channel EEG signal analog acquisition front-end, 9-axis acceleration acquisition chip and blood oxygen (SpO2) acquisition module, we acquired the EEG, posture and SpO2 signals synchronously. Secondly, in the light of the bandwidth and power consumption of information transmission in the wireless body area network, we designed a data transmission networking based on wireless radio frequency Wi-Fi, achieving high-speed signal communication with high accuracy. The attentiveness induction experiment was designed, and an objective evaluation index of attentiveness based on reaction time and accuracy rate for regression analysis and fitting was put forward. After preprocessing the raw data, a variety of features were extracted, and the performance of the attentiveness evaluation was verified. Results show that the accuracy rate of the attentiveness is up to 77.1%, which realizes the effective evaluation of attentiveness.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126160444","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

Free Range Laying Hens Monitoring System Based on Improved MobileNet Lightweight Network 基于改进型MobileNet轻量级网络的散养蛋鸡监测系统

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498163

Yuxuan Jiang, Linze Li, Fenghang Zhang, Weijie Zhang, Qun Yu

{"title":"Free Range Laying Hens Monitoring System Based on Improved MobileNet Lightweight Network","authors":"Yuxuan Jiang, Linze Li, Fenghang Zhang, Weijie Zhang, Qun Yu","doi":"10.1109/ICAICA52286.2021.9498163","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9498163","url":null,"abstract":"In order to solve the problems such as the difficulty in artificial supervision, the high cost of managing to breed, and the difficulty in monitoring the number and state of the chickens, this paper proposed a layer monitoring system based on the improved MobileNet lightweight neural network. MobileNet (target classification network) is combined with SSD (target detection network), with MobileNetV2 network selected to replace VGG-16 in the SSD network as the basic feature extraction network, and all standard convolution in SSD regression detection is replaced by deep separable convolution to construct MobileNetV2-SSD target detection network. According to the VOC standard, the layer data set was built, the layer detection model was trained, and the layer monitoring system deployed on Raspberry PI 4B was constructed. The experimental results show that the monitoring system constructed in this paper achieves a detection accuracy of 79.17% on the self-built layer data set, and the detection speed of about 10fps can be achieved when running on Raspberry PI 4B, which basically meets the requirements of accurate and real-time detection, and can effectively monitor free-range layers and assist the management of chicken houses.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"38 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126108731","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

Decision Tree for Classifying Betacoronavirus Species Using Amino Acid Frequencies 基于氨基酸频率的冠状病毒种属分类决策树

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497957

M. Alshehri, Manee M. Manee, Ghaida G. Alharthi, Mohanad A Ibrahim, Badr M. Al-Shomrani, Fahad H Alqahtani

{"title":"Decision Tree for Classifying Betacoronavirus Species Using Amino Acid Frequencies","authors":"M. Alshehri, Manee M. Manee, Ghaida G. Alharthi, Mohanad A Ibrahim, Badr M. Al-Shomrani, Fahad H Alqahtani","doi":"10.1109/ICAICA52286.2021.9497957","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9497957","url":null,"abstract":"Emerging infectious diseases have received significant global attention due to Betacoronaviruses. Researchers have used different names for the same Betacoronavirus genome or the same name for different genomes, resulting in erroneous identification. An approach for Betacoronavirus species classification is proposed, adopting amino acid bias as the feature input to a decision tree. The dataset contains sequences of the four structural proteins— spike, envelope, membrane, and nucleocapsid—of ten different species. The protein sequences are first converted to an 80-dimensional feature vector in which each element corresponds to the frequency of an amino acid. Using this input, the decision tree achieved an accuracy rate of 99%, indicating that amino acid bias is an effective attribute for the classification of Betacoronavirus species. This study finds out that we can use amino acid frequencies as features. Also, it can classify known Betacoronavirus family members and label them with common names. We also recommend that authors unify the names of these genomes to minimize ambiguity caused by alternative names.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127415716","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 classification method of hierarchical retrieval based on comment data 基于评论数据层次检索的情感分类方法

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497974

Cheng-yue Liu, Jian Zhang

引用次数: 0