2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)最新文献_第5页

Study on classification algorithm of motor imagination EEG signal 运动想象脑电信号的分类算法研究

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00120

Xian Xie, Yingchuan Yang

引用次数: 0

Research Progress of Monitoring System Based on Wearable Fall Detection Equipment for The Elderly 基于可穿戴老年人跌倒检测设备的监测系统研究进展

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00009

Yanli Li, Peng Liu, R. Xiang, Julong Pan

引用次数: 0

Hurricane Damage Prediction on Satellite Imagery based on Neural Networks 基于神经网络的卫星图像飓风灾情预测

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00082

Dongbo Hu, Zijie Lei, Siyuan Wan

{"title":"Hurricane Damage Prediction on Satellite Imagery based on Neural Networks","authors":"Dongbo Hu, Zijie Lei, Siyuan Wan","doi":"10.1109/icaice54393.2021.00082","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00082","url":null,"abstract":"Accurate categorizations of Hurricane damage on specific locations could significantly facilitate rescuing teams and rescuing resources to be deployed to where they are needed the most. In addition, it could aid analysts to predict further predict the potential damages incoming Hurricanes could bring to various locations based on previous categorizations of satellite images about damaged terrains and buildings. This study possesses a Neural-network-based prediction model, in which CNN and FFNN of various parameters and structures are performed to make predictions regarding if a certain location is damaged due to a Hurricane from images captured by Satellites. Based on various aspects of the overall performance of models, including accuracy, AUC score, Loss curve, confusion matrix, F1 score, model fitting time and training time, the best model regarding this task is AlexNet with an accuracy of 96.77% and F1 score of 0.9816 despite its slightly longer training time of 63s per epoch. The results of fellow neural network models suggest that neural network models are capable of handling images categorization and prediction problems regarding satellite images of Hurricane, thus helping optimize resources expenditure and improve efficiency for further related analyzes.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124407872","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: 1

Chinese Named Entity Recognition via Multi-Channel Attention of Lexicon 基于词典多通道关注的中文命名实体识别

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00054

Yu Tian, Huawei Chen, Dongfeng Cai

引用次数: 0

Improved DV-Hop Node location Optimization Algorithm Based on Adaptive Particle Swarm 基于自适应粒子群的改进DV-Hop节点定位优化算法

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00010

Bingquan Chen, Xingfeng Guo, Yuanfeng Huang, M. Yang

引用次数: 1

ICool: One-stop service IoT cloud platform ICool:一站式服务物联网云平台

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00133

Hana Han, Bin Zhu, Luyao Zhan, Tiantian Yu, Gangwei Shen, Ying Chen

{"title":"ICool: One-stop service IoT cloud platform","authors":"Hana Han, Bin Zhu, Luyao Zhan, Tiantian Yu, Gangwei Shen, Ying Chen","doi":"10.1109/icaice54393.2021.00133","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00133","url":null,"abstract":"Small and medium-sized industrial enterprises generally have the problem of difficulty in “on-clouding” with industrial equipment during the informatization transformation. In this regard, Icool: a one-stop service Internet of Things cloud platform was designed and implemented, which can provide small and medium-sized industrial enterprises with convenient services for fast-tracking industrial equipment to the cloud, providing data visualization services for enterprises to realize intelligent equipment operating status data. Early warning and intelligent diagnosis determine the operating status of the equipment and provide quick warning notifications through the web terminal, and offer calculation analysis tools and sample data for remote diagnosis analysts and algorithm engineers. The platform supports the integration of industrial big data, Internet of Things, artificial intelligence and other information technology; integrate the data resources of the existing information system of the enterprise. It will realize multiple functions such as equipment operation and maintenance monitoring and energy efficiency management monitoring, and that will widely support multiple application scenarios such as smart factories, smart campuses, and smart logistics. The test results show that the platform can better realize the terminal management and connection management of industrial equipment. Also it can provide refined management of the entire life cycle of industrial equipment, and that will be a good market application prospect.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131926699","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

BPNN anomaly data detection algorithm based on gray wolf algorithm to optimize K-means clustering 基于灰狼算法优化k均值聚类的BPNN异常数据检测算法

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00038

Ming Run-Yang, Xing Feng-Guo, Yuan Feng-Huang, Bing Quan-Chen

{"title":"BPNN anomaly data detection algorithm based on gray wolf algorithm to optimize K-means clustering","authors":"Ming Run-Yang, Xing Feng-Guo, Yuan Feng-Huang, Bing Quan-Chen","doi":"10.1109/icaice54393.2021.00038","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00038","url":null,"abstract":"Aiming at the situation that the K-means clustering algorithm tends to fall into the local optimal solution during the clustering process, and the clustering results are prone to errors, this paper proposes a clustering algorithm based on gray wolf optimization-means, Realize the initial selection of K-means cluster centers through the global optimization ability of the gray wolf optimization algorithm. And update the cluster centers through iterative wolf $alpha$ to optimize the K-means clustering algorithm. Aiming at BP neural network as a supervised learning algorithm, prior knowledge of data is required for training, due to the different data types generated by different events, the applicability of BP neural network is not strong, the paper proposes a combination of K-means clustering algorithm based on gray wolf algorithm optimization and BP neural network. Cluster the initial data set through K-means clustering, and label the clustered data, then import the labeled data as a training set into the BP neural network for training, and obtain the final detection model to realize online detection of large amounts of data. The experimental results show that the algorithm proposed in this paper on the IBLK dataset and the Taihu Lake water quality dataset is compared with the traditional K-means clustering algorithm and the random forest algorithm based on firefly optimization proposed in [12] in the IBLK dataset and Taihu Lake. Experimental verification on the water quality data set, the detection rate was increased by 8.9%, 17.7% and 1.15%, 12.6%; the false alarm rate was reduced by 8.1 %, 19.3% and 1.12%, 13.6% respectively.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123954448","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

Applying Active learning in Music Popularity Prediction 主动学习在音乐流行度预测中的应用

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00075

Huanran Sa

引用次数: 0

Generation and Elimination of Antenna Effect 天线效应的产生与消除

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00162

Chenjie Wu, Ying Tang, Yi Wei, Shuo Sun

引用次数: 0

Cloud detection from the hyperspectral infrared radiation using random forest model 基于随机森林模型的高光谱红外辐射云检测

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00109

Huaxiang Shi, Yi Yu, Weimin Zhang, Qi Zhang, Tengling Luo, G. Ma

{"title":"Cloud detection from the hyperspectral infrared radiation using random forest model","authors":"Huaxiang Shi, Yi Yu, Weimin Zhang, Qi Zhang, Tengling Luo, G. Ma","doi":"10.1109/ICAICE54393.2021.00109","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00109","url":null,"abstract":"To use infrared observation data from the High Spectral Infrared Atmospheric Sounder (HIRAS) which is onboard the FengYun 3D (FY-3D) satellite, we have proposed a new cloud detection method based on random forest (RF). The true cloud distribution of field of views (FOVs) is generated by the collocated cloud masks of the Medium Resolution Spectral Imager-II (MERSI). The long-wave infrared radiations of 781 channels in the HIRAS FOVs are used as the input features of the model. The matched observation data of HIRAS and MERSI in East Asia (May 2019 to April 2020) are used as training and testing datasets. Given the significant differences in the radiation characteristics between land and sea, we respectively build the sea and land cloud detection models based on random forest. Both of them have achieved good cloud detection performance. The sea model produced slightly higher performance (ACC of 0.96, a FAR of 0.03, an F1-score of 0.96, and AUC of 0.99) than the land model (ACC of 0.95, FAR of 0.04, F1-score of 0.96, and AUC of 0.99). The RF cloud detection models have adequate generalization performances for the observations of HIRAS at different times and regions. Besides, the RF cloud detection models have faster computing efficiency and lower data dependency than HIRAS-MERSI matching method. The validation experiments have shown that the RF models can detect the dense cloud scenes and the large clear-sky areas with higher accuracy. However, the RF model has relatively low detection accuracy for broken clouds and thin clouds. This may be because the infrared radiation properties of these cloud FOVs and clear-sky FOVs are relatively similar.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114583611","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