2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)最新文献_第3页

Design of Approval Workflow Engine Based on Flexible Transition 基于柔性转换的审批工作流引擎设计

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930213

Bing-Hsuan Wu, Guohong Yi, Jianting Li, Zhichao Cao, Xiaodong Xu

引用次数: 0

Prediction method of turbine engine RUL based on GA-SVR 基于GA-SVR的涡轮发动机RUL预测方法

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930303

Ye Zhu, Bo Xu, Zhenjie Luo, Zhiqiang Liu, Hao Wang, C. Du

{"title":"Prediction method of turbine engine RUL based on GA-SVR","authors":"Ye Zhu, Bo Xu, Zhenjie Luo, Zhiqiang Liu, Hao Wang, C. Du","doi":"10.1109/AICIT55386.2022.9930303","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930303","url":null,"abstract":"The remaining life prediction of turbine engine plays an indispensable role in engine health management, which is of great significance to ensure flight safety and improve maintenance efficiency. With the development of engine health management technology, the engine is terminated before failure or failure, which makes it difficult to collect enough data with failure information. In order to improve the prediction accuracy of engine remaining life with limited data samples, a joint algorithm based on genetic algorithm and support vector regression (GA-SVR) is proposed in this paper. Genetic algorithm (GA) is used to solve the hyperparametric optimization problem in support vector regression (SVR) model. Based on the C-MAPSS public data set provided by NASA, the data of 20 engines are randomly selected to construct a small sample data set to train the GA-SVR model, and compared with other existing algorithms. The experimental results show that the prediction error of GA-SVR model is smaller in the case of small samples, It is proved that the proposed model can accurately deal with the problem of turbine engine residual life prediction in the case of small samples.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122219546","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

An intelligent traffic control system based on sand table 基于沙盘的智能交通控制系统

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930209

Lianying Zou, Tao Yuan, Wenjie Liao, Yang Zhang, Xinli Cao, Yuxin Shang

{"title":"An intelligent traffic control system based on sand table","authors":"Lianying Zou, Tao Yuan, Wenjie Liao, Yang Zhang, Xinli Cao, Yuxin Shang","doi":"10.1109/AICIT55386.2022.9930209","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930209","url":null,"abstract":"With the continuous improvement of the combination of computer control technology and traffic flow theory, and the continuous enhancement of traffic management methods, it is an important task for researchers to greatly improve the level of intelligent transportation in China. In view of the current needs of the development of intelligent transportation, this paper simulates the specific traffic environment in real life in combination with the simulation environment in the laboratory, and builds a smart car that meets the environmental requirements for this environment, and sets the console host computer interface as the interface. The information exchange platform between smart cars can also control the start and stop of smart cars according to the needs of users.In order to realize the system, the system designs two schemes for the behavior of smart cars: for embedded development, it uses infrared to track and RFID radio frequency card for positioning of smart cars; for image recognition, it uses residual network. Virtual tracking, and smart car behavior using AlexNet’s marker recognition. The two behaviors are aimed at the same simulated environment, and the smart car can comprehensively consider the two behaviors, so that the response to the environment is more accurate and precise. Finally, a sand table-based intelligent traffic control system is formed.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125275285","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

SAR Image Water Extraction Based on Saliency Target Detection 基于显著性目标检测的SAR图像水分提取

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930251

Peng Wang, Haibo Zhang, Qiwen Lv, Shuwei Zhao, Lei Wang

{"title":"SAR Image Water Extraction Based on Saliency Target Detection","authors":"Peng Wang, Haibo Zhang, Qiwen Lv, Shuwei Zhao, Lei Wang","doi":"10.1109/AICIT55386.2022.9930251","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930251","url":null,"abstract":"Synthetic aperture radar (SAR) image water extraction has important research significance in water resources monitoring and other applications. In SAR images, the scattering properties of some land cover types with low backscattering coefficients, such as broad roads and mountain shadows, are very close to the scattering properties of water. Most water extraction methods are easy to identify those false water targets as water. Meanwhile, the water area in the scene is very small, and the proportion of water and background in the training concentration is extremely unbalanced. The imbalance of water samples affects the water extraction performance. Traditional machine learning water extraction methods require manual extraction of effective features, which are time-consuming and inefficient. Therefore, we decided to introduce the deep learning-based salient object detection network Pool-Net into SRA image water extraction. To overcome the effect of class imbalance, we introduce the focus loss function into the Pool-Net network. Experimental results show that the proposed method achieves good water extraction results on water class imbalanced SAR images. The water extraction accuracy of the proposed method is 2% higher than Pool-Net and also surpasses some well-known image segmentation methods.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131467221","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

Intelligent Integration of Large-scale Grid-connected Alkaline Electrolyzers for the Carbon-neutral Energy Systems 面向碳中和能源系统的大型并网碱性电解槽智能集成

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930224

Jinhui Yu, Bei Lu, Wenjing Su, Y. Zong

引用次数: 0

An Improved Algorithm for Defogging Based on Fused Underwater Images 基于融合的水下图像去雾改进算法

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930307

Xinli Cao, Junqiao Xiong, Yuxin Shang, Changrui Liu, Lianying Zou

引用次数: 0

Research on Intelligent Question Answering System for Chemical Safety Based on Knowledge Graph 基于知识图谱的化工安全智能问答系统研究

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930319

Zhenzhen Liu, Sheng Zheng, Xi Shi

{"title":"Research on Intelligent Question Answering System for Chemical Safety Based on Knowledge Graph","authors":"Zhenzhen Liu, Sheng Zheng, Xi Shi","doi":"10.1109/AICIT55386.2022.9930319","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930319","url":null,"abstract":"In recent years, the chemical industry has developed rapidly, and the production usage of chemical has continued to increase. In the face of variety of chemicals and ever-changing chemical reactions, people’s cognition of knowledge and damage characteristics lags behind, leading to frequent chemical accidents. For facilitating and assisting people to make reasonable judgments in daily work or when accidents happen, this paper utilizes the existing structured data of chemical safety constructs an intelligent question answering system based on knowledge graph, which is applied to chemical safety for the first time. Firstly, to deal with the problem of user intent understanding in question answering system, a multi-classifier combining NB (Naive Bayes) algorithm and Bert-BiLSTM-CRF model is constructed to complete the task of question classification. Secondly, according to the classification result, the most similar question template is matched. Finally, the semantic information of the template and the intention words in the question are mapped to the chemical safety knowledge graph to retrieve the answers. In addition, this paper utilizes SVM (Support Vector Machines) to compare the effect of question classification, and utilizes BiLSTM-CRF model to compare the effect of feature words recognition. The experimental results demonstrate that 91% of the questions are answered correctly, indicating that question answering system can answer the questions related to chemical safety in real time and accurately.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124797902","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

An Action Recgonition System Based on WiFi 基于WiFi的动作识别系统

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930165

Xianggang Zhang, Meina Dong, Ting Zhang

引用次数: 0

TCNet: Temporal Consistency Network for Semisupervised Change Detection 半监督变化检测的时间一致性网络

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930313

Qidi Shu, Jiarui Hu, Jun Pan, Yuchuan Bai, Zhuoer Zhang, Zongrui Li

{"title":"TCNet: Temporal Consistency Network for Semisupervised Change Detection","authors":"Qidi Shu, Jiarui Hu, Jun Pan, Yuchuan Bai, Zhuoer Zhang, Zongrui Li","doi":"10.1109/AICIT55386.2022.9930313","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930313","url":null,"abstract":"Change detection is a challenging task in earth observation. In recent years, deep learning techniques have been widely applied in change detection and achieved impressive progress. However, deep learning based change detection methods heavily rely on a large amount of annotated samples. Labeling for change detection is a time-consuming and labor-intensive task. In order to solve this problem, we propose a novel temporal consistency network (TCNet) for semisupervised change detection. Motivated by the fact that different input sequences have no effect on the prediction results of change detection, our method learns the distribution of unlabeled data by enforce the consistency of the prediction obtained with different input sequences. Specifically, for labeled samples, two segmentation networks with the same structure are trained with two different input sequences. For the unlabeled samples, we perform the forward prediction on the two segmentation networks with corresponding input sequence to obtain two results of change detection. Then, the supervised signals can be generated by minimizing the difference between two predicted results. In this way, the distribution of unlabeled data can be fully explored thus enhancing the generalization of change detection. Experiments on google dataset show the effectiveness of the proposed method.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128425549","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

Double Clustering Based Kriging Algorithm for foF2 Reconstruction 基于双聚类的Kriging算法的foF2重构

2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930291

Jia Hu, Li Cheng, Wang Li

{"title":"Double Clustering Based Kriging Algorithm for foF2 Reconstruction","authors":"Jia Hu, Li Cheng, Wang Li","doi":"10.1109/AICIT55386.2022.9930291","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930291","url":null,"abstract":"Ionospheric cut-off frequency (foF2) soundings have important implications in areas such as wireless communications and military aerospace. During periods of high solar activity, the accuracy of ionospheric foF2 acquisition as well as reconstruction decreases as solar activity intensifies. In this paper, an improved general Kriging interpolation algorithm is proposed, which takes into account the sudden changes in ionospheric electron concentration (TEC) during high-intensity solar flares and the west-to-east shift in geographic location. The remaining high-quality site information was used to reconstruct the foF2 at the site to be measured by Kriging. The remaining good site information is used to reconstruct the foF2 at the site to be measured by Kriging. In this paper, real-time ionospheric data from the Global Ionospheric Radio Observatory (GIRO) during solar flares are used to analyze the ionospheric foF2 reconstruction results for the three weeks before, during and after the solar flare frequency, and three sites at mid and low latitudes are clustered - Kriging algorithm and ordinary Kriging The improved clustering-Kriging method is validated to be superior during solar flares. The improved algorithm has significantly improved the foF2 interpolation accuracy at low latitudes compared to the ordinary Kriging algorithm.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130360058","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