2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)最新文献_第2页

Day-ahead Optimization Economic Dispatch CCHP Multi-microgrid System Based on Bargaining Game Method 基于议价博弈方法的日前优化经济调度CCHP多微网系统

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498230

Jiabao Bu, Qian Wang, Jian Xu

引用次数: 1

An Improved Whale Optimization Algorithm Based on Nonlinear Function and Local Search 基于非线性函数和局部搜索的改进鲸鱼优化算法

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498143

Jie Liu

引用次数: 0

Research on Ultrasonic Detection Technology of Overhead Distribution Line 架空配电线路超声检测技术研究

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497922

T. Li, Bin Song, Yu Liu, Bo Jiang, Chaohui Yu

引用次数: 0

Prediction of soil moisture and temperature based on deep learning 基于深度学习的土壤湿度和温度预测

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498190

Huidan Liu, Yi Yang, Xue-fen Wan, Jian Cui, Fan Zhang, Tingting Cai

{"title":"Prediction of soil moisture and temperature based on deep learning","authors":"Huidan Liu, Yi Yang, Xue-fen Wan, Jian Cui, Fan Zhang, Tingting Cai","doi":"10.1109/ICAICA52286.2021.9498190","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9498190","url":null,"abstract":"Accurate prediction of soil moisture and temperature is helpful to regulate agricultural planting parameters and optimize crop planting quality. In this paper, relevant issues are studied from two aspects: environmental data acquisition system based on Internet of Things technology and adaptive deep learning prediction model selection. An NB-IoT IoT data collection system is proposed for deep learning of long-period and equal-interval time series data collection. Using the obtained environmental temperature and humidity and soil moisture and temperature data, a deep learning model based on long short-term memory network (LSTM) is implemented to train and predict soil moisture and temperature, and the prediction effect of each deep learning method is analyzed under different step length conditions. The experimental results show that the system can effectively obtain and manage the data required for deep learning, and the deep learning-based prediction model can achieve reliable prediction of soil moisture and temperature by only relying on the environmental temperature and humidity time series data.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"80 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":"126337228","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

HeTong: A Voice Answering Enabled Multi-language Questionnaire System Based on Spring Cloud 和通:基于Spring云的语音应答多语种问卷调查系统

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498116

Yuliang Li, Liping Zhu

{"title":"HeTong: A Voice Answering Enabled Multi-language Questionnaire System Based on Spring Cloud","authors":"Yuliang Li, Liping Zhu","doi":"10.1109/ICAICA52286.2021.9498116","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9498116","url":null,"abstract":"In view of the fact that current web questionnaire systems are not suitable for use by specific groups such as those at multi-ethnic inhabited multilingual area, and the status quo that the questionnaire systems based on monolith architecture are difficult to expand and maintain, we designed and implemented HeTong based on Spring Cloud, a frame of microservice architecture that is easy to extend and upgrade. HeTong is a multi-language questionnaire system that enables voice input for subjective investigation questions apart from general functions of conventional questionnaire systems. In this paper, the microservice system design for HeTong is introduced. Firstly, the overall framework of HeTong is described. Secondly, the business logic module is designed in detail where different types of microservices are divided according to different functions. Then, the business logic module is implemented based on Spring Cloud. Finally, the interactive interface between the user and the system is designed. The software tests show that the system can well realize the functions of language switching, user management, questionnaire management, data statistics and voice answering.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"8 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":"126397103","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 Collaborative Management of Integrated Equipment in Distributed Cloud Data Centre 分布式云数据中心集成设备协同管理研究

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498008

Jun Yu, J. Mu, Yintie Zhang, Kai Jiang

引用次数: 0

Time Series K-Nearest Neighbors Classifier Based on Fast Dynamic Time Warping 基于快速动态时间翘曲的时间序列k近邻分类器

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497898

Jinghui Wang, Yuanchao Zhao

引用次数: 1

Design of Multi-storey Health Management System for Missile Equipment Based on PHM 基于PHM的导弹装备多层健康管理系统设计

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497918

Liu Yue, Lin Rifeng, Li Jiying, Liu Yanhui

引用次数: 0

Electrical Load Forecasting Based on Multi-model Combination by Stacking Ensemble Learning Algorithm 基于叠加集成学习算法的多模型组合电力负荷预测

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498248

Jianfeng Jiang, Wenjun Zhu, Chong Zhang, Xingang Wang

引用次数: 2

Research on FPGA Based Convolutional Neural Network Acceleration Method 基于FPGA的卷积神经网络加速方法研究

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498022

Tan Xiao, Man Tao

{"title":"Research on FPGA Based Convolutional Neural Network Acceleration Method","authors":"Tan Xiao, Man Tao","doi":"10.1109/ICAICA52286.2021.9498022","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9498022","url":null,"abstract":"In recent years, with the continuous breakthrough in the field of algorithms, the computational complexity of current target detection algorithms is getting higher and higher. In the forward inference stage, many practical applications often have low latency and strict power consumption restrictions. How to realize a low-power, low-cost and high-performance target detection platform has gradually attracted attention. Given the current mobile scene's requirements for high performance and low power consumption, hardware acceleration architecture suitable for different CNNs is designed by combining the working principle of CNN and the computing characteristics of FPGA. CNN’s basic operation unit is realized through high-level synthesis technology, including convolution operation unit, pool operation unit, activation function unit, etc. Optimization strategies such as pipeline, dynamic fixed-point quantization, and ping-pong caching are adopted to reduce the use of on-chip and off-chip memory access and storage resources. Finally, two convolutional neural networks with different structures, the LeNet-5 classification network and, the YOLOv2 detection network, are selected for functional verification and performance analysis. The experimental results show that the convolutional neural network FPGA accelerator designed in this paper can provide better performance with fewer resources and power consumption and can efficiently use the hardware resources on the FPGA.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"105 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":"133556542","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