2021 International Conference on Networking, Communications and Information Technology (NetCIT)最新文献

System ITAE Optimization for Muscle-Nerve Cooperative Quadruped Robot 肌肉-神经协同四足机器人系统ITAE优化

2021 International Conference on Networking, Communications and Information Technology (NetCIT) Pub Date : 2021-12-01 DOI: 10.1109/NetCIT54147.2021.00011

Shaoqing Dai, Depeng Kong, Jiu-sheng Li, Weisi Gu, Dehui Kong, Xinyue Chen

引用次数: 0

Predicting Telecommunication User Favorite Package by Using Deep Neural Network 基于深度神经网络的电信用户喜爱包预测

2021 International Conference on Networking, Communications and Information Technology (NetCIT) Pub Date : 2021-12-01 DOI: 10.1109/NetCIT54147.2021.00048

Tingshun Li, Huiyu Yang, Dadi Wang, Zesan Liu

{"title":"Predicting Telecommunication User Favorite Package by Using Deep Neural Network","authors":"Tingshun Li, Huiyu Yang, Dadi Wang, Zesan Liu","doi":"10.1109/NetCIT54147.2021.00048","DOIUrl":"https://doi.org/10.1109/NetCIT54147.2021.00048","url":null,"abstract":"With the popularity of mobile phone, telecom companies have launched more and more package services. It is very difficult to choose telecom user package suitable for him or her. This paper provides a method to build a model based on deep neural network (DNN) for multi-classification, which has high accuracy to help user to pick out a favorite one from dozens of packages. The model is trained out from telecom user big data. First, the telecom big data are preprocessed to be completed integrity, normalized, balanced., and then divided into training data set, validating data set and testing data set. Second, feature engineering needs to be done in order to improve prediction model. Two types of feature engineering are presented in this work to compare which is better. Manual feature engineering is a way to build new features from expertise, while auto feature engineering is from third-party library. Third, an experimental process is design out to obtain prediction models, and use them to predict the testing data set. Finally, some conclusions are obtained by analyzing the experimental results. So, this paper proposes a multi-classification model to recommend a suitable package to a telecom user, which is trained out from telecom big data with high accuracy, more practical. Moreover, this work show that feature engineering and data preprocessing are helpful to obtain better machine learning model.","PeriodicalId":378372,"journal":{"name":"2021 International Conference on Networking, Communications and Information Technology (NetCIT)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123434513","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

Research on internal control of accounting information system based on BP neural network 基于BP神经网络的会计信息系统内部控制研究

2021 International Conference on Networking, Communications and Information Technology (NetCIT) Pub Date : 2021-12-01 DOI: 10.1109/NetCIT54147.2021.00086

Xiangmei Ran

引用次数: 0

Speech emotion recognition based on convolutional neural network 基于卷积神经网络的语音情感识别

2021 International Conference on Networking, Communications and Information Technology (NetCIT) Pub Date : 2021-12-01 DOI: 10.1109/NetCIT54147.2021.00028

Chen Jie

{"title":"Speech emotion recognition based on convolutional neural network","authors":"Chen Jie","doi":"10.1109/NetCIT54147.2021.00028","DOIUrl":"https://doi.org/10.1109/NetCIT54147.2021.00028","url":null,"abstract":"Speech emotion recognition is a technology to automatically obtain emotion types from given attributive segments. With the increasing demand for emotion recognition in business, education and other fields, the development of high-accuracy speech emotion recognition system has become a hot research direction in the speech field. Speech emotion recognition takes speech as the carrier of emotion to study the formation and change of various emotions in speech, so that the computer can analyze the speaker's specific emotional situation through speech, so as to make human-computer interaction more humanized. In order to improve the accuracy of intelligent speech emotion recognition system, a speech emotion recognition model based on feature representation of convolutional neural network CNN( Convolution Neural Network) is proposed. Mel-frequency cepstral coefficients (MFCC), which is the most widely used method to extract speech features, is selected for the experiment. At the same time, in order to increase the feature differences between emotional speech, the mel-frequency cepstral coefficients feature data matrix obtained from speech signal preprocessing is transformed to improve the speech emotion recognition rate.","PeriodicalId":378372,"journal":{"name":"2021 International Conference on Networking, Communications and Information Technology (NetCIT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114732231","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

Design and Implementation of Smog Collection System Based on ZigBee 基于ZigBee的烟雾采集系统的设计与实现

2021 International Conference on Networking, Communications and Information Technology (NetCIT) Pub Date : 2021-12-01 DOI: 10.1109/NetCIT54147.2021.00042

Wei Li, Changling Zuo, Min Zhu

引用次数: 0

Modeling and Algorithm Implementation of Free Gait Planning for Quadruped Robot Based on Machine Vision 基于机器视觉的四足机器人自由步态规划建模与算法实现

2021 International Conference on Networking, Communications and Information Technology (NetCIT) Pub Date : 2021-12-01 DOI: 10.1109/NetCIT54147.2021.00047

B. Kong

引用次数: 1

Evaluation of Reactive Power and Voltage Support Capability of Power Grid Based on Big Data 基于大数据的电网无功电压保障能力评估

2021 International Conference on Networking, Communications and Information Technology (NetCIT) Pub Date : 2021-12-01 DOI: 10.1109/NetCIT54147.2021.00059

Qing He, Wenchao Yang, Longyan He, Yuqing Mao

{"title":"Evaluation of Reactive Power and Voltage Support Capability of Power Grid Based on Big Data","authors":"Qing He, Wenchao Yang, Longyan He, Yuqing Mao","doi":"10.1109/NetCIT54147.2021.00059","DOIUrl":"https://doi.org/10.1109/NetCIT54147.2021.00059","url":null,"abstract":"With the development of new energy power grid construction, the failure of the power grid and the accidents caused by the collapse of the whole network are increasing, which are mainly attributed to the voltage drop of the power grid caused by reactive power shortage. Therefore, it is necessary to evaluate the reactive voltage support capacity of the power grid, customize quantitative indexes based on the evaluation content, and conduct data mining for the massive data in the operation of the power grid to form visual data content for operation and maintenance personnel to maintain and monitor the power grid. Based on the above background and with the clustering method as the technical core, this paper formulated the evaluation system of power grid reactive voltage support capability based on big data to carry out technical evaluation of power grid reactive voltage support capability. On this basis, this article establishes a complete set of reactive voltage support capability evaluation systems through the basic framework of data mining to improve the versatility of the indexes by optimizing the correlation coefficient and adding dynamic correlation indexes to adapt to multiple data sample categories.","PeriodicalId":378372,"journal":{"name":"2021 International Conference on Networking, Communications and Information Technology (NetCIT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127584391","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

Fault Diagnosis Method of Ship Hydraulic System Based on KPCA-PNN 基于KPCA-PNN的船舶液压系统故障诊断方法

2021 International Conference on Networking, Communications and Information Technology (NetCIT) Pub Date : 2021-12-01 DOI: 10.1109/NetCIT54147.2021.00022

Bohao Li

引用次数: 2

Research on Stock Price Volatility Prediction Based on Generative Adversarial Network 基于生成对抗网络的股票价格波动预测研究

2021 International Conference on Networking, Communications and Information Technology (NetCIT) Pub Date : 2021-12-01 DOI: 10.1109/NetCIT54147.2021.00073

Lu Wang, Zhensheng Huang

引用次数: 0

Improved AFSA for Solving Intelligent Test Problem 解决智能测试问题的改进AFSA

2021 International Conference on Networking, Communications and Information Technology (NetCIT) Pub Date : 2021-12-01 DOI: 10.1109/NetCIT54147.2021.00030

Xu Wu, Qianyu Lin, Dezhi Wei

{"title":"Improved AFSA for Solving Intelligent Test Problem","authors":"Xu Wu, Qianyu Lin, Dezhi Wei","doi":"10.1109/NetCIT54147.2021.00030","DOIUrl":"https://doi.org/10.1109/NetCIT54147.2021.00030","url":null,"abstract":"Intelligent test paper generation problem is a multi-objective parameter optimization problem under certain constraints, which has been realized by many algorithms. However, the existing intelligent test paper generation mostly adopts a single algorithm, and each algorithm has its own shortcomings. Sometimes it is inevitable to fall into the defects of the single algorithm in the process of test paper generation. Therefore, a mathematical model of intelligent test paper generation is proposed, which combines the advantages of artificial fish swarm algorithm and genetic algorithm to form a hybrid intelligent algorithm. At the beginning of intelligent test paper generation, the artificial fish swarm algorithm is used to quickly approach the test paper goal. In the process of test paper generation, when the optimal individual has no change or extremely small change in consecutive iterations, the genetic algorithm is used to jump the artificial fish individual to improve the convergence speed. The simulation results show that the hybrid intelligent algorithm can effectively optimize the effect of intelligent test paper generation by using a single algorithm alone.","PeriodicalId":378372,"journal":{"name":"2021 International Conference on Networking, Communications and Information Technology (NetCIT)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124318538","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