2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)最新文献_第2页

Study on Dynamic Characteristics of Sliding Bearings at Different Rotations 滑动轴承在不同转速下的动态特性研究

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00085

Junwei Zhang, Qijiang Yang, Liu Yu, Ming-Ming Cheng, Ling Tian, Qinyao Xu, Mengsheng Xia

引用次数: 0

Few-shot Classification of Radar Equipment Fault Based on TF-IDF Feature Date Augmentation and BERT 基于TF-IDF特征数据增强和BERT的雷达设备故障少弹分类

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00093

Yiyuan Li, Jiangping Yang, Taiyong Fei, Yuxi Xie

引用次数: 1

Dynamic tracking method for landing trajectory of power line patrol UAV Based on Chaos Genetic Algorithm 基于混沌遗传算法的电力线巡逻无人机着陆轨迹动态跟踪方法

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00062

Jiawen Yan, Zhe Liu, Chagjin Chen, Jintao Zhao

引用次数: 1

Logistic Regression Analysis of Online Course Click Rate 网络课程点击率的逻辑回归分析

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00081

Sun Jia

{"title":"Logistic Regression Analysis of Online Course Click Rate","authors":"Sun Jia","doi":"10.1109/MLISE54096.2021.00081","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00081","url":null,"abstract":"The advantages of online learning are very significant. With the popularization of network technology and the development of network hardware equipment and mobile terminals, the development of technology provides a foundation for the rapid development of online learning. Due to the outbreak of the COVID-19 epidemic in 2020, classroom teaching cannot be carried out. Therefore, many schools use online platforms to implement online teaching. From the traditional face-to-face teaching as the main body, online teaching supplements, to a single online teaching form. At present, online teaching mainly includes three forms: live broadcast, recorded broadcast and online self-directed learning. At the same time, a large amount of student activity data is generated on the teaching platform. Take the online teaching data of colleges and universities in the first half of 2020 as an example, with the help of the mining tool RapidMiner to mine and analyze teaching data, student click-through rates and student performance data. Deeply analyze the implementation of online teaching and the mechanism of various influencing factors, and use logistic regression algorithms to predict whether students will pass the exam. Through student learning: On the online learning platform, students' learning content is the same, but the difference is their learning behavior on the platform. The click-through rate reflects the learning behavior of students in the data, and the level of performance is closely related to the degree of active learning of students.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122780516","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

Uprising E-sports Industry: machine learning/AI improve in-game performance using deep reinforcement learning 电子竞技产业:机器学习/AI通过深度强化学习提高游戏表现

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00112

Xianzuo Du, Xiwei Fuqian, Jiaxi Hu, Ze-Jun Wang, Dongju Yang

{"title":"Uprising E-sports Industry: machine learning/AI improve in-game performance using deep reinforcement learning","authors":"Xianzuo Du, Xiwei Fuqian, Jiaxi Hu, Ze-Jun Wang, Dongju Yang","doi":"10.1109/MLISE54096.2021.00112","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00112","url":null,"abstract":"With the quick development of machine learning, deep reinforcement learning will have a big influence on E-sports. It can be considered machine learning will help us train E-sports players easily and effectively and give coaches and players some new ideas to train and win the games. Flappy Bird is a game where the players try to keep the bird alive as long as possible and get a high score. Flappy Bird is a good example to prove that the thought is feasible. In this project, a flappy bird training AI is developed based on Q-learning and DQN. The game is played by the models obtained from deep reinforcement learning, and the game is also played by humans. Then get experimental data from these two ways and compare them. For the two ways of playing the game (by AI or manually), there are many similarities in the increased rate of scores as training sessions increase, which means AI can “teach” players how to train to get a higher score. It can be applied to skills and experience-based games and help us to train top players. Maybe it can also be applied to other fields, such as helping engineers escape from potential errors and accidents.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121859584","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 Medical Question Answering Model Based on Lattice CNNs 基于点阵cnn的中医问答模型

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00012

Manfu Ma, Keyan Gao, Yuanzhe Liu, Hai Jia, Xia Wang, Yong Li

{"title":"Chinese Medical Question Answering Model Based on Lattice CNNs","authors":"Manfu Ma, Keyan Gao, Yuanzhe Liu, Hai Jia, Xia Wang, Yong Li","doi":"10.1109/MLISE54096.2021.00012","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00012","url":null,"abstract":"The application demand of intelligent medicine is growing, and medical question answering (QA) is an important part of it. Chinese medical question answering has difficulty in word segmentation, which leads to insufficient feature extraction. To solve this problem, this paper constructs a user-defined dictionary and uses LCN s to build a Chinese medical question answering model. First, a user-defined dictionary for Chinese medical QA is constructed based on ICD-10 professional medical thesaur-us, and the electronic medical record is processed by using Chine-se word segmentation tool and user-defined dictionary. Secondly, use the EMR (electronic medical record) after word segmentation as the data of word vector training, and use the Skip-gram model to train the medical word vector. Finally, taking the medical word vector as the input of the model, LCNs are used to extract sentence features, calculate the similarity between question feat-ures and answer features, and then train the Chinese medical question answering model. Experiments show that the accuracy of LCNs model can reach 88%, and its performance is better than other deep learning methods such as LSTM.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127967650","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

Analysis of Mechanical Properties to a Type of Locking Mechanism of Rotating INS 一种旋转惯导系统锁紧机构的力学性能分析

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00110

He Xiaofei, Wang Feng, Yuan Meng, He Guangzhi

{"title":"Analysis of Mechanical Properties to a Type of Locking Mechanism of Rotating INS","authors":"He Xiaofei, Wang Feng, Yuan Meng, He Guangzhi","doi":"10.1109/MLISE54096.2021.00110","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00110","url":null,"abstract":"A locking mechanism for the rotating INS Unit is proposed, which is suitable for the reliable locking of the non-calibrated working state of the rotating INS Unit(IMU). The locking mechanism consists of two sets of the self-locking devices; both of them have lifting mechanisms. The difference is that one set of the self-locking devices uses end tooth positioning to lock. The lifting mechanism of this self-locking device pushes the fixed tooth plate to make a reciprocating linear motion. The fixed toothed disc and the movable toothed disc on the Inertia sensitive component can complete meshing and disengagement, which achieves precise positioning to ensure locking accuracy and locking repeatability. Another set of self-locking device uses a concave movable ejector rod and inertia-sensitive component. The conical fixed rod is pressed and disengaged to achieve locking. Two sets of self-locking devices work on the IMU at the same time, and jointly complete the positioning and locking. For both of the single-end locking and double-end locking modes, mechanical analysis model was established and simulation analysis was performed. The result shows that the double-ended locking method can meet the requirements of a certain mechanical environment.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124331354","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

High bandwidth friction compensation method based on acceleration estimation 基于加速度估计的高带宽摩擦补偿方法

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00046

Yu Yang, Zhang Gang, Gao Jun Ke, Wang Jian Gang

引用次数: 0

A Comparative Deep Learning Algorithms for Agricultural Insect Recognition 农业昆虫识别的比较深度学习算法

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00067

Haiyang He, Jing Wang, Shiguo Huang, Xiaolin Li

引用次数: 0

Design of LQFP chip pin defect detection system based on machine vision 基于机器视觉的LQFP芯片引脚缺陷检测系统设计

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00021

XingFu Ou, Wenfeng Chen, Miao Zhang

引用次数: 1