发布求助
文献互助 智能选刊 最新文献

2020 IEEE 18th International Conference on Industrial Informatics (INDIN)最新文献

筛选
英文 中文
Improved Process Fault Diagnosis by Using Neural Networks with Andrews Plot and Autoencoder 基于Andrews图和自编码器的神经网络改进过程故障诊断
2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442157
Shengkai Wang, J. Zhang
{"title":"Improved Process Fault Diagnosis by Using Neural Networks with Andrews Plot and Autoencoder","authors":"Shengkai Wang, J. Zhang","doi":"10.1109/INDIN45582.2020.9442157","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442157","url":null,"abstract":"With industrial production processes becoming more and more sophisticated, traditional fault diagnosis systems may be insufficient to meet current industrial diagnostic performance requirements. In order to improve fault diagnosis performance, this paper proposes an enhanced neural network based fault diagnosis system by integrating Andrews plot and Autoencoder. Features are first extracted from on-line measurements by Andrews plot and the high-dimensional features are compressed by autoencoder to an appropriate dimension, which are then fed to a neural network for fault classification. Application to a simulated CSTR process demonstrates that the proposed method can give more reliable and earlier diagnosis than the traditional neural network based fault diagnosis method.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114850223","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
Volume ratio prediction model during Price Limits Periods in China stock markets 中国股票市场限价期成交量比预测模型
2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442116
Jianwu Lin, Yishen Xu, Dayu Qin
{"title":"Volume ratio prediction model during Price Limits Periods in China stock markets","authors":"Jianwu Lin, Yishen Xu, Dayu Qin","doi":"10.1109/INDIN45582.2020.9442116","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442116","url":null,"abstract":"Algorithmic trading has become the major trading mechanism and one of the core technologies of electronic transactions globally. In USA, above 90% of electronic trading volumes has been done by algorithmic trading systems. However, algorithmic trading is still new in China capital market, only less than 10% of the volume has been done by algorithmic trading systems. With the rapid development of Chinese capital market and QFII capacity expansion, it will be the major trading mechanism in China. While being introduced into Chinese markets, it has to adapt to some special local trading rules, such as: Price limits (limit up and limit down). Because of the particular preferences by the Chinese investors, the market has a unique morphology forms in price limits. How to improve the model of price limits in China's algorithmic trading is the main focus of this research, especially under recent increasing volatility of global stock market in early 2020. This paper proposes a novel volume ratio prediction model, which can obtain a more accurate value of the price limit trading volume distribution. And an improved algorithmic trading logic based this model is proposed and proves its effectiveness.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114890889","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
Sustainable and Intelligent Precision Agriculture 可持续和智能精准农业
2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/indin45582.2020.9442074
{"title":"Sustainable and Intelligent Precision Agriculture","authors":"","doi":"10.1109/indin45582.2020.9442074","DOIUrl":"https://doi.org/10.1109/indin45582.2020.9442074","url":null,"abstract":"","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124194503","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
Real-time and Networked Embedded Computing and IoT 实时和网络化嵌入式计算与物联网
2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/indin45582.2020.9442122
{"title":"Real-time and Networked Embedded Computing and IoT","authors":"","doi":"10.1109/indin45582.2020.9442122","DOIUrl":"https://doi.org/10.1109/indin45582.2020.9442122","url":null,"abstract":"","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116928613","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
A multi-instance LSTM network for failure detection of hard disk drives 用于硬盘驱动器故障检测的多实例LSTM网络
2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442240
{"title":"A multi-instance LSTM network for failure detection of hard disk drives","authors":"","doi":"10.1109/INDIN45582.2020.9442240","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442240","url":null,"abstract":"Hard disk (HDD) failure is the most important reliability issue in the data center. Therefore, the prediction of hard disk failure has become the focus of attention of major data centers. However, most current research work does not notice the fact that the data on the hard disk is mostly unlabeled data. Since the degradation period in HDD is very short, the mixture of health data and erroneous data can cause serious data imbalance. This makes fault prediction a difficult task. In response to the above problems, a multi-instance long-term sequence classification method based on long-short-term memory (LSTM) network is proposed. By dividing the longterm sequence data packet into multiple instances, the relationship between the instance and the sample label is studied to predict HDD failure. Through the analysis of the hard disk data of a communication company and the Backblaze data center, this method can obtain better results than other methods.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116944831","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
Artificial Intelligence Based Distributed Fault Diagnosis and Prognosis in Industrial Applications 基于人工智能的分布式故障诊断与预测在工业中的应用
2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/indin45582.2020.9442154
{"title":"Artificial Intelligence Based Distributed Fault Diagnosis and Prognosis in Industrial Applications","authors":"","doi":"10.1109/indin45582.2020.9442154","DOIUrl":"https://doi.org/10.1109/indin45582.2020.9442154","url":null,"abstract":"","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"253 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115006923","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
Robotic Disassembly Sequence Planning Considering Robotic Collision Avoidance Trajectory in Remanufacturing 再制造中考虑机器人避碰轨迹的机器人拆卸顺序规划
2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442129
Binbin Chen, Wenjun Xu, Jiayi Liu, Zhenrui Ji, Zude Zhou
{"title":"Robotic Disassembly Sequence Planning Considering Robotic Collision Avoidance Trajectory in Remanufacturing","authors":"Binbin Chen, Wenjun Xu, Jiayi Liu, Zhenrui Ji, Zude Zhou","doi":"10.1109/INDIN45582.2020.9442129","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442129","url":null,"abstract":"Remanufacturing has enormous economic and environmental benefits in terms of resource conservation and environmental protection. Disassembly, as an essential step in remanufacturing, is always manually executed, it has the disadvantages of high labor intensive, time consuming and low efficiency while robotic disassembly can cover the shortages of manual disassembly. During the robotic disassembly process, considering the structure and movement characteristics of the industrial robot, the industrial robot need to perform collision avoidance movements considering the obstacle caused by the End-of-Life (EoL) product. The moving time considering the robotic collision avoidance trajectory is a non-negligible part of total disassembly time. In this paper, robotic disassembly sequence planning (RDSP) considering robotic collision avoidance trajectory is proposed. This method is used to obtain the collision avoidance trajectory and the moving time between different disassembly points by the robotic collision avoidance model established in this paper. Afterwards, an optimized discrete bee algorithm (ODBA) is used to generate the optimal disassembly sequence to minimize the total disassembly time. Finally, case studies based on a gear pump verify the effectiveness of proposed methods.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128982165","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: 2
Human activity recognition based on triaxial accelerometer using multi-feature weighted ensemble 基于多特征加权集合的三轴加速度计人体活动识别
2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442172
Qingnan Li, Yun Yang, Po Yang
{"title":"Human activity recognition based on triaxial accelerometer using multi-feature weighted ensemble","authors":"Qingnan Li, Yun Yang, Po Yang","doi":"10.1109/INDIN45582.2020.9442172","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442172","url":null,"abstract":"Human activity recognition (HAR) has been widely used in some areas such as smart home, health care and so on. However, there are still some low recognition accuracy cases in actual scenarios. In order to improve the accuracy of recognition, we propose a multi-feature weighted ensemble classification method on triaxial accelerometer sensor data. We perform weighted integration on five base classifiers to obtain the final prediction classification label. Among these five base classifiers, three are K-nearest neighbor (KNN) classifiers with different features respectively using three traditional feature extraction methods from original data. Another two are currently popular deep learning models—Attention Mechanisms on Long Short-Term Memory Network (Attention-LSTM) and Convolutional Neural Network (CNN), which can automatically extract features and classify. We demonstrated the feasibility of this ensemble method on a dataset containing eight human daily activities. Comparing experimental results, our method achieved the best recognition effect, with an accuracy of 95.58%.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125288775","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
Maximum Power Point Tracking of Photovoltaic Systems Using Deep Q-networks 基于深度q -网络的光伏系统最大功率点跟踪
2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442100
Kangshi Wang, Dou Hong, Jieming Ma, K. Man, Kaizhu Huang, Xiaowei Huang
{"title":"Maximum Power Point Tracking of Photovoltaic Systems Using Deep Q-networks","authors":"Kangshi Wang, Dou Hong, Jieming Ma, K. Man, Kaizhu Huang, Xiaowei Huang","doi":"10.1109/INDIN45582.2020.9442100","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442100","url":null,"abstract":"A photovoltaic (PV) generator exhibits nonlinear current-voltage characteristics and its maximum power point varies with incident atmospheric conditions. Therefore, maximum power point tracking (MPPT) control is required to maximize the output power of the PV generator. In this paper, deep Q-network based reinforcement learning strategy is proposed to optimize MPPT process for the photovoltaic system. The proposed system uses a novel control method which introduces agent to interface with the environment and finally gets the strategy of maximum reward accordingly. Simulations and experiments show the feasibility and effectiveness of the proposed system. Compared with the traditional perturb and observe (P&O) and incremental conductance (InC) methods, this method prominently saves tracking steps.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127938223","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
Stock-UniBERT: A News-based Cost-sensitive Ensemble BERT Model for Stock Trading 股票交易的基于新闻的成本敏感集成BERT模型
2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442147
Xiliu Man, Jianwu Lin, Yujiu Yang
{"title":"Stock-UniBERT: A News-based Cost-sensitive Ensemble BERT Model for Stock Trading","authors":"Xiliu Man, Jianwu Lin, Yujiu Yang","doi":"10.1109/INDIN45582.2020.9442147","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442147","url":null,"abstract":"Financial news plays an important role in investors' decisions and then influences stock markets. Previous studies mainly focus on establishing sentiment index from financial text and then making stock return prediction and trading strategy based on the index. This procedure demands costly manual label and may not directly correspond to actual stock market reaction. This paper solves this problem by using labels of stocks' residual return as sentiment labels for BERT model training. Distinct from ordinary task, buying or selling action will be taken after judgement of the stock news' sentiment. Hence, weighted cross-entropy loss and cost-sensitive accuracy are used to reveal influence and cost of judgement. Different settings of weighted cross-entropy loss are applied to learn self-adaptively and a selection method is designed to seek capable base classifiers for ensemble learning. This paper then develops a stock trading strategy based on the ensemble BERT model. Experiments and ablation study show the robust effectiveness of our strategy.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"21 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129954177","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信