2020 2nd International Conference on Industrial Artificial Intelligence (IAI)最新文献_第6页

DEM data-driven modeling of repose angle of granular materials 基于DEM数据驱动的颗粒物料休止角建模

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262219

Zhou Hu, Xiaoyan Liu, Chen Chau Chu

{"title":"DEM data-driven modeling of repose angle of granular materials","authors":"Zhou Hu, Xiaoyan Liu, Chen Chau Chu","doi":"10.1109/IAI50351.2020.9262219","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262219","url":null,"abstract":"Repose angle is an important property of granular materials and is usually simulated using Discrete Element Method (DEM). However, DEM simulation is computationally intensive and is thus unsuitable for online applications where parameters are frequently changed. To solve this problem, we propose a DEM data-driven modeling method for fast prediction of repose angle. Firstly, variables affecting the repose angle are analyzed; by Latin hypercube sampling of parameter spaces, 100 sets of DEM simulations are performed to generate data of repose angle. Based on these data, a support vector machine (SVM) model is then established and trained for fast prediction of repose angle under various conditions. Tests and comparison show that the reposed angle predicted by the SVM model is close to the DEM simulation result while the required computing time is greatly decreased (from 43.8 hours to 0.17 seconds), and it outperforms BP neural network and Kriging interpolation method in terms of prediction accuracy. The SVM model for repose angle is also verified by physical experiments, with prediction error less than ± 1 °. The established model can replace DEM, and is suitable for applications where fast prediction of repose angle is required.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128117055","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

[Copyright notice] (版权)

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/iai50351.2020.9262188

引用次数: 0

A Data Driven Condition Monitoring of Chain Type Structures by Using Nonlinear Frequency Analyses 基于非线性频率分析的链式结构状态监测

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262237

Yi Gao, Zhong Luo, Yunpeng Zhu, Yue Qiu, Yuqi Li

引用次数: 0

Abnormal Condition Detection Integrated with Kullback Leibler Divergence and Relative Importance Function for Cement Raw Meal Calcination Process 基于Kullback Leibler散度和相对重要函数的水泥生料煅烧过程异常状态检测

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262170

Jinghui Qiao, Feng Tian

{"title":"Abnormal Condition Detection Integrated with Kullback Leibler Divergence and Relative Importance Function for Cement Raw Meal Calcination Process","authors":"Jinghui Qiao, Feng Tian","doi":"10.1109/IAI50351.2020.9262170","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262170","url":null,"abstract":"This paper focus on abnormal condition detection by using Kullback Leibler (KL) divergence with relative importance function. There exist multimodal working conditions, such as normal condition, abnormal condition. KL method was proved to be more sensitive to initial faults than the Hotelling's T-squared statistic. Relative importance function estimation for condition detection has been demonstrated, and relative importance function is always smoother than corresponding ordinary density-ratios. In cement raw meal calcination process, we sampled some important variables, such as calciner temperature, preheater C1 outlet temperature, raw meal flow, and C1 and C5 cone pressure. In actual process, the product quality index is low and it is easy to cause the preheater C5 feeding tube to be blocked. To detect abnormal condition, an abnormal condition detection based on Kullback Leibler divergence with relative importance function was proposed. The actual application results shows that the model proposed can detect abnormal condition by current operating data, and far from fault condition by the practical application results.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129824826","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

An Empirical Study on The Cluster Development of Urban Smart Home Appliance Industry Based on Big Data Analysis of Books* 基于图书大数据分析的城市智能家电产业集群发展实证研究*

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262205

Jiyang Yuan, Yanbin Zhao, Xinkun Gao

引用次数: 0

Adaptive Neural Network Control of an Airborne Robotic Manipulator System 机载机械臂系统的自适应神经网络控制

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262230

Hao Xu, S. Ge, Qiong Liu, Wanyue Jiang, Ruihang Ji

引用次数: 2

Design of Stabilization Platform Control System Based on Characteristic Model 基于特征模型的稳定平台控制系统设计

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262198

Shaoping Shen, H. Pang, Jianchun Gong

引用次数: 0

Visual Analysis of Research Hotspots of Enterprise Transformation and Upgrading Based on Internet Big Data* 基于互联网大数据的企业转型升级研究热点可视化分析*

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262202

Haiqin Xie, Ailing Wang, Xinkun Gao

引用次数: 0

Research on Hardware-in-loop Simulation Platform of Anti-swing Control for Helicopter Suspension 直升机悬架抗摆控制硬件在环仿真平台研究

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262210

Zhifu Tang, D. Yuan, Junfei Zhang, Xiaojun Xing, Cai Xin, Yuquan He

引用次数: 1

Improved composite neural learning control for marine unmanned vehicles with the actuator gain constraints 基于舵机增益约束的改进复合神经学习控制

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262211

Guoqing Zhang, S. Chu, Jiqiang Li, Weidong Zhang

引用次数: 0