2022 4th International Conference on Industrial Artificial Intelligence (IAI)最新文献

Time-Attention Graph Convolutional Network Soft Sensor in Biochemical Processes 生化过程中的时间-注意力图卷积网络软传感器

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976863

Mingwei Jia, Danya Xu, Tao Yang, Y. Yao, Yi Liu

{"title":"Time-Attention Graph Convolutional Network Soft Sensor in Biochemical Processes","authors":"Mingwei Jia, Danya Xu, Tao Yang, Y. Yao, Yi Liu","doi":"10.1109/IAI55780.2022.9976863","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976863","url":null,"abstract":"Most data-driven soft sensor methods can model nonlinear time-varying characteristics of biochemical processes. However, the intrinsic relationship between variables, which is helpful for understanding model behavior, has rarely been investigated in existing data-driven methods. In this work, a novel soft sensor model of time-attention graph convolutional network (TA-GCN) is proposed, which jointly leverages variable relationships and long-term temporal dependencies to improve interpretability and prediction accuracy. This model first uses the maximum information coefficient to construct a topology graph and trains edge strengths end-to-end. The data are then encoded in the spatial-temporal dimension based on GCN and attention mechanism. Finally, the empirical knowledge that analyzes the operating state of the process and graph are combined to explain the model behavior. In comparison to existing soft sensors, TA-GCN enables efficient and scalable training for long-term spatial-temporal dependencies. Experimental results on InPenSim dataset demonstrate that TA-GCN is competitive with state-of-the-art methods.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115482785","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

Optimal Antenna Pairing of A Miniaturized Radar Array for Smart Sensing of Soil Carbon Content 一种用于土壤碳含量智能感知的小型化雷达阵列天线优化配对

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976505

Di An, Michael Difrieri, Yangquan Chen

{"title":"Optimal Antenna Pairing of A Miniaturized Radar Array for Smart Sensing of Soil Carbon Content","authors":"Di An, Michael Difrieri, Yangquan Chen","doi":"10.1109/IAI55780.2022.9976505","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976505","url":null,"abstract":"The foundation of soil carbon management is the measurement of soil carbon content, which potentially enables many carbon-negative or carbon-neutral technologies for fighting climate change and improving soil health for greater crop yield. Several researchers used a non-intrusive method to quantify soil organic carbon content using ground penetrating radar (GPR) with a fixed sensor configuration. The sensor we used in this study, however, is compactly comprised of an array of 18 radar transmitter (TX) and receiver (RX) pairs. It is necessary to propose an assessment of sensing performance which can avoid possible failure in identifying the correct soil carbon spatial-temporal changes. In this paper, we provide a comprehensive assessment of the evaluation of non-intrusive methods for sensing soil carbon content when a radar array is used. Specifically, our proposed evaluation score utilizes explicit physical knowledge as a data-driven metric to find the optimal antenna pair combination for our radar array sensor under different sensing tasks and environments. We evaluated our soil carbon sensing score (SCSS) using the data collected from real-world soil sample experiments. The results show that the optimal antenna pair has the greatest sensing ability to measure soil carbon content in a variety of sensing environments and sensing distances, with a 36% increase in classification accuracy.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115720815","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

Target tracking trajectory generation for quadrotors in static complex environments 静态复杂环境下四旋翼机目标跟踪轨迹生成

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976773

Yang Ji

引用次数: 0

Flexibility of Seru Production System: An Input-Process-Output System View* Seru生产系统的灵活性:一个输入-过程-输出系统视图*

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976524

Yuhong Ren, Jiafu Tang

引用次数: 0

Detection of arc shape in ultra-narrow gap welding based on improved YOLOv5s 基于改进YOLOv5s的超窄间隙焊接电弧形状检测

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976556

Weilong He, Ping Wang, A. Zhang, Jing Ma, Shengming Ma, Yanpeng Feng

引用次数: 0

Spatial-temporal Pattern Recognition for Data Identification and Tagging Based on Power Curve in Wind Turbines 基于功率曲线的风电数据识别与标注的时空模式识别

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976653

Linsong Yuan, Shenwei Chen, Guanglun Liu

引用次数: 0

Fault diagnosis study of elevator based on stochastic configuration networks 基于随机组态网络的电梯故障诊断研究

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976875

Tianwei Dong, C. Zang, Peng Zeng

引用次数: 1

Adaptive Control for A Class of Nonholonomic Vehicle Systems 一类非完整车辆系统的自适应控制

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976558

Tianqun Ren, Xiang Chen

引用次数: 1

A Train Cooperative Operation Optimization Method based on Improved Reinforcement Learning Algorithm* 基于改进强化学习算法的列车协同运行优化方法*

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976538

Xingguo Wang, Deqing Huang, Huanlai Xing

{"title":"A Train Cooperative Operation Optimization Method based on Improved Reinforcement Learning Algorithm*","authors":"Xingguo Wang, Deqing Huang, Huanlai Xing","doi":"10.1109/IAI55780.2022.9976538","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976538","url":null,"abstract":"This paper mainly focuses on the high-speed train cooperative operation problem. To solve this problem, this paper presents a speed curve optimization method based on improved reinforcement learning algorithm. First, according to the train dynamics system, we build the speed curve optimization object. In order to realize the cooperative operation of trains, we use the artificial potential field method to establish the reward function for train spacing. At the same time, to ensure passenger comfort, train jerk rate also needs to be added into the reward function. And then, agent of improved reinforcement learning is established. The improved reinforcement learning algorithm is different from the general reinforcement learning algorithm in that the observation dimension of policy network is manually reduced compared with that of the Q value network to improve the learning speed of the algorithm. At the same time, in order to reduce the agent's attempts to perform useless actions in some states, a reference controller is added to the system to further accelerate the learning process. In addition, training parameters need to be set, such as training termination conditions, maximum number of steps, desired global reward value, and so on. After the training. The Agent can generate a desirable speed curve of train based on constraints of vehicle output and jerk rate under cooperative operation.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125732252","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

Deep Learning Assisted Online Multi-Step Demand Forecasting of Fused Magnesia Smelting Processes 深度学习辅助熔镁熔炼过程在线多步需求预测

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976577

Mingyu Li, Jingwen Zhang, Tianyou Chai

{"title":"Deep Learning Assisted Online Multi-Step Demand Forecasting of Fused Magnesia Smelting Processes","authors":"Mingyu Li, Jingwen Zhang, Tianyou Chai","doi":"10.1109/IAI55780.2022.9976577","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976577","url":null,"abstract":"This paper proposes a multi-step ahead power demand model for fused magnesia smelting processes (FMSP) which combines a linear model and an unknown nonlinear term to predict the electricity demand and its variation tendency for the next 5 steps. The linear model is identified by the multi-output fast recursive algorithm (MFRA) while the unknown nonlinear term is fitted with a long-short term memory (LSTM) model. The hyperparameters in the LSTM are estimated by the Bayesian optimization (BO) algorithm. Since the sampling period of the power is only 7 seconds, and we have to predict the next 5 steps electricity demand and its tendency within one sampling period, we therefore update parameters of the linear model by the MFRA while parameters of the dense layer of the LSTM are updated by the gradient descent algorithm within the online multi-step demand forecasting framework. The experimental results using the real-time data of a FMSP confirm the effectiveness of the proposed algorithm, achieving up to 52% error reduction in 5-step ahead demand forecasting when compared with other approaches.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"357 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115863075","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