2022 4th International Conference on Industrial Artificial Intelligence (IAI)最新文献_第3页

Block-structured echo state network based on error reduction mechanism 基于纠错机制的块结构回波状态网络

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976790

Xingshang Li, Fanjun Li, Shoujing Zheng, Qianwen Liu

引用次数: 0

Adaptive backstepping sliding mode control based on MLP neural network for trajectory tracking of USV 基于MLP神经网络的USV轨迹跟踪自适应反步滑模控制

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976722

Yuan Yu, Lin Pan, Jun-an Bao, Hao Tian

{"title":"Adaptive backstepping sliding mode control based on MLP neural network for trajectory tracking of USV","authors":"Yuan Yu, Lin Pan, Jun-an Bao, Hao Tian","doi":"10.1109/IAI55780.2022.9976722","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976722","url":null,"abstract":"This study proposes an adaptive sliding mode control (SMC) strategy based on neural networks and backstepping method for trajectory tracking control of the underactuated unmanned surface vessel (USV). The controller is decomposed into two loops of kinematics and dynamics by using the back-stepping control. In the kinematics loop, the surge and sway reference velocities of USV are designed and regarded as virtual control laws to stabilize the position errors. In the dynamics loop, the SMC is used to design the control laws. To avoid chattering of SMC, the exponential approach rate is improved by using the arctangent function, which forms the sliding mode controller with the variable parameter approach rate. The neural network based on the minimum learning parameter method (MLP) is used to approximate the uncertain terms of the model to enhance the robustness of the system and reduce the computational complexity. The adaptive laws are proposed to compensate for the approximation errors of neural networks and disturbances. By constructing the Lyapunov function, it is demonstrated the proposed control scheme can guarantee the uniform final boundedness of all signals in the closed-loop system. Finally, simulation results on an underactuated USV further illustrate the effectiveness.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"1998 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":"125715361","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

Prediction of Element Component Content Based on Mechanism Analysis and Error Compensation 基于机理分析和误差补偿的元素含量预测

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976647

Rongxiu Lu, Biao Deng, Kanghao Ding, Hui Yang, Jianyong Zhu, Hongliang Liu

{"title":"Prediction of Element Component Content Based on Mechanism Analysis and Error Compensation","authors":"Rongxiu Lu, Biao Deng, Kanghao Ding, Hui Yang, Jianyong Zhu, Hongliang Liu","doi":"10.1109/IAI55780.2022.9976647","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976647","url":null,"abstract":"To solve the difficulty of rapid and accurate detection of component content in the rare earth extraction process, a component content modeling method combining mechanism model and error compensation model based on just-in-time learning (JITL) was proposed. Considering the different dynamic characteristics of each section, the extraction section is simplified using the segmented-aggregation method, and the mechanism model of the rare earth extraction process based on material balance is established; in view of the error caused by the simplification of the model and the characteristics of some rare earth solutions with color features, the color features of rare earth solution samples are extracted by machine vision technology, and the error compensation model of the mechanism model is established by the just-in-time learning algorithm. Through the experimental verification of the field sample data of the praseodymium/neodymium (Pr/Nd) extraction process, the results show that the modeling method proposed in this paper is suitable for rapid and accurate detection of elemental component content in the rare earth extraction process with ionic color features.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"241 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114049631","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

Relative-Time-Delay-Aware Self-Optimizing-Control for First-Order-Plus-Delay-Time Systems 一阶多时滞系统的相对时滞感知自优化控制

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976779

J. Viola, Yangquan Chen

引用次数: 1

Intelligent Interpretation of High-resolution Remote Sensing Images based on Deep Learning 基于深度学习的高分辨率遥感图像智能解译

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976690

Bitong Huai, Han Liu, Guo Xie, Youmin Zhang

引用次数: 0

Event-Triggered Online Learning Distributionally Robust Energy Management of Ammonia-Based Multi-Energy Microgrid 基于氨的多能微电网事件触发在线学习分布式鲁棒能量管理

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976794

Longyan Li, C. Ning

{"title":"Event-Triggered Online Learning Distributionally Robust Energy Management of Ammonia-Based Multi-Energy Microgrid","authors":"Longyan Li, C. Ning","doi":"10.1109/IAI55780.2022.9976794","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976794","url":null,"abstract":"This paper proposes a novel uncertainty-aware energy management framework for Multi-energy Microgrid (MEMG), which comprehensively comprises electricity, heat, natural gas, hydrogen, and ammonia. In particular, green ammonia is produced from hydrogen, which is derived from electrolysis powered by renewable energy. The proposed framework seamlessly integrates day-ahead optimal scheduling with data-driven model predictive control. To offer a just-in-time resilience to uncertainties of renewable energy and load, we further develop event-triggered online learning distributionally robust model predictive control (ET-OLDRMPC). Specifically, an event trigger mechanism is designed to enable the controller to intelligently switch between certainty-equivalence and distributionally robust schemes as per their respective advantageous regimes, thereby ensuring operation safety while mitigating unnecessary conservatism. For the distributionally robust scheme, we leverage a nonparametric Bayesian model to construct online ambiguity sets of uncertainty distributions, which encode statistical multimodality and local moment information. The effectiveness of the proposed framework is validated in a case study.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"30 7 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":"132762501","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 Novel Defect Detection Method of Liquid Crystal Display Based on Machine Vision 一种基于机器视觉的液晶显示器缺陷检测方法

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976633

Shengping Yu, Wenju Zhou, Jun Liu

{"title":"A Novel Defect Detection Method of Liquid Crystal Display Based on Machine Vision","authors":"Shengping Yu, Wenju Zhou, Jun Liu","doi":"10.1109/IAI55780.2022.9976633","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976633","url":null,"abstract":"As an important information display tool closely related to people's daily life, the liquid crystal display (LCD) has become an inseparable part of people's lives. In the manufacturing process of LCD, screen defect detection is an indispensable step which directly affects the yield and quality of LCD. In order to improve the accuracy and efficiency of defect detection for LCD screen, this paper proposes a novel defect detection method for LCD based on machine vision. Firstly, preprocessing operations including grayscale, binarization, filtering and dilation are used to reduce background noise and enhance the useful features of LCD screens. Secondly, the maximum connected region (MCR) and minimum external rectangle (MER) are adopted to initially locate the position of the LCD screen; Then, the affine transformation is introduced to correct the tilted screen and horizontal projection (HP) and vertical projection (VP) are presented to extract the LCD screen. Finally, a regional template matching algorithm is proposed to detect defects of LCD screens. Experiments show the effectiveness and robustness of the proposed method.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"5 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":"116229365","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

BDS Multipath Signal Classification Using Support Vector Machine 基于支持向量机的北斗多径信号分类

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976714

Yahang Qin, Zhenni Li, Shengli Xie, Rong Yuan, Junming Xie

{"title":"BDS Multipath Signal Classification Using Support Vector Machine","authors":"Yahang Qin, Zhenni Li, Shengli Xie, Rong Yuan, Junming Xie","doi":"10.1109/IAI55780.2022.9976714","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976714","url":null,"abstract":"In urban environments, multipath can significantly deteriorate the positioning precision of the global navigation satellite system (GNSS). BeiDou navigation satellite system independently established by China plays an important role in the GNSS market. Eliminating the multipath is a crucial problem to contribute to the development of the BeiDou navigation satellite system (BDS). In this paper, we use the machine learning algorithm support vector machine (SVM) to classify the BeiDou satellite signals into line-of-sight (LOS), multipath, and non-line-of-sight signals (NLOS). Single and multiple feature classification of the signal was performed by using the carrier to noise ratio (C/N0), elevation angle (ELE), and pseudorange residuals (PR). We use SVM with radial basis function (RBF), which can effectively handle nonlinear and high-dimensional data, and this feature is just suitable for the effective classification of nonlinear and high-dimensional data in this paper. It is a challenging problem to select the appropriate features from receiver independent exchange (RINEX) format signals for the diverse forms of signals output from BeiDou signal receivers. In this paper, we analyze the selected features C/N0, ELE, and PR, and it is proved that they can be used for BeiDou satellite signal classification. In the experimental study, BeiDou satellite signals are collected with static receivers in an urban canyon. The experimental results show that the highest classification accuracy of 78.48% is achieved based on the PR using a single feature aspect. The SVM classification accuracy based on feature C/N0, ELE, and PR can reach 87.22%. The classification using multiple features is significantly higher than that of single feature.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"33 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":"122700444","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

Nonsigular fast terminal sliding mode control based on Extended state observer for trajectory tracking of USV 基于扩展状态观测器的USV轨迹跟踪非奇异快速终端滑模控制

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976827

Jun-an Bao, Lin Pan, Jiying Wang, Yuan Yu, Hao Tian

引用次数: 0

Grid Cell Detection of Dandelion Weed Centers via Convolutional Neural Network 蒲公英杂草中心的卷积神经网络网格细胞检测

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976823

Ibrahim Babiker, Jiacai Liao, W. Xie

{"title":"Grid Cell Detection of Dandelion Weed Centers via Convolutional Neural Network","authors":"Ibrahim Babiker, Jiacai Liao, W. Xie","doi":"10.1109/IAI55780.2022.9976823","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976823","url":null,"abstract":"This paper presents a novel method for detecting dandelion weed (Taraxacum officinale) plant centers in perennial ryegrass using partial information gathered only from plant leaves. A primitive region proposal method generates proposals from original birds-eye view images of whole dandelion weeds in grass. The proposals containing dandelion weed leaves are taken and plant centers are labeled with a point based on the novel concept of the most “prominent” leaf. The samples are divided into a grid of cells and the cell containing the labeled point is considered the truth cell. A radial map and its inverse are generated based on the spatial location of the cells w.r.t. the truth cell. A fully convolutional network is trained to detect the positive truth cell using novel loss functions based on these maps. Using a relatively small dataset, the loss functions with the terms that compute regression loss on the maps yield significantly better model performance than those without. In addition, some errors are simply the result of the center of an alternate “prominent” leaf being automatically detected. Further, the comparison results with segmentation models reveal some advantages in detecting only plant centers as opposed to training computationally costly inference models.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"20 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":"130212746","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