2020 2nd International Conference on Industrial Artificial Intelligence (IAI)最新文献

{"title":"Study of Modeling Earthquake Emergency Rescue Material Scheduling Problems by Multi-objective Optimization Algorithms","authors":"Jingbang Chen, Mingtao Hu, Haolang Shen, Hongyi Lan, Zujian Wu","doi":"10.1109/IAI50351.2020.9262178","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262178","url":null,"abstract":"To save lives and reduce the property loss, it is important to effectively plan and schedule rescue material distribution after earthquake disaster happening. Due to different constraint factors, such as the time, the number of rescue resource, it is necessary to intelligently analyze and design the distribution solutions based on multi-objective methodologies. Mathematical modeling methods and multi-objective optimization algorithms can be applied to support decision-makers to have an overview on resource distribution with balancing the costs of constrain factors. In this work, a multi-objective model is constructed and analyzed by multi-objective optimization algorithms. Furthermore, an embedded global and local optimization algorithm based on particle swarm optimization and simulated annealing is proposed and applied to the model analysis. Simulation results show that applied and proposed algorithms can effectively analyze the constructed model and adaptively analyze the materials distribution. It can also be a beneficial reference for decision making on relevant disaster rescue scheduling issues.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"39 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132575716","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}

{"title":"Application of Fiber Bragg Grating Sensing Technology in Key Equipment Monitoring of Offshore Platform","authors":"C. Jiang, Tingjun Yan, Mingxue Song, J. Shi","doi":"10.1109/IAI50351.2020.9262234","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262234","url":null,"abstract":"Due to the special marine environment, it is difficult for the traditional electronic sensors to play a long-term real-time on-line monitoring role in the key equipment of offshore platforms under the environment of high humidity, high salt, high pressure, corrosion and electromagnetic interference. Based on the principle of fiber grating sensing and material properties, Based on fiber Bragg grating sensing principle and material properties, the fiber grating sensing technology is introduced to monitor the key equipment status of offshore platform in real-time, and the key and possible dangerous position location monitoring and establish the early warning system, which helps on-site staffs to find out the problems of equipment structure in time and take corresponding measures to ensure the safe production operation of the offshore platform. In this paper, combined with the monitoring cases of offshore platforms, such as jacket platform, workover rig derrick, dry-type transformer cabinet, etc. The status monitoring methods, advantages and existing problems of FBG sensing technology in the field of offshore engineering application are discussed, so as to provide basis for the future development of FBG sensing technology in the field of offshore engineering.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"725 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":"132630520","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}

{"title":"Multi-objective multifactorial evolutionary algorithm enhanced with the weighting helper-task","authors":"Yongjin Zheng, Zexuan Zhu, Yutao Qi, Lei Wang, Xiaoliang Ma","doi":"10.1109/IAI50351.2020.9262200","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262200","url":null,"abstract":"Recently, transfer learning has received more and more attention in the field of computational intelligence. The multi-task paradigm is a recent research hotspot. Among them, multi-objective multitasking optimization aims to optimize multiple multi-objective optimization problems simultaneously. The first evolutionary algorithm for multi-objective multitasking optimization is multi-objective multifactorial algorithm (MO-MFEA). However, MO-MFEA has slow convergence due to irrelevance or weakly relevance among tasks. To deal with this issue, we introduce an additional helper-task, i.e., a weight sum of component tasks, into MO-MFEA to improve the effectiveness of inter-task knowledge transfer. Experimental results on a set of benchmark problems have validated the effectiveness and efficiency of the proposed method as compared with MOMFEA and NSGA-II.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"24 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":"121792678","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}

{"title":"Adaptive Trajectory Tracking Control for a Rehabilitative Training Walker with Center of Gravity Shift","authors":"Hongbin Chang, Xiaojie Su, Shuoyu Wang","doi":"10.1109/IAI50351.2020.9262159","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262159","url":null,"abstract":"The improvement in living conditions and the increasing life expectancy have led to the gradual aging of the population worldwide, resulting in a significant increase in the incidence of age-related health issues. Declining motor function (e.g., decreased muscle strength, coordination, and dexterity) is commonly associated with aging and can lead to potentially disabling falls. However, the workforce of healthcare providers and nurses is decreasing and, therefore, cannot meet the demands of the people who require physical rehabilitation; as the birth rate drops, this situation is likely to worsen. Therefore, the author's laboratory has been focusing on the development of rehabilitation robots for many years. The main contribution of this paper is to design suitable control algorithms to improve the rehabilitation effect of the rehabilitative training walker (RTW). In order to make the RTW has the same therapeutic effect as therapist, it must imitate the training prescription provided by therapist. Then, the RTW needs to track the trajectory designed by the therapist with high precision. However, some problems affect the precision of trajectory tracking, which makes accurate trajectory tracking impossible. Thus, we should solve these problems by improving the control algorithm, to enhance the effect of rehabilitation training. Firstly, when the RTW works together with its user, the center of gravity of the RTW will be shifted. It will further lead to some parameters in the system to change randomly. In order to solve this problem, we construct a reasonable stochastic model to describe the motion of the robot. Based on this model, we design an appropriate controller to converge the tracking error of the robot. Additionally, by designing appropriate control parameters, we make the error system is asymptotically stable. As we know, the RTW usually works In a narrow and complex environment. Therefore, In this study, three omniwheels are mounted on the RTW to allow it to move omnidirectionally; however, this also presents a challenge in the control of the robot. Unlike vehicles with standard wheels, omnidirectional vehicles suffer from noticeable jitter in the orientation angle. Most of the previous studies neglected the problem while others attempted to reduce this jitter by adjusting the gains in the controller. Here, we also investigate the structure of the omniwheels touchdown characteristics to reveal the cause of the vibration in the orientation angle. Then, we apply an adaptive technique to eliminate this vibration. A limitation of the previously published tacking methods is that they require at least the measurement of velocity on the link side or motor side. However, in robotic applications, velocity sensors are frequently omitted because of their considerable the production cost, and the size and the weight of the servo-drives. Moreover, in practical robotic systems, the velocity measurements obtained through tachometers are easily perturbed ","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"138 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120868684","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}

{"title":"Unsupervised Detection for Burned Area with Fuzzy C-Means and D-S Evidence Theory","authors":"Guangyi Wang, Youmin Zhang, W. Xie, Y. Qu","doi":"10.1109/IAI50351.2020.9262167","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262167","url":null,"abstract":"Mapping the burned area of forest fires can contribute significantly to the understanding, quantification, and evaluation of forest fire severity and its impacts on the forest ecosystem. In this paper, an unsupervised detection for burned region based on the Fuzzy C-Means (FCM) and Dempster-Shafer (D-S) evidence theory with the bi-temporal images is proposed. Specifically, according to difference maps from the delta normalized burn ratio and spectral angle index, the Expectation-Maximization (EM) algorithm is used to separate the study area into the definitely burned region and indefinitely burned region. Then, under the enlightenment of the multi-source information fusion theory, the indefinite region is discriminated against further with FCM and D-S evidence theory. Finally, the final fire-burned map can be inferred from the results obtained from the aforementioned steps. The experimental results on two forest fires with bi-temporal Landsat-8 images have shown the potential of the proposed burned area mapping method, in the field of detecting the forest landscape change based on multispectral remote sensing images.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"12 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":"126595066","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}

{"title":"A WeChat Mini-program System with LSTM for The Emotional EEG Signal Recognition","authors":"Liang Wang, Xin Deng, Xiangwei Lv, Ke Liu, Qing-Yun Yang, Can Long","doi":"10.1109/IAI50351.2020.9262189","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262189","url":null,"abstract":"As one of the advanced functions for human being, the emotion has a great influence on people's personality and mental health. EEG serves as a rapid measure method for neural signals that becomes an important way to evaluate different emotions. Some traditional machine learning techniques do not take into account the crucial temporal dynamic information in the EEG signals. However, with the recursive structure in time, the long and short time memory (LSTM) network in deep learning technology can solve this problem well. In this paper, a LSTM is designed and trained well to classify the emotional EEG, and then a WeChat mini-program system is constructed. The mini-program system incorporates with the LSTM to perform the EEG preprocessing, feature extraction, emotion classifying, and user management functions and so on. It can give feedback to the users about the emotional changes degree of pleasure and sobriety according to their EEG, which could serve as the emotion inspector as well as the entertainment tool for personal use.","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":"129667671","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}

{"title":"Analysis of Military Theoretical Research Based on Subject Knowledge Map","authors":"Xiaosong Li, Xinran Peng, Shuai Lei, Tian Liu","doi":"10.1109/IAI50351.2020.9262163","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262163","url":null,"abstract":"This paper defined the concept of military theory, conducted the analysis of the current state of military theoretical research based on knowledge maps, data cleansing and analysis of military theoretical research literature. Through multidimensional scale analysis, Co-word analysis, social network graph analysis, centrality analysis and factor analysis, this paper carried out knowledge map research on the status of military theoretical research. On this basis, summarized the current situation of military theoretical research, which includes great development potential, relatively limited research, generalization of content, lack of quantitative analysis, and insufficient basic conditions. The research conclusions have certain reference value for further development of military theoretical research and practical application.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"4 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":"127818393","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}

{"title":"Comprehensive Forecasting Method of Monthly Electricity Consumption Based on Time Series Decomposition and Regression Analysis","authors":"Changfeng Luan, Xinfu Pang, Yanbo Wang, Li Liu, S. You","doi":"10.1109/IAI50351.2020.9262169","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262169","url":null,"abstract":"Power consumption prediction is the basis of implementing planned power consumption and preparing production plan. It is one of the main projects in the design of industrial and mining enterprises. It is also an important link to ensure the balance between national economic needs and power supply. Due to the influence of distributed energy and the change of power demand and load characteristics of the user side compared with the past, the power consumption prediction starts to face small-scale users and is more easily disturbed by various influencing factors, so the traditional prediction method is not fully suitable for today's power consumption prediction. Firstly, STL is used to decompose the power consumption sequence of corresponding month into trend component, season component and random component. Secondly, the BP neural network model is used to predict the seasonal component of the month when the seasonal mutation and major festivals are located. ARIMA model is used to predict the trend component. The average value is used to predict the random components. Then, the predicted values of the three components are reconstructed into the final predicted values. Finally, the algorithm is compiled by R language, and the validity of the proposed method is verified by the actual monthly electricity sales data of a University Park in the north. And further consider the prediction method of economic factors.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"78 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":"125927592","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}

{"title":"A Degradation Prediction Algorithm for Maritime Distress Reporting Based on Deep Learning","authors":"Fang Fang, S. Kuo, Ge Yaowu","doi":"10.1109/IAI50351.2020.9262221","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262221","url":null,"abstract":"Owing to the bad weather, equipment immersion with water, antenna difficult to point at the satellite and so on, the accuracy and the reliability of the positional information of the pilot in distress sent by the distress message signal sending device is low, which will be reduced with the increase of working time. In order to improve the reliability of distress message signal sending device based on BeiDou satellite, a prediction method for signal sending time and a prediction method for signal transmitting delay time are firstly proposed based on the deep neural network. In the process of prediction, a lot of sensor information is used, especial in the prediction of signal transmitting delay time, multiple-sampling information from the sensors is adopted. The experimental results show that the probability of successful message signal sending is increased from 36.3% to 73.3%, moreover, the working time of the equipment was extended from 6.0 hours to 8.6 hours.","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":"128361827","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}

{"title":"Adaptive Leaderless Consensus Control of a class of Second order Nonlinear Multi-agent Systems with Unknown Control Directions and External Disturbances","authors":"Jiangshuai Huang, T. Gao, Yong Zhou","doi":"10.1109/IAI50351.2020.9262174","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262174","url":null,"abstract":"This paper solves the leaderless consensus problem of a class of uncertain nonlinear multi-agent systems with unknown control directions and external disturbances. Without using the Nussbaum function approach, a novel control scheme is proposed by means of the switching mechanism. The control algorithm guarantees that consensus errors converge to the origin asymptotically, and the amplitude of the control signals is much smaller compared with those using Nussbaum functions.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"64 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":"124426038","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}