2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)最新文献_第7页

Analyzing Risky Behavior in Traffic Accidents 交通事故中的危险行为分析

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283330

Mayank Chaudhari, S. Sarkar, Divyasheel Sharma

{"title":"Analyzing Risky Behavior in Traffic Accidents","authors":"Mayank Chaudhari, S. Sarkar, Divyasheel Sharma","doi":"10.1109/SMC42975.2020.9283330","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283330","url":null,"abstract":"Among all the transportation systems that people use, the public traffic-ways are most common and dangerous resulting in a significant number of fatalities per day worldwide. Statistics have shown that the mortality rates related to traffic accident are more among youth. Although various road safety strategies and rules are developed by the government and law-enforcement agencies to combat the situation, these methods mainly target design, operation, and usability of traffic-ways. Most of the recent data-driven analysis papers model the traffic patterns or predict accidents from the past data. In this paper, we consider a comprehensive, year long fatality analysis reporting system (FARS) data to analyze the role of various factors related to humans, weather and physical conditions (e.g., road surface, light condition etc.) involved in traffic accidents. We build an intelligent risk prediction model that can help decision-makers to ensure road safety. The proposed model estimates (i.) the accident risk over a future time frame, and (ii.) the risk associated with the drivers present on the traffic-way based on the driver’s behavior, history, environmental conditions and physical conditions related to traffic-way.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"3 1","pages":"464-471"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72785159","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

Effectiveness of neural language models for word prediction of textual mammography reports 神经语言模型对文本乳房x光检查报告词预测的有效性

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283304

Mihai David Marin, Elena Mocanu, C. Seifert

引用次数: 1

Preliminary Investigation of Visual Information Influencing Driver’s Steering Control based on CNN 基于CNN的视觉信息对驾驶员转向控制的影响初探

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283290

Yuki Okafuji, Toshihito Sugiura, T. Wada

引用次数: 1

Design and Characterization of low frequency Capacitive Micromachined Ultrasonic Transducer (CMUT) 低频电容式微机械超声换能器(CMUT)的设计与表征

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9282903

Mayank B. Thacker, D. Buchanan

引用次数: 3

Robust Vaccination Strategy based on Dynamic Game for Uncertain SIR Time-Delay Model 基于动态博弈的不确定SIR时滞模型鲁棒疫苗接种策略

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283389

Hiroya Kikuchi, H. Mukaidani, Ramasamy Saravanakumar, W. Zhuang

引用次数: 2

Population Size Specification for Fair Comparison of Multi-objective Evolutionary Algorithms 多目标进化算法公平比较的种群大小规范

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9282850

H. Ishibuchi, Lie Meng Pang, Ke Shang

{"title":"Population Size Specification for Fair Comparison of Multi-objective Evolutionary Algorithms","authors":"H. Ishibuchi, Lie Meng Pang, Ke Shang","doi":"10.1109/SMC42975.2020.9282850","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9282850","url":null,"abstract":"In general, performance comparison results of optimization algorithms depend on the parameter specifications in each algorithm. For fair comparison, it may be needed to use the best specifications for each algorithm instead of using the same specifications for all algorithms. This is because each algorithm has its best specifications. However, in the evolutionary multi-objective optimization (EMO) field, performance comparison has usually been performed under the same parameter specifications for all algorithms. Especially, the same population size has always been used. In this paper, we discuss this practice from a viewpoint of fair comparison of EMO algorithms. First, we demonstrate that performance comparison results depend on the population size. Next, we explain a new trend of performance comparison where each algorithm is evaluated by selecting a pre-specified number of solutions from the examined solutions (i.e., by selecting a solution subset with a pre-specified size). Then, we discuss the selected subset size specification. Through computational experiments, we show that performance comparison results do not strongly depend on the selected subset size while they depend on the population size.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"35 1","pages":"1095-1102"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75106546","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

Evolutionary Generative Contribution Mappings 进化生成贡献映射

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283014

Masayuki Kobayashi, Satoshi Arai, T. Nagao

{"title":"Evolutionary Generative Contribution Mappings","authors":"Masayuki Kobayashi, Satoshi Arai, T. Nagao","doi":"10.1109/SMC42975.2020.9283014","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283014","url":null,"abstract":"Although convolutional neural networks (CNNs) have significantly evolved and demonstrated outstanding performance, their uninterpretable nature is still considered to be a major problem. In this study, we take a closer look at CNN interpretability and propose a new method called Evolutionary Generative Contribution Mappings (EGCM). In EGCM, CNN models incorporate both a classification mechanism and an interpreting mechanism in an end-to-end training process. Specifically, the network generates the class contribution maps, which indicate the discriminative regions for the model to identify a specific class. Additionally, these maps can be directly used for classification tasks; all that is needed is a global average pooling and a softmax function. The network is represented by a directed acyclic graph and optimized using a genetic algorithm. Architecture search enables EGCM to deliver reasonable classification performance while maintaining high interpretability. We apply the EGCM framework on several datasets and empirically demonstrate that the EGCM not only achieves excellent classification performance but also maintains high interpretability.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"34 1","pages":"1657-1664"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76510285","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

Slip Ratio Optimization in Vehicle Safety Control Systems Using Least-Squares Based Adaptive Extremum Seeking 基于最小二乘自适应极值求法的车辆安全控制系统滑移率优化

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283109

Nursefa Zengin, Halit Zengin, B. Fidan, A. Khajepour

引用次数: 2

A Deep Learning Approach for Fault Detection and Diagnosis of Industrial Processes using Quantum Computing 基于量子计算的工业过程故障检测与诊断的深度学习方法

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283034

Akshay Ajagekar, F. You

引用次数: 0

Optimal Trajectory Planning for a Robotic Manipulator Palletizing Tasks * 机器人码垛作业的最优轨迹规划[j]

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9282868

F. Parisi, A. M. Mangini, M. P. Fanti

{"title":"Optimal Trajectory Planning for a Robotic Manipulator Palletizing Tasks *","authors":"F. Parisi, A. M. Mangini, M. P. Fanti","doi":"10.1109/SMC42975.2020.9282868","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9282868","url":null,"abstract":"In recent years, the employment of robots has become a value-added entity in the industries in gaining their competitive advantages. Moreover, thanks to Industry 4.0 paradigm, many production tasks have grown in terms of dimensionality, complexity and higher precision and need to be performed by robots. Among them, the palletizing task is still highly dependent on the particular problem to solve, and its optimization needs to be performed basing on the ground condition. In this paper a palletizing task problem performed by a robotic manipulator is studied. More in detail, some objects have to be transported from a pre-determined storage area to a delivery area. In the storage area the objects are stacked one on the other in columns, while in the delivery area the robotic manipulator poses the objects in horizontal levels, one over another. The process is optimized by minimizing the total distance travelled by the robotic manipulator to transport all the objects from the storage area to the delivery area. An Integer Linear Programming (ILP) problem is formalized and tested by simulations and experimental results.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"7 1","pages":"2901-2906"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81927965","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