2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)最新文献_第4页

The optimization of vehicle routing of communal waste in an urban environment using a nearest neighbirs' algorithm and genetic algorithm: Communal waste vehicle routing optimization in urban areas 基于最近邻算法和遗传算法的城市环境下公共垃圾车辆路径优化:城市公共垃圾车辆路径优化

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2017-02-01 DOI: 10.1109/ICACI.2017.7974519

M. Misic, A. Dordevic, A. Arsic

{"title":"The optimization of vehicle routing of communal waste in an urban environment using a nearest neighbirs' algorithm and genetic algorithm: Communal waste vehicle routing optimization in urban areas","authors":"M. Misic, A. Dordevic, A. Arsic","doi":"10.1109/ICACI.2017.7974519","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974519","url":null,"abstract":"The current effects of rapid development, high population density in large residential areas and pressures on organizations to protect the environment, create a provocative framework for waste management in modern cities. The complexity of the process of garbage collection is large, and therefore a major concern for public authorities in terms of collection, transport and further processing of solid waste. In this paper, the authors have presented a two-step solution formed from a nearest neighbor search and genetic algorithm to optimize the path of trucks with a specified capacity for garbage collection. This method firstly performs a search for the optimal solution with a nearest neighbors' algorithm (NNA) over a set of possible solutions, and then in the second step gives that solution with other random solutions to a genetic algorithm (GA) for further improvement; the goal is to extract the solution with minimal trajectory and maximum capacity utilization of trucks that are available. Testing was done on a range of problems with a certain number of trucks, with a given capacity and the number and location of sites for waste collection.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116440030","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: 5

Transformation of discriminative single-task classification into generative multi-task classification in machine learning context 机器学习环境下判别式单任务分类向生成式多任务分类的转化

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2017-02-01 DOI: 10.1109/ICACI.2017.7974487

Han Liu, Ella Haig, Alaa Mohasseb, Mohamed Bader

{"title":"Transformation of discriminative single-task classification into generative multi-task classification in machine learning context","authors":"Han Liu, Ella Haig, Alaa Mohasseb, Mohamed Bader","doi":"10.1109/ICACI.2017.7974487","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974487","url":null,"abstract":"Classification is one of the most popular tasks of machine learning, which has been involved in broad applications in practice, such as decision making, sentiment analysis and pattern recognition. It involves the assignment of a class/label to an instance and is based on the assumption that each instance can only belong to one class. This assumption does not hold, especially for indexing problems (when an item, such as a movie, can belong to more than one category) or for complex items that reflect more than one aspect, e.g. a product review outlining advantages and disadvantages may be at the same time positive and negative. To address this problem, multi-label classification has been increasingly used in recent years, by transforming the data to allow an instance to have more than one label; the nature of learning, however, is the same as traditional learning, i.e. learning to discriminate one class from other classes and the output of a classifier is still single (although the output may contain a set of labels). In this paper we propose a fundamentally different type of classification in which the membership of an instance to all classes(/labels) is judged by a multiple-input-multiple-output classifier through generative multi-task learning. An experimental study is conducted on five UCI data sets to show empirically that an instance can belong to more than one class, by using the theory of fuzzy logic and checking the extent to which an instance belongs to each single class, i.e. the fuzzy membership degree. The paper positions new research directions on multi-task classification in the context of both supervised learning and semi-supervised learning.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130376900","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: 15

Safety helmet wearing detection based on image processing and machine learning 基于图像处理和机器学习的安全帽佩戴检测

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2017-02-01 DOI: 10.1109/ICACI.2017.7974509

Jie Li, Huanming Liu, Tianzheng Wang, Min Jiang, Shuai Wang, Kang Li, Xiaoguang Zhao

引用次数: 61

Consensus in multi-agent networks with switching topology and nonlinear dynamics 具有交换拓扑和非线性动力学的多智能体网络的一致性

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2017-02-01 DOI: 10.1109/ICACI.2017.7974480

Huiwei Wang, X. Liao, Tingwen Huang

引用次数: 0

A study of pushing service framework of the personalized learning resources on network learning platform 网络学习平台个性化学习资源推送服务框架研究

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2017-02-01 DOI: 10.1109/ICACI.2017.7974510

Xiaolin Xu, Lin Zhang

引用次数: 1

A machine competence based analytical model to study trust calibration in supervised autonomous systems 基于机器能力的有监督自治系统信任标定分析模型

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2017-02-01 DOI: 10.1109/ICACI.2017.7974516

Kamran Shafi

{"title":"A machine competence based analytical model to study trust calibration in supervised autonomous systems","authors":"Kamran Shafi","doi":"10.1109/ICACI.2017.7974516","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974516","url":null,"abstract":"Modern day autonomous systems are moving away form mere automation of manual tasks to true autonomy that require them to apply human-like judgment when dealing with uncertain situations in performing complex tasks. Trust in these systems is a key enabler to fully realize this dream. A lack of trust leads to inefficient use of these systems and increases the supervision workload for humans. Conversely, an over trust in these systems leads to increased risks and exposure to catastrophic events. This paper presents a high-level analytical model to study trust dynamics in supervised autonomous system environments. Trust, in this context, is defined as a function of machine competence and the level of human control required to achieve this competence. A parametric model of machine competence is presented that allows generating different machine competence behaviors based on the task difficulty, level of supervision and machine's learning ability. The notions of perceived and desired or optimal trust, computed based on perceived and observed machine competence respectively, are introduced. This allows treating trust calibration as an optimization or control problem. The presented models provide a formal framework for developing higher-fidelity simulation models to study trust dynamics in supervised autonomous systems and develop appropriate controllers for optimizing the trust between humans and machines in these systems.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117350821","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

Toward understanding how the human vigilance contains the prevalence of computer viruses 向着了解人类如何警惕计算机病毒的流行而迈进

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2017-02-01 DOI: 10.1109/ICACI.2017.7974478

Xianxiu Zhang, Chuandong Li, Wangshu Peng, Tingwen Huang

引用次数: 0

Robust controller design for an autonomous underwater vehicle 自主水下航行器鲁棒控制器设计

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2017-02-01 DOI: 10.1109/ICACI.2017.7974515

A. Wadood, S. Anavatti, O. Hassanein

引用次数: 4

The P-th moment asymptotic stability of stochastic system with variable-time impulses 变时脉冲随机系统的p阶渐近稳定性

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2017-02-01 DOI: 10.1109/ICACI.2017.7974506

J. Tan, Chuandong Li

引用次数: 0

Navigation of mobile robot with cooperation of quadcopter 四轴联动的移动机器人导航

2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2017-02-01 DOI: 10.1109/ICACI.2017.7974481

M. Kurdi, Alex K. Dadykin, I. Elzein

引用次数: 4