2018 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第7页

Measurement-Type “Calibration” of Expert Estimates Improves Their Accuracy and Their Usability: Pavement Engineering Case Study 测量型“校准”专家评估提高其准确性和可用性:路面工程案例研究

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628665

Edgar Daniel Rodriguez Velasquez, C. C. Albitres, V. Kreinovich

引用次数: 2

Towards an agent-driven scenario awareness in remote sensing environments 面向遥感环境中agent驱动的场景感知

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628882

Danilo Cavaliere, S. Senatore

{"title":"Towards an agent-driven scenario awareness in remote sensing environments","authors":"Danilo Cavaliere, S. Senatore","doi":"10.1109/SSCI.2018.8628882","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628882","url":null,"abstract":"In dynamic environments, autonomous and unmanned vehicle systems (UVSs) represent a reliable solution, especially when the request of high performance is a stringent constraint for complex and risky tasks, such as searching survival points, multiple target monitoring, and tracking, etc. In these cases, cooperative activities among all the involved UVSs are strategic for the achievement of a collective goal. When UVS teams work collaboratively, they collect heterogeneous data from multiple sources and bring benefits through an enhanced situational awareness (SA). Multi-UVS scenarios are, by their nature, easy to be modeled as multi-agent systems. This paper presents an agent-based modeling, governing different types of unmanned vehicles that are sent ahead in an area of interest to gather environmental, sensing, image data in order to provide a complete multi-view scenario understanding. The agent model is instantiated in each vehicle, and depending on the vehicle features, encapsulates a semantic mental modeler, customized for the specific vehicle features. The agents collect raw data from the environment and translate them into high-level knowledge, i.e., a conceptualization of the data semantics (i.e., a set of pixels assumes the meaning of a car). The proposed agent-based modeling lays on a synergy between Semantic Web technologies and Fuzzy Cognitive Map (FCM) models, producing a high-level description of the evolving scenes, and then a comprehensive scenario situational awareness.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114409418","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: 7

Reconstructing positive surveys from negative surveys by improved artificial immune network 利用改进的人工免疫网络从阴性调查中重建阳性调查

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628929

Ran Liu, Mengxi Xie, S. Sun

引用次数: 1

Intelligent Facial Expression Recognition Using Particle Swarm Optimization Based Feature Selection 基于粒子群优化特征选择的智能面部表情识别

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628747

Adam Robson, Li Zhang

{"title":"Intelligent Facial Expression Recognition Using Particle Swarm Optimization Based Feature Selection","authors":"Adam Robson, Li Zhang","doi":"10.1109/SSCI.2018.8628747","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628747","url":null,"abstract":"Particle Swarm Optimization (PSO) has become a popular method of feature selection in classification problems, due to its powerful search capability and computational simplicity. Classification problems, such as facial emotion recognition, often involve data sets containing high volumes of features, not all of which are useful for classification. Redundant and irrelevant features have the potential to negatively impact the performance and accuracy of facial emotion recognition systems. The feature selection process identifies the most relevant features to achieve improved classification performance. While the use of PSO as a feature selection method in facial emotion recognition systems has seen some successes, it is still susceptible to the issue of premature convergence. This work presents seven PSO variants which mitigate against the premature convergence problem through the incorporation of three random probability distributions (Cauchy, Gaussian and Lévy). At each iteration of the proposed PSO models, probability distributions are used to increase search diversity and reduce the number of redundant features used for classification. The seven PSO variants presented in this study have demonstrated positive results when tested on real world data sets, outperforming the standard PSO model and other related work within the field.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123114610","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

A Parametric Study of Crossover Operators in Multi-objective Evolutionary Algorithm 多目标进化算法中交叉算子的参数化研究

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628707

Katsuhiro Sekine, T. Tatsukawa

{"title":"A Parametric Study of Crossover Operators in Multi-objective Evolutionary Algorithm","authors":"Katsuhiro Sekine, T. Tatsukawa","doi":"10.1109/SSCI.2018.8628707","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628707","url":null,"abstract":"The performance of Multi-Objective Evolutionary Algorithms (MOEAs) depends on the various parameter settings such as population size, generation size, crossover, mutation and so on. It is often difficult to know the appropriate parameter setting for a real-world optimization problem in advance. Besides, the optimal parameter values might depend on each optimization problem and MOEA itself. However, there are few studies for investigating the effect of parameters even in benchmark problems. Therefore, in this study, the effects on performance due to the crossover operators and MOEAs are widely investigated by using eight benchmark problems, including DTLZ and WFG benchmark problems. The number of objectives is set to three and six. We consider five major crossover operators: Simulated Binary crossover (SBX), Simplex crossover (SPX), Differential Evolution operator (DE), Parent Centric crossover (PCX), and Unimodal Normal Distribution crossover (UNDX). As MOEAs, we adopt Non-dominated sorting genetic algorithm-II (NSGAII), Non-dominated sorting genetic algorithm-III (NSGA-III),-Dominance-based Evolutionary Algorithm (-MOEA), Indicator-Based Evolutionary Algorithm (IBEA) and Multi-Objective Evolutionary Algorithm with decomposition (MOEA/D) in this study. The experimental results on benchmark problems show that the effect of the crossover operator on each MOEA is almost the same in both three and six objectives. This indicates that the knowledge has been obtained so far could adapt to the other MOEAs and more than three objectives. In addition, parameters of some crossover operators such as SBX have little impact on the performance. This indicates that these crossover operators can be set to a value used so far without the need of tuning.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124785104","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

Genetic Algorithms for Portfolio Optimization with Weighted Sum Approach 加权和法组合优化的遗传算法

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628773

Ricardo Faia, T. Pinto, Z. Vale, J. Corchado, J. Soares, F. Lezama

引用次数: 5

Multi-Population Ant Colony System for Multiple Path Planning of Food Delivery Applications 多种群蚁群系统在外卖多路径规划中的应用

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628684

Yi-Bin Cheng, Ting Huang, Huntley Ting Huang, Yue-jiao Gong, Jun Zhang

引用次数: 2

Influence of Pointing on Learning to Count: A Neuro-Robotics Model 指向对学习计数的影响:一个神经机器人模型

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628811

Leszek Pecyna, A. Cangelosi

引用次数: 3

Hindi Sentence Classification for Expressive Storytelling Systems 表达性故事系统的印地语句子分类

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628858

Shivli Agrawal, Yukti Kirtani, Y. Girdhar, Swati Aggarwal

引用次数: 0

Classification of Physiological Data in Affective Exergames 情感游戏中生理数据的分类

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628695

A. Kamenz, V. Bibaeva, Arne Bernin, Sobin Ghose, K. Luck, Florian Vogt, Larissa Müller

引用次数: 0