2016 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第4页

Solving Assembly Line Balancing Problems with Fish School Search algorithm 用鱼群搜索算法求解装配线平衡问题

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849991

I. M. C. Albuquerque, J. M. Filho, Fernando Buarque de Lima-Neto, A. Silva

{"title":"Solving Assembly Line Balancing Problems with Fish School Search algorithm","authors":"I. M. C. Albuquerque, J. M. Filho, Fernando Buarque de Lima-Neto, A. Silva","doi":"10.1109/SSCI.2016.7849991","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849991","url":null,"abstract":"Assembly lines constitute the main production paradigm of the contemporary manufacturing industry. Thus, many optimization problems have been studied aiming to improve the efficacy of its use. In this context, the problem of balancing an assembly line plays a key role. This problem is of combinatorial nature and also NP-Hard. For this reason, many researchers on computational intelligence and industrial engineering have been conceiving algorithms for tackling many versions of assembly line balancing problems using different procedures. In this paper, the Fish School Search algorithm and a variation of it that incorporates a routine to avoid stagnation of the search process were applied in order to solve the Simple Assembly Line Balancing Problem-type 1. The results were compared with an exact solution procedure named SALOME and also with the Particle Swarm Optimization algorithm. Both proposed procedures were able to achieve good results and the stagnation avoidance routine incorporated to FSS allowed more uniform distributions of tasks among workstations in the assembly line and converged faster to optimal solutions.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129943294","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: 12

Feature extraction and target classification of side-scan sonar images 侧扫声纳图像特征提取与目标分类

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850074

J. Rhinelander

引用次数: 14

Connections of reference vectors and different types of preference information in interactive multiobjective evolutionary algorithms 交互多目标进化算法中参考向量与不同类型偏好信息的连接

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850220

Jussi Hakanen, Tinkle Chugh, Karthik Sindhya, Yaochu Jin, K. Miettinen

引用次数: 25

Influence of dynamic environments on agent strategies 动态环境对agent策略的影响

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850159

Franz Pieper, Sanaz Mostaghim

引用次数: 1

Pedestrian detection aided by scale-discriminative network 基于尺度判别网络的行人检测

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850112

Zongqing Lu, Wenjian Zhang, Q. Liao

引用次数: 2

User-aided footprint extraction for appliance modelling in Non-Intrusive Load Monitoring 非侵入式负荷监测中设备建模的用户辅助足迹提取

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849843

Roberto Bonfigli, E. Principi, S. Squartini, Marco Fagiani, M. Severini, F. Piazza

{"title":"User-aided footprint extraction for appliance modelling in Non-Intrusive Load Monitoring","authors":"Roberto Bonfigli, E. Principi, S. Squartini, Marco Fagiani, M. Severini, F. Piazza","doi":"10.1109/SSCI.2016.7849843","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849843","url":null,"abstract":"In the area of Non-Intrusive Load Monitoring (NILM), many approaches need a supervised procedure of appliance modelling, in order to provide the informations about the appliances to the disaggregation algorithm and to obtain the disaggregated consumptions related to each one of them. In many approaches, the appliance modelling relies on the consumption footprint, which is a typical working cycle of the appliance. Since the NILM system has only the aggregated power consumption available, the recorded footprint might be corrupted by other appliances, which can not be turned off during this period, i.e., the fridge and freezer in the household. Furthermore, the user needs a facilitated procedure, in order to obtain a clean footprint from the aggregated power signal in real scenario. Therefore, a user-aided footprint extraction procedure is needed. In this work, this procedure is defined as a NILM problem with two sources, i.e., the desired appliance and the fridge-freezer combination. One of the resulting disaggregated profiles of the algorithm corresponds to the extracted footprint. Then, this is used for the appliance modelling stage to create te corresponding Hidden Markov Model (HMM), suitable for the Additive Factorial Approximate Maximum a Posteriori (AFAMAP) algorithm. The effectiveness of the footprint extraction procedure is evaluated through the confidence of the disaggregation output of a real problem, using a span of 30 days data taken from two different datasets (AMPds, ECO). The experiments are conducted using the HMM from the extracted footprint, compared to the confidence of the same problem using the HMM from the true footprint, as appliance level consumption. The results show that the performance are comparable, with the worst relative F1 loss of 3.83%, demonstrating the effectiveness of the footprint extraction procedure.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123678790","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: 6

Entropy rates of physiological aging on microscopy 显微镜下生理老化的熵率

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849867

T. Pham

引用次数: 0

A cellular automata model for forest fire spreading simulation 森林火灾蔓延模拟的元胞自动机模型

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849971

Xuehua Wang, Chang Liu, Jiaqi Liu, Xuezhi Qin, Ning Wang, Wenjun Zhou

引用次数: 7

Computational intelligence based machine learning methods for rule-based reasoning in computer vision applications 计算机视觉应用中基于规则推理的基于计算智能的机器学习方法

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850050

T. Dhivyaprabha, P. Subashini, M. Krishnaveni

{"title":"Computational intelligence based machine learning methods for rule-based reasoning in computer vision applications","authors":"T. Dhivyaprabha, P. Subashini, M. Krishnaveni","doi":"10.1109/SSCI.2016.7850050","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850050","url":null,"abstract":"In robot control, rule discovery for understanding of data is of critical importance. Basically, understanding of data depends upon logical rules, similarity evaluation and graphical methods. The expert system collects training examples separately by exploring an anonymous environment by using machine learning techniques. In dynamic environments, future actions are determined by sequences of perceptions thus encoded as rule base. This paper is focused on demonstrating the extraction and application of logical rules for image understanding, using newly developed Synergistic Fibroblast Optimization (SFO) algorithm with well-known existing artificial learning methods. The SFO algorithm is tested in two modes: Michigan and Pittsburgh approach. Optimal rule discovery is evaluated by describing continuous data and verifying accuracy and error level at optimization phase. In this work, Monk's problem is solved by discovering optimal rules that enhance the generalization and comprehensibility of a robot classification system in classifying the objects from extracted attributes to effectively categorize its domain.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116297737","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

Very short-term solar forecasting using multi-agent system based on Extreme Learning Machines and data clustering 基于极限学习机和数据聚类的多智能体极短期太阳预报

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850162

C. A. Severiano, F. Guimarães, Miri Weiss-Cohen

引用次数: 4