2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)最新文献_第8页

On sequential selection of attributes to be discretized for authorship attribution 作者归属的离散化属性的顺序选择

2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2017-07-01 DOI: 10.1109/INISTA.2017.8001162

Grzegorz Baron

引用次数: 3

Reasoning and communicative strategies in a model of argument-based negotiation 基于论证的谈判模式中的推理和交际策略

2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2017-07-01 DOI: 10.1080/24751839.2018.1448504

M. Koit

引用次数: 6

Analysis of dynamic service oriented systems for security related problems detection 动态分析面向服务系统的安全相关问题检测

2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2017-07-01 DOI: 10.1109/INISTA.2017.8001206

G. Kolaczek, Jolanta Mizera-Pietraszko

引用次数: 1

The analysis of the low-cost flexibility corridors 低成本灵活走廊的分析

2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2017-07-01 DOI: 10.1109/INISTA.2017.8001207

M. Dudek

引用次数: 1

Opposition-based initialization and a modified pattern for Inertia Weight (IW) in PSO PSO中基于对立的初始化及惯性权值的改进模式

2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2017-07-01 DOI: 10.1109/INISTA.2017.8001139

Mehr Umer Farooq, Akhlaque Ahmad, Abdul Hameed

{"title":"Opposition-based initialization and a modified pattern for Inertia Weight (IW) in PSO","authors":"Mehr Umer Farooq, Akhlaque Ahmad, Abdul Hameed","doi":"10.1109/INISTA.2017.8001139","DOIUrl":"https://doi.org/10.1109/INISTA.2017.8001139","url":null,"abstract":"Particle Swarm Optimization (PSO) is an evolutionary computing algorithm and is successfully used to solve complex real world optimization problems. Due to the complex nature of optimization problems, PSO endures the problems like premature convergence or being trapped in local minima, to avoid such situation the role of swarm initialization is very important. In this research we propose a new method to initialize the swarm particles on the basis of Generalized Opposition-based Learning (GOBL). The aim for GOBL strategy is to have an initial swarm with already fittest particles to set a solid ground for the rest of PSO algorithm to execute. Moreover, a strategy for linearly decreasing Inertia Weight has been proposed to equalize the proportions of exploration as well as exploitation capabilities of particles during the search process. The motivation behind incorporating the changes in standard PSO is to evade the earlier convergence and to help the algorithm in escaping from being trapped in local minimum. To assess the performance of proposed PSO variant, we practiced this algorithm on 8 different benchmark functions and results were compared with 4 other PSO versions found in literature. From the results analysis it is apparent that projected changes in the PSO increases its overall performance and efficiency especially when dealing with the noisy optimization problems. Also the proposed algorithm performs better and is more robust as compared to other algorithms for achieving desired results.","PeriodicalId":314687,"journal":{"name":"2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125798769","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: 9

The impact of diversity on the quality of collective prediction 多样性对集体预测质量的影响

2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2017-07-01 DOI: 10.1109/INISTA.2017.8001148

Van Du Nguyen, N. Nguyen

引用次数: 2

A multi-biometric recognition system based on deep features of face and gesture energy image 基于人脸深层特征和手势能量图像的多生物特征识别系统

2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 1900-01-01 DOI: 10.1109/INISTA.2017.8001186

Onur Can Kurban, T. Yıldırım, Ahmet Bilgic

{"title":"A multi-biometric recognition system based on deep features of face and gesture energy image","authors":"Onur Can Kurban, T. Yıldırım, Ahmet Bilgic","doi":"10.1109/INISTA.2017.8001186","DOIUrl":"https://doi.org/10.1109/INISTA.2017.8001186","url":null,"abstract":"Nowadays, with the increasing use of biometric data, it is expected that systems work robustly and they can give successful results against difficult situations and forgery. In face recognition systems, variables such as direction of light, facial expression and reflection makes identification difficult. With biometric fusion, both safe and high performance results can be achieved. In this work, Eurocom Kinect Face dataset and BodyLogin Gesture Silhouettes dataset are used to create a virtual dataset and they were fused with score level. For face database, VGG Face deep learning model was used as feature extractor and energy imaging method was used for extracting gesture features. Afterwards the features reduced by principal component analysis and similarity scores were produced with standard deviation Euclidean distance. The results show that face recognition achieved a high performance with deep learning features under different light and expression conditions, however, multi-biometric results have reached higher genuine match rate (GMR) performance and lower false acceptance rate (FAR). As a result of this process, gesture energy imaging can be used for person recognition and for multi biometric data.","PeriodicalId":314687,"journal":{"name":"2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122557787","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: 23

A framework for improving process robustness with quantification of uncertainties in Industry 4.0 工业4.0中通过量化不确定性提高过程鲁棒性的框架

2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 1900-01-01 DOI: 10.1109/INISTA.2017.8001155

C. Giannetti

{"title":"A framework for improving process robustness with quantification of uncertainties in Industry 4.0","authors":"C. Giannetti","doi":"10.1109/INISTA.2017.8001155","DOIUrl":"https://doi.org/10.1109/INISTA.2017.8001155","url":null,"abstract":"Digitalisation of industrial processes, also called the fourth industrial revolution, is leading to availability of large volume of data containing measurements of many process variables. This offers new opportunities to gain deeper insights on process variability and its effects on quality and performance. Manufacturing facilities already use data driven approaches to study process variability and find improvement opportunities through methodologies such as Design of Experiment (DOE) and Six Sigma. However, current approaches are not adequate to model the complexity of modern manufacturing systems, especially when these systems exhibit non-linear interactions between high numbers of variables. In this paper a methodology to improve process robustness is proposed. This methodology uses non-parametric estimation of quantiles of response to discover new tolerance limits of factors. This method does not make any stringent assumption of linearity and works well in finding the interactions effects of covariates on response quantiles. Process robustness, which is defined as the ability of a process to have acceptable quality whilst tolerating variability of the input, is measured through calculation of Likelihood Ratios (LR) associated to the new tolerance limits. Uncertainty of this estimation is quantified via simulations using the bootstrapping method. The novel contribution of this paper is the application of quantile regression and likelihood ratios to the tolerance synthesis problem applied to a low alloy foundry. It shows the validity of the methodology in modelling behaviours of complex manufacturing processes using data driven approaches to gain new insights on causes of process variabilities and discover new product specific process knowledge. This work contributes to bridging the gap between theory and application towards implementing Industry 4.0 predictive analytics.","PeriodicalId":314687,"journal":{"name":"2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124409930","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