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2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)最新文献

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Intelligent fault diagnosis of induction motors based on multi-objective feature selection using NSGA-II 基于NSGA-II的异步电动机多目标特征选择智能故障诊断
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802137
Amir-Hossein Arjmand-M, N. Sargolzaei
{"title":"Intelligent fault diagnosis of induction motors based on multi-objective feature selection using NSGA-II","authors":"Amir-Hossein Arjmand-M, N. Sargolzaei","doi":"10.1109/ICCKE.2016.7802137","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802137","url":null,"abstract":"The aim of this paper is to present an intelligent method for fault diagnosis of induction motors which doesn't need any expert to analyze the signals. A new intelligent fault diagnosis scheme based on multi-objective feature selection using non-dominated sorting genetic algorithm II (NSGA-II) is proposed. Firstly, to improve the signal-to-noise ratio, wavelet packet decomposition is performed. Multiple statistical features are then extracted from the decomposed signals. Some of these features contain unhelpful information, so the most superior features are selected using NSGA-II. Finally, the classification of type and severity of faults is performed using a multilayer perceptron (MLP) neural network. The proposed scheme is tested on a bearing fault dataset, and the results show that it, unlike signal processing techniques, is able to detect the faults of induction motor without any expert. It also achieves a better classification rate comparing with the methods based on conventional feature selection algorithms.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125777711","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
Multi-objective Estimation of Distribution Algorithm based on Voronoi and local search 基于Voronoi和局部搜索的多目标分布估计算法
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802115
Elham Mohagheghi, M. Akbarzadeh-T.
{"title":"Multi-objective Estimation of Distribution Algorithm based on Voronoi and local search","authors":"Elham Mohagheghi, M. Akbarzadeh-T.","doi":"10.1109/ICCKE.2016.7802115","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802115","url":null,"abstract":"In this paper we propose an Estimation of Distribution Algorithm (EDA) equipped with Voronoi and local search based on leader for multi-objective optimization. We introduce an algorithm that can keep the balance between the exploration and exploitation using the local information in the searched areas through the global estimation of distribution algorithm. Moreover, the probability model in EDA, receives special statistical information about the amount of the variables and their important dependency. The proposed algorithm uses the Voronoi diagram in order to produce the probability model. By using this model, there will be a selection based on the area instead of selection based on the individual, and all individual information could use to produce new solution. In the proposed algorithm, considering the simultaneous use of global information about search area, local information of the solutions and the Voronoi based probability model lead to produce more diverse solutions and prevent sticking in local optima. Also, in order to reduce the data dimension, the principle component analysis is proposed. Several benchmarks functions with different complexity like linear and non-linear relationship between the variables, the continues-discontinues and convexnon-convex optima fronts use to show the algorithm performance.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125897143","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
A Fuzzy Decision tree approach for imbalanced data classification 不平衡数据分类的模糊决策树方法
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802155
Sahar Sardari, M. Eftekhari
{"title":"A Fuzzy Decision tree approach for imbalanced data classification","authors":"Sahar Sardari, M. Eftekhari","doi":"10.1109/ICCKE.2016.7802155","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802155","url":null,"abstract":"Recently, new Fuzzy Decision Tree (FDT) approaches have been developed for doing classification tasks. In this paper, one of these FDTs is adapted for performing the imbalanced classification tasks. First, our proposed method utilizes k-means algorithm to cluster the majority class samples into some clusters. Then, each cluster is labeled as a new class and thereby the binary imbalanced classification problem is converted to the multi-class classification problem. Eventually, FDT algorithm is employed for classifying the new data set. The obtained results show that our proposed method outperforms almost all the other fuzzy rule based approaches over highly imbalanced data sets.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129512913","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
Improving person recognition by weight adaptation of soft biometrics 利用软生物特征的权重适应提高人的识别能力
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802112
Shaghayegh Gharghabi, Bita Azari, Faraz Shamshirdar, R. Safabakhsh
{"title":"Improving person recognition by weight adaptation of soft biometrics","authors":"Shaghayegh Gharghabi, Bita Azari, Faraz Shamshirdar, R. Safabakhsh","doi":"10.1109/ICCKE.2016.7802112","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802112","url":null,"abstract":"One of the main challenges of current person recognition techniques lies on difficulties of recognition in various poses. Recently, attention has been focused on using soft biometric information extracted from the human body to overcome the biometric recognition system's limitation in unconstrained environments. In this paper, we integrate the face and body information in a linear combination. We propose a novel approach in which the weights of features in the recognition system are adapted based on the reliability of the detected joints extracted from the body and the correlation between features. We evaluate the proposed approach in recognizing a five person group in various poses such as sitting and circular walking. The method was applied to a service robot equipped with the Kinect sensor. The results show a mean improvement of 4.39% after weight adaptation based on the correlation between features and 6.88% after consideration of the reliability of the features.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116152122","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
Feature weighting for cancer tumor detection in mammography images using gravitational search algorithm 基于引力搜索算法的乳房x线影像肿瘤特征加权检测
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802158
Fatemeh Shirazi, E. Rashedi
{"title":"Feature weighting for cancer tumor detection in mammography images using gravitational search algorithm","authors":"Fatemeh Shirazi, E. Rashedi","doi":"10.1109/ICCKE.2016.7802158","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802158","url":null,"abstract":"Optimization methods have been widely used in image processing and computer vision. In this paper, k-nearest neighbor (KNN) and real-valued gravitational search algorithm (RGSA) are used to detect the breast cancer tumors in mammography images. GSA is used as a tool for optimization of the features weighting (FW) and tuning the classifier. FW-KNN based on GSA is employed to enhance the K-NN classification accuracy. The weighted features and the tuned K-NN classifier are utilized for detecting tumors. The obtained results show good efficiency of GSA-based FW-KNN classification for breast cancer tumor detection.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123169564","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
Antenna selection: A novel approach to improve energy efficiency in massive MIMO systems 天线选择:一种提高大规模MIMO系统能量效率的新方法
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802124
Masoud Arash, Ehsan Yazdian, M. Fazel
{"title":"Antenna selection: A novel approach to improve energy efficiency in massive MIMO systems","authors":"Masoud Arash, Ehsan Yazdian, M. Fazel","doi":"10.1109/ICCKE.2016.7802124","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802124","url":null,"abstract":"Due to take advantage of large number of antennas, massive MIMO systems are able to serve high data rates. On the other hand, using more antennas will rise the hardware power consumption and therefore total power consumption. So, a trade-off between data rate and power consumption is made which can be reflected well in Energy-Efficiency parameter. Considering hardware power consumption results to more realistic model. In this paper, a model for Energy-Efficiency by taking hardware power consumption into account is introduced and then, an antenna selection algorithm is presented to improve Energy-Efficiency. As dimensions of system is high, this algorithm is designed simple. Next, different selection scenario will be investigated and at the end an optimization for a special case will be represented. Our simulations results show significant improvement in Energy-Efficiency for antenna selection case using proposed method.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134342878","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: 3
Design a new algorithm to count white blood cells for classification leukemic blood image using machine vision system 设计了一种新的白细胞计数算法，利用机器视觉系统对白血病血液图像进行分类
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802148
Zahra Khandan Khadem Alreza, A. Karimian
{"title":"Design a new algorithm to count white blood cells for classification leukemic blood image using machine vision system","authors":"Zahra Khandan Khadem Alreza, A. Karimian","doi":"10.1109/ICCKE.2016.7802148","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802148","url":null,"abstract":"White blood cells protect the immune system against viruses and bacteria. Data extraction from white blood cells may cause problems such as loosing form, dimensions and edges. In this study, a complete and automatic method to identify and classify white blood cells using microscopic images has been presented. In the proposed method, in the first step, white blood cells are identified using color space conversion models. Then leukocytes group are separated using division of watershed conversion. In the next step image cleanup is done and all leukocytes available on the edge of images and abnormal components are removed. This is accomplished by cutting the image with the smallest rectangle that has connected components. The second level of division relates to the detection of the nucleus and cytoplasm. In the last step feature extraction is performed which causes the pathologists can have the best interpretation of them. All the above steps have been performed in MATLAB software. At the end, the proposed method was examined by a database belonging to Imam Reza (AS) hospital in Mashhad, consisting of 29 images of blood cells, and showed the accuracy of 93% in the detection of white blood cells.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127768917","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: 22
Research on a location-based task assignment for mobile sensing 基于位置的移动传感任务分配研究
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802114
Xiulan Wang, Shiyan Wang
{"title":"Research on a location-based task assignment for mobile sensing","authors":"Xiulan Wang, Shiyan Wang","doi":"10.1109/ICCKE.2016.7802114","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802114","url":null,"abstract":"in recent years, mobile phone sensing applications has been regarded as new paradigm to obtain ubiquitous environment data. Research on motivating smart phone users to participate in mobile sensing contributing their resources and maximizing the profit of platform is in full swing. Unfortunately, the location-based optimal task assignment problem in mobile sensing is an NP-hard problem. We transform the problem to the maximum weight independent set problem (MWIS) and proposed a polynomial-time approximation scheme (PTAS) to approximate this problem in the intersection graph models. We conducted the simulation experiment and it turns out that the practical performance of the proposed near optimal task assignment algorithm corroborates the theoretical analysis.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122743367","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
A new approach in single Image Super Resolution 单幅图像超分辨率的一种新方法
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802119
S. Sarmadi, Zari Shamsa
{"title":"A new approach in single Image Super Resolution","authors":"S. Sarmadi, Zari Shamsa","doi":"10.1109/ICCKE.2016.7802119","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802119","url":null,"abstract":"Super Resolution image reconstruction tries to obtain a high resolution image from one or more observed low resolution images of the same scene, using signal processing techniques. Variety of super resolution methods have been proposed in last decades. In this paper, we propose a new super resolution algorithm based on single low resolution image. As the super resolution reconstruction is an inverse problem, our method consists of three phases up-sampling, deblurring and denoising. Experimental results show the effectiveness of the proposed method.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123958376","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: 4
Robust crowdsourcing-based linear regression 稳健的基于众包的线性回归
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2016-10-01 DOI: 10.1109/ICCKE.2016.7802130
Saeid Abbaasi, M. Mohammadi, Ehsan Shams Davodly
{"title":"Robust crowdsourcing-based linear regression","authors":"Saeid Abbaasi, M. Mohammadi, Ehsan Shams Davodly","doi":"10.1109/ICCKE.2016.7802130","DOIUrl":"https://doi.org/10.1109/ICCKE.2016.7802130","url":null,"abstract":"In most machine learning problems, the labeling of the training data is an expensive or even impossible task. Crowdsourcing-based learning uses uncertain labels from many non-expert annotators instead of one reference label. Crowdsourcing based linear regression is an efficient way for function estimation when many labels are available for each instance. However, methods in literature have a poor performance against large noise and outliers in labels. To tackle this problem, we proposed a novel robust crowdsourcing-based linear regression derived from information theoretic learning. The proposed problem is not convex, but it can be efficiently solved by half quadratic programming. The proposed model has a close relation with weighted crowdsourcing-based linear regression, in which labels of each annotator weight adaptively and iteratively. The Performance of the proposed method evaluated on several artificial data sets in different circumstances. Experimental Results demonstrate the efficacy and robustness of the proposed method.","PeriodicalId":205768,"journal":{"name":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117148920","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
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