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2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)最新文献

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A domain specific language oriented to fault detection, isolation and recovery 面向故障检测、隔离和恢复的领域特定语言
2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054156
M. Vitelli, M. Tipaldi, L. Troiano
{"title":"A domain specific language oriented to fault detection, isolation and recovery","authors":"M. Vitelli, M. Tipaldi, L. Troiano","doi":"10.1109/SOCPAR.2013.7054156","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054156","url":null,"abstract":"Reliability of complex systems requires to take into account possible failures and strategies to detect and recover from system faults. This leads designers to consider models and algorithms capable of simulating and verifying fault detection, isolation and recovery (FDIR) strategies in different scenarios, characterized by uncertainty and partial information. Different solutions have been proposed. In this paper we present Trouble, a domain specific language aimed at describing and simulating troubleshooting algorithm. Different examples highlight advantages of such an approach.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130924912","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 visualization method of Kansei texture 感性纹理的一种可视化方法
2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054122
Hidenori Sakaniwa, Fanqyan Dong, K. Hirota
{"title":"A visualization method of Kansei texture","authors":"Hidenori Sakaniwa, Fanqyan Dong, K. Hirota","doi":"10.1109/SOCPAR.2013.7054122","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054122","url":null,"abstract":"A visualization method of Kansei Texture is proposed, where the Kansei Texture (\"Shokushitsu-kan\" in Japanese) has been defined as a quantitative sensation index to represent visual and/or texture information of an object photo/movie. It aims to compensate the information gap between the real object and its photo/movie image for the applications such as net shopping, robot vision, telemedicine, and e-learning, where the real objects are not available but only their still/dynamic images with brief text explanation are obtainable. A questionnaire experiment is done for 10 subjects by showing the conventional net shopping web site and the proposal site using Kansei Texture illustration, and the applicability of the proposal in net shopping is confirmed. Further precise Kansei Texture representation method is also investigated with a higher order visualization method.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126461904","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
Identifying power profiles in the photovoltaic power station data by self-organizing maps and dimension reduction by Sammon's projection 利用自组织图和Sammon投影降维方法识别光伏电站数据中的功率剖面
2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054150
M. Radvanský, M. Kudelka, V. Snás̃el
{"title":"Identifying power profiles in the photovoltaic power station data by self-organizing maps and dimension reduction by Sammon's projection","authors":"M. Radvanský, M. Kudelka, V. Snás̃el","doi":"10.1109/SOCPAR.2013.7054150","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054150","url":null,"abstract":"This paper presents results of the identification of clusters in the hourly recorded data of power from a small photovoltaic power station. Our main aim was to find a method of how to identify typical patterns of generated power. Although one can think that sunny days are the same, the power of the sun light is very volatile during a day. We were not interested in finding the absolute values of this power but just its patterns according to the day's maximal power. Our proposed method is based on several techniques. We used network algorithm as a method for removing noise from the data, Sammon's projection for visualization and dimensionality reduction and final clustering by the self-organizing maps.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133674510","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
An efficient method for vision-based fire detection using SVM classification 基于SVM分类的视觉火灾检测方法
2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054125
Ha Dai Duong, Dao Thanh Tinh
{"title":"An efficient method for vision-based fire detection using SVM classification","authors":"Ha Dai Duong, Dao Thanh Tinh","doi":"10.1109/SOCPAR.2013.7054125","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054125","url":null,"abstract":"In this paper, we present a new vision-based algorithm for fire detection problem. The algorithm consists of three main tasks: pixel-based processing to identify potential fire blobs, blob-based statistical feature extraction, and a support vector machine classifier. In pixel-based processing phase, five feature vectors based on RGB color space are used to classify a pixel by using a Bayes classifier to build a potential fire mask (PFM) of image. Next step, a potential fire blob mask (PFBM) is computed by using the difference between two consecutive PFM and a recover technique. In blob-based phase, for each potential blob in a potential fire blobs image (PFBI) an 7-feature vector are evaluated; this vector includes three statistical features of colour, four texture parameters and one shape roundness parameter. Finally, a SVM classifier is designed and trained for distinguish a potential fire blob are fire or fire-like object. Experimental results demonstrate the effectiveness and robustness of the proposed method.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114657141","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: 13
An intelligent link adaptation scheme for OFDM based hyperlans 一种基于OFDM的超局域网智能链路自适应方案
2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054159
Atta-ur-Rahman, M. Salam, M. T. Naseem, M. Z. Muzaffar
{"title":"An intelligent link adaptation scheme for OFDM based hyperlans","authors":"Atta-ur-Rahman, M. Salam, M. T. Naseem, M. Z. Muzaffar","doi":"10.1109/SOCPAR.2013.7054159","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054159","url":null,"abstract":"Adaptive communication is becoming requirement of every communication system for effective utilization of available radio resources. In this approach the transmission parameters like code rate, modulation size and available power are dynamically chosen so that the overall system throughput is maximized while certain constraints like bit error rate are satisfied. In this paper a similar constrained optimization problem is solved by optimally choosing the said parameters with the help of Differential Evolution algorithm with a Fuzzy Rule Based System (DE-FRBS) in an OFDM environment, in this proposal the FRBS is used to adapt the code rate and the modulation scheme according to channel state information (CSI) and desired quality of service (QoS) while DE is employed to find the optimum power vector (OPV) to be transmitted over OFDM subcarriers. Product codes and Quadrature Amplitude Modulation (QAM) are used as coding and modulation schemes respectively. Product codes are considered much powerful in terms of error correction capability. Significance of the proposed scheme is shown by the simulations.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124843795","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
Massive training in artificial immune recognition algorithm for enhancement of lung CT scans 肺CT扫描增强人工免疫识别算法的大规模训练
2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054115
S. Hang, S. Shamsuddin, A. Ralescu
{"title":"Massive training in artificial immune recognition algorithm for enhancement of lung CT scans","authors":"S. Hang, S. Shamsuddin, A. Ralescu","doi":"10.1109/SOCPAR.2013.7054115","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054115","url":null,"abstract":"We proposed a pixel-based machine learning algorithm in the training of artificial immune recognition system (AIRS) to detect lung lesions in two-dimensional computed tomography (CT) scans. AIRS is an immune based algorithm which inspired by several biological mechanisms in mammalian immune system such as mutation, clonal expansion and immune memory generation. The proposed framework implements the concept of pixel machine learning (PML) where no segmentation and features calculation are required in the pre-processing of pixels. Hounsfield (HU) values in the selected region of interest (ROI) in CT scan are used directly to form a large number of learning sub-regions for massive training process. By using raw data in training, the loss of pixel information during detection of abnormality on medical images can be avoided. There are two versions of the AIRS (AIRS1 and AIRS2) algorithms are involved in the experiments of comparing their performance in the classification of medical images. The main advantage of these AIRS algorithms is to remove surplus training data while remain only relevant features in the processing of large amount of data training. The validation of results based on visualization validation and quantitative comparison using Kullback Leibler Divergence (KLD) are introduced. In this research, the massive training AIRS (MTAIRS) algorithms have generated promising results in visualization for lesions enhancement and detection in CT scans.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124929245","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
Multi-labeled document classification using semi-supervived mixture model of Watson distributions on document manifold 基于Watson分布的半监督混合模型的多标签文档分类
2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054113
N. K. Anh, Ngo Van Linh, Nguyen Khac Toi, Nguyen The Tarn
{"title":"Multi-labeled document classification using semi-supervived mixture model of Watson distributions on document manifold","authors":"N. K. Anh, Ngo Van Linh, Nguyen Khac Toi, Nguyen The Tarn","doi":"10.1109/SOCPAR.2013.7054113","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054113","url":null,"abstract":"Classification of multilabel documents is essential to information retrieval and text mining. Most of existing approaches to multilabel text classification do not pay attention to relationship between class labels and input documents and also rely on labeled data all the time for classification. In fact, unlabeled data is readily available whereas generation of labeled data is expensive and error prone as it needs human annotation. In this paper, we propose a novel multilabel document classification approach based on semi-supervised mixture model of Watson distributions on document manifold which explicitly considers the manifold structure of document space to exploit efficiently both labeled and unlabeled data for classification. Our proposed approach models all labels within a dataset simultaneously, which lends itself well to the task of considering the relationship between these labels. The experimental results show that proposed method outperforms the state-of-the-art methods applying to multilabeled text classification.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126185275","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
Geometrical feature based ranking using grey relational analysis (GRA) for writer identification 基于几何特征排序的灰色关联分析(GRA)作者识别
2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054118
I. E. A. Jalil, A. Muda, S. Shamsuddin, A. Ralescu
{"title":"Geometrical feature based ranking using grey relational analysis (GRA) for writer identification","authors":"I. E. A. Jalil, A. Muda, S. Shamsuddin, A. Ralescu","doi":"10.1109/SOCPAR.2013.7054118","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054118","url":null,"abstract":"The author's unique characteristic is determined by the variation of generated features from feature extraction process. Different sets of features produced are based on different feature extraction methods (local or global). Thus, the process has led to the production of high dimensional datasets that contributing to many irrelevant or redundant features. The main problem however is to find a way to identify the most significant features. The features ranking method using Grey Relational Analysis (GRA) is proposed to find the significance of each feature and give ranking to the features. This study presents the Higher-Order United Moment Invariant (HUMI) as the global feature extraction methods. The combinations of features with the higher ranking are discretized and used as the subsets of features to identify the writer. The result demonstrates that the average classification accuracy of five classifiers by using just the combination of two most significant features have yielded a better performance than using all features.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129889594","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
Fault detection and resolution based on extended time failure propagation graphs
2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054155
L. Troiano, A. Cerbo, M. Tipaldi, M. Vitelli
{"title":"Fault detection and resolution based on extended time failure propagation graphs","authors":"L. Troiano, A. Cerbo, M. Tipaldi, M. Vitelli","doi":"10.1109/SOCPAR.2013.7054155","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054155","url":null,"abstract":"FDIR functionalities are investigated since the very beginning of a space mission and play a relevant role in the definition of its autonomy, reliability and availability objectives. In this paper, an analytical methodology derived from the Timed Failure Propagation Graph (ETFPG) is proposed. TPFG is a causal model that captures the temporal aspects of failure propagation in a wide variety of engineering systems. It has been extended in order to incorporate the recovery actions as well as to accommodate the dependencies on the mission phases and spacecraft operational modes in the related graphs. The proposed methodology has proved to be very useful in the context of the trouble identification and shooting of complex system, such a satellite.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122324830","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
Hiding data in audio using modified CPT scheme 使用改进的CPT方案隐藏音频中的数据
2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054098
Huynh Ba Dieu, N. X. Huy
{"title":"Hiding data in audio using modified CPT scheme","authors":"Huynh Ba Dieu, N. X. Huy","doi":"10.1109/SOCPAR.2013.7054098","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054098","url":null,"abstract":"This paper presents a method to hide information in audio based on CPT scheme. The proposed method is done by modifying CPT scheme used for hiding data in binary image. We do not use two matrices as key for embedding and extracting process to reduce the processing time. To ensure the security, we use Arnold transform to scramble the secret message. Experimental results show that the proposed method is inaudible and suitable for hiding data in audio.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116385040","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
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