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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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International Journal of Fuzzy Logic and Intelligent Systems
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Korean Institute of Intelligent Systems
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Volume 16, Issue 2 - Jun 2016
Volume 16, Issue 1 - Mar 2016
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Robust Algorithms for Combining Multiple Term Weighting Vectors for Document Classification
Kim, Minyoung ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 16, issue 2, 2016, Pages 81~86
DOI : 10.5391/IJFIS.2016.16.2.81
Term weighting is a popular technique that effectively weighs the term features to improve accuracy in document classification. While several successful term weighting algorithms have been suggested, none of them appears to perform well consistently across different data domains. In this paper we propose several reasonable methods to combine different term weight vectors to yield a robust document classifier that performs consistently well on diverse datasets. Specifically we suggest two approaches: i) learning a single weight vector that lies in a convex hull of the base vectors while minimizing the class prediction loss, and ii) a mini-max classifier that aims for robustness of the individual weight vectors by minimizing the loss of the worst-performing strategy among the base vectors. We provide efficient solution methods for these optimization problems. The effectiveness and robustness of the proposed approaches are demonstrated on several benchmark document datasets, significantly outperforming the existing term weighting methods.
Sensitivity Analysis of Width Representation for Gait Recognition
Hong, Sungjun ; Kim, Euntai ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 16, issue 2, 2016, Pages 87~94
DOI : 10.5391/IJFIS.2016.16.2.87
In this paper, we discuss a gait representation based on the width of silhouette in terms of discriminative power and robustness against the noise in silhouette image for gait recognition. Its sensitivity to the noise in silhouette image are rigorously analyzed using probabilistic noisy silhouette model. In addition, we develop a gait recognition system using width representation and identify subjects using the decision level fusion based on majority voting. Experiments on CASIA gait dataset A and the SOTON gait database demonstrate the recognition performance with respect to the noise level added to the silhouette image.
An Adaptive Goal-Based Model for Autonomous Multi-Robot Using HARMS and NuSMV
Kim, Yongho ; Jung, Jin-Woo ; Gallagher, John C. ; Matson, Eric T. ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 16, issue 2, 2016, Pages 95~103
DOI : 10.5391/IJFIS.2016.16.2.95
In a dynamic environment autonomous robots often encounter unexpected situations that the robots have to deal with in order to continue proceeding their mission. We propose an adaptive goal-based model that allows cyber-physical systems (CPS) to update their environmental model and helps them analyze for attainment of their goals from current state using the updated environmental model and its capabilities. Information exchange approach utilizes Human-Agent-Robot-Machine-Sensor (HARMS) model to exchange messages between CPS. Model validation method uses NuSMV, which is one of Model Checking tools, to check whether the system can continue its mission toward the goal in the given environment. We explain a practical set up of the model in a situation in which homogeneous robots that has the same capability work in the same environment.
Adaptive Bayesian Object Tracking with Histograms of Dense Local Image Descriptors
Kim, Minyoung ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 16, issue 2, 2016, Pages 104~110
DOI : 10.5391/IJFIS.2016.16.2.104
Dense local image descriptors like SIFT are fruitful for capturing salient information about image, shown to be successful in various image-related tasks when formed in bag-of-words representation (i.e., histograms). In this paper we consider to utilize these dense local descriptors in the object tracking problem. A notable aspect of our tracker is that instead of adopting a point estimate for the target model, we account for uncertainty in data noise and model incompleteness by maintaining a distribution over plausible candidate models within the Bayesian framework. The target model is also updated adaptively by the principled Bayesian posterior inference, which admits a closed form within our Dirichlet prior modeling. With empirical evaluations on some video datasets, the proposed method is shown to yield more accurate tracking than baseline histogram-based trackers with the same types of features, often being superior to the appearance-based (visual) trackers.
Frequentist and Bayesian Learning Approaches to Artificial Intelligence
Jun, Sunghae ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 16, issue 2, 2016, Pages 111~118
DOI : 10.5391/IJFIS.2016.16.2.111
Artificial intelligence (AI) is making computer systems intelligent to do right thing. The AI is used today in a variety of fields, such as journalism, medical, industry as well as entertainment. The impact of AI is becoming larger day after day. In general, the AI system has to lead the optimal decision under uncertainty. But it is difficult for the AI system can derive the best conclusion. In addition, we have a trouble to represent the intelligent capacity of AI in numeric values. Statistics has the ability to quantify the uncertainty by two approaches of frequentist and Bayesian. So in this paper, we propose a methodology of the connection between statistics and AI efficiently. We compute a fixed value for estimating the population parameter using the frequentist learning. Also we find a probability distribution to estimate the parameter of conceptual population using Bayesian learning. To show how our proposed research could be applied to practical domain, we collect the patent big data related to Apple company, and we make the AI more intelligent to understand Apple`s technology.
Improved Post-Filtering Method Using Context Compensation
Kim, Be-Deu-Ro ; Lee, Jee-Hyong ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 16, issue 2, 2016, Pages 119~124
DOI : 10.5391/IJFIS.2016.16.2.119
According to the expansion of smartphone penetration and development of wearable device, personal context information can be easily collected. To use this information, the context aware recommender system has been actively studied. The key issue in this field is how to deal with the context information, as users are influenced by different contexts while rating items. But measuring the similarity among contexts is not a trivial task. To solve this problem, we propose context aware post-filtering to apply the context compensation. To be specific, we calculate the compensation for different context information by measuring their average. After reflecting the compensation of the rating data, the mechanism recommends the items to the user. Based on the item recommendation list, we recover the rating score considering the context information. To verify the effectiveness of the proposed method, we use the real movie rating dataset. Experimental evaluation shows that our proposed method outperforms several state-of-the-art approaches.
Pseudoinverse Matrix Decomposition Based Incremental Extreme Learning Machine with Growth of Hidden Nodes
Kassani, Peyman Hosseinzadeh ; Kim, Euntai ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 16, issue 2, 2016, Pages 125~130
DOI : 10.5391/IJFIS.2016.16.2.125
The proposal of this study is a fast version of the conventional extreme learning machine (ELM), called pseudoinverse matrix decomposition based incremental ELM (PDI-ELM). One of the main problems in ELM is to determine the number of hidden nodes. In this study, the number of hidden nodes is automatically determined. The proposed model is an incremental version of ELM which adds neurons with the goal of minimization the error of the ELM network. To speed up the model the information of pseudoinverse from previous step is taken into account in the current iteration. To show the ability of the PDI-ELM, it is applied to few benchmark classification datasets in the University of California Irvine (UCI) repository. Compared to ELM learner and two other versions of incremental ELM, the proposed PDI-ELM is faster.
Three-dimensional Head Tracking Using Adaptive Local Binary Pattern in Depth Images
Kim, Joongrock ; Yoon, Changyong ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 16, issue 2, 2016, Pages 131~139
DOI : 10.5391/IJFIS.2016.16.2.131
Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.
Control of a Segway with unknown control coefficient and input constraint
Park, Bong Seok ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 16, issue 2, 2016, Pages 140~146
DOI : 10.5391/IJFIS.2016.16.2.140
This paper proposes a control method of the Segway with unknown control coefficient and input saturation. To design a simple controller for the Segway with the model uncertainty, the prescribed performance function is used. Furthermore, an auxiliary variable is introduced to deal with unknown time-varying control coefficient and input saturation problem. Due to the auxiliary variable, function approximators are not used in this paper although all model uncertainties are unknown. Thus, the controller can be simple. From the Lyapunov stability theory, it is proved that all errors of the proposed control system remain within the prescribed performance bounds. Finally, the simulation results are presented to demonstrate the performance of the proposed scheme.