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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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Journal DOI :
Korean Institute of Intelligent Systems
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Volume & Issues
Volume 12, Issue 4 - Dec 2012
Volume 12, Issue 3 - Sep 2012
Volume 12, Issue 2 - Jun 2012
Volume 12, Issue 1 - Mar 2012
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A Basic Study on the Conversion of Sound into Color Image using both Pitch and Energy
Kim, Sung-Ill ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 12, issue 2, 2012, Pages 101~107
DOI : 10.5391/IJFIS.2012.12.2.101
This study describes a proposed method of converting an input sound signal into a color image by emulating human synesthetic skills which make it possible to associate an sound source with a specific color image. As a first step of sound-to-image conversion, features such as fundamental frequency(F0) and energy are extracted from an input sound source. Then, a musical scale and an octave can be calculated from F0 signals, so that scale, energy and octave can be converted into three elements of HSI model such hue, saturation and intensity, respectively. Finally, a color image with the BMP file format is created as an output of the process of the HSI-to-RGB conversion. We built a basic system on the basis of the proposed method using a standard C-programming. The simulation results revealed that output color images with the BMP file format created from input sound sources have diverse hues corresponding to the change of the F0 signals, where the hue elements have different intensities depending on octaves with the minimum frequency of 20Hz. Furthermore, output images also have various levels of chroma(or saturation) which is directly converted from the energy.
A Clustering Approach to Wind Power Prediction based on Support Vector Regression
Kim, Seong-Jun ; Seo, In-Yong ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 12, issue 2, 2012, Pages 108~112
DOI : 10.5391/IJFIS.2012.12.2.108
A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly wind energy is unlimited in potential. However, due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. It is reported that, compared with physical persistent models, statistical techniques and computational methods are more useful for short-term forecasting of wind power. Among them, support vector regression (SVR) has much attention in the literature. This paper proposes an SVR based wind speed forecasting. To improve the forecasting accuracy, a fuzzy clustering is adopted in the process of SVR modeling. An illustrative example is also given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.
A Note on the Fuzzy Linear Maps over the Fuzzy Quotient Spaces
Kim, Chang-Bum ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 12, issue 2, 2012, Pages 113~120
DOI : 10.5391/IJFIS.2012.12.2.113
In this paper we study various properties of the fuzzy linear maps over the fuzzy quotient spaces. In particular we obtain some exact sequences of the fuzzy linear maps over the fuzzy quotient spaces.
Associative Motion Generation for Humanoid Robot Reflecting Human Body Movement
Wakabayashi, Akinori ; Motomura, Satona ; Kato, Shohei ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 12, issue 2, 2012, Pages 121~130
DOI : 10.5391/IJFIS.2012.12.2.121
This paper proposes an intuitive real-time robot control system using human body movement. Recently, it has been developed that motion generation for humanoid robots with reflecting human body movement, which is measured by a motion capture. However, in the existing studies about robot control system by human body movement, the detailed structure information of a robot, for example, degrees of freedom, the range of motion and forms, must be examined in order to calculate inverse kinematics. In this study, we have proposed Associative Motion Generation as humanoid robot motion generation method which does not need the detailed structure information. The associative motion generation system is composed of two neural networks: nonlinear principal component analysis and Jordan recurrent neural network, and the associative motion is generated with the following three steps. First, the system learns the correspondence relationship between an indication and a motion using training data. Second, associative values are extracted for associating a new motion from an unfamiliar indication using nonlinear principal component analysis. Last, the robot generates a new motion through calculation by Jordan recurrent neural network using the associative values. In this paper, we propose a real-time humanoid robot control system based on Associative Motion Generation, that enables user to control motion intuitively by human body movement. Through the task processing and subjective evaluation experiments, we confirmed the effective usability and affective evaluations of the proposed system.
Control Lyapunov Function Design by Cancelling Input Singularity
Yeom, Dong-Hae ; Joo, Young-Hoon ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 12, issue 2, 2012, Pages 131~136
DOI : 10.5391/IJFIS.2012.12.2.131
If one can find a control Lyapunov function (CLF) for a given nonlinear system, the control input stabilizing the system can be easily obtained. To find a CLF, the time derivative of an energy function should be negative definite. This procedure frequently requires a control input which is a rational function or includes an inverse function. The control input is not defined on the specific state-space where the denominator of the rational function is equal to 0 or the inverse function does not exist. In this region with singularities, the trajectory of the control system cannot be generated, which is one of the most important reasons why it is hard to make the origin of a nonlinear system be globally asymptotically stable. In this paper, we propose a smooth control law ensuring the globally asymptotic stability by means of cancelling the singularity in the control input.
Detection of Lung Nodule on Temporal Subtraction Images Based on Artificial Neural Network
Tokisa, Takumi ; Miyake, Noriaki ; Maeda, Shinya ; Kim, Hyoung-Seop ; Tan, Joo Kooi ; Ishikawa, Seiji ; Murakami, Seiichi ; Aoki, Takatoshi ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 12, issue 2, 2012, Pages 137~142
DOI : 10.5391/IJFIS.2012.12.2.137
The temporal subtraction technique as one of computer aided diagnosis has been introduced in medical fields to enhance the interval changes such as formation of new lesions and changes in existing abnormalities on deference image. With the temporal subtraction technique radiologists can easily detect lung nodules on visual screening. Until now, two-dimensional temporal subtraction imaging technique has been introduced for the clinical test. We have developed new temporal subtraction method to remove the subtraction artifacts which is caused by mis-registration on temporal subtraction images of lungs on MDCT images. In this paper, we propose a new computer aided diagnosis scheme for automatic enhancing the lung nodules from the temporal subtraction of thoracic MDCT images. At first, the candidates regions included nodules are detected by the multiple threshold technique in terms of the pixel value on the temporal subtraction images. Then, a rule-base method and artificial neural networks is utilized to remove the false positives of nodule candidates which is obtained temporal subtraction images. We have applied our detection of lung nodules to 30 thoracic MDCT image sets including lung nodules. With the detection method, satisfactory experimental results are obtained. Some experimental results are shown with discussion.
Distance Sensitive AdaBoost using Distance Weight Function
Lee, Won-Ju ; Cheon, Min-Kyu ; Hyun, Chang-Ho ; Park, Mi-Gnon ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 12, issue 2, 2012, Pages 143~148
DOI : 10.5391/IJFIS.2012.12.2.143
This paper proposes a new method to improve performance of AdaBoost by using a distance weight function to increase the accuracy of its machine learning processes. The proposed distance weight algorithm improves classification in areas where the original binary classifier is weak. This paper derives the new algorithm`s optimal solution, and it demonstrates how classifier accuracy can be improved using the proposed Distance Sensitive AdaBoost in a simulation experiment of pedestrian detection.
Fuzzy Almost Strongly (r, s)-Semicontinuous Mappings
Lee, Seok-Jong ; Kim, Jin-Tae ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 12, issue 2, 2012, Pages 149~153
DOI : 10.5391/IJFIS.2012.12.2.149
In this paper, we introduce the concept of fuzzy almost strongly (r, s)-semicontinuous mappings on intuitionistic fuzzy topological spaces in
ostak`s sense. The relationships among fuzzy strongly (r, s)-semicontinuous, fuzzy almost (r, s)-continuous, fuzzy almost (r, s)-semicontinuous, and fuzzy almost strongly (r, s)-semicontinuous mappings are discussed. The characterization for the fuzzy almost strongly (r, s)-semicontinuous mappings is obtained.
Fuzzy Logic Application to a Two-wheel Mobile Robot for Balancing Control Performance
Kim, Hyun-Wook ; Jung, Seul ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 12, issue 2, 2012, Pages 154~161
DOI : 10.5391/IJFIS.2012.12.2.154
This article presents experimental studies of fuzzy logic application to control a two-wheel mobile robot(TWMR) system. The TWMR system is composed of two systems, an inverted pendulum system and a mobile robot system. Although linear controllers can stabilize the TWMR, fuzzy controllers are expected to have robustness to uncertainties so that the resulting performances are expected to be better. Nominal fuzzy rules are used to control balance and position of TWMR. Fuzzy logic is embedded on a DSP chip to control the TWMR. Balancing performances of the PID controller and the fuzzy controller under disturbances are compared through extensive experimental studies.
Generalized Higher Order Energy Based Instantaneous Amplitude and Frequency Estimation and Their Applications to Power Disturbance Detection
Iem, Byeong-Gwan ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 12, issue 2, 2012, Pages 162~166
DOI : 10.5391/IJFIS.2012.12.2.162
The instantaneous amplitude (IA) based on the higher order differential energy operator is proposed. And its general form for arbitrary order is also proposed. The various definitions of the IA and the instantaneous frequency (IF) estimators are considered. The IA and IF estimators based on the energy operators need less computational cost than the conventional IF and IA estimators exploiting the Hilbert transform. The IF and IA estimators are compared in terms of the frequency and amplitude tracking accuracy of the AM-FM signals. For noiseless case, the IA and IF estimators based on the Teager-Kaiser energy operator show better tracking performance than the IF and IA estimators based on the higher energy operators. However, under noisy condition, the IF and IA estimator based on the higher order energy operators with the order 3 and 4 show better tracking than the Teager-Kaiser energy based estimators. The IF and IA estimators are applied to signals in the various power anomalies to show their usefulness as the disturbance detectors.
Pathway Retrieval for Transcriptome Analysis using Fuzzy Filtering Technique andWeb Service
Lee, Kyung-Mi ; Lee, Keon-Myung ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 12, issue 2, 2012, Pages 167~172
DOI : 10.5391/IJFIS.2012.12.2.167
In biology the advent of the high-throughput technology for sequencing, probing, or screening has produced huge volume of data which could not be manually handled. Biologists have resorted to software tools in order to effectively handle them. This paper introduces a bioinformatics tool to help biologists find potentially interesting pathway maps from a transcriptome data set in which the expression levels of genes are described for both case and control samples. The tool accepts a transcriptome data set, and then selects and categorizes some of genes into four classes using a fuzzy filtering technique where classes are defined by membership functions. It collects and edits the pathway maps related to those selected genes without analyst` intervention. It invokes a sequence of web service functions from KEGG, which an online pathway database system, in order to retrieve related information, locate pathway maps, and manipulate them. It maintains all retrieved pathway maps in a local database and presents them to the analysts with graphical user interface. The tool has been successfully used in identifying target genes for further analysis in transcriptome study of human cytomegalovirous. The tool is very helpful in that it can considerably save analysts` time and efforts by collecting and presenting the pathway maps that contain some interesting genes, once a transcriptome data set is just given.
Water Flowing and Shaking Optimization
Jung, Sung-Hoon ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 12, issue 2, 2012, Pages 173~180
DOI : 10.5391/IJFIS.2012.12.2.173
This paper proposes a novel optimization algorithm inspired by water flowing and shaking behaviors in a vessel. Water drops in our algorithm flow to the gradient descent direction and are sometimes shaken for getting out of local optimum areas when most water drops fall in local optimum areas. These flowing and shaking operations allow our algorithm to quickly approach to the global optimum without staying in local optimum areas. We experimented our algorithm with four function optimization problems and compared its results with those of particle swarm optimization. Experimental results showed that our algorithm is superior to the particle swarm optimization algorithm in terms of the speed and success ratio of finding the global optimum.
Web Page Evaluation based on Implicit User Reactions and Neural Networks
Lee, Dong-Hoon ; Kim, Jae-Kwang ; Lee, Jee-Hyong ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 12, issue 2, 2012, Pages 181~186
DOI : 10.5391/IJFIS.2012.12.2.181
This paper proposes a method for evaluating web pages by considering implicit user reaction on web pages. Usually users spend more time and make more reactions, such as clicking, dragging and scrolling, while reading interesting pages. Based on this observation, a web page evaluation method by observing implicit user reaction is proposed. The system is designed with Ajax for observing user reactions, and neural networks for learning correlation between user reactions and usefulness of pages. The amounts of each type of user reactions are inputted to neural networks. Also the numbers of characters and images of pages are used as inputs because the amount of users` behaviors has a tendency to increase as the length of pages increase. The experiment is conducted with 113 people and 74 pages. Each page is ranked by users with a questionnaire. The proposed method shows more close ranking results to the user ranks than Google. That is, our system evaluates web pages more closely to users` viewpoint than Google. Although our experiment is limited, our result shows powerful potential of new element for web page evaluation. Some approaches evaluate web pages with their contents and some evaluate web pages with structural attributes, particularly links, of pages. Web page evaluation is for users, so the best evaluation can be done by users themselves. So, user feedback is one of the most important factors for web page evaluation. This paper proposes a new method which reflects user feedbacks on web pages.