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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of Korean Institute of Intelligent Systems
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Korean Institute of Intelligent Systems
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Volume & Issues
Volume 25, Issue 6 - Dec 2015
Volume 25, Issue 5 - Oct 2015
Volume 25, Issue 4 - Aug 2015
Volume 25, Issue 3 - Jun 2015
Volume 25, Issue 2 - Apr 2015
Volume 25, Issue 1 - Feb 2015
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Development of the Context-Aware System for Senior Citizen based on Case-Based Reasoning
Kim, Jung-Sook ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 419~424
DOI : 10.5391/JKIIS.2015.25.5.419
The entry of an aging society require a safety of senior citizens against emergency. However, a home and a residence of senior citizens should not consider a characteristic of safety and the unfortunate accidents could happen in the house or the residence. Especially if the elders live alone then they have a help function using a call button in a bathroom or a closed area. But, a sliding or an overbalance in a bathroom or a closed area of a home may happen suddenly how can require a help in real time. That is a very serious accident to senior citizens. In this paper, we developed the context-aware system using the various sensors for collecting the data which is an activities of daily living of elders and we designed the recognition method using case-based reasoning for detecting the anomaly and the emergency context in the bathroom or the closed area in house. After that, if the anomaly or the emergency are detected then it call to a family or a relative or an administration in real-time.
A Study on Rotating Object Classification using Deep Neural Networks
Lee, Yong-Kyu ; Lee, Yill-Byung ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 425~430
DOI : 10.5391/JKIIS.2015.25.5.425
This paper is a study to improve the classification efficiency of rotating objects by using deep neural networks to which a deep learning algorithm was applied. For the classification experiment of rotating objects, COIL-20 is used as data and total 3 types of classifiers are compared and analyzed. 3 types of classifiers used in the study include PCA classifier to derive a feature value while reducing the dimension of data by using Principal Component Analysis and classify by using euclidean distance, MLP classifier of the way of reducing the error energy by using error back-propagation algorithm and finally, deep learning applied DBN classifier of the way of increasing the probability of observing learning data through pre-training and reducing the error energy through fine-tuning. In order to identify the structure-specific error rate of the deep neural networks, the experiment is carried out while changing the number of hidden layers and number of hidden neurons. The classifier using DBN showed the lowest error rate. Its structure of deep neural networks with 2 hidden layers showed a high recognition rate by moving parameters to a location helpful for recognition.
TV Program Recommender System Using Viewing Time Patterns
Bang, Hanbyul ; Lee, HyeWoo ; Lee, Jee-Hyong ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 431~436
DOI : 10.5391/JKIIS.2015.25.5.431
As a number of TV programs broadcast today, researches about TV program recommender system have been studied and many researchers have been studying recommender system to produce recommendation with high accuracy. Recommender system recommends TV program to user by using metadata like genre, plot or calculating users` preferences about TV programs. In this paper, we propose a new TV program Collaborative Filtering Recommender System that exploits viewing time pattern like viewing ratio, relation with finish time and recently viewing history to calculate preference for high-quality of recommendation. To verify usefulness of our research, we also compare our method which utilizes viewing time patterns and baseline which simply recommends TV program of user`s most frequently watched channel. Through this experiments, we show that our method very effectively works and recommendation performance increases.
Design of an observer-based decentralized fuzzy controller for discrete-time interconnected fuzzy systems
Kong, Seong G. ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 437~443
DOI : 10.5391/JKIIS.2015.25.5.437
This paper presents face recognition based on the fusion of visible image and thermal infrared (IR) texture estimated from the face image in the visible spectrum. The proposed face recognition scheme uses a multi- layer neural network to estimate thermal texture from visible imagery. In the training process, a set of visible and thermal IR image pairs are used to determine the parameters of the neural network to learn a complex mapping from a visible image to its thermal texture in the low-dimensional feature space. The trained neural network estimates the principal components of the thermal texture corresponding to the input visible image. Extensive experiments on face recognition were performed using two popular face recognition algorithms, Eigenfaces and Fisherfaces for NIST/Equinox database for benchmarking. The fusion of visible image and thermal IR texture demonstrated improved face recognition accuracies over conventional face recognition in terms of receiver operating characteristics (ROC) as well as first matching performances.
Performance Enhancement of the Attitude Estimation using Small Quadrotor by Vision-based Marker Tracking
Kang, Seokyong ; Choi, Jongwhan ; Jin, Taeseok ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 444~450
DOI : 10.5391/JKIIS.2015.25.5.444
The accuracy of small and low cost CCD camera is insufficient to provide data for precisely tracking unmanned aerial vehicles(UAVs). This study shows how UAV can hover on a human targeted tracking object by using CCD camera rather than imprecise GPS data. To realize this, UAVs need to recognize their attitude and position in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for an UAV to estimate of his attitude by environment recognition for UAV hovering, as one of the best important problems. In this paper, we describe a method for the attitude of an UAV using image information of a maker on the floor. This method combines the observed position from GPS sensors and the estimated attitude from the images captured by a fixed camera to estimate an UAV. Using the a priori known path of an UAV in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a marker on the floor and the estimated UAV`s attitude. Since the equations are based on the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the UAV. The Kalman filter scheme is applied for this method. its performance is verified by the image processing results and the experiment.
Design of an observer-based decentralized fuzzy controller for discrete-time interconnected fuzzy systems
Koo, Geun Bum ; Joo, Young Hoon ; Park, Jin Bae ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 451~456
DOI : 10.5391/JKIIS.2015.25.5.451
In this paper, an observer-based decentralized fuzzy controller is designed for discrete-time interconnected fuzzy systems. Based on the fuzzy subsystem of the interconnected fuzzy system, the observer-based decentralized fuzzy controller is considered. By using the fuzzy subsystem and the observer-based decentralized fuzzy controller, the closed-loop system is obtained. From the closed-loop system, the stability condition with the maximum interconnection bound is developed, and its sufficient condition is represented as the linear matrix inequality (LMI). Finally, the numerical example is provided to verify the effectiveness of the proposed technique.
Collective Intelligence based Wrong Answer Note System
Ha, Jin Seog ; Kim, Chang Suk ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 457~463
DOI : 10.5391/JKIIS.2015.25.5.457
This paper presents the need for the concept of collective intelligence based system for the timely learning and incorrect notes show the utilization and satisfaction. The old wrong answer note system is characterized by the provision of uniform right answer explanations for the questions whose answers were wrong by checking whether the evaluation items were answered right or wrong. The characteristic requires a lot of improvements in terms of wrong answer analysis and feedback since it cannot properly receive feedback on the items that a learner got right by luck in spite of poor understanding of them and on the errors in the selection process of wrong answers by individual learners. The SERO wrong answer note was designed to propose new ways to identify and capture such "score errors" and compensate for the practical weaknesses of learners. The Stability Emergency Risk Opportunity (SERO) wrong answer note is based on a method of categorizing and analyzing evaluation items answered by the examinee into four types (S, E, R and O type), and commentary correct as well as incorrect answers by presenting a variety of commentary notes using the collective intelligence of the study show that satisfaction is high.
Smart Windows and Doors Platform for Providing Optimized Inner Environment
Cho, Yong-Hyun ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 464~469
DOI : 10.5391/JKIIS.2015.25.5.464
This paper presents the smart system platform for remotely controlling the windows and doors system(WDS), which gathers and analyzes the state of WDS and the environmental data for preventing crimes and keeping a pleasant indoor. In particular, standard API between the smart WDS platform and the smart home platform has been presented to be easy to a home services, such as security, safety, and home appliance control. The private gateway of wire and wireless communication interfaces has been developed to remotely control and monitor the WDS for anytime and anyplace solving the crime prevention and ventilation problem. Web-and App-based user interface in order to detect the opening and shutting states and remotely control WDS have also been developed to support the mobile environment, respectively.
A Big Data Preprocessing using Statistical Text Mining
Jun, Sunghae ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 470~476
DOI : 10.5391/JKIIS.2015.25.5.470
Big data has been used in diverse areas. For example, in computer science and sociology, there is a difference in their issues to approach big data, but they have same usage to analyze big data and imply the analysis result. So the meaningful analysis and implication of big data are needed in most areas. Statistics and machine learning provide various methods for big data analysis. In this paper, we study a process for big data analysis, and propose an efficient methodology of entire process from collecting big data to implying the result of big data analysis. In addition, patent documents have the characteristics of big data, we propose an approach to apply big data analysis to patent data, and imply the result of patent big data to build R&D strategy. To illustrate how to use our proposed methodology for real problem, we perform a case study using applied and registered patent documents retrieved from the patent databases in the world.
Comparison of Linear and Nonlinear Regressions and Elements Analysis for Wind Speed Prediction
Kim, Dongyeon ; Seo, Kisung ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 477~482
DOI : 10.5391/JKIIS.2015.25.5.477
Linear regressions and evolutionary nonlinear regression based compensation techniques for the short-range prediction of wind speed are investigated. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS for wind speed prediction. The proposed method is compared to various linear regression methods for prediction of wind speed. Also, statistical analysis of distribution for UM elements for each method is executed. experiments are performed for KLAPS(Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea.
A Texture Classification Based on LBP by Using Intensity Differences between Pixels
Cho, Yong-Hyun ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 483~488
DOI : 10.5391/JKIIS.2015.25.5.483
This paper presents a local binary pattern(LBP) for effectively classifying textures, which is based on the multidimensional intensity difference between the adjacent pixels in the block image. The intensity difference by considering the a extent of 4 directional changes(verticality, horizontality, diagonality, inverse diagonality) in brightness between the adjacent pixels is applied to reduce the computation load as a results of decreasing the levels of histogram for classifying textures of image. And the binary patterns that is represented by the relevant intensities within a block image, is also used to effectively classify the textures by accurately reflecting the local attributes. The proposed method has been applied to classify 24 block images from USC Texture Mosaic #2 of 128*128 pixels gray image. The block images are different in size and texture. The experimental results show that the proposed method has a speedy classification and makes a free size block images classify possible. In particular, the proposed method gives better results than the conventional LBP by increasing the range of histogram level reduction as the block size becomes larger.
Analysis of the outcome for the Korean Pro-Basketball games using Regression models
Jhang, Hyo Jin ; Kwak, Hyun ; Choi, Seung Hoe ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 489~494
DOI : 10.5391/JKIIS.2015.25.5.489
The purpose of this paper is to analyse outcomes of Korean Pro-basketball games using regression models. Both Classic Fuzzy Regression Model and Fuzzy Regression Model applying linguistic variables were used to meet the purpose of the paper. In General Regression Analysis, in which the results of games are expressed and analyzed through score differences, a regression model is proposed considering influential variables for the score differences of the two teams. In Fuzzy Regression Analysis, the results are sorted into six different literal expressions, `win with large margin, win with moderate margin, win with narrow margin, defeat with narrow margin, defeat with moderate margin, and defeat with large margin`. Athletic performances and team work of each teams were expressed in fuzzy number to analyse how much athletic performances and team work affect results of games. This paper referred back to 2013-2014 season data provided by KBL(Korean Basketball League) and professional columns on Korean basketball analysis.
Effective Response Time Verify of Active Decoy Against Anti-Ship Missile Using DEVS Simulation
Choi, Soon-Ho ; Cho, Tae-Ho ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 495~501
DOI : 10.5391/JKIIS.2015.25.5.495
Abroad warships are confronted with various menaces. The most critical threat of the warship is an Anti-Ship Missile (ASM). The ASM is able to be launched at a variety of environments and platforms. The ASM can evades conventional naval radar systems and electronic countermeasure techniques for providing a fatal damage to the warship. To cope with the ASM, an active decoy is an effective method to minimize the direct damage to the warship. The active decoy increases survivability of the warship because the ASM can lure pursuit of the active decoy instead of the warship. In this paper, our proposed method verifies an available response time of the active decoy to deal with the ASM using the active decoy of the warship in marine environments. We defined models of the warship, the ASM, and the active decoy, and executed simulation by combining the models. By the simulation result, the proposed method demonstrated the superiority of the mobile active decoy of the response time decoy among various active decoys, and estimated a protection area to prevent the ASM according the response time of the mobile active decoy against the ASM.
Improvement of Disparity Map using Loopy Belief Propagation based on Color and Edge
Kim, Eun Kyeong ; Cho, Hyunhak ; Lee, Hansoo ; Wibowo, Suryo Adhi ; Kim, Sungshin ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 502~508
DOI : 10.5391/JKIIS.2015.25.5.502
Stereo images have an advantage of calculating depth(distance) values which can not analyze from 2-D images. However, depth information obtained by stereo images has due to following reasons: it can be obtained by computation process; mismatching occurs when stereo matching is processing in occlusion which has an effect on accuracy of calculating depth information. Also, if global method is used for stereo matching, it needs a lot of computation. Therefore, this paper proposes the method obtaining disparity map which can reduce computation time and has higher accuracy than established method. Edge extraction which is image segmentation based on feature is used for improving accuracy and reducing computation time. Color K-Means method which is image segmentation based on color estimates correlation of objects in an image. And it extracts region of interest for applying Loopy Belief Propagation(LBP). For this, disparity map can be compensated by considering correlation of objects in the image. And it can reduce computation time because of calculating region of interest not all pixels. As a result, disparity map has more accurate and the proposed method reduces computation time.
Design of Fuzzy Pattern Classifier based on Extreme Learning Machine
Ahn, Tae-Chon ; Roh, Sok-Beom ; Hwang, Kuk-Yeon ; Wang, Jihong ; Kim, Yong Soo ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 509~514
DOI : 10.5391/JKIIS.2015.25.5.509
In this paper, we introduce a new pattern classifier which is based on the learning algorithm of Extreme Learning Machine the sort of artificial neural networks and fuzzy set theory which is well known as being robust to noise. The learning algorithm used in Extreme Learning Machine is faster than the conventional artificial neural networks. The key advantage of Extreme Learning Machine is the generalization ability for regression problem and classification problem. In order to evaluate the classification ability of the proposed pattern classifier, we make experiments with several machine learning data sets.
A New Complexity Analysis of the SymMerge Algorithm
Kim, Pok-Son ;
Journal of Korean Institute of Intelligent Systems, volume 25, issue 5, 2015, Pages 515~521
DOI : 10.5391/JKIIS.2015.25.5.515
The SymMerge algorithm is an efficient merging algorithm for input sequences u and v of sizes $\left|u \right|