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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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Journal DOI :
Korean Institute of Intelligent Systems
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Volume & Issues
Volume 24, Issue 6 - Dec 2014
Volume 24, Issue 5 - Oct 2014
Volume 24, Issue 4 - Aug 2014
Volume 24, Issue 3 - Jun 2014
Volume 24, Issue 2 - Apr 2014
Volume 24, Issue 1 - Feb 2014
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Design of a Silicon Neuron Circuit using a 0.18 ㎛ CMOS Process
Han, Ye-Ji ; Ji, Sung-Hyun ; Yang, Hee-Sung ; Lee, Soo-Hyun ; Song, Han-Jung ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 457~461
DOI : 10.5391/JKIIS.2014.24.5.457
CMOS process silicon neuron circuit of the pulse type for modeling biological neurons, were designed in the semiconductor integrated circuit. Neuron circuiSt providing is formed by MOS switch for initializing the input terminal of the capacitor to the input current signal, a pulse signal and an amplifier stage for generating an output voltage signal. Synapse circuit that can convert the current signal output of the input voltage signal, using a bump circuit consisting of NMOS transistors and PMOS few. Configure a chain of neurons for verification of the neuron model that provides synaptic neurons and two are connected in series, were performed SPICE simulation. Result of simulation, it was confirmed the normal operation of the synaptic transmission characteristics of the signal generation of nerve cells.
The System of Converting Muscular Sense into both Color and Sound based on the Synesthetic Perception
Bae, Myung-Jin ; Kim, Sung-Ill ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 462~469
DOI : 10.5391/JKIIS.2014.24.5.462
As a basic study on both engineering applications and representation methods of synesthesia, this paper aims at building basic system which converts a muscular sense into both visual and auditory elements. As for the building method, data of the muscular sense can be acquired through roll and pitch signals which are calculated from both three-axis acceleration sensor and the two-axis gyro sensor. The roll and pitch signals are then converted into both visual and auditory information as outputs. The roll signals are converted into both intensity elements of the HSI color model and octaves as one of auditory elements. In addition, the pitch signals are converted into both hue elements of the HSI color model and scales as another one of auditory elements. Each of the extracted elements of the HSI color model is converted into each of the three elements of the RGB color model respectively, so that the real-time output color signals can be obtained. Octaves and scales are also converted and synthesized into MIDI signals, so that the real-time sound signals can be obtained as anther one of output signals. In experiments, the results revealed that normal color and sound output signals were successfully obtained from roll and pitch values that represent muscular senses or physical movements, depending on the conversion relationship based on the similarity between color and sound.
Categorization of POIs Using Word and Context information
Choi, Su Jeong ; Park, Seong-Bae ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 470~476
DOI : 10.5391/JKIIS.2014.24.5.470
A point of interest is a specific point location such as a cafe, a gallery, a shop, or a park. It consists of a name, a category, a location, and so on. Its information is necessary for location-based application, above all category is basic information. However, category information should be automatically gathered because it costs high to gather it manually. In this paper, we propose a novel method to estimate category of POIs automatically using an inner word and local context. An inner word is a word that contains POI's name. Their name sometimes expose category information. Thus, their name is used as inner word information in estimating category of POIs. Local context information means words around a POI's name in a document that mentioned the name. The context include information to estimate category. The evaluation of the proposed method is performed on two data sets. According to the experimental results, proposed model using combination inner word and local context show higher accuracy than that of model using each.
Analysis of Gait Characteristics of Walking in Various Emotion Status
Dang, Van Chien ; Tran, Trung Tin ; Kim, Jong-Wook ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 477~481
DOI : 10.5391/JKIIS.2014.24.5.477
Human has various types of emotions which affect speculation, judgement, activity, and the like at the moment. Specifically, walking is also affected by emotions, because one's emotion status can be easily inferred by his or her walking style. The present research on biped walking with humanoid robots is mainly focused on stable walking irrespective of ground condition. For effective human-robot interaction, however, walking pattern needs to be changed depending on the emotion status of the robot. This paper provides analysis and comparison of gait experiment data for the men and women in four representative emotion states, i.e., joy, sorrow, ease, and anger, which was acquired by a gait analysis system. The data and analysis results provided in this paper will be referenced to emotional biped walking of a humanoid robot.
Semi-supervised learning for sentiment analysis in mass social media
Hong, Sola ; Chung, Yeounoh ; Lee, Jee-Hyong ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 482~488
DOI : 10.5391/JKIIS.2014.24.5.482
This paper aims to analyze user's emotion automatically by analyzing Twitter, a representative social network service (SNS). In order to create sentiment analysis models by using machine learning techniques, sentiment labels that represent positive/negative emotions are required. However it is very expensive to obtain sentiment labels of tweets. So, in this paper, we propose a sentiment analysis model by using self-training technique in order to utilize "data without sentiment labels" as well as "data with sentiment labels". Self-training technique is that labels of "data without sentiment labels" is determined by utilizing "data with sentiment labels", and then updates models using together with "data with sentiment labels" and newly labeled data. This technique improves the sentiment analysis performance gradually. However, it has a problem that misclassifications of unlabeled data in an early stage affect the model updating through the whole learning process because labels of unlabeled data never changes once those are determined. Thus, labels of "data without sentiment labels" needs to be carefully determined. In this paper, in order to get high performance using self-training technique, we propose 3 policies for updating "data with sentiment labels" and conduct a comparative analysis. The first policy is to select data of which confidence is higher than a given threshold among newly labeled data. The second policy is to choose the same number of the positive and negative data in the newly labeled data in order to avoid the imbalanced class learning problem. The third policy is to choose newly labeled data less than a given maximum number in order to avoid the updates of large amount of data at a time for gradual model updates. Experiments are conducted using Stanford data set and the data set is classified into positive and negative. As a result, the learned model has a high performance than the learned models by using "data with sentiment labels" only and the self-training with a regular model update policy.
Design of Multiple Channel Wireless Remote Control System for Unmanned Vehicle
Kim, Jin-Kwan ; Ryoo, Young-Jae ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 489~494
DOI : 10.5391/JKIIS.2014.24.5.489
In this paper, a design of multiple channel wireless remote control system for unmanned vehicle is proposed. One of serious problems of the previous wireless remote control system is that it does not work when a control channel is damaged in case of emergency because it's composed of single control channel. Therefore, we propose the multiple channel wireless remote system which is composed of a portable wireless remote controller and a stationary wireless remote controller. The portable wireless remote controller and stationary wireless remote controller are designed and the multiple channel wireless remote control system for unmanned vehicles in developed. By applying to the unmanned vehicle to check its performance. The wireless remote control system is tested. Emergency stop using the portable wireless remote controller is tested when the stationary wireless remote controller is damaged. Also, emergency stop using the stationary wireless remote controller is tested when the portable wireless remote controller is damaged. The result of emergency stop test shows satisfied performance.
IDS Model using Improved Bayesian Network to improve the Intrusion Detection Rate
Choi, Bomin ; Lee, Jungsik ; Han, Myung-Mook ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 495~503
DOI : 10.5391/JKIIS.2014.24.5.495
In recent days, a study of the intrusion detection system collecting and analyzing network data, packet or logs, has been actively performed to response the network threats in computer security fields. In particular, Bayesian network has advantage of the inference functionality which can infer with only some of provided data, so studies of the intrusion system based on Bayesian network have been conducted in the prior. However, there were some limitations to calculate high detection performance because it didn't consider the problems as like complexity of the relation among network packets or continuos input data processing. Therefore, in this paper we proposed two methodologies based on K-menas clustering to improve detection rate by reforming the problems of prior models. At first, it can be improved by sophisticatedly setting interval range of nodes based on K-means clustering. And for the second, it can be improved by calculating robust CPT through applying weighted-leaning based on K-means clustering, too. We conducted the experiments to prove performance of our proposed methodologies by comparing K_WTAN_EM applied to proposed two methodologies with prior models. As the results of experiment, the detection rate of proposed model is higher about 7.78% than existing NBN(Naive Bayesian Network) IDS model, and is higher about 5.24% than TAN(Tree Augmented Bayesian Network) IDS mode and then we could prove excellence our proposing ideas.
Image Recognition Based on Nonlinear Equalization and Multidimensional Intensity Variation
Cho, Yong-Hyun ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 504~511
DOI : 10.5391/JKIIS.2014.24.5.504
This paper presents a hybrid recognition method, which is based on the nonlinear histogram equalization and the multidimensional intensity variation of an images. The nonlinear histogram equalization based on a adaptively modified function is applied to improve the quality by adjusting the brightness of the image. The multidimensional intensity variation by considering the a extent of 4-step changes in brightness between the adjacent pixels is also applied to reflect accurately the attributes of image. The statistical correlation that is measured by the normalized cross-correlation(NCC) coefficient, is applied to comprehensively measure the similarity between the images. The NCC is considered by the intensity variation of each 2-direction(x-axis and y-axis) image. The proposed method has been applied to the problem for recognizing the 50-face images of 40*40 pixels. The experimental results show that the proposed method has a superior recognition performances to the method without performing the histogram equalization, or the linear histogram equalization, respectively.
Flow Characteristic of Cyclone Dust Separator for Marine Sweeping Machine
Park, MinJae ; Jin, Taeseok ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 512~517
DOI : 10.5391/JKIIS.2014.24.5.512
This paper describes the development of new sweeping machine based on Cyclone Technology, which maintains constant suction power and uses it in a industrial applications as a method for dust removed from grinding work. The performance of a cyclone separator is determined by the turbulence characteristics and particle-particle interaction. To achieve this goal, we design cyclone technology based dust separator for sweeping machine has been proposed as a system which is suitable to work utilizing dust suction alternative to conventional manual system. and Numerical analysis with computational fluid dynamics(CFD) was carried out to investigate the working fluid that flow into cyclone dust separator in order to design optimal structure of the sweeping machine. The validation of cyclone model with CFD is carried out by comparing with experimental results.
Recognizing the Direction of Action using Generalized 4D Features
Kim, Sun-Jung ; Kim, Soo-Wan ; Choi, Jin-Young ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 518~528
DOI : 10.5391/JKIIS.2014.24.5.518
In this paper, we propose a method to recognize the action direction of human by developing 4D space-time (4D-ST, [x,y,z,t]) features. For this, we propose 4D space-time interest points (4D-STIPs, [x,y,z,t]) which are extracted using 3D space (3D-S, [x,y,z]) volumes reconstructed from images of a finite number of different views. Since the proposed features are constructed using volumetric information, the features for arbitrary 2D space (2D-S, [x,y]) viewpoint can be generated by projecting the 3D-S volumes and 4D-STIPs on corresponding image planes in training step. We can recognize the directions of actors in the test video since our training sets, which are projections of 3D-S volumes and 4D-STIPs to various image planes, contain the direction information. The process for recognizing action direction is divided into two steps, firstly we recognize the class of actions and then recognize the action direction using direction information. For the action and direction of action recognition, with the projected 3D-S volumes and 4D-STIPs we construct motion history images (MHIs) and non-motion history images (NMHIs) which encode the moving and non-moving parts of an action respectively. For the action recognition, features are trained by support vector data description (SVDD) according to the action class and recognized by support vector domain density description (SVDDD). For the action direction recognition after recognizing actions, each actions are trained using SVDD according to the direction class and then recognized by SVDDD. In experiments, we train the models using 3D-S volumes from INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset and recognize action direction by constructing a new SNU dataset made for evaluating the action direction recognition.
A Novel Approach towards use of Adaptive Multiple Kernels in Interval Type-2 Possibilistic Fuzzy C-Means
Joo, Won-Hee ; Rhee, Frank Chung-Hoon ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 529~535
DOI : 10.5391/JKIIS.2014.24.5.529
In this paper, we propose a hybrid approach towards multiple kernels interval type-2 possibilistic fuzzy C-means(PFCM) based on interval type-2 possibilistic fuzzy c-means(IT2PFCM) and possibilistic fuzzy c-means using multiple kernels( PFCM-MK). In case of noisy data or overlapping cluster prototypes, fuzzy C-means gives poor performance in comparison to possibilistic fuzzy C-means(PFCM). Moreover, to address the uncertainty associated with fuzzifier parameter m, interval type-2 possibilistic fuzzy C-means(PFCM) is used. Most of the practical data available are complex and non-linearly separable. In such cases using Gaussian kernels proves helpful. Therefore, in order to overcome all these issues, we have integrated multiple kernels possibilistic fuzzy C-means(PFCM) into interval type-2 possibilistic fuzzy C-means(IT2PFCM) and propose the idea of multiple kernels based interval type-2 possibilistic fuzzy C-means(IT2PFCM-MK).
Design of Meteorological Radar Pattern Classifier Using Clustering-based RBFNNs : Comparative Studies and Analysis
Choi, Woo-Yong ; Oh, Sung-Kwun ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 536~541
DOI : 10.5391/JKIIS.2014.24.5.536
Data through meteorological radar includes ground echo, sea-clutter echo, anomalous propagation echo, clear echo and so on. Each echo is a kind of non-precipitation echoes and the characteristic of individual echoes is analyzed in order to identify with non-precipitation. Meteorological radar data is analyzed through pre-processing procedure because the data is given as big data. In this study, echo pattern classifier is designed to distinguish non-precipitation echoes from precipitation echo in meteorological radar data using RBFNNs and echo judgement module. Output performance is compared and analyzed by using both HCM clustering-based RBFNNs and FCM clustering-based RBFNNs.
Development of the Dripping Speed Measurement System of Medical Liquid using Heuristic
Kim, Jung-Sook ; Jeong, Junho ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 542~547
DOI : 10.5391/JKIIS.2014.24.5.542
This paper describes the medical and IT convergence system using a smart phone and a heuristic method for the measurement of the dripping speed of the liquid in a drip chamber, which can estimate the remaining time using pattern recognition and difference image from video frame information based on Android technology. The video frames were first made using a smartphone camera and we calculated the difference image between the n image and the (n-1) image and then changed into binary images using the threshold value. At this point, it is very important to find an optimal threshold value using heuristic method to recognize the dripping of the liquids. In addition, the user can adjust the dripping speed according to the doctor's prescription, exactly like watching the progress bar of a mobile application. The experiment results show that our method using video processing technique accurately measures the dripping speed for a wide range of speeds that are sufficient for ordinary practice.
A Study of Arrow Performance using Artificial Neural Network
Jeong, Yeongsang ; Kim, Sungshin ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 548~553
DOI : 10.5391/JKIIS.2014.24.5.548
In order to evaluate the performance of arrow that manufactures through production process, it is used that personal experiences such as hunters who have been using bow and arrow for a long time, technicians who produces leisure and sports equipment, and experts related with this industries. Also, the intensity of arrow's impact point which obtains from repeated shooting experiments is an important indicator for evaluating the performance of arrow. There are some ongoing researches for evaluating performance of arrow using intensity of the arrow's impact point and the arrow's flying image that obtained from high-speed camera. However, the research that deals with mutual relation between distribution of the arrow's impact point and characteristics of the arrow (length, weight, spine, overlap, straightness) is not enough. Therefore, this paper suggests both the system that could describes the distribution of the arrow's impact point into numerical representation and the correlation model between characteristics of arrow and impact points. The inputs of the model are characteristics of arrow (spine, straightness). And the output is MAD (mean absolute distance) of triangular shaped coordinates that could be obtained from 3 times repeated shooting by changing knock degree 120. The input-output data is collected for learning the correlation model, and ANN (artificial neural network) is used for implementing the model.
Development of Efficient Operational Mode for Wind-Diesel Hybrid System
Asghar, Furqan ; Kim, Se-Yoon ; Kim, Sung Ho ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 554~561
DOI : 10.5391/JKIIS.2014.24.5.554
Hybrid wind Diesel stand-alone power systems are considered economically viable and effective to create balance between production and load demand in remote areas where the wind speed is considerable for electric generation, and also, electric energy is not easily available from the grid. In Wind diesel hybrid system, the wind energy system is the main constitute and diesel system forms the back up. This type of hybrid power system saves fuel cost, improves power capacity to meet the increasing demand and maintains the continuity of supply in the system. Problem we face in this system is that even after producing enough power through wind turbine system, considerable portion of this power needs to be dumped due to short term oversupply of power and to maintain the frequency within close tolerances. As a result remaining portion of total energy supplied comes from the diesel generator to overcome the temporal energy shortage. This scenario decreases the overall efficiency of hybrid power system. In this study, efficient Simulink modeling for wind-diesel hybrid system is proposed and some simulations study is carried out to verify the feasibility of the proposed scheme.
Design of Meteorological Radar Echo Classifier Using Fuzzy Relation-based Neural Networks : A Comparative Studies of Echo Judgement Modules
Ko, Jun-Hyun ; Song, Chan-Seok ; Oh, Sung-Kwun ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 562~568
DOI : 10.5391/JKIIS.2014.24.5.562
There exist precipitation echo and non-precipitation echo in the meteorological radar. It is difficult to effectively issue the right weather forecast because of a difficulty in determining these ambiguous point. In this study, Data is extracted from UF data of meteorological radar used. Input and output data for designing two classifier were built up through the analysis of the characteristics of precipitation and non-precipitation. Selected input variables are considered for better performance and echo classifier is designed using fuzzy relation-based nueral network. Comparative studies on the performance of echo classifier are carried out by considering both echo judgement module 1 and module 2.
On Generalized Intuitionistic Soft Equality
Park, Jin Han ; Kwun, Young Chel ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 5, 2014, Pages 569~577
DOI : 10.5391/JKIIS.2014.24.5.569
Park et al. (2011) introduced the concept of generalized intuitionistic fuzzy soft sets, which can be seen as an effective mathematical tool to deal with uncertainties. In this paper, the concept of generalized intuitionistic fuzzy soft equality is introduced and some related properties are derived. It is proved that generalized intuitionistic fuzzy soft equality is congruence relation with respect to some operations and the generalized intuitionistic fuzzy soft quotient algebra is established.