Go to the main menu
Skip to content
Go to bottom
REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
Journal of Korean Institute of Intelligent Systems
Journal Basic Information
Journal DOI :
Korean Institute of Intelligent Systems
Editor in Chief :
Volume & Issues
Volume 23, Issue 6 - Dec 2013
Volume 23, Issue 5 - Oct 2013
Volume 23, Issue 4 - Aug 2013
Volume 23, Issue 3 - Jun 2013
Volume 23, Issue 2 - Apr 2013
Volume 23, Issue 1 - Feb 2013
Selecting the target year
Assessment of External Force Acting on Ship Using Big Data in Maritime Traffic
Kim, Kwang-Il ; Jeong, Jung Sik ; Park, Gyei-Kark ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 379~384
DOI : 10.5391/JKIIS.2013.23.5.379
For effective ship management in VTS(Vessel Traffic Service), it needs to assess the external force acting on ship. Big data in maritime traffic can be roughly categorized into two groups. One is the traffic information including ship's particulars. The other is the external force information e.g., wind, sea wave, tidal current. This paper proposes the method to assess the external force acting on ship using big data in maritime traffic. To approach Big data in maritime traffic, we propose the Waterway External Force Code(WEF code) which consist of wind, wave, tidal and current information, Speed Over the Water(SOW) of each ship, weather information. As a results, the external force acting a navigating ship is estimated.
The Efficient Method of Parallel Genetic Algorithm using MapReduce of Big Data
Hong, Sung-Sam ; Han, Myung-Mook ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 385~391
DOI : 10.5391/JKIIS.2013.23.5.385
Big Data is data of big size which is not processed, collected, stored, searched, analyzed by the existing database management system. The parallel genetic algorithm using the Hadoop for BigData technology is easily realized by implementing GA(Genetic Algorithm) using MapReduce in the Hadoop Distribution System. The previous study that the genetic algorithm using MapReduce is proposed suitable transforming for the GA by MapReduce. However, they did not show good performance because of frequently occurring data input and output. In this paper, we proposed the MRPGA(MapReduce Parallel Genetic Algorithm) using improvement Map and Reduce process and the parallel processing characteristic of MapReduce. The optimal solution can be found by using the topology, migration of parallel genetic algorithm and local search algorithm. The convergence speed of the proposal method is 1.5 times faster than that of the existing MapReduce SGA, and is the optimal solution can be found quickly by the number of sub-generation iteration. In addition, the MRPGA is able to improve the processing and analysis performance of Big Data technology.
The Model of Network Packet Analysis based on Big Data
Choi, Bomin ; Kong, Jong-Hwan ; Han, Myung-Mook ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 392~399
DOI : 10.5391/JKIIS.2013.23.5.392
Due to the development of IT technology and the information age, a dependency of the network over the most of our lives have grown to a greater extent. Although it provides us to get various useful information and service, it also has negative effectiveness that can provide network intruder with vulnerable roots. In other words, we need to urgently cope with theses serious security problem causing service disableness or system connected to network obstacle with exploiting various packet information. Many experts in a field of security are making an effort to develop the various security solutions to respond against these threats, but existing solutions have a lot of problems such as lack of storage capacity and performance degradation along with the massive increase of packet data volume. Therefore we propose the packet analysis model to apply issuing Big Data technology in the field of security. That is, we used NoSQL which is technology of massive data storage to collect the packet data growing massive and implemented the packet analysis model based on K-means clustering using MapReudce which is distributed programming framework, and then we have shown its high performance by experimenting.
Knowledge Extractions, Visualizations, and Inference from the big Data in Healthcare and Medical
Kim, Jin Sung ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 400~405
DOI : 10.5391/JKIIS.2013.23.5.400
The purpose of this study is to develop a composite platform for knowledge extractions, visualizations, and inference. Generally, the big data sets were frequently used in the healthcare and medical area. To help the knowledge managers/users working in the field, this study is focused on knowledge management (KM) based on Data Mining (DM), Knowledge Distribution Map (KDM), Decision Tree (DT), RDBMS, and SQL-inference. The proposed mechanism is composed of five key processes. Firstly, in Knowledge Parsing, it extracts logical rules from a big data set by using DM technology. Then it transforms the rules into RDB tables. Secondly, through Knowledge Maintenance, it refines and manages the knowledge to be ready for the computing of knowledge distributions. Thirdly, in Knowledge Distribution process, we can see the knowledge distributions by using the DT mechanism.Fourthly, in Knowledge Hierarchy, the platform shows the hierarchy of the knowledge. Finally, in Inference, it deduce the conclusions by using the given facts and data.This approach presents the advantages of diversity in knowledge representations and inference to improve the quality of computer-based medical diagnosis.
A Big Data Learning for Patent Analysis
Jun, Sunghae ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 406~411
DOI : 10.5391/JKIIS.2013.23.5.406
Big data issue has been considered in diverse fields. Also, big data learning has been required in all areas such as engineering and social science. Statistics and machine learning algorithms are representative tools for big data learning. In this paper, we study learning tools for big data and propose an efficient methodology for big data learning via legacy data to practical application. We apply our big data learning to patent analysis, because patent is one of big data. Also, we use patent analysis result for technology forecasting. To illustrate how the proposed methodology could be applied in real domain, we will retrieve patents related to big data from patent databases in the world. Using searched patent data, we perform a case study by text mining preprocessing and multiple linear regression of statistics.
Probabilistic Modeling of Photovoltaic Power Systems with Big Learning Data Sets
Cho, Hyun Cheol ; Jung, Young Jin ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 412~417
DOI : 10.5391/JKIIS.2013.23.5.412
Analytical modeling of photovoltaic power systems has been receiving significant attentions in recent years in that it is easy to apply for prediction of its dynamics and fault detection and diagnosis in advanced engineering technologies. This paper presents a novel probabilistic modeling approach for such power systems with a big data sequence. Firstly, we express input/output function of photovoltaic power systems in which solar irradiation and ambient temperature are regarded as input variable and electric power is output variable respectively. Based on this functional relationship, conditional probability for these three random variables(such as irradiation, temperature, and electric power) is mathematically defined and its estimation is accomplished from ratio of numbers of all sample data to numbers of cases related to two input variables, which is efficient in particular for a big data sequence of photovoltaic powers systems. Lastly, we predict the output values from a probabilistic model of photovoltaic power systems by using the expectation theory. Two case studies are carried out for testing reliability of the proposed modeling methodology in this paper.
Two-Dimensional Localization Problem under non-Gaussian Noise in Underwater Acoustic Sensor Networks
Lee, DaeHee ; Yang, Yeon-Mo ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 418~422
DOI : 10.5391/JKIIS.2013.23.5.418
This paper has considered the location estimation problem in two dimension space by using a non-linear filter under non-Gaussian noise in underwater acoustic sensor networks(UASNs). Recently, the extended Kalman filter (EKF) is widely used in location estimation. However, the EKF has a lot of problems in the non-linear system under the non-gaussian noise environment like underwater environment. In this paper, we propose the improved Two-Dimension Particle Filter (TDPF) using the re-interpretation distribution techniques based on the maximum likelihood (ML). Through the simulation, we compared and analyzed the proposed TDPF with the EKF under the non-Gaussian underwater sensor networks. Finally, we determined that the TDPF's result shows more accurate localization than EKF's result.
Face Tracking and Recognition in Video with PCA-based Pose-Classification and (2D)
PCA recognition algorithm
Kim, Jin-Yul ; Kim, Yong-Seok ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 423~430
DOI : 10.5391/JKIIS.2013.23.5.423
In typical face recognition systems, the frontal view of face is preferred to reduce the complexity of the recognition. Thus individuals may be required to stare into the camera, or the camera should be located so that the frontal images are acquired easily. However these constraints severely restrict the adoption of face recognition to wide applications. To alleviate this problem, in this paper, we address the problem of tracking and recognizing faces in video captured with no environmental control. The face tracker extracts a sequence of the angle/size normalized face images using IVT (Incremental Visual Tracking) algorithm that is known to be robust to changes in appearance. Since no constraints have been imposed between the face direction and the video camera, there will be various poses in face images. Thus the pose is identified using a PCA (Principal Component Analysis)-based pose classifier, and only the pose-matched face images are used to identify person against the pre-built face DB with 5-poses. For face recognition, PCA, (2D)PCA, and
algorithms have been tested to compute the recognition rate and the execution time.
Classification of Aroma Using Neural Network
Kim, Yong Soo ; Kim, Han-Soo ; Kim, Sun-Tae ; Lim, Mi-Hye ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 431~435
DOI : 10.5391/JKIIS.2013.23.5.431
Aroma has been used for healing for a long time. The healing effects depend on aroma used. We made gas sensor array system to classify aromas systematically. We used outputs of sensors as the input to IAFC neural network. Results show that the neural network successfully classified jasmine, orange, roman chamomile, and lavender into 4 classes, and classified without any error.
Operator Modeling and Design of Fuzzy Controller for a Wire-Driven Heavy Material Lifting System
Song, Bo-Wei ; Seo, Hyun-Duk ; Lee, Yun-Jung ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 436~443
DOI : 10.5391/JKIIS.2013.23.5.436
This paper presents design methods of a fuzzy controller and an operator model for a wire-driven heavy material lifting system helping human workers. The wire-driven heavy material lifting system is a kind of human-assistive systems in which a human is involved in the control loop. Thus, human's control characteristics and requirement of reducing worker's force to lift a heavy material are considered in the design process of the proposed fuzzy controller. An automatic weight measurement algorithm during the early stage of lifting is also introduced. Finally, the effectiveness and performance of the proposed system are proved by experiments.
Prediction of Centerlane Violation for vehicle in opposite direction using Fuzzy Logic and Interacting Multiple Model
Kim, Beomseong ; Choi, Baehoon ; An, Jhonghyen ; Lee, Heejin ; Kim, Euntai ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 444~450
DOI : 10.5391/JKIIS.2013.23.5.444
For intelligent vehicle technology, it is very important to recognize the states of around vehicles and assess the collision risk for safety driving of the vehicle. Specifically, it is very fatal the collision with the vehicle coming from opposite direction. In this paper, a centerlane violation prediction method is proposed. Only radar signal based prediction makes lots of false alarm cause of measurement noise and the false alarm can make more danger situation than the non-prediction situation. We proposed the novel prediction method using IMM algorithm and fuzzy logic to increase accuracy and get rid of false positive. Fuzzy logic adjusts the radar signal and the IMM algorithm appropriately. It is verified by the computer simulation that shows stable prediction result and fewer number of false alarm.
Fuzzy Relation-Based Analysis of Korean Foods and Adjectives for Taste Evaluation
Lee, Joonwhoan ; Park, Keunho ; Rho, Jeong-Ok ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 451~459
DOI : 10.5391/JKIIS.2013.23.5.451
In this paper we analyze the Korean foods and sensory adjectives that can be used for the taste expression of corresponding food based on the fuzzy relation. In order to construct fuzzy relation we gathered and chose 87 related Korean adjectives for expressing not only taste but also smell from foods. After then we performed a sensory evaluation for 51 Korean foods with 20 subjects to check the proper adjectives when they take a food. Based on the data collected by the evaluation a fuzzy relation is constructed and used for the analysis of the properties of food and adjectives. In addition the composition of the fuzzy relation provides the fuzzy tolerance(compatibility) relation among foods as well as that among adjectives. From the fuzzy complete
-cover of the relations we could explore the taxonomy of food or adjectives. We expect that the fuzzy relation-based scheme in the paper can be utilized for analysis of the sensory adjectives like smelling and tactile sensation.
Tracking Path Generation of Mobile Robot for Interrupting Human Behavior
Jin, Taeseok ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 460~465
DOI : 10.5391/JKIIS.2013.23.5.460
In this paper, we describe a security robot system to control human's behavior in the security area. In order to achieve these goals, we present a method for representing, tracking and human blocking by laserscanner systems in security area, with application to pedestrian tracking in a crowd. When it detects walking human who is for the security area, robot calculates his velocity vector, plans own path to forestall and interrupts him who want to head restricted area and starts to move along the estimated trajectory. While moving the robot continues these processes for adapting change of situation. After arriving at an opposite position human's walking direction, the robot advises him not to be headed more and change his course. The experimental results of estimating and tracking of the human in the wrong direction with the mobile robot are presented.
An Efficient Gait Generation Method for Quadruped Robot with Waist Joints
Kim, Dong Sub ; Choi, Yoon Ho ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 466~472
DOI : 10.5391/JKIIS.2013.23.5.466
In this paper, we propose a gait generation method for a quadruped robot using the waist joints which can minimize the body shake during the locomotion. In this proposed method, we first calculate the hip coordinate of tilted body using the geometrical model of a quadruped robot, and then move the CoG(Center of Gravity) of a quadruped robot using 2-DOF waist joints to minimizes the body shake. In addition, the gait of a quadruped robot is generated based on the wave gait method. Finally, we verify the effectiveness of the proposed method by comparing with that of the previous method through the computer simulations.
Design of Heavy Rain Advisory Decision Model Based on Optimized RBFNNs Using KLAPS Reanalysis Data
Kim, Hyun-Myung ; Oh, Sung-Kwun ; Lee, Yong-Hee ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 473~478
DOI : 10.5391/JKIIS.2013.23.5.473
In this paper, we develop the Heavy Rain Advisory Decision Model based on intelligent neuro-fuzzy algorithm RBFNNs by using KLAPS(Korea Local Analysis and Prediction System) Reanalysis data. the prediction ability of existing heavy rainfall forecasting systems is usually affected by the processing techniques of meteorological data. In this study, we introduce the heavy rain forecast method using the pre-processing techniques of meteorological data are in order to improve these drawbacks of conventional system. The pre-processing techniques of meteorological data are designed by using point conversion, cumulative precipitation generation, time series data processing and heavy rain warning extraction methods based on KLAPS data. Finally, the proposed system forecasts cumulative rainfall for six hours after future t(t=1,2,3) hours and offers information to determine heavy rain advisory. The essential parameters of the proposed model such as polynomial order, the number of rules, and fuzzification coefficient are optimized by means of Differential Evolution.
Lateral Control of High Speed Flight Based on Type-2 Fuzzy Logic
Song, Jin-Hwan ; Jeon, Hong-Tae ;
Journal of Korean Institute of Intelligent Systems, volume 23, issue 5, 2013, Pages 479~486
DOI : 10.5391/JKIIS.2013.23.5.479
There exist two major difficulties in developing flight control system: nonlinear dynamic characteristics and time-varying properties of parameters of aircraft. Instead of the difficulties, many high reliable and efficient control methodologies have been developed. But, most of the developed control systems are based on the exact mathematical modelling of aircraft and, in the absence of such a model, it is very difficult to derive performance, robustness and nominal stability. From these aspects, recently, some approaches to utilizing the intelligent control theories such as fuzzy logic control, neural network and genetic algorithm have appeared. In this paper, one advanced intelligent lateral control system of a high speed fight has been developed utilizing type-2 fuzzy logic, which can deduce the uncertainty problem of the conventional fuzzy logic. The results will be verified through computer simulation.