• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1032-1038
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• 2008

KTX train system is composed of TORNAD* network for transmitting information of train's internal equipments and OBCS which proceed information within train. OBCS of one trainset consisted of 28 equipments takes intelligent and dynamic composition according to equipment handling, train command and control flow. Each OBCS which is installed within trainset handle and supervise mutually action information about equipments, transmit it to driver to transmit information about train operation and preventive management. This mutual supervision and information transmission use KTX TORNAD* network system. TORNAD* network system is the one which is uniquely developped by GEC ALSTHOM, the KTX trainset manafacturing provider and this field is excluded from technical know-how transfer item. Through the research on analysis method of KTX TORNAD* system data structure which is operating on Seoul-Pusan Line, I hope that this thesis can contribute to train network system's standardization after applying it to improvement of train network system maintenance, enhancing quality of train service and applying it to future Korean rolling stock network system development.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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• pp.154-161
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• 2018

This study conducted to predict the stock market prices based on the assumption that internet news articles might have an impact and effect on the rise and fall of stock market prices. The internet news articles were tested to evaluate the accuracy by comparing predicted values of the actual stock index and the forecasting models of the companies. This paper collected stock news from the internet, and analyzed and identified the relationship with the stock price index. Since the internet news contents consist mainly of unstructured texts, this study used text mining technique and multiple regression analysis technique to analyze news articles. A company H as a representative automobile manufacturing company was selected, and prediction models for the stock price index of company H was presented. Thus two prediction models for forecasting the upturn and decline of H stock index is derived and presented. Among the two prediction models, the error value of the prediction model (1) is low, and so the prediction performance of the model (1) is relatively better than that of the prediction model (2). As the further research, if the contents of this study are supplemented by real artificial intelligent investment decision system and applied to real investment, more practical research results will be able to be developed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.18 no.4
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• pp.335-342
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• 2000

This study is to integrate and to analyzegraphic and attribute data in features which should be preceding for the ITS construction. A 1:5000 scale map as a national basemap was used and was checked for feasibility. For the convenience of position confirmation, the analysis component was developed and was linked to addresses. Also, the attribute information was considered on the shortest route, and optimal route was selected by investigating traffic volumes on main crossroads depending on the time zone. The optimal route could be selected by analyzing user's demands, position and statistical data, and the position information could be provided efficiently by combining the graphic information with the land register information.

• IE interfaces
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• v.16 no.1
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• pp.8-15
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• 2003

With rapid growth of internet technology, companies have developed an information system such as the electronic catalog for product data in the E-Business. Due to the heuristic nature of the catalog search for proper tools in the specific process, the intelligent and user friendly methods residing in the search process give a comfortable environment even for the beginners in the field. In this paper, we develop a web based catalog for machining tools especially in Milling process. It has two distinct procedures for the users of the catalog; Search and Analysis. The Search is to select a proper cutter, insert, component combination in the developed relational database based on the cutting process and material. The Analysis is to suggest a recommended optimal cutting conditions based on the machining tools and selected materials. All of these procedures are stored in a server with a program based on the ASP and Java Script where the procedure is initiated by the client using the internet which is accessed through insert. With the success on implementing the above engineering database in the internet, we can provide the foundation for developing PDM with heuristic procedure.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.12
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• pp.1192-1203
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• 2005

A technical analysis was conducted to predict the development trend for heat pump system. The study was based on a submitted patent from 1983 to 2002 in Korea, U.S.A. and Japan. The total number of raw data from the registered database was 19,261 and the obtained data to be analyzed through the filtering process was 5,143. Technical development of compression type heat pump was more dominant than the other types, absorption, adsorption, and chemical heat pump. The patents for compression type made up over $80\%$ in each country, Most of patents were developed for the defrosting and controlling technology of the compression type heat pump system. Approximately $24\%\;and\;62\%$ of the patents about compression type heat pump were for defrosting and control technologies, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6145-6158
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• 2019

It is a challenge for the current security industry to respond to a large number of malicious codes distributed indiscriminately as well as intelligent APT attacks. As a result, studies using machine learning algorithms are being conducted as proactive prevention rather than post processing. The k-NN algorithm is widely used because it is intuitive and suitable for handling malicious code as unstructured data. In addition, in the malicious code analysis domain, the k-NN algorithm is easy to classify malicious codes based on previously analyzed malicious codes. For example, it is possible to classify malicious code families or analyze malicious code variants through similarity analysis with existing malicious codes. However, the main disadvantage of the k-NN algorithm is that the search time increases as the learning data increases. We propose a fast k-NN algorithm which improves the computation speed problem while taking the value of the k-NN algorithm. In the test environment, the k-NN algorithm was able to perform with only the comparison of the average of similarity of 19.71 times for 6.25 million malicious codes. Considering the way the algorithm works, Fast k-NN algorithm can also be used to search all data that can be vectorized as well as malware and SSDEEP. In the future, it is expected that if the k-NN approach is needed, and the central node can be effectively selected for clustering of large amount of data in various environments, it will be possible to design a sophisticated machine learning based system.

• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.121-128
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• 2019

Computing systems have various vulnerabilities to cyber attacks. In particular, various cyber attacks that are intelligent in the information society have caused serious social problems and economic losses. Traditional security systems are based on misuse-based technology, which requires the continuous updating of new attack patterns and the real-time analysis of vast amounts of data generated by numerous security devices in order to accurately detect. However, traditional security systems are unable to respond through detection and analysis in real time, which can delay the recognition of intrusions and cause a lot of damage. Therefore, there is a need for a new security system that can quickly detect, analyze, and predict the ever-increasing cyber security threats based on machine learning and big data analysis models. In this paper, we present a IDS model that combines machine learning and big data technology.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.154-159
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• 2007

Wake distribution data of stem flow fields have been accumulated systematically by model tests. If the correlation between geometrical hull information and wake distribution is grasped through the accumulated data, this correlation can be helpful to designing similar ships. In this paper, Neuro-Fuzzy system that is emerging as a new knowledge over a wide range of fields nowadays is tried to estimate the wake distribution on the propeller plan. Neuro-Fuzzy system is well known as one of prospective and representative analysis method for prediction, classification, diagnosis of real complicated world problem, and it is widely applied even in the engineering fields. For this study three-dimensional stern hull forms and nominal wake values from a model test ate structured as processing elements of input and output layer, respectively. The proposed method is proved as an useful technique in ship design by comparing measured wake distribution with predicted wake distribution.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.3
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• pp.29-37
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• 2008

In this parer, we purpose for applying the RFID(Radio Frequency IDentification) system in National Road Traffic Volume Survey. Because there is limitation for shipping RFID Tag on every car, we firstly defined Expansion (process of making the number of all cars which passed survey point from sampling data) and determined the best methodology among 3 methodologies (Time factor Model, Fuzzy Model, Artificial Neural Network). As a result of analysis, Time Factor Model was chosen as the best methodology for Expansion. Also, we analyzed to find an application of the RFID system in National Road Traffic Volume Survey and obtained a possibility applying it. It is expected that if the RFID system is used in Traffic Volume Survey, the survey cost is saved than before.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.5 no.2 s.10
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• pp.19-28
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• 2006

In the IEEE 802.15.4a system, they require simple, economical and low power consumption transmitter and receiver to transmit low rate data and to identify distance and location. To meet these requirements, LR-WPAN system use transmitter and receiver with simple modulation and demodulation scheme. In this paper, use PPM+BPSK modulation and windowing scheme to overcome multipath fading effect. Then we apply this channel estimation scheme to LR-WPAN system and compare performance depends on transmitter scheme. Proposed method using preamble to find channel characteristic out and we compensate distorted data with that information. Therefore we can detect signal easily at the demodulation part. Simulation result shows that performance evaluation is greater at the NLOS channel than LOS channel no matter what the receiver scheme.