• Journal of the Korean Society for Railway
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• v.11 no.2
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• pp.188-194
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• 2008

Brake disks for rolling stock are exposed to thermal fatigue during braking, and thermal cracks occur on surface of disks. Thermal cracks can cause serious accidents, deterioration of braking performance and increase of maintenance cost due to frequent exchange of friction materials. In this study, candidate materials with high-heat resistance were selected by searching the literature. By using cast specimens made of the candidate materials, chemical composition, crystal structure and graphite type were analyzed. In addition, friction coefficient and wear were measured and compared with values for the disk material in service. As a result, it was shown that the NiCrMo has highest tensile strength and lowest friction coefficient and the disk material in service has the most stable friction characteristics.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.718-723
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• 2008

Bearing is one of the important mechanical elements used in various industrial equipments. Most of failures occurred during the equipment operation result from bearing defects and breakages. Therefore, monitoring of bearings is essential in preventing equipment breakdowns and reducing unexpected loss. The purpose of this paper is to present an online monitoring method to predict bearing states using vibration signals. Bearing vibrations, which are collected as a form of profile signal, are first analyzed by a discrete wavelet transform. Next, some statistical features are obtained from the resultant wavelet coefficients. In order to select significant ones among them, analysis of variance (ANOVA) is employed in this paper. Statistical features screened in this way are used as input variables to support vector machine (SVM). An hierarchical SVM tree is proposed for dealing with multi-class problems. The result of numerical experiments shows that the proposed SVM tree has a competent performance for classifying bearing fault states.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1153-1160
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• 2018

As the number of online game users increases and the market size grows, various kinds of cheating are occurring. Game bots are a typical illegal program that ensures playtime and facilitates account leveling and acquisition of various goods. In this study, we propose a method to detect game bots based on user action time interval (ATI). This technique observes the behavior of the bot in the game and selects the most frequent actions. We distinguish between normal users and game bots by applying Machine Learning to feature frequency, ATI average, and ATI standard deviation for each selected action. In order to verify the effectiveness of the proposed technique, we measured the performance using the actual log of the 'Aion' game and showed an accuracy of 97%. This method can be applied to various games because it can utilize all actions of users as well as character movements and social actions.

• Journal of Korea Multimedia Society
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• v.17 no.1
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• pp.69-76
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• 2014

It is hard to represent massive terrain data in real-time even using recent graphics hardware. In order to process massive terrain data, mesh simplification method such as continuous Level-of-Detail is commonly used. However, existing GPU-based methods using quad-tree structure such as geometry splitting, produce lots of vertices to traverse the quad-tree and retransmit those vertices back to the GPU in each tree traversal. Also they have disadvantage of increase of tree size since they construct the tree structure using texture. To solve the problem, we proposed GPU-base chunked LOD technique for real-time terrain rendering. We restrict depth of tree search and generate chunks with tessellator in GPU. By using our method, we can efficiently render the terrain by generating the chunks on GPU and reduce the computing time for tree traversal.

• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.1-8
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• 2017

Accurate forecasting enables to effectively prepare for future phenomenon. Especially, meteorological phenomenon is closely related with human life, and it can prevent from damage such as human life and property through forecasting of weather and disaster that can occur. To respond quickly and effectively to oil spill accidents, it is important to accurately predict the movement of oil spills and the weather in the surrounding waters. In this paper, we selected four representative machine learning techniques: support vector machine, Gaussian process, multilayer perceptron, and radial basis function network that have shown good performance and predictability in the previous studies related to oil spill detection and prediction in meteorology such as wind, rainfall and ozone. we suggest the applicability of oil spill prediction model based on machine learning.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.525-531
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• 2009

Data mining is the process of extracting hidden patterns from collected data. At this time, for collected data which take important role as the basic information for prediction and recommendation, the process to discriminate incorrect data in order to enhance the performance of analysis result, is needed. The existing methods to discriminate unexpected data from collected data, mainly relies on methods which are based on statistics or simple distance between data. However, for these methods, the problematic point that even meaningful data could be excluded from analysis due that the environment and characteristic of the relevant data are not considered, exists. This study proposes a method to endow human heuristic knowledge with weight value through the comparison between collected data and human heuristic knowledge, and to use the value for creating a decision tree. The data discrimination by the method proposed is more credible as human knowledge is reflected in the created tree. The validity of the proposed method is verified through an experiment.

• Korean Journal of Microbiology
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• v.31 no.4
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• pp.292-299
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• 1993

Agrobacterium tumefaciens KU12 isolated from Korea is able to induce tumors on various plants and catabolize octopine as a sole carbon and nitrogen source. A, tumefaciens KU12 contains three plasmids. Their sizes are 45.5 kb. 240 kb. and > 240 kb. respectively. For the purpose of identification of octopine type Ti plasmid, avirulent A, tumefacients A136 is transformed with plasmids isolated from KU12 by direct transformation. Transformants containing Ti plasmid were grown on AB medium containing octopine as a sole nitrogen source. The isolated strain, named KU911, contains only 240 kb plasmid. As a result of induction of crown gall and Southern hybridization with other octopine Ti plasmid pTiAch5, 240 kb plasmid named pTiKU12 was Ti plasmid.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.147-154
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• 2018

In this paper, we propose methods to acquire enemy object information, which is frequently required in multi-resolution model based war game and affects the simulation performance. In the information request method, a multi-resolution model selects information of a specific resolution and provides them to external objects. In the information announcement method, a multi-resolution model announces all information, and external objects select information of a specific resolution. In the information sharing method, external objects obtains information of a specific resolution by inquiring all information of a multi-resolution model which are stored in a shared space. Simulation results show that the information sharing method is more efficient than the information request method and the information announcement method because the information acquisition is fast. In addition, the proposed methods will increase the efficiency of war game operation by shortening the time for acquiring enemy forces' information.

• Bulletin of the Korean Space Science Society
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• 2010.04a
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• pp.37.5-38
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• 2010

경희대학교 우주탐사학과에서는 우주공간 탐사를 위해 Trio(TRiplet Ionospheric Observatory)-CINEMA(Cubesat for Ions, Neutrals, Electrons and MAgnetic fields)로 명명된 초소형 위성을 개발하고 있다. 과학임무는 지구 저궤도에서 고에너지 입자를 관측하는 것이며, 이를 위해 고에너지 (2~300keV) 입자 검출기와 자기장 측정기가 탑재된다. 저에너지 입자 검출기 시스템인 STEIN(SupraThermal Electrons, Ions, Neutrals)은 $1\times4$ Array의 개선된 실리콘 검출기와 이온, 전자, 중성입자를 분리할 수 있는 정전장 편향기, 그리고 신호를 처리하는 전자회로로 구성되어있다. 설계된 전자회로는 매우 작은 검출기 기판, 아날로그 기판과 디지털 기판으로 이루어져 있고, 475mW 이하의 저 전력으로 동작한다. 또한 2~100keV의 에너지를 1keV이하의 해상도로 30,000event/sec/pixel 까지 관측 할 수 있도록 회로를 설계하였다. 센서로 들어온 입자로 인해 발생한 펄스의 신호는 4개의 아날로그 회로가 담당하게 되는데, Folded cascode amplifier를 배치하여 증폭률을 높인 Charge sensitive amplifier를 통해 신호를 증폭하고, $2{\mu}s$ unipolar gaussian shaping amplifier를 통해 읽기 쉽게 처리된 신호를 상한파고선별기와 하한파고 선별기를 통해 유효 값 여부를 판단하고, 피크 검출기를 통해 피크의 타이밍을 측정한 뒤 신호를 아날로그-디지털 변환 회로를 통하여 8bit의 값으로 나타내어, 입자들의 Spectrum을 측정하게 된다. 크기와 소비전력이 적음에도 검출성능이 우수하기 때문에 이 시스템은 향후 우주탐사 시스템에 있어 매우 중요한 역할을 수행 할 것으로 생각한다.

• Smart Media Journal
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• v.6 no.1
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• pp.22-29
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• 2017

Due to the rapid advancement and distribution of smart devices of late, document data on the Internet is on the sharp increase. The increment of information on the Web including a massive amount of documents makes it increasingly difficult for users to understand corresponding data. In order to efficiently summarize documents in the field of automated summary programs, various researches are under way. This study uses TextRank algorithm to efficiently summarize documents. TextRank algorithm expresses sentences or keywords in the form of a graph and understands the importance of sentences by using its vertices and edges to understand semantic relations between vocabulary and sentence. It extracts high-ranking keywords and based on keywords, it extracts important sentences. To extract important sentences, the algorithm first groups vocabulary. Grouping vocabulary is done using a scale of specific weight. The program sorts out sentences with higher scores on the weight scale, and based on selected sentences, it extracts important sentences to summarize the document. This study proved that this process confirmed an improved performance than summary methods shown in previous researches and that the algorithm can more efficiently summarize documents.