• 한국물환경학회지
• /
• 제32권2호
• /
• pp.135-141
• /
• 2016

This study investigated changes of hydrology, soil characteristics, riparian vegetation communities, and geomorphology in sandbars before and after sand-mining to determine the effect of sand-mining at upstream of Guemgang and Bochungcheon streams in Korea. Sand-mining events affected the mining area. They supplied organic matters and nutrients during flood. Sediment deposition caused soil texture change and expansion of vegetation area. However, riverbeds were stabilized after the disturbance. According to the analyses of aerial photographs, the vegetation area was significantly expanded in both dam-regulated streams and dam-unregulated streams after sand-mining. Willow shrubs advanced in disturbed area at an average of 10 years after sand-mining. It took willows trees 10.6 years to become dominant communities. Therefore, it took a total of 20.6 years for new riparian forest to form in sandbar after sand-mining. Our results confirmed that stream flow condition were dependent on vegetation recruitment in dam-regulated streams and dam-unregulated streams. For willow recruitment in unregulated streams, calculation of water level below dimensionless bed shear stress is important because low water level variation is a limiting factor of vegetation recruitment.

• 한국물환경학회지
• /
• 제26권3호
• /
• pp.474-481
• /
• 2010

This study was conducted to describe the characteristics of Non-point source (NPS) Pollution discharge from a coal mining area in Korea. The study areas is located on the Dogye site, Samchuk, Kangwon Province Coal Corporation and the Jangsung site, Taebaek, Kangwon Province Coal Corporation. The monitoring system was installed at a drainage channel and water samples and rainfall events were collected during March 2008 to February 2009. The collected water samples were analyzed with respect to SS, BOD, $COD_{Cr}$, $COD_{Mn}$, T-N, T-P, and TOC, respectively. It was observed that the runoff and water quality were largely influenced by mine drainage. Also a significant relationship was observed from the correlation between flow and water quality, flow and NPS. And estimated Event Mean Concentration (EMC), NPS pollution loads were Dogey coal mine and Taeback coal mine respectively. As the study progresses in the future, runoff and pollution loads will be updated.

• 한국정보시스템학회지:정보시스템연구
• /
• 제14권2호
• /
• pp.277-292
• /
• 2005

This paper uses a data mining approach to develop bankruptcy prediction models suitable for traditional (off-line) companies and electronic (on-line) companies. It observes the differences in the composition prediction models between these two types of companies and provides interpretation of bankruptcy classifications. The bankruptcy prediction models revealed the major variables in predicting bankruptcy to be 'cash flow to total assets' and 'gross value-added to net sales' for traditional off-line companies while 'cash flow to liabilities','gross value-added to net sales', and 'current ratio' for electronic companies. The accuracy rates of final prediction models for traditional off-line and electronic companies were found to be $84.7\%\;and\;82.4\%$, respectively. When the model for traditional off-line companies was applied for electronic companies, prediction accuracy dropped significantly in the case of bankruptcy classification (from $70.4\%\;to\;45.2\%$) at the level of a blind guess ($41.30\%$). Therefore, the need for different models for traditional off-line and electronic companies is justified.

• 한국컴퓨터정보학회논문지
• /
• 제23권12호
• /
• pp.43-48
• /
• 2018

In this paper, more efficient classification result could be obtained by applying the combination of the Hidden Markov Model and SVM Model to HMSV algorithm gene expression data which simulated the stochastic flow of gene data and clustering it. In this paper, we verified the HMSV algorithm that combines independently learned algorithms. To prove that this paper is superior to other papers, we tested the sensitivity and specificity of the most commonly used classification criteria. As a result, the K-means is 71% and the SOM is 68%. The proposed HMSV algorithm is 85%. These results are stable and high. It can be seen that this is better classified than using a general classification algorithm. The algorithm proposed in this paper is a stochastic modeling of the generation process of the characteristics included in the signal, and a good recognition rate can be obtained with a small amount of calculation, so it will be useful to study the relationship with diseases by showing fast and effective performance improvement with an algorithm that clusters nodes by simulating the stochastic flow of Gene Data through data mining of BigData.

• Journal of Power Electronics
• /
• 제19권3호
• /
• pp.655-664
• /
• 2019

In order to analyze the nonlinear phenomena of the bifurcation and chaos caused by the switching of nonlinear switching devices in inductively coupled power transfer (ICPT) systems, a Jacobian matrix model, based on discrete mapping numerical modeling, is established to judge the system stability of the periodic closed orbit and to study the nonlinear behavior of Hopf bifurcation in a system under full resonance. The general flow of the parameter design, based on the stability principle for ICPT systems, is proposed to avoid the chaos and bifurcation phenomena caused by unreasonable parameter selection. Firstly, based on the state equation of SS-type compensation, a three-dimensional bifurcation diagram with the coupling coefficient as the bifurcation parameter is established with a numerical simulation to observe the nonlinear phenomena in the system. Then Filippov's method based on a Jacobian matrix model is adopted to deduce the boundary of stable operation and to judge the type of the bifurcation in the system. Then the general flow of the parameter design based on the stability principle for ICPT systems is proposed through the above analysis to realize stable operation under the conditions of weak coupling. Finally, an experimental platform is built to confirm the correctness of the numerical simulation and modeling.

• 한국정보전자통신기술학회논문지
• /
• 제12권3호
• /
• pp.231-241
• /
• 2019

웹 사용 마이닝 (WUM)은 웹 마이닝과 데이터 마이닝 기술의 응용 중의 하나다. 웹 마이닝 기술은 사용자가 웹 사이트에 액세스 할 때 웹 사용자가 생성 한 웹 서버 로그 데이터를 사용하여 사용자의 액세스 패턴을 식별하고 분석하는데 사용된다. 따라서 우선 데이터 마이닝 기술을 적용하여 웹 로그에서 사용자 액세스 패턴을 발견하기 전에 합리적인 방법으로 데이터를 수집해야 한다. 데이터 수집의 중요한 일은 사용자의 웹 사이트 방문 과정에서 사용자의 자세한 클릭 동작을 효율적으로 얻는 것이다. 이 논문은 주로 데이터 수집 전략 및 필드 추출 알고리즘과 같은 웹 사용 마이닝 데이터 프로세스의 첫 단계 이전의 데이터 수집 단계에 중점을 둔다. 필드 추출 알고리즘은 로그 파일에서 필드를 분리하는 프로세스를 수행하며 대용량의 사용자 데이터에 대한 실제 응용에도 사용된다.

• Geomechanics and Engineering
• /
• 제15권4호
• /
• pp.1017-1027
• /
• 2018

To explore the influence of coal thickness on the mechanical behavior and the failure characteristics of rock-coal-rock (RCR) mass, the experimental investigation of uniaxial compressive tests was conducted first and then a systematic numerical simulation by particle flow code (PFC2D) was performed to deeply analyze the failure mechanical behavior of RCR specimens with different coal thicknesses in conventional compression tests. The overall elastic modulus and peak stress of RCR specimens lie between the rock and the coal. Inter-particle properties were calibrated to match the physical sample strength and the stiffness response. Numerical simulation results show that the deformation and strength behaviors of RCR specimens depend not only on the coal thickness, but also on the confining pressure. Under low confining pressures, the overall failure mechanism of RCR specimen is the serious damage of coal section when the coal thickness is smaller than 30 mm, but it is shear failure of coal section when the coal thickness is larger than 30 mm. Whereas under high confining pressures, obvious shear bands exist in both the coal section and the rock section when the coal thickness is larger than 30 mm, but when the coal thickness is smaller than 30mm, the failure mechanism is serious damage of coal section and shear failure of rock section.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권6호
• /
• pp.3230-3253
• /
• 2017

Android malware steals users' private information, and embedded unsafe advertisement (ad) libraries, which execute unsafe code causing damage to users. The majority of such traffic is HTTP and is mixed with other normal traffic, which makes the detection of malware and unsafe ad libraries a challenging problem. To address this problem, this work describes a novel HTTP traffic flow mining approach to detect and categorize Android malware and unsafe ad library. This work designed AndroCollector, which can automatically execute the Android application (app) and collect the network traffic traces. From these traces, this work extracts HTTP traffic features along three important dimensions: quantitative, timing, and semantic and use these features for characterizing malware and unsafe ad libraries. Based on these HTTP traffic features, this work describes a supervised classification scheme for detecting malware and unsafe ad libraries. In addition, to help network operators, this work describes a fine-grained categorization method by generating fingerprints from HTTP request methods for each malware family and unsafe ad libraries. This work evaluated the scheme using HTTP traffic traces collected from 10778 Android apps. The experimental results show that the scheme can detect malware with 97% accuracy and unsafe ad libraries with 95% accuracy when tested on the popular third-party Android markets.

• 디지털콘텐츠학회 논문지
• /
• 제19권2호
• /
• pp.363-370
• /
• 2018

최근 랜섬웨어 탐지는 디지털 콘텐츠 보호를 위한 컴퓨터 보안 분야에서 중요한 주요한 이슈가 되고 있다. 그러나 불행하게도 현재 시그니쳐 기반이나 정적 탐지 모델의 경우 압축 및 암호화 등의 기법을 이용하여 탐지를 피해갈 수 있다. 이를 극복하기 위해 본 논문에서는 RF, SVM, SL, NB 알고리즘 같은 데이터 마이닝 기법을 이용한 다이나믹 랜섬웨어 탐지 시스템을 제안하였다. 이 기법은 실제 소프트웨어를 구동 시켜 동작 행위를 추출해 API 호출 흐름 그래프를 만들고 그 특징을 분석에 이용하였다. 그 후 데이터 정규화, 특징 선택 작업을 진행하였다. 우리는 이러한 분석과정을 더욱더 개선 시켰다. 마지막으로 데이터 마이닝 알고리즘을 적용시켜 랜섬웨어인지를 판별하였다. 제안한 알고리즘의 성능 측정을 위해 더 적합한 추가 샘플 랜섬웨어 데이터를 수집하여 실험하였고 탐지성능이 향상되었음을 보여주었다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제6권12호
• /
• pp.3315-3337
• /
• 2012

Crowded scenes analysis is a challenging topic in computer vision field. How to detect diverse motion patterns in crowded scenarios from videos is the critical yet hard part of this problem. In this paper, we propose a novel approach to mining motion patterns by utilizing motion information during both long-term period and short interval simultaneously. To capture long-term motions effectively, we introduce Motion History Image (MHI) representation to access to the global perspective about the crowd motion. The combination of MHI and optical flow, which is used to get instant motion information, gives rise to discriminative spatial-temporal motion features. Benefitting from the robustness and efficiency of the novel motion representation, the following motion pattern mining is implemented in a completely unsupervised way. The motion vectors are clustered hierarchically through automatic hierarchical clustering algorithm building on the basis of graphic model. This method overcomes the instability of optical flow in dealing with time continuity in crowded scenes. The results of clustering reveal the situations of motion pattern distribution in current crowded videos. To validate the performance of the proposed approach, we conduct experimental evaluations on some challenging videos including vehicles and pedestrians. The reliable detection results demonstrate the effectiveness of our approach.