• 한국가스학회:학술대회논문집
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• 한국가스학회 2003년도 추계학술발표회 논문집
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• pp.126-130
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• 2003

• Geomechanics and Engineering
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• 제34권3호
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• pp.267-284
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• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.1187-1192
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• 2008

In this paper, a versatile multi-objective optimization concept for fatigue life prediction is introduced. Multi-objective decision making in engineering design refers to obtaining a preferred optimal solution in the context of conflicting design objectives. Compromise decision support problems are used to model engineering decisions involving multiple trade-offs. These methods typically rely on a summation of weighted attributes to accomplish trade-offs among competing objectives. This paper gives an interpretation of the decision parameters as governing both the relative importance of the attributes and the degree of compensation between them. The approach utilizes a response surface model, the compromise decision support problem, which is a multi-objective formulation based on goal programming. Examples illustrate the concepts and demonstrate their applicability.

• Journal of the Korean Data and Information Science Society
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• 제15권3호
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• pp.565-574
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• 2004

Classification tree algorithms including as CART by Brieman et al.(1984) in some aspects, recursively partition the data space with the aim of making the distribution of the class variable as pure as within each partition and consist of several steps. SUPPORT(smoothed and unsmoothed piecewise-polynomial regression trees) method of Chaudhuri et al(1994), a weighted averaging technique is used to combine piecewise polynomial fits into a smooth one. We focus on applying SUPPORT to a binary class variable. Logistic model is considered in the caculation techniques and the results are shown good classification rates compared with other methods as CART, QUEST, and CHAID.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.5023-5038
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• 2017

Big data is an emerging technology which deals with wide range of data sets with sizes beyond the ability to work with software tools which is commonly used for processing of data. When we consider a huge network, we have to process a large amount of network information generated, which consists of both normal and abnormal activity logs in large volume of multi-dimensional data. Intrusion Detection System (IDS) is required to monitor the network and to detect the malicious nodes and activities in the network. Massive amount of data makes it difficult to detect threats and attacks. Sequential Pattern mining may be used to identify the patterns of malicious activities which have been an emerging popular trend due to the consideration of quantities, profits and time orders of item. Here we propose a sequential pattern mining algorithm with fuzzy logic feature selection and fuzzy weighted support for huge volumes of network logs to be implemented in Apache Hadoop YARN, which solves the problem of speed and time constraints. Fuzzy logic feature selection selects important features from the feature set. Fuzzy weighted supports provide weights to the inputs and avoid multiple scans. In our simulation we use the attack log from NS-2 MANET environment and compare the proposed algorithm with the state-of-the-art sequential Pattern Mining algorithm, SPADE and Support Vector Machine with Hadoop environment.

• Journal of Information Processing Systems
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• 제16권2호
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• pp.329-342
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• 2020

In recent years, emotional text classification is one of the essential research contents in the field of natural language processing. It has been widely used in the sentiment analysis of commodities like hotels, and other commentary corpus. This paper proposes an improved W-LDA (weighted latent Dirichlet allocation) topic model to improve the shortcomings of traditional LDA topic models. In the process of the topic of word sampling and its word distribution expectation calculation of the Gibbs of the W-LDA topic model. An average weighted value is adopted to avoid topic-related words from being submerged by high-frequency words, to improve the distinction of the topic. It further integrates the highest classification of the algorithm of support vector machine based on the extracted high-quality document-topic distribution and topic-word vectors. Finally, an efficient integration method is constructed for the analysis and extraction of emotional words, topic distribution calculations, and sentiment classification. Through tests on real teaching evaluation data and test set of public comment set, the results show that the method proposed in the paper has distinct advantages compared with other two typical algorithms in terms of subject differentiation, classification precision, and F1-measure.

• 한국통신학회논문지
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• 제27권7B호
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• pp.653-663
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• 2002

3세대 비동기 IMT-2000 패킷망인 UMTS/GPRS는 독자적인 QoS 구조를 가지고 있으나 인터넷 서비스를 위해서는 IP-QoS의 수용이 불가피하다. 본 논문에서는 MPLS를 기반으로 하는 UMTS/GPRS에서의 IP-QoS 제공 방안을 소개하고자 한다. MPLS를 적용한 GPRS 망의 기능 구조를 포함한 QoS 지원 프레임워크를 제시하였으며, 패킷 스케줄링 부분에서는 Diffserv 모델을 근간으로 한 효율적인 스케줄링 방식을 제시하였다. 본 논문에서 제시하고 있는 스케줄링 방식은 특히 실시간 서비스에 대한 QoS 지원에 초점을 맞추어 Priority Queueing (PQ)과 Weighted Round Robin (WRR)이 결합된 형태의 새로운 버퍼 관리 방안을 포함하고 있다. NS-2 시뮬레이터를 이용하여 제안한 스케줄링 방식에 대한 성능 평가를 수행하였으며, 시뮬레이션 결과 그 타당성을 입증할 수 있었다.

• 정보처리학회논문지C
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• 제12C권5호
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• pp.687-694
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• 2005

ATM과 IP 망에서 혼잡발생시 높은 우선순위를 가진 패킷은 낮은 우선순위를 가진 패킷에 비해 영향을 적게 받도록 설계되어야 한다. 이러한 문제 해결을 위해, 본 논문에서는 기존 If 망에서 사용되는 가중치 CSFQ(Weighted Core-Stateless Fair Queueing)를 ATM 노드의 공간 우선순위(space priority) 정책에 적용하였다. 성능분석을 위해 임계치(threshold)를 갖는 MMPP/D/1/K 큐잉모델의 트래픽 클래스별 셀 손실률을 유도하고 그 결과를 논하였다. 분석결과를 통해 가중치 CSFQ 기법이 ATM 혹은 IP 노드에서 차별화된 서비스 제공에 매우 유용함을 보였다.

• Asia pacific journal of information systems
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• 제30권4호
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• pp.847-878
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• 2020

In the era of disruptive change, a data-driven approach is vital to Human Resource Management (HRM) of any leading organization, for it is used to gain a competitive advantage. HR analytics (HRA) has emerged as innovative technologies since advanced analytics, i.e., predictive or prescriptive analytics, were widely used in the High Performing Organizations (HPOs). Therefore, many organizations elevate themselves to become HPOs through Data Science on the "people side." This paper proposes a systematic literature review using the Literature Weighted Scoring (LWS) to develop a conceptual framework based on three adoption theories, which are the Technology-Organization-Environment (TOE), Diffusion of Innovation (DOI), and Unified Theory of Acceptance and Use of Technology (UTAUT). The results show that a total of 13 theory-derived factors are determined as influential factors affecting HRA adoption, and the top three factors are "Quantitative Self-Efficacy," "Top Management Support," and "Data Availability." The conceptual framework with hypotheses is proposed to provide a foundation for further studies on organizational HRA adoption.

• 한국항공운항학회지
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• 제24권4호
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• pp.44-52
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• 2016

The departure flow management is the planning tool to optimize the schedule of the departure aircraft and allows them to join smoothly into the overhead traffic flow. To that end, the arrival time prediction to the merge point for the cruising aircraft is necessary to determined. This paper proposes a trajectory prediction model for the cruising aircraft based on the machine learning approach. The proposed method includes the trajectory vectored from the procedural route and is applied to the historical data to evaluate the prediction performances.