• Genomics & Informatics
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• 제14권4호
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• pp.166-172
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• 2016

Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.

• 터널과지하공간
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• 제3권1호
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• pp.63-79
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• 1993

The design of tunnels in rock masses often demands more informations on geologic features and rock mass properties than acquired by usual field survey and laboratory testings. In practice, the situation that a perfect set of geological and mechanical input data is given to geomechanics design engineer is rare, while the engineers are asked to achieve a high level of reliability in their design products. This study presents an artificial neural network which is developed to resolve the difficulties encountered in conventional design techniques, particulary the problem of deteriorating the confidence of existing numerical techniques such as the finite element, boundary element and distinct element methods due to the incomplete adn vague input data. The neural network has inferring capabilities to identify the possible failure modes, support requirements and its timing for underground openings, from previous case histories. Use of the neural network has resulted in a better estimate of the correlation between systems of rock mass classifications such as the RMR and Q systems. A back propagation learning algorithm together with a multi-layer network structure is adopted to enhance the inferential accuracy and efficiency of the neural network. A series of experiments comparing the results of the neural network with the actual field observations are performed to demonstrate the abilities of the artificial neural network as a new tunnel design assistance system.

• 한국지형공간정보학회:학술대회논문집
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• 한국지형공간정보학회 1993년도 학술발표회 개요집
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• pp.141-145
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• 1993

수치변화탐지에 있어서 종래의 단일 밴드 분석법에 대한 대안으로, 선정된 조합에 의한 복합 밴드의 정보를 활용하는 기하거리분석법이라는 새로운 앨고리듬을 개발하였으며, 분석된 두 앨고리듬 중 기하거리분석법이 변화탐지에 보다 좋은 결과를 나타냈다. 기하거리분석법은 식생 형 변화에 대한 복합 밴드의 정보를 활용할 수가 있고, 데이타의 양을 줄일 수 있는 장점이 있다. 하지만, 이 방법에 대해서는 여러 환경에서의 보다 세밀한 정량적 분석이 요구되어진다. 각 변화영상에 대한 최적영역수준은 여러가지 정확도지수를 분석하여 결정하였으며, (변화)구분도에 대한 표준정확도로는 카파일도계수를 적용하였다.

• 한국추진공학회지
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• 제16권6호
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• pp.62-72
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• 2012

우주 비행체와 유도 미사일에 적용되는 궤도천이 및 자세제어 시스템(이하 DACS)은 비행체의 궤도를 천이시키거나 미세한 자세 제어를 수행하게 된다. DACS를 개발하기 위해서는 추력변화 최대화를 위한 핀틀/노즐의 형상 조합, 핀틀 구동력 최소화를 위한 공력하중 저감, 다축 제어 알고리즘에 대한 연구가 중요하다. 본 논문에서는 이러한 DACS 시스템에 대한 소개와 분류, 국내외 연구 개발 동향에 대해 살펴보고 향후 연구 개발 전망을 제시하였다.

• Industrial Engineering and Management Systems
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• 제11권4호
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• pp.371-384
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• 2012

This study proposes a multi-level data analysis approach to identify both superficial and latent relationships among variables in the data set obtained from a vocational rehabilitation (VR) services program of people with significant disabilities. At the first layer, data mining and statistical predictive models are used to extract the superficial relationships between dependent and independent variables. To supplement the findings and relationships from the analysis at the first layer, association rule mining algorithms at the second layer are employed to extract additional sets of interesting associative relationships among variables. Finally, nonlinear nonparametric canonical correlation analysis (NLCCA) along with clustering algorithm is employed to identify latent nonlinear relationships. Experimental outputs validate the usefulness of the proposed approach. In particular, the identified latent relationship indicates that disability types (i.e., physical and mental) and severity (i.e., severe, most severe, not severe) have a significant impact on the levels of self-esteem and self-confidence of people with disabilities. The identified superficial and latent relationships can be used to train education program designers and policy developers to maximize the outcomes of VR training programs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3794-3820
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• 2019

In recent years, a cloud environment with the ability to detect illegal behaviours along with a secured data storage capability is much needed. This study presents a cloud storage framework, wherein a 128-bit encryption key has been generated by combining deoxyribonucleic acid (DNA) cryptography and the Hill Cipher algorithm to make the framework unbreakable and ensure a better and secured distributed cloud storage environment. Moreover, the study proposes a DNA-based encryption technique, followed by a 256-bit secure socket layer (SSL) to secure data storage. The 256-bit SSL provides secured connections during data transmission. The data herein are classified based on different qualitative security parameters obtained using a specialized fuzzy-based classification technique. The model also has an additional advantage of being able to decide on selecting suitable storage servers from an existing pool of storage servers. A fuzzy-based technique for order of preference by similarity to ideal solution (TOPSIS) multi-criteria decision-making (MCDM) model has been employed for this, which can decide on the set of suitable storage servers on which the data must be stored and results in a reduction in execution time by keeping up the level of security to an improved grade.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2013년도 추계학술발표대회
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• pp.1370-1373
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• 2013

영상에서 사람의 얼굴 영상을 추출하여 성별 및 연령대를 자동으로 분석하는 시스템은 광고판 등을 이용한 마케팅, 보안, 통계 분야 등 여러 가지 적용이 가능하다. 이러한 시스템의 개발을 위해서는 얼굴 인식 알고리즘과 특성 분류 알고리즘이 요구된다. 그러나 기존 알고리즘의 경우 문제점이 존재한다. 얼굴 인식 알고리즘으로 가장 많이 사용되는 HAAR 특징은 오탐률이 높으며, 특성 분류 알고리즘으로 사용하는 Fisherface 기법의 경우 분류 Class가 3가지 이상시 분류 성공률이 현저히 떨어지는 문제점이 있다. 본 논문에서는 이 두 알고리즘의 문제점을 개선한 새로운 알고리즘을 제안한다. 얼굴 인식을 위해 기존 HAAR 특징과 LBP 특징을 결합하여 오탐률을 크게 감소시켰다. 또한 특성 분류를 위하여 3 Class 이상의 분류를 대체할 방법으로 2 Class-multi-level 반복 분류방식을 사용하였다. 대량의 데이터에 대한 실험을 통하여 제안한 방법이 기존 방법들보다 성능이 향상되었음을 보인다.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.103-111
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• 2021

Obesity affects individuals of all gender and ages worldwide; consequently, several studies have performed great works to define factors causing it. This study develops an effective method to trace obesity levels based on supervised data mining techniques such as Random Forest and Multi-Layer Perception (MLP), so as to tackle this universal epidemic. Notably, the dataset was from countries like Mexico, Peru, and Colombia in the 14- 61year age group, with varying eating habits and physical conditions. The data includes 2111 instances and 17 attributes labelled using NObesity, which facilitates categorization of data using Overweight Levels l I and II, Insufficient Weight, Normal Weight, as well as Obesity Type I to III. This study found that the highest accuracy was achieved by Random Forest algorithm in comparison to the MLP algorithm, with an overall classification rate of 96.7%.

• Nuclear Engineering and Technology
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• 제55권1호
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• pp.52-62
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• 2023

Various decontamination technologies have been developed for removing contaminated areas in industries. Although it is important to consider parameters such as safety, cost, and time when selecting the decontamination technology, till date their comparative study is missing. Furthermore, different decontamination technologies influence the decontamination effects in different ways. Therefore, this study compares different decontamination techniques for the steam generator using a multicriteria decision-making method. A steam generator is a large device comprising both low- and very low-level waste (LLW, VLLW) and reflects the difference in weights of the standards according to the classification of the waste. For LLW and VLLW decontaminations, chemical oxidizing reduction decontamination (CORD) and decontamination grit blasting were used as the preferred techniques, respectively, considering the purpose of decontamination differs based on the initial state of waste. An expert survey revealed that safety in LLW and waste minimization in VLLW exhibited high preference. This evaluation method can be applied not only to the comparison between each process, but also to the creation of process scenarios. Therefore, determining the decontamination approach using logical decision-making methods may improve the safety and economic feasibility of each step in the decommissioning process and ensure a public acceptance.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.534-541
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• 2011

Risk evaluation approaches for bidding on international construction projects are typically partitioned into three stages: country selection, project classification, and bid-cost evaluation. However, previous studies are frequently under attack in that they have several crucial limitations: 1) a dearth of studies about country selection risk tailored for the overseas construction market at a corporate level; 2) no consideration of uncertainties for input variable per se; 3) less probabilistic approaches in estimating a range of cost variance; and 4) less inclusion of covariance impacts. This study thus suggests a three-staged risk evaluation model to resolve these inherent problems. In the first stage, a country portfolio model that maximizes the expected construction market growth rate and profit rate while decreasing market uncertainty is formulated using multi-objective genetic analysis. Following this, probabilistic approaches for screening bad projects are suggested through applying various data mining methods such as discriminant logistic regression, neural network, C5.0, and support vector machine. For the last stage, the cost overrun prediction model is simulated for determining a reasonable bid cost, while considering non-parametric distribution, effects of systematic risks, and the firm's specific capability accrued in a given country. Through the three consecutive models, this study verifies that international construction risk can be allocated, reduced, and projected to some degree, thereby contributing to sustaining stable profits and revenues in both the short-term and the long-term perspective.