• Asia pacific journal of information systems
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• 제19권2호
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• 국제초고층학회논문집
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• 제9권2호
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• pp.127-154
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• 2020

Disproportionate collapse triggered by local structural failure may cause huge casualties and economic losses, being one of the most critical civil engineering incidents. It is generally recognized that ensuring robustness of a structure, defined as its insensitivity to local failure, is the most acceptable and effective method to arrest disproportionate collapse. To date, the concept of robustness in its definition and quantification is still an issue of controversy. This paper presents a detailed review on about 50 quantitative measures of robustness for building structures, being classified into structural attribute-based and structural performance-based measures (deterministic and probabilistic). The definition of robustness is first described and distinguished from that of collapse resistance, vulnerability and redundancy. The review shows that deterministic measures predominate in quantifying structural robustness by comparing the structural responses of an intact and damaged structure. The attribute-based measures based on structural topology and stiffness are only applicable to elastic state of simple structural forms while the probabilistic measures receive growing interest by accounting for uncertainties in abnormal events, local failure, structural system and failure-induced consequences, which can be used for decision-making tools. There is still a lack of generalized quantifications of robustness, which should be derived based on the definition and design objectives and on the response of a structure to local damage as well as the associated consequences of collapse. Critical issues and recommendations for future design and research on quantification of robustness are provided from the views of column removal scenarios, types of structures, regularity of structural layouts, collapse modes, numerical methods, multiple hazards, degrees of robustness, partial damage of components, acceptable design criteria.

• 대한간호학회지
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• 제29권5호
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• pp.1087-1102
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• 1999

Diabetes mellitus, a universal and prevalent chronic disease, is projected to be one of the most formidable worldwide health problems in the 21st century. For those living with diabetes, there is a need for self-care skills to manage a complex medical regimen. Self-efficacy which refers to one's belief in his/her capability to monitor and perform the daily activities required to manage diabetes has be found to be related to self-care. The concept of self-efficacy comes from social cognitive theory which maintains that cognitive mechanism mediate the performance of behavior. The literature cites several research studies which show a strong relationship between self-efficacy and self-care behavior. Meta-analysis is a technique that enables systematic review and quantitative integration of the results from multiple primary studies that are relevant to a particular research question. Therefore, this study was done using meta-analysis to quantitatively integrate the results of independent research studies to obtain numerical estimates of the overall effect of a self-efficacy with diabetic patient on self-care behaviors. The research proceeded in three stages : 1) literature search and retrieval of studies in which self-efficacy was related to self-care, 2) coding, and 3) calculation of mean effect size and data analysis. Seventeen studies which met the research criteria included study population of adults with diabetes, measures of self-care and measures of self-efficacy as a predictive variable. Computation of effect size was done on DSTAT which is a statistical computer program specifically designed for meta-analysis. To determine the effect of self-efficacy on self-care practice homogeneity tests were conducted. Pooled effect size estimates, to determine the best subvariable for composite variables, metabolic control variables and component of self-efficacy and self-care, indicated that the effect of self-efficacy composite on self-care composite was moderate to large. The weighted mean effect size of self-efficacy composite and self-care composite were ＋.76 and the confidence interval was from ＋.66 to ＋.86 with the number of subjects being 1,545. The total for this meta-analysis result showed that the weighted mean effect sizes ranged from ＋.70 to ＋1.81 which indicates a large effect. But since reliabilities of the instruments in the primary studies were low or not stated, caution must be applied in unconditionally accepting the results from these effect sizes. Meta-analysis is a useful took for clarifying the status of knowledge development and guiding decision making about future research and this study confirmed that there is a relationship between self-efficacy and self-care in patients with diabetes. It, thus, provides support for nurses to promote self-efficacy in their patients. While most of the studies included in this meta-analysis used social cognitive theory as a framework for the study, some studies use Fishbein & Ajzen's attitude model as a model for active self-care. Future research is needed to more fully define the concept of self-care and to determine what it is that makes patients feel competent in their self-care activities. The results of this study showed that self-efficacy can promote self-care. Future research is needed with experimental design to determine nursing interventions that will increase self-efficacy.

• 정보처리학회논문지C
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• 제17C권3호
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• pp.271-280
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• 2010

차세대 이동통신 시스템에서는 3세대 진화망인 LTE(long-Term Evolution), WiMAX/WiBro, 차세대 WLAN등 다양한 무선 접속 기술이 All-IP 기반의 핵심망을 중심으로 통합되는 형태로 발전하고 있다. 이러한 발전에 따라 중첩된 다양한 무선 이종망 환경에서 최적의 조건을 제공하는 망으로의 접속을 제공하는 수직적 핸드오버가 필요하다. 그러나 현재까지는 각각의 네트워크가 독자적 서비스 제공을 위해 독립적인 무선자원관리 기능을 제공하여 왔으므로, 이종망 환경에서의 다양한 네트워크를 끊김없이 서비스를 제공하기 위해서는 개별 네트워크의 무선자원들을 통합적으로 관리하여 최적의 서비스를 제공할 수 있어야 할 것이다. 최근 이러한 무선 이종망 환경에서의 문제점을 해결하기 위해 적응적이동성을 위한 범용링크계층(GLL)과 통합무선자원관리(CRRM) 방식의 개념이 도입되고 있다. 본 논문에서는 LTE와 WLAN 사이에서의 효율적인 수직적 핸드오버를 위한 범용링크계층을 기반으로 정책기반과 다기준 의사결정법(MCDM)을 혼합한 수직적 핸드오버 알고리즘을 제안하고, 퍼지 로직 제어기(FLC)를 이용하여 핸드오버 시점을 적응적으로 결정하는 방안을 제안한다. 시뮬레이션 연구 결과 본 논문에서 제안하는 수직적 핸드오버 기법은 수신신호의 세기를 기반으로하는 방법과 MCDM 만을 사용하는 방법에 비해 데이터 처리량, 핸드오버 성공확률, 서비스 사용비용 그리고 핸드오버 시도 횟수 측면에서 우수한 성능을 보였다.

• 대한교통학회지
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• 제21권1호
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• pp.103-113
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• 2003

본 연구는 교통 경로 탐색 가운데, 우회 경로 탐색과 선호 경로 탐색을 하였으며, 계층 분석법을 적용한 퍼지비가법 제어기 사용을 제안한다. 이것은 기존의 경로 탐색과는 달리, 인간의 사고과정에 착안한 것으로, 애매한 주관적 판단을 정량적으로 분석, 평가하였다. 그리고 중요도를 운전 전문가로부터 의견 수렴한 것을 기초로 도출하였으며, 실제효용성을 진단하고자 경로 모델의 예를 사용하였다. 모델 평가는 평가 요소에 대한 속성 소속 함수화 및 평가치 규정, 계층 분석법에 의한 중요도 결정, $\lambda$-퍼지 척도에 의한 중요도의 비 가법적 표현, Choquet 퍼지 적분 등으로 수행하였다. 결국, 우회 경로 탐색 결과, 시시각각 변하는 교통환경에 적응할 수 있는 실 시간적인 교통 경로 제어가 가능하였으며, 선호 경로 탐색 결과, 본 연구의 알고리즘이 운전자 개인의 교통 경로 선택 성향을 잘 반영함을 보여 주었다. 논문은 5 가지의 중요한 의미가 있다. (1) 제안된 접근 방법은 운전자의 경로 선택 결정 과정과 유사하다. (2) 제안된 접근 방법은 다 속성의 경로 평가 기준을 제어 할 수 있다. (3) 제안된 접근 방법은 운전자의 주관적 판단을 비가법적으로 객관화 할 수 있다. (4) 제안된 접근 방법은 우회 경로 탐색에서 동적인 경로 탐색을 보여주고 있다 (5) 제안된 접근 방법은 선호 경로 탐색에서 개개 운전자 속성을 고려할 수 있다.

• 인터넷정보학회논문지
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• 제15권3호
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• pp.101-107
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• 2014

본 논문에서는 랜드마크 윈도우 기반의 빈발 패턴 마이닝 기법을 분석하고 성능을 평가한다. 본 논문에서는 Lossy counting 알고리즘과 hMiner 알고리즘에 대한 분석을 진행한다. 최신의 랜드마크 알고리즘인 hMiner는 트랜잭션이 발생할 때 마다 빈발 패턴을 마이닝 하는 방법이다. 그래서 hMiner와 같은 랜드마크 기반의 빈발 패턴 마이닝을 온라인 마이닝이라고 한다. 본 논문에서는 랜드마크 윈도우 마이닝의 초기 알고리즘인 Lossy counting와 최신 알고리즘인 hMiner의 성능을 평가하고 분석한다. 우리는 성능평가의 척도로 마이닝 시간과 트랜잭션 당 평균 처리 시간을 평가한다. 그리고 우리는 저장 구조의 효율성을 평가하기 위하여 최대 메모리 사용량을 평가한다. 마지막으로 우리는 알고리즘이 안정적으로 마이닝이 가능한지 평가하기 위해 데이터베이스의 아이템 수를 변화시키면서 평가하는 확장성 평가를 수행한다. 두 알고리즘의 평가 결과로, 랜드마크 윈도우 기반의 빈발 패턴 마이닝은 실시간 시스템에 적합한 마이닝 방식을 가지고 있지만 메모리를 많이 사용했다.