• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.243-246
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• 2003

Data mining is a process of discovering useful patterns for decision making from an amount of data. It has recently received much attention in a wide range of business and engineering fields Classifying a group into subgroups is one of the most important subjects in data mining Tree-based methods, known as decision trees, provide an efficient way to finding classification models. The primary concern in tree learning is to minimize a node impurity, which is evaluated using a target variable in the data set. However, there are situations where multiple target variables should be taken into account, for example, such as manufacturing process monitoring, marketing science, and clinical and health analysis. The purpose of this article is to present several methods for measuring the node impurity, which are applicable to data sets with multiple target variables. For illustrations, numerical examples are given with discussion.

• 한국전기전자재료학회논문지
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• 제21권11호
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• pp.987-994
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• 2008

The optimal structure of the Tonpilz transducer was designed. First, the FE model of the transducer was constructed, that included all the details of the transducer which used practical environment. The validity of the FE model was verified through the impedance analysis of the transducer. Second, the resonance frequency, the sound pressure, the bandwidth, and the impulsive shock pressure of the transducer in relation to its structural variables were analyzed. Third, the design method of $2^n$ experiments was employed to reduce the number of analysis cases, and through statistical multiple regression analysis of the results, the functional forms of the transducer performances that could consider the cross-coupled effects of the structural variables were derived. Based on the all results, the optimal geometry of the Tonpilz transducer that had the highest sound pressure level at the desired working environment was determined through the optimization with the SQP-PD method of a target function composed of the transducer performance. Furthermore, for the convenience of a user, the automatic process program making the optimal structure of the acoustic transducer automatically at a given target and a desired working environment was made. The developed method can reflect all the cross-coupled effects of multiple structural variables, and can be extended to the design of general acoustic transducers.

• Journal of Korea Trade
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• 제24권6호
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• pp.1-18
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• 2020

Purpose - This study investigates whether a listing effect exists in cross-border M&As and whether the effect can be attributed to the uncertainty of the GDP growth rate in the target firm's home country. We apply a joint variable analysis using M&A announcement data from the Korea Exchange (KRX), Shanghai Stock Exchange (SSE), and the Taiwan Stock Exchange (TWSE) from 2004 to 2013. We also conduct an event study using the measure of the uncertainty of the GDP growth rate (based on IMF statistics) in 55 target countries. Design/methodology - We measure the abnormal return (AR) using the market-adjusted model. We test the significance of the AR and the cumulative abnormal return (CAR) using a one-sample t-test. We examine the characteristics of the CARs depending on whether the target company is listed by applying a difference analysis using CAR as a test variable. In addition, we set CAR (-5, +5) as a dependent variable to identify the cause of the listing effect, and test both the financial characteristic variables of the acquirer and the collective characteristic variables of the merger as independent variables in the multiple regression analysis. Findings - First, we find the listing effect of cross-border M&As in the KRX, SSE, and TWSE, which represent the capital markets in Korea, China, and Taiwan, respectively. This listing effect persists during the global financial crisis and has a negative effect on the wealth of acquiring shareholders, especially when the target countries are emerging markets. Second, greater uncertainty regarding the target countries' economic growth in cross-border M&As has a negative effect on the wealth of acquiring firms' shareholders. Third, our empirical analysis demonstrates that the listing effect is attributable to the fact that firms listed in a target country with greater uncertainty of economic growth are more directly and greatly exposed to uncertain capital markets through stock markets, than are unlisted firms. Originality/value - This study is significant in that it presents a new strategic perspective in the study of cross-border M&As by demonstrating empirically that the listing effect is attributable to the uncertainty regarding the economic development of the target firms' home countries.

• 정보처리학회논문지C
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• 제18C권2호
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• pp.89-96
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• 2011

Address Space Randomization(ASR)은 성능 부하가 없고 광범위한 데이터 메모리 영역의 보호가 가능한 우수한 방어 기법이다. ASR은 사용 가능한 데이터 메모리 영역 내에서 변수를 재배치 함으로써 공격자에게 변수의 주소를 숨기는데, 데이터 메모리 영역의 크기가 한정되어서 무차별 공격에 취약한 단점이 있다. 본 논문은 기존 ASR의 단점을 제거하기 위한 다중 ASR 기법을 제시한다. 다중 ASR 기법은 데이터 메모리 영역을 원본 및 복사 영역으로 나누고 각 메모리 영역의 변수 값을 비교함으로써 공격을 탐지하고 방어한다. 다중 ASR에서 각 데이터 메모리 영역의 변수는 서로 다른 순서로 배치되므로 한 번의 공격을 통해 동시에 동일한 변수 값을 조작하는 것은 불가능하다. 다중 ASR이 적용된 프로그램은 중복 수행으로 인해 비교적 높은 성능 부하를 보이나, 실제 공격 대상이 되는 웹서버 등 I/O 처리가 많이 요구되는 프로그램의 경우 40%~50% 정도의 성능 부하를 보인다. 아울러 본 논문에서는 프로그램에 다중 ASR을 적용하기 위한 변환프로그램을 개발하였다.

• Journal of the Korean Data and Information Science Society
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• 제22권5호
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• pp.877-884
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• 2011

의사결정나무는 데이터마이닝의 대표적인 알고리즘으로서, 의사결정 규칙을 도표화하여 관심대상이 되는 집단을 몇 개의 소집단으로 분류하거나 예측을 수행하는 방법이다. 일반적으로 의사결정나무의 모형 생성 시, 입력 변수의 수가 많을 경우 생성된 의사결정모형은 복잡한 형태가 될 수 있고, 모형 탐색 및 분석에 있어 어려움을 겪기도 한다. 이때 입력변수들 간의 내재적인 관련성은 없으나, 외적 변수에 의하여 각 변수가 우연히 어떤 다른 변수와 연결됨으로써 관련성이 있는 것으로 나타나는 것을 종종 볼 수 있다. 이에 본 논문에서는 의사결정나무 생성 시, 입력 변수에 대한 외적 관계를 파악할 수 있는 다중외적연관성규칙을 이용하여 의사결정나무 생성에 불필요한 입력변수를 제거하는 방법을 제시하고 그 효율성을 파악하기 위하여 실제 자료에 적용하고자 한다.

• Food Science and Biotechnology
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• 제16권4호
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• pp.557-564
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• 2007

We prepared caviar analogs encapsulated by calcium-alginate gel membranes as a means to replace higher priced natural caviars. Processing the caviar analogs (beluga type) was optimized by response surface methodology with central composite design. Concentrations of sodium alginate ($X_1$) and $CaCl_2\;(X_2)$ were chosen as the independent variables. In order to compare characteristics of the caviar analogs with the natural caviar, sphericity ($Y_1$), diameter ($Y_2$), membrane thickness ($Y_3$), rupture strength ($Y_4$), rupturing deformation ($Y_5$), and sensory score ($Y_6$) were used as the dependent variables. The sphericity of the caviar analogs showed a similar value to that of natural caviar (over 94%) in the range of independent variables. Generally, the $CaCl_2$ concentration ($X_2$) affected all dependent variables to a greater extent than the sodium alginate concentration ($X_l$), For the multiple response optimization of the 5 dependent variables ($Y_1,\;Y_2,\;Y_4,\;Y_5$, and $Y_6$), the desirability function was defined as the following conditions: target values ($Y_1\;=\;100%,\;Y_2\;=\;3.0\;mm,\;Y_4\;=\;1,470\;g,\;Y_5\;=\;1.1\;mm,\;and\;Y_6\;=\;10\;points$). Membrane thickness ($Y_3$) was eliminated from the dependent variables for multiple response optimization because it could not be measured with an image analyzer. The values of the independent variables as evaluated by multiple response optimization were $X_1\;=\;-0.093$ (78%) and $X_2\;=\;-0.322$ (1.07%), respectively.

• 품질경영학회지
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• 제23권3호
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• pp.84-92
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• 1995

A method is proposed for the simultaneous optimization of several response functions that depend on the same set of controllable variables and are adequately represented by a response surface model (polynomial regression model) with the same degree and with constraint that the individual responses have the target values. First, the multiple responses data are checked for linear dependencies among the responses by eigenvalue analysis. Thus a set of responses with no linear functional relationships is used in developing a function that measures the distance estimated responses from the target values. We choose the optimal condition that minimizes this measure. Also, under the different degree of importance two step procedures are proposed.

• Nuclear Engineering and Technology
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• 제56권6호
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• pp.2266-2273
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• 2024

A machine learning platform is proposed for the diagnosis of a severe accident progression in a nuclear power plant. To predict the key parameters for accident management including lost signals, a long short term memory (LSTM) network is proposed, where multiple accident scenarios are used for training. Training and test data were produced by MELCOR simulation of the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident at unit 3. Feature variables were selected among plant parameters, where the importance ranking was determined by a recursive feature elimination technique using RandomForestRegressor. To answer the question of whether a reduced order ML model could predict the complex transient response, we performed a systematic sensitivity study for the choices of target variables, the combination of training and test data, the number of feature variables, and the number of neurons to evaluate the performance of the proposed ML platform. The number of sensitivity cases was chosen to guarantee a 95 % tolerance limit with a 95 % confidence level based on Wilks' formula to quantify the uncertainty of predictions. The results of investigations indicate that the proposed ML platform consistently predicts the target variable. The median and mean predictions were close to the true value.

• 품질경영학회지
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• 제27권2호
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• pp.218-236
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• 1999

The purpose of parameter design is to determine optimal settings of design parameters of a product or a process such that the performance characteristics of a product exhibit small variabilities around their target values. Taguchi made significant contributions in this area. However, his analysis of the problem focused on only one performance characteristic or response, although in product and process design, multiple characteristics are more common. The critical problem in dealing with multiple characteristics is how to compromise the conflict among the selected levels of the design parameters for each individual characteristic. In this paper, Methodology using SN ratio optimized by univariate technique is proposed and a parameter design procedure to achieve the optimal balance among several different response variables is developed. Existing case studies are solved by the proposed method and the results are compared with ones by the sum of SN ratios, the expected weighted loss, the desirability function, and EXTOPSIS model.

• 한국지능시스템학회논문지
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• 제13권4호
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• pp.386-390
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• 2003

데이터마이닝은 많은 양의 데이터로부터 의사결정에 유용한 패턴을 발견하는 과정으로서 최근 경영 및 공학 분야의 폭넓은 영역에서 많은 관심을 모으고 있다. 어떤 그룹을 여러 하위그룹으로 분류해내는 일은 데이터마이닝의 주요 내용 중 하나이다. 의사결정나무로 알려진 트리기반 기법은 그러한 분류모형을 수립하는 데 효율적인 방안을 제공한다 트리학습에 있어서 우선적인 관건은 목표변수에 의해 측정되는 노드불순도를 최소화하는 것이다. 하지만 공정관측, 마케팅과학, 임상분석 등과 같은 문제에서는 여러 목표변수를 동시에 고려해야 하는 상황이 쉽게 등장하는 데, 본 논문의 목적은 이처럼 다변량 목표변수를 갖는 데이터셋에서 활용할 수 있는 노드불순도 측정방안을 제시하는 데 있다. 아울러 수치 예를 이용하여 적용결과에 대해 논의한다.