• Nuclear Engineering and Technology
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• 제44권6호
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• pp.663-672
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• 2012

As software based digital I&C (Instrumentation and Control) systems are used more prevalently in nuclear plants, enhancement of software dependability has become an important issue in the area of nuclear I&C systems. Critical attributes of software dependability are safety and reliability. These attributes are tightly related to software failures caused by faults. Software testing and V&V (Verification and Validation) activities are hence important for enhancing software dependability. If the risky modules of safety-critical software can be predicted, it will be possible to focus on testing and V&V activities more efficiently and effectively. It should also make it possible to better allocate resources for regulation activities. We propose a prediction technique to estimate risky software modules by adopting machine learning models based on software complexity metrics. An empirical study with various machine learning algorithms was executed for comparing the prediction performance. Experimental results show SVMs (Support Vector Machines) perform as well or better than the other methods.

• 한국시뮬레이션학회논문지
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• 제9권3호
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• pp.77-89
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• 2000

This paper suggests a multi-attribute dispatching rule for an automated guided vehicle(AGV). The attributes to be considered are the number of queues in outgoing buffers of workstations, distance between an idle AGV and a workstation with a job waiting for the service of vehicle, and the number of queues in input buffers of the destination workstation of a job. The suggested rule is based on the simple additive weighting method using a normalized score for each attribute. A neural network approach is applied to obtain an appropriate weight vector of attributes based on the current status of the manufacturing system. Backpropagation algorithm is used to train the neural network model. The proposed dispatching rules and some single attribute rules are compared and analyzed by simulation technique. A number of simulation runs are executed under different experimental conditions to compare the several performance measures of the suggested rules and some existing single attribute dispatching rules each other.

• ETRI Journal
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• 제36권6호
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• pp.1023-1031
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• 2014

Failure analysis is necessary to clarify the root cause of a failure, predict the next time a failure may occur, and improve the performance and reliability of a system. However, it is not an easy task to analyze and interpret failure data, especially for complex systems. Usually, these data are represented using many attributes, and sometimes they are inconsistent and ambiguous. In this paper, we present a scalable approach for the analysis and interpretation of failure data of high-performance computing systems. The approach employs rough sets theory (RST) for this task. The application of RST to a large publicly available set of failure data highlights the main attributes responsible for the root cause of a failure. In addition, it is used to analyze other failure characteristics, such as time between failures, repair times, workload running on a failed node, and failure category. Experimental results show the scalability of the presented approach and its ability to reveal dependencies among different failure characteristics.

• 한국식생활문화학회지
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• 제34권4호
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• pp.432-439
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• 2019

The purpose of this study is to measure the effect of customers' waiting time on their revisit intention through their emotion. Also this study assessed the effect of restaurant selection attributes on consumers' revisit intention in Korea. This study used experimental scenario questionnaires for collecting data. Frequency analysis, Cronbach's alpha, correlation, t-tests and multiple regression analysis were assessed using SPSS. Customers preferred taste, sanitation and service when selecting a restaurant to dine out. The results of this study found that there were no significant differences between positive and negative emotions due to waiting time. Findings of this study suggested that waiting time, convenience, nutritional value, and emotion influenced consumers' revisit intention. Therefore, reducing waiting time and providing proper service will help consumers have positive emotions to return to dine at a restaurant.

• 한국인지과학회:학술대회논문집
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• 한국인지과학회 2006년도 춘계학술대회
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• pp.53-58
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• 2006

We examined the cerebral activation of the emotional and linguistic attributes during the visual word recognition. This research investigated the affective priming effect preserving the behavioral paradigm. We used the primed-evaluation task in which the participants classify the target as positive or negative, and manipulated the emtional attributes by emtional relations of the prime-target word pairs(PP, PN, NP, NN). ROIs analyses for the semantic processing and emotional processing were performed. The results showed that the semantic processing areas including the IPL, SMG, and aSTS were activated differently according to the experimental condition. The activations of the IPL were increased only on the NN condition, whereas the activation of the SMG was decreased only on the PP condition. Furthmore, the activation of the emotional processing areas including the mPFC and ACC, was different according to the emotional realtions of word pairs. Similar to the SMG, the BOLD signal of the mPFC was decreaed only on the PP condition, whereas the activation of ACC was Increased only on the NN condition. These results were seemed to show the interact ive cerebral activations for processing the emtoional and linguistic attributes in a word, during visual word recognition.

• 동아시아식생활학회지
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• 제16권1호
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• pp.29-36
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• 2006

The purpose of this study was to measure the pizza purchasing behavioral characteristics of respondents and importances of factors affecting pizza purchase, to estimate the effects of attributes on pizza restaurant choice, and to predict probability of selecting a particular pizza restaurant The questionnaire consisted of two parts: The paired experimental profiles, purchasing behavior and importances of factors affecting pizza purchase. This study generated profiles of 16 hypothetical pizza restaurant based on the seven attributes. The profiles comprised 16 discrete sets of variables, each of which had two levels. For this study, researcher randomly selected 150 students of university as respondents. Twenty students did not complete the survey instrument, resulting in a final sample size of 129. All estimations were carried out using frequency, correlation, phreg procedure of SAS package. The results were as followed Based on the estimated model, the -2LL(B) statistic for a model with all explanatory variables was 5585.761 and the Chi-square statistic is 134.786 with 7 df (p<0.001). At p<0.001, we would reject the null hypothesis that the attributes do not influence choice. The parameter estimate for price was highest, followed by late delivery time, promised delivery time, money-back guarantee, discount, pizza variety, and pizza temperature. The result from this study suggested that there was an opportunity to increase market share and profit by improving operations so that customers receive discount and money-back guarantee simultaneously, and by reducing price, delivery time.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제16권1호
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• pp.60-64
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• 2010

비 트랜잭션 데이터를 대상으로 연관 규칙을 도출하기 위해서, 데이터의 속성들을 구간화하는 기법들이 활발하게 연구되었다. 이러한 기존의 연구들은 구간화 단계에서 구간 범위의 변화에 따른 연관 규칙의 신뢰도 변화를 반영하지 않고, 구간화 단계와 연관 규칙을 도출하는 단계들을 독립적으로 수행하였다. 이로 인해 속성들의 구간이 부적절하게 설정되고, 이 결과 높은 신뢰도를 갖는 연관 규칙들이 최종 결과에서 누락된다. 따라서 본 논문에서는 속성들을 구간화하는 단계와 연관 규칙들을 도출하는 단계를 병합하여 동시에 수행함으로써, 가장 신뢰도가 높은 연관규칙들을 도출할 수 있는 구간을 설정하는 방안을 제안한다. 이를 위해서 연관 규칙의 우변의 속성들을 대상으로 계층적 클러스터링을 수행하고, 각 클러스터들에 대해서 특성 분석을 수행한다. 실험 결과, 제안하는 기법은 기존의 기법들에 비해서 높은 신뢰도를 갖는 연관 규칙들을 발견하는 것으로 나타났다.

• 대한건축학회논문집:계획계
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• 제36권5호
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• pp.83-91
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• 2020

Environmental conditions are known to impact human health and behavior, emotions such as pleasure, anxiety, and depression, and reduce stress. Interior design that elevates emotional comfort and satisfaction can help improve mental health and well-being. This study is a systematic review that analyzed previous empirical studies that explored the effect of interior design elements on the user's emotional response which is quantitatively evaluated by bio-signal and qualitatively evaluated through self-reported questionnaire surveys. This paper aims to derive the attributes of interior design and biometric indicators that affect the user's positive emotion through the synthesis of previous studies and to confirm the feasibility of measuring bio-signals as an objective evaluation tool for architectural design and as a quantitative research method. As a result of the review, the biometric data from EEG, fMRI, ECG, EMG, GSR, and eye-tracking were used to measure the participants' emotional responses, which were manifested as positive or negative depending on certain attributes of interior design such as the form, color, lighting, material and furniture. The attributes of interior design related to the positive emotional response were the curved shape, high ceiling, openness of space, and subdued tone colors. Standard lighting conditions and wooden spaces were related to stress reduction in terms of comfort and relaxation. The free arrangement of furniture was related to the user's positive emotions. On the other hand, consistent experimental protocols could not be found, and although the sample sizes of the studies were small, the studies have demonstrated the feasibility of the emotional response measurement by using the biometric data. Therefore this method can be a useful objective tool in the measurement of human-centric data in architectural design, and to develop the evidence-based design to induce positive emotions and minimize stress.

• 한국안전학회지
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• 제8권4호
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• pp.139-148
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• 1993

Thermosyphons are simple devices that can passively transport thermal energy over relatively long distance with little temperature degradation. These attributes permit the use of low grade thermal energy for thermal control of structures including the snow melting and deicing to the pavement surface. The thermosyphon system requires no costly energy input and Is completely maintenance free. This paper presents the experimental results of the snow melting system in which thermosyphon was utilized to transfer the geothermal energy to the pavement to obviate slipping traffic accidents due to freezing of pavement in winter.

• 한국통신학회논문지
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• 제22권3호
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• pp.423-431
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• 1997

A new decision tree generator MEC is proposed in this paper, which uses the difference of multi-base entropy as a consistent criterion for discretization and selection of attributes. To evaluate the performance of the proposed generator, it is compared to other generators which use criteria based on entropy and adopt different discretization styles. As an experimental result, it is shown that the proposed generator produces the most efficient classifiers, which have the least number of leaves at the same error rate, regardless of whether attribute values constituting the training set are discrete or continuous.