• 한국식생활문화학회지
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• 제23권3호
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• pp.301-307
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• 2008

The purpose of this study was to assess the causal relationships among belief, subjective norm, moral feeling, attitude, and the intention to consume organic beef. A total of 326 questionnaires were completed by adult in Jeonnam area. Structural equation modeling was used to measure the causal relationships among the constructs. The structural analysis results indicated an excellent model-data fit. The covariance effects of belief and subjective norm, and belief and moral feeling were statistically significant. The effects of belief and moral feeling on attitude and intention to consume were also statistically significant. As expected, attitude had significant effects on intention to consume. Moreover, attitude played a mediating roles in the relationship between belief and intention to consume. Finally, based on structural analysis, a model was proposed for the interrelations among belief, subjective norm and moral feeling towards organic food, attitude and intention to consume. It should be noted that the original organic food consumption model was modified, and should preferably be validated by future research. Other constructs, such as perceived behavior control and consumption, may be incorporated to form models that consist of new antecedent and consequence pairs.

• 유통과학연구
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• 제21권11호
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• pp.67-75
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• 2023

Purpose: The study is focused on understanding consumer behaviour related to credit card use in retail payment or identifying factors that influence risk perception. Research design, data and methodology: Based on data collecting from structured self-administered questionnaires of 247 Vietnamese bank account payers, this study uses the Cronbach alpha analysis, the factor analyses, the structural equation modeling to assess the research's measurement model and structural model with the presence of knowledge, propensity to trust, self-efficacy, risk perception, intended use and their complex, intertwined relationships. Results: The results reveal that customer's perceived risk, which is affected by their self-efficacy and propensity to trust, negatively impact on their intended use of credit cards in retail payment. However, there is no evidence of the significant influence of consumer knowledge on how they assess potential losses of credit card. Conclusions: These findings provide a better understanding of consumer risk perception, its antecedents and consequence in a direction of credit card adoption. Bank managers or marketers should focus on increasing the information about credit cards and issues related to credit card use in retail payment, promoting mechanisms to encourage customers to participate in the credit card experience.

• 한국방재안전학회논문집
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• 제16권3호
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• pp.1-15
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• 2023

본 연구는 복합화력발전소 내 배연탈질 설비에서 원료 및 중간재로 많이 사용되는 암모니아의 연속 누출에 대한 유해 위험 거리 예측 및 정량적 평가를 위한 피해영향 모델링이다. 피해 예측은 사고 영향 평가와 확산모델을 기반으로 암모니아 저장탱크 누출사고의 최적 시나리오를 모델링 하기 위해 변수들의 조업조건을 표준조건으로 하였다. 대상지역인 한국서부발전 태안발전본부의 복합화력발전소는 온도, 습도, 풍속, 대기 안정도, 풍향 등의 계절적 요인에 따른 기상 조건과 지형적 조건을 설정하기에 최적의 조건이며, 시나리오에 따른 모델링 결과가 누출 사고에 대한 특성을 분석하기에 가장 적합하였다. 암모니아 가스는 Gaussian 확산모델을 기반으로 하여 확산에 따른 LOC 별 농도치를 도출하였다. 본 연구를 통한 ALOHA 모델링 결과는 대기 조건의 경우 온도가 높고 안정도가 표준수치 범위이며, 풍속이 낮을수록 암모니아 확산에 의한 인체 피해 영향 정도가 높아지는 것으로 분석되었다.

• 전자공학회논문지CI
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• 제39권3호
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• pp.18-31
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• 2002

본 연구는 뉴로퍼지 네트워크와 다항식 뉴럴네트워크를 합성한 하이브리드 모델링 구조인 고급 뉴로퍼지 다항식 네트워크(Advanced neurofuzzy polynomial networks ; ANFPN)를 제안한다. 제안된 네트워크 구조는 높은 비선형 규칙 기반 모델로, CI(Computational Intelligence)의 기술, 즉 퍼지집합, 뉴럴네트워크, 유전자 알고리즘에 의해 설계되어진다. 뉴로퍼지 네트워크는 ANFPN 구조의 전반부를, 다항식 뉴럴네트워크는 후반부를 구성한다. ANFPN의 전반부에서, 뉴로퍼지 네트워크는 간략추론, 오류역전파 학습 규칙을 이용한다. 멤버쉽함수의 파라미터, 학습율, 모멘텀 계수는 유전자 최적화를 이용하여 조절된다. ANFPN의 후반부 구조로서 다항식 뉴럴네트워크는 학습을 통해 생성되는(전개되는) 유연한 네트워크 구조이다. 특히 다항식 뉴럴네트워크의 층과 노드 수는 고정되어 있지 않고 동적으로 생성된다. 본 연구에서는, 2가지 형태의 ANFPN 구조를 제안한다. 즉 기본 구조와 변형된 구조이다. 여기서 기본 구조와 변형된 구조는 다항식 뉴럴네트워크 구조의 각 층에서 입력변수의 수와 회귀다항식의 차수에 의존한다. 두 결합 구조의 특징 때문에 공정 시스템의 비선형적인 특성을 고려할 수 있고 보다 우수한 예측능력을 가진 좋은 출력선응을 얻을 수 있게 한다. ANFPN의 유용성과 실용성은 2개의 수치 예제를 통해 논의된다. 제안된 ANFPN은 기존의 모델보다 높은 정밀도와 예측능력을 가진 모델을 생성함을 보인다.

• 한국안전학회지
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• 제32권5호
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• pp.168-178
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• 2017

In order to prevent major and chemical accidents, some of the plants which would like to install and operate hazard chemicals handling facilities must submit Off-site Consequence Analysis due to recent arisen leak accidents since 2015. A lot of chemical industrials choose gas detectors as mitigation equipment to early detect gas vapor. The way of placement of gas detectors has two methods; Code-based Design(CBD) and Performance-based Design. The CBD has principles for gas detectors to be installed with consideration for the place that is expected to accumulate gas, and the leak locations according to legal standards and technical guidelines, and has a possibility to be unable to detect by these rules to locate gas detectors by vapor density information. The PBD has two methods; a Geographic Method and Scenario based Method. The Scenario-based Method has been suggested to make up for the Geographic Coverage Method. This Scenario-based Method draw the best optimum placement of gas detectors by considering leak locations, leak speed information, leak directions and etc. However, the domestic placement guidelines just refers to the CBD. Therefore, this study is to compare existing placement location of gas detectors by the domestic CBD with placement locations, coverages and the number of gas detectors in accordance with the Scenario-based Method. Also this study has measures for early detecting interest of Vapor Cloud and suitable placement of gas detectors to prevent chemical accidents. The Phast software was selected to simulate vapor cloud dispersion to predict the consequence. There are two cases; an accident hole size of leak(8 mm) from API which is the highst accident hole size less than 24.5 mm, and a normal leak hole size from KOSHA Guide (1.8 mm). Detect3D was also selected to locate gas detectors efficiently and compare CBD results and PBD results. Currently, domestic methods of gas detectors do not consider any risk, but just depend on domestic code methods which lead to placement of gas detectors not to make personnels recognize tolerable or intolerable risks. The results of the Scenario-based Method, however, analyze the leak estimated range by simulating leak dispersion, and then it is able to tell tolerable risks. Thus it is considered that individuals will be able to place gas detectors reasonably by making objectives and roles flexibly according to situations in a specific plant.

• 한국유통학회지:유통연구
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• 제17권3호
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• pp.93-113
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• 2012

갈등에 대한 다양한 연구가 이루어졌음에도 불구하고, 갈등해결을 통한 관계학습의 관점에서 조직적(interorganizational)으로 접근한 연구는 매우 부족한 실정이다. 본 연구에서는 갈등해결 매커니즘을 통해, 유통경로 구성원들이 어떻게 관계학습을 구축할 수 있는지, 그리고 이것들이 경로관계의 성과에 어떠한 영향을 미치는지 살펴보고 있다. 이와 같은 목적으로 국내 유통업체의 협력업체 영업담당자 490명을 대상으로 설문조사를 실시한 결과, 갈등해결에 있어 협력행동은 관계학습의 세 가지 과정인 정보공유, 공동이해와 해석, 관계특유기억 모두를 강화한 반면, 회피행동은 정보공유만 약화시키는 것으로 나타났다. 공동이해와 해석, 관계특유기억은 유통경로의 성과인 효과성과 효율성을 강화시킨 반면, 정보공유는 성과에 영향을 미치지 않았다.

• 한국가스학회지
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• 제19권4호
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• pp.29-34
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• 2015

화학물질을 다루는 실험실의 안전사고는 매년 끊이지 않고 있다. 특히 대학 내 실험실은 학생들이 주된 연구자로써 지식과 능력을 쌓아가고 경험을 축적하는 곳이기에 실험실의 안전은 더욱 중요하다. 학 내 실험실에서 주로 사용하는 5가지의 가스(CO, $NH_3$, $H_2$, $CH_4$, $N_2$)를 선정하여 이 가스들이 누출 될 경우의 시나리오들로부터 확산 과정을 PHAST v.6.7로 계산, 분석함으로써 피해 정도를 예측하였다. 실험실 내부 확산과정은 Computational Fluid Dynamics(CFD) 프로그램 FLUENT를 통하여 모델링하였다. 가스 누출 시 실험실 창을 통해 외부로 유출 될 경우 실내와 실외의 피해 영향에 대해서도 비교하였다. 각 가스들의 모델링 결과를 보면, 실험실 외부보다는 실험실 내부에서 그 피해 정도가 훨씬 큰 데, 학 내 실험실에서 일반적으로 사용하는 가스 용기, 즉 실린더의 용량은 47 L(혹은 그 이하)로 ton 단위로 사용하는 사업장에 비하면 그 양이 현저히 적기 때문에 실외의 영향이 플랜트와 비교해서 현저히 작다. 하지만 시뮬레이션 결과 작은 양으로도 실내에서는 큰 피해가 발생할 수 있음을 보여준다.

• 한국운동역학회지
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• 제24권1호
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• pp.35-42
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• 2014

Tendon elasticity is an important factor affecting muscle function and thus human movements. It has been reported that the mechanical properties of tendon are adaptable to external loading condition. Based on the adaptability of muscle and tendon to external loading conditions, one can assume that there might be an optimum ratio between muscle strength and tendon stiffness. The present study aimed to investigate whether there is correlation between plantar flexor muscle strength and stiffness of the achilles tendon (AT). Twenty two male subjects (age: $23.2{\pm}1.5yrs$, height: $175.5{\pm}6.2cm$, weight: $75.4{\pm}9.8kg$) performed maximum voluntary isometric plantarflexion on a custom-built dynamometer and muscle-tendon junction of the medial gastrocnemius muscle was simultaneously monitored using a real-time ultrasound imaging machine. The averages of muscle force and tendon stiffness were $366.38{\pm}79.37N$, $35.34{\pm}10.42N/mm$, respectively. Significant positive correlation was observed between muscle strength and tendon stiffness (r=0.8507), indicating that the muscle force is proportional to tendon stiffness. The results might have been used in computational modeling and criterion of training progress level in the fields of training and rehabilitation.

• 전기학회논문지
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• 제62권5호
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• pp.696-704
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• 2013

In this paper, we introduce the LED emotional lighting system realized with the aid of both intelligent algorithm and RGB LED combined with White LED. Generally, the illumination is known as a design factor to form the living place that affects human's emotion and action in the light- space as well as the purpose to light up the specific space. The LED emotional lighting system that can express emotional atmosphere as well as control the quantity of light is designed by using both RGB LED to form the emotional mood and W LED to get sufficient amount of light. RBFNNs is used as the intelligent algorithm and the network model designed with the aid of LED control parameters (viz. color coordinates (x and y) related to color temperature, and lux as inputs, RGBW current as output) plays an important role to build up the LED emotional lighting system for obtaining appropriate color space. Unlike conventional RBFNNs, Fuzzy C-Means(FCM) clustering method is used to obtain the fitness values of the receptive function, and the connection weights of the consequence part of networks are expressed by polynomial functions. Also, the parameters of RBFNN model are optimized by using PSO(Particle Swarm Optimization). The proposed LED emotional lighting can save the energy by using the LED light source and improve the ability to work as well as to learn by making an adequate mood under diverse surrounding conditions.

• 전기학회논문지
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• 제59권5호
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• pp.981-989
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• 2010

In this study, we introduce a new architecture of fuzzy inference system. In the fuzzy inference system, we use Fuzzy C-Means clustering algorithm to form the premise part of the rules. The membership functions standing in the premise part of fuzzy rules do not assume any explicit functional forms, but for any input the resulting activation levels of such radial basis functions directly depend upon the distance between data points by means of the Fuzzy C-Means clustering. As the consequent part of fuzzy rules of the fuzzy inference system (being the local model representing input output relation in the corresponding sub-space), four types of polynomial are considered, namely constant, linear, quadratic and modified quadratic. This offers a significant level of design flexibility as each rule could come with a different type of the local model in its consequence. Either the Least Square Estimator (LSE) or the weighted Least Square Estimator (WLSE)-based learning is exploited to estimate the coefficients of the consequent polynomial of fuzzy rules. In fuzzy modeling, complexity and interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. The performance of the fuzzy inference system is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules(clusters) and the order of polynomial in the consequent part of the rules. Accordingly we can obtain preferred model structure through an adjustment of such parameters of the fuzzy inference system. Moreover the comparative experimental study between WLSE and LSE is analyzed according to the change of the number of clusters(rules) as well as polynomial type. The superiority of the proposed model is illustrated and also demonstrated with the use of Automobile Miles per Gallon(MPG), Boston housing called Machine Learning dataset, and Mackey-glass time series dataset.