• 한국조리학회지
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• 제20권2호
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• pp.38-53
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• 2014

본 연구는 식사대용으로 떡의 소비촉진과 대중화를 위해 서비스, 품질 향상, 상품개발에 필요한 기초자료를 얻고자 서울지역에 거주하는 20세 이상의 성인 남녀를 대상으로 식사대용으로 전통떡류를 이용하는 현황과 구매에 미치는 선택속성을 분석하였다. 조사는 2013년 9월 23부터 9월 30일까지 약 7일간에 진행되었고 250부의 유효한 설문지를 최종분석에 사용하였다. 본 연구의 분석결과 인구통계학적 특성에서는 성별, 결혼, 연령대, 직업, 월평균 수입, 주거형태 등은 유의한 차이가 없었으나 대졸학력에서는 유의한 차이가 있었고 식사대용으로 전통떡을 구매 할 때에는 맛, 신선도, 매장의 편의성을 중요하게 생각하는 것으로 나타났다. 따라서 식사대용으로서 먹을 수 있는 다양한 상품이 개발된다면 전통 떡의 소비가 대중화되고 활성화될 것으로 기대된다.

• 대한토목학회논문집
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• 제31권6B호
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• pp.551-563
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• 2011

수자원사업은 대규모의 예산이 투입된다. 따라서 수자원사업은 합리적이고 신뢰성 있는 의사결정이 필요한데 그동안 주로 경제성분석에 의존하여 왔다. 본 연구목적은 경제성분석위주의 기존 의사결정이 아닌, 사업의 필요성과 투자의 타당성을 다양한 각도로 평가할 수 있는 수자원사업 대안선정과 투자우선순위결정을 위한 다기준의사결정방법을 도출하는 것이다. 본 논문은 경제성 분석, 정책적 분석, 취약성 분석과 각각의 하위평가항목으로 평가기준을 구성하였다. 또한, 전문가 설문을 통한 사전 가중치를 제시함으로써, 일관성 있는 평가가 이루어지도록 하였다. 그리고 전문가 설문을 통해 평가항목의 속성별로 위험성향을 고려한 효용함수를 도출하였다. 종합평가점수는 평가항목별 가중치와 속성별 효용점수로 산정된다. 적용결과, 평가기준은 취약성 기준에 큰 영향을 받는 것으로 나타났다. 본 연구는 수자원사업에 대한 효율성과 취약지역에 대한 형평성을 제고하는데 기여할 수 있을 것으로 판단된다.

• 대한지리학회지
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• 제43권6호
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• pp.938-950
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• 2008

본 연구의 목적은 문화목적지 선택속성 추출과 집단별 시장세분화를 통해 문화목적지 선택속성과 여행만족도와의 영향관계 및 차이를 비교하는 것이다. 본 조사를 위한 설문은 기존 문헌연구와 데이터를 기초로 하여 개발되었으며, 설문조사는 부여와 공주지역을 중심으로 실시하였다. 연구결과에서는 문화목적지 선택속성이 6개로 추출되었으며, 군집분석에서는 3개의 그룹으로 세분화되었다. 다중회귀분석에서는 3개의 세분화된 집단과 여행 만족간의 관계를 알아보고자 Fisher's Z 값을 이용하였으며, 결과는 다음과 같다. 첫째, 문화 목적지 선택속성은 여행 만족도에 영향을 주는 요인으로 밝혀졌으며, 선택속성요인은 3개의 시장으로 세분화되었다. 둘째, 문화목적지 선택속성으로 세분화된 3개의 그룹은 여행 만족도에 영향을 주는 것으로 조사되었다. 따라서 세분화된 그룹 간 문화목적지 선택속성의 선택이 다르게 분석되었으며, 이와 관련한 문화관광을 하는 관광객들을 위한 상품과 서비스를 차별화 할 수 있는 마케팅적 정책과 방법을 강구하여야 할 것으로 보여진다.

• 한국수자원학회논문집
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• 제32권2호
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• pp.163-176
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• 1999

GIS기법을 이용하여 추출되는 수문지형정보는 기존 주제도의 선정, 사용된 알고리즘 등에 따라 달라질 수 있다. 따라서 본 연구에서는 토양도 및 토지이용도의 선정에 따른 유출수문곡선번호의 변화를 분석하여 미계측 유역의 강우유출모의에 효과적인 GIS의 활용방안을 제시하고자 한다. 이를 위해 미계측 유역에서의 유효강우량 산정에 필요한 GIS 공간자료(개략토양도, 정밀토양도, 토지이용계획도,위성영상)를 구축하였다. 미국 토양보존국(SCS)의 유출곡선번호(runoff curve number; CN)방법의 적용을 위한 수문학적 속성의 입력과정에서 발생할 수 있는 문제점을 분석하였다. 또한 SCS CN값 산정을 위한 GIS 공간자료의 선정에 따른 유출응답특성을 검토하였다. 실측 수문곡선과의 검증을 통해 미계측 유역에서의 강우유출모의에서 GIS의 적용성을 확인할 수 있었다. SCS CN값을 산정하기 위한 GIS 공간자료로서는 정밀토양도와 위성영상자료를 이용하는 것이 적합한 것으로 나타났다.

• 대한통합의학회지
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• 제9권1호
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• pp.69-90
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• 2021

Purpose : This study explores unmet medical services within a region for patients admitted to a single medical institution in one region and was to analyze the importance and satisfaction of hospital selection attributes. Through this, we tried to solve the unmet medical needs of patients and provide useful basic data in terms of hospital management in the region. Methods : It were collected to a total of 250 questionnaires for patients admitted to the regional integrative medical hospital. However, 232 samples were used for the final analysis, excluding 18 copies not reported in good faith. For the analysis, first, demographic frequency analysis of inpatients and inpatients was performed, and second, characteristics of patients, including frequent disease receiving treatment, were analyzed. Next, descriptive statistics analysis was conducted on unmet medical service intentions. In terms of hospital selection attribute, the items of continuity maintenance (I quadrant), priority visibility (II quadrant), low priority (III quadrant), and excessive effort (IV quadrant) were derived using the IPA (importance-performance analysis) matrix technique. Results : The derived results were classified by item and area. In the priority administration area, it was the reputation and recognition of medical institutions and the service area of medical institutions. In the case of items, there were 6 items including the importance of surgery and medical expenses, and diet at hospitalization. 1) Conclusion : Thus a result of this study, resources are efficiently allocated to priority correction areas with high importance but low satisfaction and circulatory medical treatment is performed in the departments required by patients who use medical care and, various methods, such as preparing a policy to support medical expenses, should be sought.

• 한국식생활문화학회지
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• 제37권5호
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• pp.429-437
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• 2022

This study evaluated the quality of school milk programs and analyzed the relative importance of school milk program selection attributes using conjoint analysis. The survey was conducted on students from middle and high schools in metropolitan cities that provide school milk programs. Responses were received from 414 students and the data was subjected to frequency analysis, t-test, and conjoint analysis using the SPSS Statistics Package. While evaluating white milk in the school milk program, middle school students rated 'packaging condition' (4.23) the highest, high school students rated 'nutrition' (4.64) the highest, and their evaluation of all the quality attributes was significantly different from that of middle school students (p<0.001). Overall satisfaction scores too, showed a significant difference between high school (4.46) and middle school students (4.01) (p<0.001). Processed milk & dairy products had the highest satisfaction score in the attribute of 'serving time' (4.57). The relative importance of the choice attributes of the school milk program was in the order of 'number per item' (62.260%), 'temperature' (25.708%), and 'serving method' (12.032%) for all students. The school milk program most preferred by all students and middle school students was to provide milk at a refrigerated temperature, select white milk three times a week, processed milk, fermented milk, and cheese twice a week, and provide it at the desired time.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• 한국병원경영학회지
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• 제29권2호
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• pp.18-36
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• 2024

This study delved into the complex nature of medical services as experience goods and trust services, investigating the profound impact of online word-of-mouth on medical consumers' decisions to visit hospitals. Considering the restrictive legal framework for medical advertising, consumers are increasingly dependent on unrestricted sources of information like online reviews. This research aimed to provide empirical evidence for the significant role online word-of-mouth plays in hospital selection. Utilizing data from Naver reviews, hospital choice factors were classified based on the Kano model, revealing the subtle yet significant influence that word-of-mouth has on consumers' hospital visit intentions beyond merely positive or negative messages. In particular, the study provided insights into how the categorized positive and negative information, along with the presence or absence of emotional expression, affects the efficacy of word-of-mouth. The experiment targeted medical consumers aged over 20 and, through analysis using the SPSS statistical program, yielded important findings. The direction of online word-of-mouth, the presence of emotional expression, and the interaction of Kano attributes all created significant differences in hospital visit intentions. Notably, emotional expression included in negative word-of-mouth concerning one-dimensional attributes markedly decreased visit intentions, whereas the absence of emotional expression in attractive attributes actually enhanced reliability and increased visit intentions. These findings offer critical implications for redefining strategies in medical marketing and online review management. The discoveries of this study underscore the importance of active engagement and strategic management of online reviews by medical service providers, urging careful consideration of the various elements of online word-of-mouth that influence medical consumers' hospital visit intentions.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2004년도 춘계학술발표회논문집
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• pp.395-400
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• 2004

This study consists of three steps of data modeling procedures. The first step is to identify possible items for the data model based on literature review and expert interviews. The second step is to design delineate possible sub-themes, feature classes, feature types, attributes, attribute domains, and their relationships. These are presented in various UML class diagrams, and each feature type is clearly defined and modeled. The data model also shows geometry objects and their topological relationships in UML diagrams. Finally, a standardized data model has been provided to avoid possible conflicts in the field of geographic and Facility Area, and thus this study and the data model will eventually assist in alleviating efforts to build standardized geographic information databases for Facility Area.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.713-716
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• 2005

In this paper, in order to identify and recognize attack patterns, we propose a Bayesian classification using frequent patterns. In theory, Bayesian classifiers guarantee the minimum error rate compared to all other classifiers. However, in practice this is not always the case owing to inaccuracies in the unrealistic assumption{ class conditional independence) made for its use. Our method addresses the problem of attribute dependence by discovering frequent patterns. It generates frequent patterns using an efficient FP-growth approach. Since the volume of patterns produced can be large, we propose a pruning technique for selection only interesting patterns. Also, this method estimates the probability of a new case using different product approximations, where each product approximation assumes different independence of the attributes. Our experiments show that the proposed classifier achieves higher accuracy and is more efficient than other classifiers.