• 대한영양사협회학술지
• /
• 제12권3호
• /
• pp.235-242
• /
• 2006

The purpose of this study of was to provide basic data for preparing event menus to increase customer's satisfaction by investigating university students' participation and preference for the event menus in the foodservice operations. The questionnaires were distributed to 300 customers from August 1 to 31, 2005. 88.0% of the questionnaires were analyzed. Statistical analysis of data was performed using SAS package program(version 8.2) for descriptive analysis and $χ^2$-test, t-test, one-way ANOVA, Duncan multiple range test. The results of this study can be summarized as follows : 50.4% of the students have participated in foodservice operation's event and the average degree of the satisfaction was 2.67 out of 5. The type of the events customers have most frequently participated in were the national holiday·subdivisions of the season event(47.3%), the day event(34.1%), environment event(26.9%) and so on. In large classification, preferred were season event(85.2%), international food event(76.9%), and healthy food event(73.1%) and so on. In small classification, orgarnic food event(53.0%), summer fruits festival(41.3%), midsummer event(36.6%) and christmas event(34.4%) and so on. From now on, the event reflecting customers' expectation and requirement should be planned and implemented.

• 한국군사과학기술학회지
• /
• 제21권2호
• /
• pp.147-157
• /
• 2018

There are increasing demands to provide early warning against intruding drones and cope with potential threats. Commercial anti-drone systems are mostly based on simple target detection by radar reflections. In real scenario, however, it becomes essential to obtain drone radar signatures so that hostile targets are recognized in advance. We present experimental test results that micro-Doppler radar signature delivers partial information on multi-rotor platforms and exhibits limited performance in drone recognition and classification. Afterward, we attempt to generate high resolution profile of flying drone targets. To this purpose, wide bands radar signals are employed to carry out inverse synthetic aperture radar(ISAR) imaging against moving drones. Following theoretical analysis, experimental field tests are carried out to acquire real target signals. Our preliminary tests demonstrate that high resolution ISAR imaging provides effective measures to detect and classify multiple drone targets in air.

• 정보관리연구
• /
• 제37권4호
• /
• pp.25-47
• /
• 2006

다국어로 구축된 학술정보 시스템의 통합검색 환경을 구현하기 위해서는 다국어 전문용어에 대한 해석을 제공하고 전문용어의 분야별 분류정보를 제공할 수 있는 시스템이 필요하다. 본 연구는 이러한 다국어 환경의 통합 정보검색 시스템을 운용할 수 있도록 기반시스템을 구축하는 것을 목적으로 한다. 다국어 의미망으로 상호 연결된 과학기술 전문용어 체계를 구축하는 방법과 다단계 연결노드에 대한 최단거리 탐색 기법을 소개하였다. 또한, 생성된 용어군집 결과를 해석하기 위한 기초분석을 수행하여 향후 심도있는 분석연구를 수행하기 위한 기반을 마련하고자 하였다.

• 한국멀티미디어학회논문지
• /
• 제25권5호
• /
• pp.703-720
• /
• 2022

Based on the love styles theory used in psychology, this paper classifies users(Passionate Love, Game-playing Love, Friendship Love, Practical Love, Possessive Love, Altruistic Love) and investigates satisfaction with the motivation for using TikTok love videos(Entertainment, Social Relationship, Love skills-learning, Self-verification, Problem-solving) according to the theory of use and satisfaction. First, 414 users were selected to conduct TikTok surveys to collect data. Then, through the analysis of the research results, among the six love types, game-playing type and possessive type have a positive (+) impact on entertainment motivation and love skill-learning motivation. Game-playing type also have a positive (+) impact on social relationship motivation and self-verification motivation. In addition, altruistic type and possessive type are also factors to strengthen the motivation of self-verification. The altruistic type, possessive type and practical type will improve the problem-solving motivation. Finally, through hierarchial multiple regression analysis, it is confirmed that game-playing love type, entertainment motivation, love skill-learning motivation and self-verification motivation can improve user satisfaction. The above results enrich the research of user classification as well as providing inspiration for improving the quality and communication efficiency of TikTok's video and enhancing user experience.

• 한국식생활문화학회지
• /
• 제26권6호
• /
• pp.629-640
• /
• 2011

This research analyzed the naming standard of Korea menu names divided into two groups, main dish and side dish. The research was conducted by contents analysis of selected literature articles and multiple-response cross tabulation analysis. The result demonstrated that the naming standard of Korea food consisted of the main ingredient name - sub ingredient name - main condiment name and main recipe. On the other hand, the menu name that is in native language or has a historical origin is exempt from this classification. Therefore, this study proposes a new standard, 'Hansik Menu Naming', to assist the food service industry and correct the names of unknown foreign dishes.

• 동굴
• /
• 제90호
• /
• pp.63-68
• /
• 2009

동굴의 유형 분류를 학술적 근거에 두지 않고 동굴 내부의 다양한 특성 즉, 길이, 지형지물의 종류, 성인, 광장유무를 변수로 이용하였다. 분석 기법으로는 군집분석을 이용하였다. 분석 결과 5개의 유형으로 분류되었으며, 이러한 결과는 관람객의 요구에 따른 다양한 분류가 가능하다는 결과를 말해준다.

• 동굴
• /
• 제92호
• /
• pp.49-54
• /
• 2009

동굴의 유형 분류를 학술적 근거에 두지 않고 동굴 외부의 다양한 특성 즉, 숙박시설, 주변 관광지, 음식점수, 배후도시수를 변수를 이용하였다. 분석 기법으로는 군집분석을 이용하였다. 분석 결과 5개의 유형으로 분류되었으며, 이러한 결과는 관람객의 요구에 따른 다양한 분류가 가능하다는 결과를 말해준다.

• Journal of Power Electronics
• /
• 제17권4호
• /
• pp.972-982
• /
• 2017

The Park's vector of stator current is a popular technique for the detection of induction motor faults. While the detection of the faulty condition using the Park's vector technique is easy, the classification of different types of faults is intricate. This problem is overcome by the Multiple Park's Vector (MPV) approach proposed in this paper. In this technique, the characteristic fault frequency component (CFFC) of stator winding faults, rotor winding faults, unbalanced voltage and bearing faults are extracted from three phase stator currents. Due to constructional asymmetry, under the healthy condition these characteristic fault frequency components are unbalanced. In order to balanced them, a correction factor is added to the characteristic fault frequency components of three phase stator currents. Therefore, the Park's vector pattern under the healthy condition is circular in shape. This pattern is considered as a reference pattern under the healthy condition. According to the fault condition, the amplitude and phase of characteristic faults frequency components changes. Thus, the pattern of the Park's vector changes. By monitoring the variation in multiple Park's vector patterns, the type of fault and its severity level is identified. In the proposed technique, the diagnosis of faults is immune to the effects of unbalanced voltage and multiple faults. This technique is verified on a 7.5 hp three phase wound rotor induction motor (WRIM). The experimental analysis is verified by simulation results.

• 한국멀티미디어학회논문지
• /
• 제20권8호
• /
• pp.1406-1420
• /
• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• 지능정보연구
• /
• 제27권1호
• /
• pp.83-102
• /
• 2021

2020년 8월 정부는 한국판 뉴딜을 뒷받침하기 위한 공공기관의 역할 강화방안으로서 각 공공기관별 역량을 바탕으로 5대 분야에 걸쳐 총 20가지 과제를 선정하였다. 빅데이터(Big Data), 인공지능 등을 활용하여 대국민 서비스를 제고하고 공공기관이 보유한 양질의 데이터를 개방하는 등의 다양한 정책을 통해 한국판 뉴딜(New Deal)의 성과를 조기에 창출하고 이를 극대화하기 위한 다양한 노력을 기울이고 있다. 그중에서 한국무역보험공사(KSURE)는 정책금융 공공기관으로 국내 수출기업들을 지원하기 위해 여러 제도를 운영하고 있는데 아직까지는 본 기관이 가지고 있는 빅데이터를 적극적으로 활용하지 못하고 있는 실정이다. 본 연구는 한국무역보험공사의 수출신용보증 사고 발생을 사전에 예측하고자 공사가 보유한 내부 데이터에 기계학습 모형을 적용하였고 해당 모형 간에 예측성과를 비교하였다. 예측 모형으로는 로지스틱(Logit) 회귀모형, 랜덤 포레스트(Random Forest), XGBoost, LightGBM, 심층신경망을 사용하였고, 평가 기준으로는 전체 표본의 예측 정확도 이외에도 표본별 사고 확률을 구간으로 나누어 높은 확률로 예측된 표본과 낮은 확률로 예측된 경우의 정확도를 서로 비교하였다. 각 모형별 전체 표본의 예측 정확도는 70% 내외로 나타났고 개별 표본을 사고 확률 구간별로 세부 분석한 결과 양 극단의 확률구간(0~20%, 80~100%)에서 90~100%의 예측 정확도를 보여 모형의 현실적 활용 가능성을 보여주었다. 제2종 오류의 중요성 및 전체적 예측 정확도를 종합적으로 고려할 경우, XGBoost와 심층신경망이 가장 우수한 모형으로 평가되었다. 랜덤포레스트와 LightGBM은 그 다음으로 우수하며, 로지스틱 회귀모형은 가장 낮은 성과를 보였다. 본 연구는 한국무역보험공사의 빅데이터를 기계학습모형으로 분석해 업무의 효율성을 높이는 사례로서 향후 기계학습 등을 활용하여 실무 현장에서 빅데이터 분석 및 활용이 활발해지기를 기대한다.