• 안보군사학연구
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• 통권3호
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• pp.197-242
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• 2005

Active catalog is a kind of digital content that enables consumers to test the functions and features of products from their PCs as if they were using it in real life, by simulating the actions and responses of the product. This new type of interactive digital content can be used extensively to make sales personnel training manuals, sales tools, user manuals and user trouble shooting documents. With active catalogs, companies will be able to compare different designs, show actions according to different functions, and evaluate user reaction to new products without having to produce a single physical prototype or mock-up. At the same time, consumers will be able to understand and 'operate' the product and make well-informed purchase decisions. In this paper, we present a visual event-driven modeling tool, PlayMo, for creating active catalogs, analyze the advantages of using PlayMo, describe the event-driven method used by PlayMo and also introduce two enhanced characteristics of the Event Flow Chart with which the events in PlayMo are structured. Interactive digital content by using the PlayMo3D makes easy, simple and effective design for e-learning, e-catalogue, e-marketing/sales, e-prototyping, customer support, etc. Through its application-ready 3D function visualization solution, engineers and designers can rapidly turn a CAD design model into a 3D interactive virtual product, and the effective function prototyping job can be also completed within a minute.

• 대한간호학회지
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• 제24권2호
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• pp.206-215
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• 1994

Intuition is an abstract concept which is most often thought of as a nonrational, nonscientific mode of thought. However, since there are so many amorphous definitions of intuition that it seems important to clarify the meaning of this concept. Therefore, this study use the process of Walker & Avant’s concept analysis to define of the concept of intuition Attributes of intuition were defined as 1) Knowledge of truth that is difficult to explicate ; 2) A type of immediate knowing ; 3) Knowlwdge without reasining analysis ; 4) Knowledge that is attained based on virtue character which integrates all matter and is not attained through individual experience. Antecedents of intuition consists of 1) as ground for knowledge or truth that is not availables to trace through the analytic procedures ; & 2) the flow of Ki which unites human beings and the universe. Consequences of intuition events or incidents occuring as a result of the concept consist of verification of the truth though analytic procedures and application of knowledge in both theoretical and practical ways. To develop intuitive ability, as an educator should not only make studies in recognizine, analysing and teaching concepts related to logical, rational decision making but should also recognize and teach concepts related to intuitive components of making decisions in clinical practice and classroom learning as well.

• 반도체디스플레이기술학회지
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• 제14권3호
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• pp.67-72
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• 2015

This paper proposes an object tracking system based on keypoints using homography in mobile environments. The proposed system is based on markerless tracking, and there are four modules which are recognition, tracking, detecting and learning module. Recognition module detects and identifies an object to be matched on current frame correspond to the database using LSH through SURF, and then this module generates a standard object information. Tracking module tracks an object using homography information that generate by being matched on the learned object keypoints to the current object keypoints. Then update the window included the object for defining object's pose. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. The experimental results show that the proposed system is able to recognize and track objects with updating object's pose for the use of mobile platform.

• 제어로봇시스템학회논문지
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• 제4권3호
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• pp.321-328
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• 1998

When we control the level of the steam generator in the nuclear power plants, a swell and shrink arises from many disturbances such as feed water rate, feed water temperature, main steam flow rate, and coolant temperature. If we use the conventional type of PI controller in this system, we will not have stability during controlling at lower power, the removal function of disturbances, and a load follow-up control effectively. In this paper, we study the application of a 2-Degree of Freedom(2-DOF) PID controller to the level control of the steam. generator of nuclear power plants through the simulation and the experimental steam generator. We use the parameters $\alpha$, $\beta$, $\gamma$ of the 2-DOF PID controller for the removal of disturbances and the parameters Kp,Ti,Td of the conventional type of PID controller for controlling setpoint. The back-propagation learning algorithm of neural network is used for tuning the 2-DOF PID controller. We can find satisfactory results of the removal of the disturbances and the tracking function in the change of setpoint through the simulation and experimental steam generator.

• Maxillofacial Plastic and Reconstructive Surgery
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• 제40권
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• pp.25.1-25.4
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• 2018

Background: Some of head and neck cancer patients are in compromised general condition after ablation surgery and chemoradiation therapy, which makes secondary free tissue transfer quite challenging. Elderly cancer patients also have some risk for microvascular surgery with lengthened general anesthesia. In those cases, the pedicled flap vascularized by supraclavicular artery could be considered as an alternative to free flap. Despite several authors have demonstrated the clinical reliability of supraclavicular artery island flap (SCAIF), to date, SCAIF has not been widely used among reconstructive surgeon. In this article, we clarified vascular flow pattern and introduce simple surgical technique of SCAIF with a literature review. Case presentation: Three patients who had underwent previous neck surgery and adjuvant therapy received maxillofacial reconstruction using SCAIF. It required only a few landmarks, flap harvesting was carried out, and the elapsed time gradually decreased to 15 min with experiences. There were no remarkable morbidities in both donor and recipient sites. Conclusion: SCAIF exhibited minimal anatomic variations and short learning curve of surgical techniques, which might be valuable reconstruction modality for beginning surgeon. And it can be beneficial option for the patients with vessel-depleted neck, medically compromised status for lengthened general anesthesia and failed free tissue transfer.

• 산업경영시스템학회지
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• 제28권3호
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• pp.42-57
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• 2005

The National Education Information System(NEIS) has been utilized in primary and secondary schools. In this paper, we consider the NEIS should be used not only for educational administration affairs, but also for a lifelong management of national human resource. The current School Information Management System(SIMS), including the NEIS, is unsatisfactory due to the insufficiency of actual field suitability and end-user's conveniency. To this, we have devised improvements of the SIMS in the seven problem areas: ) The core business process of the school should be analyzed sufficiently and reflected in SIMS. (2) We should fully utilize groupware functions which activate the learning organization. (3) We might apply and use the CRM techniques of enterprises in SIMS. (4) The SIMS should be easy to make necessary school assessment data. (5) We should complement functions of the SIMS for a lifelong healthcare information management of national human resource. (6) The SIMS should support the school lunch management. (7) We should bring BOM and work-flow concepts into the SIMS.

• 한국재난정보학회:학술대회논문집
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• 한국재난정보학회 2017년 정기학술대회
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• pp.366-368
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• 2017

본 연구는 국내 산사태 발생 데이터를 기반으로 시뮬레이션 모델을 머신러닝 기법을 통해 학습시켜 산사태의 토석류 흐름을 구현하는 알고리즘에 대한 연구이다. 전통적인 프로그래밍을 통한 산사태 시뮬레이션 모델 개발을 해당 시스템에 더 많은 고도의 물리학 법칙을 통합 적용시켜 토석류의 흐름을 공학적으로 재현해내는데 중점을 두고 개발이 진행되지만, 본 연구에서 다루는 머신러닝 기법을 통한 산사태 시뮬레이션 모델 개발의 경우 시스템에 입력되는 데이터를 기반으로한 학습을 통하여 토석류 흐름에 영향을 미치는 변수와 파라메터를 산출하고 정의는데 중점을 두고 개발이 진행된다. 본 연구에서 산사태 시뮬레이션 모델 개발에 활용하는 머신러닝 알고리즘은 강화학습 알고리즘으로 기존 산사태 발생 지점을 기반으로 에이전트를 설정해 시간에 따라 시뮬레이션의 각 스텝에서 토석류의 흐름 즉 액션을 환경에 따른 가중치를 기준으로 산정하게 된다. 여기서 환경에 따른 가중치는 시뮬레이션 모델에 정의된 메서드에 따라 산정된다. 시간이 목표값에 도달하여 결과가 출력되면 출력된 결과와 해당 산사태 발생 지점의 실제 산사태 피해 지역 데이터 즉 시뮬레이션 결과 이상치와의 비교를 통하여 시뮬레이션을 평가하게 된다. 이러한 평가는 시뮬레이션 데이터와 실제 데이터간의 유사도 비교를 통해 손실률을 도출하게 되고 이러한 손실률을 경사하강법등의 최적화 알고리즘을 통해 최소화 하여 입력된 데이터를 기반으로한 최적의 토석류 흐름 구현 알고리즘을 도출한다.

• 한국항공운항학회지
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• 제22권4호
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• pp.42-55
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• 2014

The purpose of this research was to identify expertise in ait traffic control by using cognitive skill analysis for novices and experts in routine and non-routine situations. The result of study was to understand expertise in air traffic control tasks in terms of what cognitive processes are responsible for the expert's high performance levels. The problem solving task was difficult for novices, but performed relatively automatically by experts in a routine situation. The difficulty could indicate the presence of controlled processing. Rather than rules and strategies, novices focused more on environmental factors, which merely increase cognitive load. In a non-routine situation, novices showed that they did not categorize the information consistently and alternative resources were not available for them. Experts, however, performed automatically a task by arranging and organizing information related to problem solving components in contexts without regard to a routine and non-routine situation. Especially experts developed a stable representation and directed alternative resources for air traffic flow and efficiency. Based on the results, cognitive processes of experts could be useful to understand expert performance and analyze the learning process, which imply the necessity of developing expertise systematically.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• 대한건축학회논문집:계획계
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• 제36권4호
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• pp.41-49
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• 2020

The purpose of this study was to investigate the use of the Deep Neural Networks(DNN) model to classify user's emotions, in particular Electroencephalography(EEG) toward Virtual-Reality(VR) based 3D design alternatives. Four different types of VR Space were constructed to measure a user's emotion and EEG was measured for each stimulus. In addition to the quantitative evaluation based on EEG data, a questionnaire was conducted to qualitatively check whether there is a difference between VR stimuli. As a result, there is a significant difference between plan types according to the normalized ranking method. Therefore, the value of the subjective questionnaire was used as labeling data and collected EEG data was used for a feature value in the DNN model. Google TensorFlow was used to build and train the model. The accuracy of the developed model was 98.9%, which is higher than in previous studies. This indicates that there is a possibility of VR and Fast Fourier Transform(FFT) processing would affect the accuracy of the model, which means that it is possible to classify a user's emotions toward VR based 3D design alternatives by measuring the EEG with this model.