• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.123-123
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• 2012

일강수량은 수공구조물 설계 및 수자원계획을 수립하기 위한 입력 자료로 이용된다. 일반적으로 수자원계획은 장기적인 목적을 가지고 수행되어지며, 장기간의 일강수량 자료를 필요로 한다. 하지만 장기간의 일강수량 자료의 획득의 어려움으로 단기간의 일강수량자료를 이용하여 모의한 장기간 강수자료를 이용하게 된다. 이처럼 수자원계획의 수립에 있어서 일강수량 모의기법의 성능은 수자원계획의 신뢰성 및 결과에 큰 영향을 준다. 일강수량 모의기법은 국내외적으로 매우 활발하게 이루어지고 있으며, 수자원계획 및 수공구조물 설계 외에도 매우 다양한 목적으로 활용되어 지고 있다. 일강수량을 모의기법 중 강수계열의 단기간의 기억(memory)을 활용한 Markov Chain 모형이 가장 일반적이지만, 기존 Markov Chain 모형을 통한 일강수량 모의는 극치강수량을 재현하기 어렵다는 문제점이 있다. 또한, 일강수량 모의 기법의 목적인 수자원계획 및 수공구조물 설계 등의 입력자료로 활용되어지기 위해서는 모의 결과가 유역내 지점별 공간 상관성을 재현함으로써 모형의 우수성과 자료결과의 신뢰성을 확보할 수 있어야 하겠다. 이러한 점에서 본 연구에서는 내삽에서 우수한 재현능력을 갖는 핵 밀도함수와 극치강수량 재현에 유리한 GPD분포의 특징을 함께 고려할 수 있는 불연속 Kernel-Pareto Distribution 기반에 공간상관성 재현 알고리즘을 결합한 일강수량모의기법을 개발하였다. 한강유역의 18개 강수지점에 대해서 기존 Gamma분포를 사용한 Markov Chain 모형과 본 연구에서 제안한 방법을 적용하여 모형을 평가해 보고자 한다. Gamma 분포기반 Markov Chain 모형의 경우 일강수량 모의 시 1차모멘트인 평균과 2-3차 모멘트 모두 효과적으로 재현하지 못하는 문제점이 나타났다. 그러나 본 연구에서 적용한 다지점 불연속 Kernel-Pareto 분포 모형은 강수계열의 평균적인 특성뿐만 아니라 표준편차 및 왜곡도의 경우에도 관측치의 통계특성을 매우 효과적으로 재현하며, 100년빈도 강수량 모의결과 기존 모의모형의 문제점을 보완할 수 있는 개선된 결과를 보여주었다. 본 연구에서 제시한 방법론은 유역내의 공간상관성을 재현하며, 평균 및 중간값 등 낮은 차수의 모멘트 등 일강수량 분포특성을 더욱 효과적으로 모의할 수 장점을 확인하였다.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.1-9
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• 2019

The purposes of the study are to investigate Koreans' perception about Vietnam by analyzing social big data and to seek changing direction in perception. For the purposes, the texts about Vietnam in Naver Blog and Twitter and the number of search and click for Vietnam in Naver were analyzed by Social Metrics of Daum Soft and Datalab of Naver. The study also analyzed the annual change of their interest in Vietnam based on social media. The results showed that Koreans still remember the Vietnam war, have a positive emotion toward Vietnam, and view Vietnam as a country where we can gain mutual benefit by exchange. The findings also indicated that Koreans perceive Vietnam as a favorite tourist spot regardless of age. Meanwhile, children under 12 showed a different pattern of an annual change in perception. It might be a positive sign that Koreans' interest region toward Vietnam would be diversified because children under 12 would be the central axis of cultural contents.

• Design Convergence Study
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• v.16 no.6
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• pp.137-152
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• 2017

This research aims to develop the companion robot experience design for elderly in korea based on needs-function deploy matrix of robot and emotion expression research of robot in multimodal interaction. First, Elder users' main needs were categorized into 4 groups based on ethnographic research. Second, the functional elements and physical actuators of robot were mapped to user needs in function- needs deploy matrix. The final UX design prototype was implemented with a robot type that has a verbal non-touch multi modal interface with emotional facial expression based on Ekman's Facial Action Coding System (FACS). The proposed robot prototype was validated through a user test session to analyze the influence of the robot interaction on the cognition and emotion of users by Story Recall Test and face emotion analysis software; Emotion API when the robot changes facial expression corresponds to the emotion of the delivered information by the robot and when the robot initiated interaction cycle voluntarily. The group with emotional robot showed a relatively high recall rate in the delayed recall test and In the facial expression analysis, the facial expression and the interaction initiation of the robot affected on emotion and preference of the elderly participants.

• Journal of Preventive Medicine and Public Health
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• v.30 no.1 s.56
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• pp.171-179
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• 1997

This study was performed to see whether neurobehavioral tests was affected by the exposure-free time in the workers chronically exposed to organic solvents. Thirty-four female workers were participated and four items among neurobehavioral core test battery of World Health Organization, including digit span, Santa Ana Dexterity, digit symbol and Benton Visual Retention, were administered to the workers. Test was conducted three times-preshift on Monday, preshift on Weekday and during shift on Weekday-per person and the interval of tests was 2 weeks. Digit span forward, Santa Ana Dexterity, digit symbol, and Benton Visual Retention showed significant decrements by performing time, especially during shift on Week-day versus preshift on Monday and preshift on Weekday. In addition, the score at preshift on Weekday was significantly lower than preshift on Monday, in preferred Santa Ana Dexterity and digit symbol. Generally, those who were exposed to high concentration, over 50 years and under 6 years of education showed marked decrease of score at during shift. So, it would be desirable that neurobehavioral test is conducted at preshift on Monday and items related to short term memory could be considerable to be done at preshift on Weekday.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.185-191
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• 2018

This study examined the type and extent of interruption between induced emotion and succeeding arithmetic operation. The experiment was carried out to determine the influence of the induced emotions (anger, joy, and sorrow) and stimulus types (picture and sentence) on the cognitive process load that may block the interactions among the constituents of working memory. The study subjects were 32 undergraduates who were similar with respect to age and education parameters and were especially instructed to attend to induced emotion by imitation of facial expression and to make a correct decision during the remainder calculation task. In the results, the stimulus types did not exhibit any difference but there was a significant difference among the induced emotion types. The difference was observed in slower response time at positive emotion(joy condition) as compared with other emotions(anger and sorrow). More specifically, error and delayed correct response rate for emotion types were analysed to determine which phase the slower response was associated with. Delayed responses of the joy condition by sentence-inducing stimulus were identified with the error rate difference, and those by picture-inducing stimulus with the delayed correct response rate. These findings not only suggest that induced positive emotion increased response time compared to negative emotions, but also imply that picture-inducing stimulus easily affords arithmetic fluctuation whereas sentence-inducing stimulus results in arithmetic failure.

• Journal of Intelligence and Information Systems
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• v.16 no.1
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• pp.17-43
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• 2010

This research considers the role of knowledge as marketing message, designs and analyses the personal experience management (PEM) business model using Human-Reader system. It is difficult to save and manage person's daily experience and relevant contents due to the lack of proper infrastructure and system. On the contrary, using Human-Reader infrastructure, person's experience and various relevant contents can be easily saved and managed because seamlessness between offline and online and the various devices that person can always carry along in ubiquitous environment. Since person can store and manage information, contents and advertisements through Human-Reader system and u-PEMS, marketing messages and advertisements do not have to be repetitive and stimulating. Instead, marketing messages and advertisements in Human-Reader environment should be granting values that can be saved and managed. We propose various scenarios, processes and its issues. And we analyze the expected value of RFID tag used on the proposed business model by so-called 'Tag Evaluation Model' and assess the assumptions that are basis of the proposed business model for evaluate the feasibility of the u-PEM business model.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.503-514
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• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.

• Journal of Korean Society of Archives and Records Management
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• v.6 no.2
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• pp.157-184
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• 2006

Presidential records which have produced during a presidency as a national center are the evidence of the presidency and main historical records. We have the responsibility to establish fundamental systems to produce such main historical records and to manage such main historical records which could help people and history to judge the presidency based upon the evidence of their activities. The historical appraisal could be achieved not by memory but by evidence. A draft of a proposed law on the management of presidential records which includes the establishment of presidential libraries for the presidential records Mecca is being moored at the National Assembly now. The presidential library is to be considered as a multi-functional national institution which is carrying out the role as an Archives, Museums and Center for the education. In addition, it is imperative for a presidential library to provide user-oriented services to enrich the usability and the value of records, recognizing the change of administration paradigm from a supplier-oriented system to a customer-oriented system. This dissertation, in order to develop presidential library service programs focusing on customers rather than the convenience of administration, reviewed programs of the U.S. presidential libraries as a developed case and proposes guidelines and applicable samples for the development of the Korean presidential library service programs.

• Journal of Korean Society of Archives and Records Management
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• v.19 no.3
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• pp.293-300
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• 2019

Gyeongsangnam-do Archives is the first permanent record management organization built in the province. In 2007, the Local Archives Management Agency was obliged to establish under the "Public Archives Management Act," and it was not until about 10 years after that the first plan was made for the construction of all 17 attempts. Unlike the initial plan, many parts of the building were reduced because of the building remodeling; nonetheless, the construction was successful. The Gyeongsangnam-do Archives, which first came to the fore in Gyeongnam, is constantly committed to the development of records management and the spread of recording culture from 2014 to 2019. However, the first time was unfamiliar and difficult because of many challenges. Furthermore, it is necessary to carry out various tasks that have been set aside such as the actual implementation of declarative laws and realization of the spread of the recording culture with citizens. Therefore, the first trial may require more responsibility than glory. As the researcher, I will manage various tasks and results of the work done at Gyeongsangnam-do Archives, as well as record every day to be recognized as a leader and director of spreading the recording culture.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.