• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.59-59
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• 2023

기후변화로 인한 집중호우와 홍수는 하천의 범람, 내수침수 등을 일으킨다. 최근 발생한 2022년9월 태풍 '힌남노'는 포항시 10명의 인명 피해와 1조 7000억원의 재산 피해로 막대한 피해를 야기시켰다. 본 연구는 2011년부터 2018년까지 시군구 단위의 행정구역별 홍수 기간 강우량, 피해액, 홍수 지역의 인구 자료를 NAVER DATA LAB(2016년부터 자료 제공) '홍수' 검색량 데이터와 비교 분석하였다. 본 연구에서는 다량의 강우량 또는 높은 피해액이 발생한 시기에 홍수 검색량이 낮았던 지역을 홍수에 대한 대중 인지도가 취약한 지역으로 정의하였다. '홍수' 검색량과 강우량, 피해액, 홍수 지역 인구와의 상관관계를 분석한 결과, 강우량과 인구는 각각 0.86, 0.81의 높은 상관계수를 보인 반면, 피해액은 0.52로 상대적으로 낮은 상관관계를 보였다. 2016-2018년 특/광역시단위 분석 결과, 총 17번의 홍수 발생 중 '인천광역시'와 '세종특별시'에서 피해액 규모가 각각 2, 3순위로 높았던 반면 홍수 인지도는 각각 6, 11순위로 홍수 인지도가 취약한 지역으로 평가되었다. 도 단위 평가 시, 총 34번의 홍수 발생 중 '강원도'와 '경상북도'에서 피해액 규모 3순위, 강우량 10순위 일 때, 홍수 인지도는 27순위로 홍수 인지도가 취약한 지역으로 평가되었다. 다중 선형회귀 기법을 통해 2016년부터의 데이터를 기반으로 모델을 훈련하여 2016년 이전의 '홍수' 검색량 예측 자료를 재생산하였다. 2011-2015년 특/광역시 중심의 평가에서, 총 25번의 홍수 발생 중 부산광역시에서 피해액 규모가 1순위, 강우량이 2순위로 높았던 반면 홍수 인지도는 6순위로 홍수인지도가 취약한 지역으로 평가되었다. 도 단위 평가 시, 총 50번의 홍수 발생 중 '충청남도'와 '경기도'에서 피해액 규모가 3순위일 때 홍수 인지도가 7순위로 홍수 인지도가 취약한 지역으로 평가되었다. 본 연구는 물리·사회시스템의 빅데이터를 분석하여, 사회수문학적 접근 방식으로 홍수에 대한 사회적 취약성을 새롭게 제시하며 사회과학과 수자원 분야의 융합연구 필요성을 강조하였다.

• The Journal of Bigdata
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• v.7 no.2
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• pp.1-9
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• 2022

In the global era, the importance of imported food safety management is increasing. Address information of overseas food companies is key information for imported food safety management, and must be verified for prompt response and follow-up management in the event of a food risk. However, because each country's address system is different, one verification system cannot verify the addresses of all countries. Also, the purpose of address verification may be different depending on the field used. In this paper, we deal with the problem of classifying a given overseas food business address into the administrative district level of the country. This is because, in the event of harm to imported food, it is necessary to find the administrative district level from the address of the relevant company, and based on this trace the food distribution route or take measures to ban imports. However, in some countries the administrative district level name is omitted from the address, and the same place name is used repeatedly in several administrative district levels, so it is not easy to accurately classify the administrative district level from the address. In this study we propose a deep learning-based administrative district level classification model suitable for this case, and verify the actual address data of overseas food companies. Specifically, a method of training using a label powerset in a multi-label classification model is used. To verify the proposed method, the accuracy was verified for the addresses of overseas manufacturing companies in Ecuador and Vietnam registered with the Ministry of Food and Drug Safety, and the accuracy was improved by 28.1% and 13%, respectively, compared to the existing classification model.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.35-49
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• 2024

Video captioning technology, as a significant outcome of the integration between computer vision and natural language processing, has emerged as a key research direction in the field of artificial intelligence. This technology aims to achieve automatic understanding and language expression of video content, enabling computers to transform visual information in videos into textual form. This paper provides an initial analysis of the research trends in deep learning-based video captioning and categorizes them into four main groups: CNN-RNN-based Model, RNN-RNN-based Model, Multimodal-based Model, and Transformer-based Model, and explain the concept of each video captioning model. The features, pros and cons were discussed. This paper lists commonly used datasets and performance evaluation methods in the video captioning field. The dataset encompasses diverse domains and scenarios, offering extensive resources for the training and validation of video captioning models. The model performance evaluation method mentions major evaluation indicators and provides practical references for researchers to evaluate model performance from various angles. Finally, as future research tasks for video captioning, there are major challenges that need to be continuously improved, such as maintaining temporal consistency and accurate description of dynamic scenes, which increase the complexity in real-world applications, and new tasks that need to be studied are presented such as temporal relationship modeling and multimodal data integration.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.55-57
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• 2001

강화학습은 한 에이전트가 자신이 놓여진 환경으로부터의 보상을 최대화할 수 있는 최적의 행동 전략을 학습하는 것이다. 따라서 강화학습은 입력(상태)과 출력(행동)의 쌍으로 명확한 훈련 예들이 제공되는 교사 학습과는 다르다. 특히 Q-학습과 같은 비 모델 기반(model-free)의 강화학습은 사전에 환경에 대한 별다른 모델을 설정하거나 학습할 필요가 없으며 다양한 상태와 행동들을 충분히 자주 경험할 수만 있으면 최적의 행동전략에 도달할 수 있어 다양한 응용분야에 적용되고 있다. 하지만 실제 응용분야에서 Q-학습과 같은 강화학습이 겪는 최대의 문제는 큰 상태 공간을 갖는 문제의 경우에는 적절한 시간 내에 각 상태와 행동들에 대한 최적의 Q값에 수렴할 수 없어 효과를 거두기 어렵다는 점이다. 이런 문제점을 고려하여 본 논문에서는 로봇 축구 시뮬레이션 환경에서 각 선수 에이전트의 동적 위치 결정을 위해 효과적인 새로운 Q-학습 방법을 제안한다. 이 방법은 원래 문제의 상태공간을 몇 개의 작은 모듈들로 나누고 이들의 개별적인 Q-학습 결과를 단순히 결합하는 종래의 모듈화 Q-학습(Modular Q-Learning)을 개선하여, 보상에 끼친 각 모듈의 기여도에 따라 모듈들의 학습결과를 적응적으로 결합하는 방법이다. 이와 같은 적응적 중재에 기초한 모듈화 Q-학습법(Adaptive Mediation based Modular Q-Learning, AMMQL)은 종래의 모듈화 Q-학습법의 장점과 마찬가지로 큰 상태공간의 문제를 해결할 수 있을 뿐 아니라 보다 동적인 환경변화에 유연하게 적응하여 새로운 행동 전략을 학습할 수 있다는 장점을 추가로 가질 수 있다. 이러한 특성을 지닌 AMMQL 학습법은 로봇축구와 같이 끊임없이 실시간적으로 변화가 일어나는 다중 에이전트 환경에서 특히 높은 효과를 볼 수 있다. 본 논문에서는 AMMQL 학습방법의 개념을 소개하고, 로봇축구 에이전트의 동적 위치 결정을 위한 학습에 어떻게 이 학습방법을 적용할 수 있는지 세부 설계를 제시한다.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.139-146
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• 2002

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless these algorithms can learn the optimal policy if the agent can visit every state-action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem, we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL) as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. In this paper we use the AMMQL algorithn as a learning method for dynamic positioning of the robot soccer agent, and implement a robot soccer agent system called Cogitoniks.

• 한국노년학
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• v.30 no.4
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• pp.1129-1143
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• 2010

The purposes of this study were to investigate the major determinants influencing on health promoting behaviors(HPB) of the elderly living in Seoul. The conceptual framework of the study was Pender's health promoting model and the ecological perspectives. The study was conducted with 495 elderly persons whom 60 years old. For the analysis of data, descriptive statistics and hierarchical regression were used for the statistical analysis with SPSS program. The results were as following: 1) The mean score of the HPB was 3.11(SD=0.41). 2) Hierarchical regression analysis found that ModelIV accounted for 55.7% of the variance in HPB. 3) The Major determinants on HPB among the elderly persons were prior related perceived benefits of action, social support, perceived self-efficacy, community environment, perceived health status, education, and age. In conclusions, first, we should develop to various levels of educational and supportive programs for the HPB among the elderly persons. Second, we should examine more with environment, the accessibility to senior welfare agencies. Third, we should be organized the self-help groups for the elderly persons to improve health promoting behaviors. Fourth, the government should established more secure environment for the HPB, and find better solutions that are provided by various social welfare agencies connected with the coordination of the services in the local communities. Finally, we should develop professional education training programs of the HPB for the practitioners in the field of Gerontological Social Work.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.25-36
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• 2008

In this paper, we present a robust hand recognition approach to sudden illumination changes. The proposed approach constructs a background model with respect to hue and hue gradient in HSI color space and extracts a foreground hand region from an input image using the background subtraction method. Eighteen features are defined for a hand pose and multi-class SVM(Support Vector Machine) approach is applied to learn and classify hand poses based on eighteen features. The proposed approach robustly extracts the contour of a hand with variations in illumination by applying the hue gradient into the background subtraction. A hand pose is defined by two Eigen values which are normalized by the size of OBB(Object-Oriented Bounding Box), and sixteen feature values which represent the number of hand contour points included in each subrange of OBB. We compared the RGB-based background subtraction, hue-based background subtraction and the proposed approach with sudden illumination changes and proved the robustness of the proposed approach. In the experiment, we built a hand pose training model from 2,700 sample hand images of six subjects which represent nine numerical numbers from one to nine. Our implementation result shows 92.6% of successful recognition rate for 1,620 hand images with various lighting condition using the training model.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.619-628
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• 2022

Although face recognition and face generation have been growing in popularity, the privacy issues of using facial images in the wild have been a concurrent topic. In this paper, we propose a face editing network that can reduce privacy issues by generating face images with various properties from a small number of real face images and facial mask information. Unlike the existing methods of learning face attributes using a lot of real face images, the proposed method generates new facial images using a facial segmentation mask and texture images from five parts as styles. The images are then trained with our network to learn the styles and locations of each reference image. Once the proposed framework is trained, we can generate various face images using only a small number of real face images and segmentation information. In our extensive experiments, we show that the proposed method can not only generate new faces, but also localize facial attribute editing, despite using very few real face images.

• 재활복지
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• v.20 no.4
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• pp.1-29
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• 2016

The research regarding employees' job satisfaction is one of the most important indicators of their vocational adjustment or outcome. The purpose of this study is to investigate the level of job satisfaction of South Korean masseurs with visual impairments and what variables predict to this. The work satisfaction model of Social Cognitive Career Theory (Lent Brown, 2006a) was grounded. a total of 221 South Korean masseurs with visual impairments participated in this study. Multiple regression analysis indicated that as masseurs in this study experienced having a better fit with their job regarding their monetary aspects, as they felt efficacious with their massage skills, as they felt more positive, and as they considered their job duties fit their education or skills they learned, their level of job satisfaction was higher. However, fit with their organization values or cultures or how much they get social support from their family, friends, or significant others did not predict their job satisfaction. In addition, the analysis supported the existence of a moderating effect of positive affect on the relationship between subjective fit and job satisfaction, in addition to the moderating effect of social support on the relationship between work related self-efficacy and job satisfaction among study participants. Implications for policy makers, researchers, and career counselors were also provided.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.