• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.205-227
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• 2002

Our study is focused on a multiple-agent system to provide efficient collaborative work by automating the conference calling process with the help of intelligent agents. Automating the meeting scheduling requires a careful consideration of the individual official schedule as well as the privacy and personal preferences. Therefore, the automation of conference calling needs the distributed processing task where a separate calendar management process is associated for increasing the reliability and inherent parallelism. This paper describes in detail the design and implementation issues of a multiple-agent system for conference calling that allows the convener and participants to minimize their efforts in creating a meeting. Our system is based on the client-sewer model. In the sewer side, a scheduling agent, a negotiating agent, a personal information managing agent, a group information managing agent, a session managing agent, and a coordinating agent are operating. In the client side, an interface agent, a media agent, and a collaborating agent are operating. Agents use a standardized knowledge manipulation language to communicate amongst themselves. Communicating through a standardized knowledge manipulation language allows the system to overcome heterogeneity which is one of the most important problems in communication among agents for distributed collaborative computing. The agents of our system propose the dates on which as many participants as possible are available to attend the conference using the forward chaining algorithm and the back propagation network algorithm.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.414-414
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• 2017

토양수분은 지면환경에서 일어나는 수문 및 에너지 순환을 이해하는 데 있어 중요한 기상인자이다. 토양수분 현장관측은 땅속에 매설된 센서에 의해 상당히 정확하게 이루어지만, 관측점 수가 충분치 않아 공간적 연속성을 확보하지 못하는 어려움이 존재한다. 이에 광역적 및 연속적 관측이 가능한 마이크로파 위성센서가 토양수분 정보 획득을 위한 보조수단으로서 그 중요성이 부각되고 있다. 마이크로파 위성센서는 구름 등 기상조건의 제약을 받지 않으며, 1978년 이래 현재까지 여러 위성에 의해 25 km 및 10 km 해상도의 전지구 토양수분자료가 생산되어 왔다. 마이크로파 센서를 이용한 토양수분자료는 동일지점에 대하여 하루 2회 정도 산출되므로 적절한 시간분해능을 가지지만, 공간해상도가 최고 10 km로서 지역규모의 수문분석에 적용하기에는 충분치 않다. 이러한 토양수분자료의 공간해상도 문제 해결을 위하여 다양한 지면환경요소를 활용한 통계적 다운스케일링이 대안으로 제시되었다. 최근의 선행연구들은 대부분 방정식을 이용한 결합모형을 통해 통계적 다운스케일링을 수행하였는데, 회귀식과 같은 선형결합뿐 아니라 신경망이나 기계학습 등의 비선형결합에서도, 불가피하게 발생할 수밖에 없는 잔차(residual)로 인하여 다운스케일링 전후의 공간분포 패턴이 달라져버리는 문제를 안고 있었다. 회귀분석에 잔차의 공간내삽을 결합시킨 회귀크리깅(regression kriging)은 잔차보정을 통해 이러한 문제를 해결함으로써 다운스케일링 전후의 공간분포 일관성을 보장하는 기법이다. 이 연구에서는 회귀크리깅을 이용하여 일자별 AMSR2(Advanced Microwave Scanning Radiometer 2) 토양수분 자료를 10 km에서 1 km 해상도로 다운스케일링하고, 다운스케일링 전후의 자료패턴 일관성을 평가한다. 지면온도(LST), 지면온도상승률(RR), 식생온도건조지수(TVDI)는 일자별로 DB를 구축하였고, 식생지수(NDVI), 수분지수(NDWI), 지면알베도(SA)는 8일 간격으로 DB를 구축하였다. 이러한 8일 간격의 자료를 일자별로 변환하기 위하여 큐빅스플라인(cubic spline)을 이용하여 시계열내삽을 수행하였다. 또한 상이한 공간해상도의 자료는 최근린법을 이용하여 다운스케일링 목표해상도인 1 km에 맞도록 변환하였다. 우선 저해상도 스케일에서 추정치를 산출하기 위해서는 저해상도 픽셀별로 이에 해당하는 복수의 고해상도 픽셀을 평균화하여 대응시켜야 하며, 이를 통해 6개의 설명변수(LST, RR, TVDI, NDVI, NDWI, SA)와 AMSR2 토양수분을 반응변수로 하는 다중회귀식을 도출하였다. 이식을 고해상도 스케일의 설명변수들에 적용하면 고해상도 토양수분 추정치가 산출되는데, 이때 추정치와 원자료의 차이에 해당하는 잔차에 대한 보정이 필요하다. 저해상도 스케일로 존재하는 잔차를 크리깅 공간내삽을 통해 고해상도로 변환한 후 이를 고해상도 추정치에 부가해주는 방식으로 잔차보정이 이루어짐으로써, 다운스케일링 전후의 자료패턴 일관성이 유지되는(r>0.95) 공간상세화된 토양수분 자료를 생산할 수 있다.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.483-497
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• 2022

Recently, due to COVID-19, non-face-to-face online lectures are being held all over the world. In higher education in the post-corona era, distance learning has become the main teaching and learning method. At this time, Metaverse is being proposed as a new alternative. Metaverse has basic elements such as avatars, 3D space, and activities accompanied by interaction, which can be seen as a difference compared to existing VR (Virtual Reality) contents. This study designed and built an educational metaverse platform that can be applied to actual lectures by reflecting the three elements of the metaverse.In addition, we implemented a cross-device-platform that supports various devices such as HMDs, smartphones, tablets, and PCs by reflecting user requirements through usability tests such as middle school, high school, college students, and parents, so that anyone can easily participate in Metaverse lectures. Currently, the metaverse platform is being developed and serviced in various ways, but there are hardly any services designed for education. Just as services such as Zoom, the existing video conferencing solution, were used for non-face-to-face education, some functions of the currently serviced metaverse are utilized for education and used in the form of a one-time event. The educational metaverse platform developed through this study is expected to be a reference in constructing the metaverse for education in the future.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.117-129
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• 2022

The ability to creatively pursue new knowledge and perspectives across various disciplines has established itself as a basic literacy for living in the 21st-century convergence era. With the development of various creative education programs, assessment tools that can objectively and systematically evaluate learners' academic achievement are also required. Therefore, this paper proposed the self assessment, peer assessment, creativity assessment, and reflection tool based on the hyper blended practical model as assessment tools for creative convergence education programs for elementary and secondary school students. The developed assessment tools attempted to develop more completed evaluation methods by modifying two items and deleting four items through validity tests. In addition, the evaluation tool was applied to 596 elementary and secondary school students nationwide, and the application results were analyzed through one-way ANOVA and Wordcloud system. As a result of the analysis, it was found that the self assessment and the reflection tool need to develop questions according to the grade group. In addition, we proposed to use these assessment tools in blended classes or various educational activities in the changing classroom environment. We hope that this paper provides implications for developing evaluation systems and tools for creative convergence education.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.239-247
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• 2022

Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process.

• Journal of the Korean Society of Floral Art and Design
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• no.42
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• pp.97-116
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• 2020

In this paper, the need for lifelong education and distance education is increasing due to the decrease of population and the increase of life expectancy. In addition, the popularization and everydayization of education, which combines daily life and learning, is an educational feature. Individuals can more easily access knowledge, and video plays an important role. Video content is the most basic medium that leads to the popularization and daily life of education. With the development of information and communication technology, popularization of media content production and editing technology, anyone can easily create and share. The video education contents business is expected to increase globally through SNS. Especially, the video contents education industry related to flower design is regarded as a suitable content field in an era where environment is essential. In the modern era, characterized by the "one-person household, one-person media" era, the environment of plants protects people from stress by restoring human emotional efficiency, environmental comfort, and stability. In other words, because humans have a preference for nature, plants play an important role for humanity recovery. Against this backdrop, flower design is expected to be a promising industrial sector with high growth, high value added and high job creation effects. In the era of the fourth revolution of the human race, competitive video contents are expected to influence the growth of the future country. will be.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.42-42
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• 2022

지금까지도 유역에서의 녹조 모니터링은 현장채수를 통한 점 단위 모니터링에 크게 의존하고 있어 기후, 유속, 수온조건 등에 따라 수체에 광범위하게 발생하는 녹조를 효율적으로 모니터링하고 대응하기에는 어려운 점들이 있어왔다. 또한, 그동안 제한된 관측 데이터로 인해 현장 측정된 실측 데이터 보다는 녹조와 관련이 높은 NDVI, FGAI, SEI 등의 파생적인 지수를 산정하여 원격탐사자료와 매핑하는 방식의 분석연구 등이 선행되었다. 본 연구는 녹조의 모니터링시 정확도와 효율성을 향상을 목표로 하여, 우선은 녹조 측정장비를 활용, 7000개 이상의 녹조 관측 데이터를 확보하였으며, 이를 바탕으로 동기간의 고해상도 위성 자료와 실측자료를 매핑하기 위해 다양한Machine Learning기법을 적용함으로써 그 효과성을 검토하고자 하였다. 연구대상지는 낙동강 내성천 상류에 위치한 영주댐 유역으로서 데이터 수집단계에서는 면단위 현장(in-situ) 관측을 위해 2020년 2~9월까지 4회에 걸쳐 7291개의 녹조를 측정하고, 동일 시간 및 공간의 Sentinel-2자료 중 Band 1~12까지 총 13개(Band 8은 8과 8A로 2개)의 분광특성자료를 추출하였다. 다음으로 Machine Learning 분석기법의 적용을 위해 algae_monitoring Python library를 구축하였다. 개발된 library는 1) Training Set과 Test Set의 구분을 위한 Data 준비단계, 2) Random Forest, Gradient Boosting Regression, XGBoosting 알고리즘 중 선택하여 적용할 수 있는 모델적용단계, 3) 모델적용결과를 확인하는 Performance test단계(R², MSE, MAE, RMSE, NSE, KGE 등), 4) 모델결과의 Visualization단계, 5) 선정된 모델을 활용 위성자료를 녹조값으로 변환하는 적용단계로 구분하여 영주댐뿐만 아니라 다양한 유역에 범용적으로 적용할 수 있도록 구성하였다. 본 연구의 사례에서는 Sentinel-2위성의 12개 밴드, 기상자료(대기온도, 구름비율) 총 14개자료를 활용하여 Machine Learning기법 중 Random Forest를 적용하였을 경우에, 전반적으로 가장 높은 적합도를 나타내었으며, 적용결과 Test Set을 기준으로 NSE(Nash Sutcliffe Efficiency)가 0.96(Training Set의 경우에는 0.99) 수준의 성능을 나타내어, 광역적인 위성자료와 충분히 확보된 현장실측 자료간의 데이터 학습을 통해서 조류 모니터링 분석의 효율성이 획기적으로 증대될 수 있음을 확인하였다.

• Journal of Korean Home Economics Education Association
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• v.30 no.1
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• pp.15-28
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• 2018

Teachers are not completed by appointment, but gradually made through self-development and training for a long time. In order to improve a sense of responsibility of home economics teachers, and also to suggest the purpose and direction of program through job training, the needs of training subjects should be preferentially understood. Thus, this study aims to provide basic data for establishing the developmental operation measures of training for home economics teachers, by researching the needs for training performed after the qualification training for first-grade teachers, targeting the teachers participating in the qualification training program for first-grade teachers of home economics in 2017. About the half of the research subjects received the home economics training one time or less for last three years. Through the training for first-grade teachers, the technical improvement of lesson instruction was demanded the most. As professional qualifications that should be cultivated through training, the ability to develop teaching methods and teaching/learning materials was the highest. Regarding the theme of training, the development of teaching/learning materials for home economics was desired the most. They wanted the training method including direct participation with high utilization for lesson, sublation of competition-centered evaluation, preference of instructors with field experience, continuous opportunity of home economics training, and communicative training. Regarding the needs for the 2015 revised curriculum, the demand for the training of 'human development and family' area was the highest. Therefore, in order to improve the professionalism of teachers through home economics training, it would be necessary to improve the educational environment such as temporal room for training and administrative support, and also to provide diverse types of training like group training, remote training, and smartphone app training suitable for changes in the generation of teachers. Also, on top of forming communities of home economics teachers, and sharing great contents of training, there should be individually-customized training for practice and sharing lesson cases.

• Journal of the Korean earth science society
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• v.42 no.3
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• pp.247-263
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• 2021

Climate change has been accelerating in coastal waters recently; therefore, the importance of coastal environmental monitoring is also increasing. Chlorophyll-a concentration, an important marine variable, in the surface layer of the global ocean has been retrieved for decades through various ocean color satellites and utilized in various research fields. However, the commonly used chlorophyll-a concentration algorithm is only suitable for application in clear water and cannot be applied to turbid waters because significant errors are caused by differences in their distinct components and optical properties. In addition, designing a standard algorithm for coastal waters is difficult because of differences in various optical characteristics depending on the coastal area. To overcome this problem, various algorithms have been developed and used considering the components and the variations in the optical properties of coastal waters with high turbidity. Chlorophyll-a concentration retrieval algorithms can be categorized into empirical algorithms, semi-analytic algorithms, and machine learning algorithms. These algorithms mainly use the blue-green band ratio based on the reflective spectrum of sea water as the basic form. In constrast, algorithms developed for turbid water utilizes the green-red band ratio, the red-near-infrared band ratio, and the inherent optical properties to compensate for the effect of dissolved organisms and suspended sediments in coastal area. Reliable retrieval of satellite chlorophyll-a concentration from turbid waters is essential for monitoring the coastal environment and understanding changes in the marine ecosystem. Therefore, this study summarizes the pre-existing algorithms that have been utilized for monitoring turbid Case 2 water and presents the problems associated with the mornitoring and study of seas around the Korean Peninsula. We also summarize the prospective for future ocean color satellites, which can yield more accurate and diverse results regarding the ecological environment with the development of multi-spectral and hyperspectral sensors.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.81-94
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• 2022

This study analyzed the effect of the entrepreneurship planning course provided by an women's university in Seoul on the enhancement of the core competencies of entrepreneurship of university students. To this end, pre- and post-test of core entrepreneurship competency were conducted on 63 female university students (32 in experimental group, 31 in control group) and then the results were analyzed. The course in which the experimental group participated was a team-based project learning course and it required a team of three people to draw an entrepreneurship plan containing social problem solving as the final result. The course was operated for a total of 8 weeks. To measure the level of entrepreneurship core competency in the pre- and post- test, the survey tool that was developed by the Ministry of Education and Korea Entrepreneurship Foundation (2020) was used. This tool composed by 'value creation', 'challenge', 'self-directed', and 'group creativity' competencies. As analyses methods, i) covariance analysis was performed using the pretest as a covariate, and then a two-way ANOVA was performed with treatment (experimental group, control group) and time point (pre test, post test) as two independent variables. Results show while there was no significant difference between the experimental group and the control group in the value creation competency, it significantly contributed to the enhancement of challenge, self-directed, and collective creativity competencies. Based on these results, implications and limitations were discussed, followed by future research direction.