• Title/Summary/Keyword: 시나리오기반 학습

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Implementation of Image Block Linked Contents to Improve Children's Visual Perception and Cognitive Function (유아의 시지각 인지기능 개선을 위한 이미지 블록 연동형 콘텐츠 구성과 구현)

  • Kwak, Chang-Sub;Lee, Young-Soon
    • The Journal of the Korea Contents Association
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    • v.22 no.9
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    • pp.76-84
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    • 2022
  • In this paper, in order to compose the visual perception cognitive function training content that can be linked with the IPUZZLE image block, an interactive content device that utilizes photos and videos of smartphones. Four areas of visual memory, visual continuity, spatial relationship, and visual discrimination were derived and the content operation, application method, and scenario were written. It was intended to continuously give and induce children's desire to participate in training by designing the content image and developing the existing learning terrain visual and perceptual cognitive function training materials in the form of mobile mini-games. Experiential activities were conducted for general children and their guardians using the developed contents, and the results were found to be significant in terms of concentration, effect, and effect compared to basic puzzle toys. It is expected that this thesis will be a meaningful data for the study of cognitive function improvement activities based on digital toys and contents.

Simulation-Based Analysis of C System in C3 System of Systems Via Machine-Learning Based Abstraction of C2 System (머신러닝 기반의 C2 시스템 추상화를 통한 C3 복합체계에서의 시뮬레이션 기반 통신 시스템 분석)

  • Kang, Bong Gu;Seo, Kyung Min;Kim, Byeong Soo;Kim, Tag Gon
    • Journal of the Korea Society for Simulation
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    • v.27 no.1
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    • pp.61-73
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    • 2018
  • In the defense modeling and simulation, for the detailed analysis of the communication system, many studies have carried out the analysis under the C3 SoS(system of systems) which consists of C2(command and control) and C(communication). However, it requires time and space constraints of the C2 system. To solve this problem, this paper proposes a communication analysis method in the standalone system environment which is combined with the C system after abstracting the C2 system. In the abstraction process, we hypothesize the traffic model and mobility model for C system analysis and learn the parameters in the model based on machine learning. Through the proposed method, it is possible to construct traffic and mobility model with different output according to the battlefield. This case study shows how the process can be applied to the C3 SoS and the enhanced accuracy than the existing method. We expect that it is possible to carry out the efficient communication analysis against many experimental scenarios with various communication parameters.

Using Requirements Engineering to support Non-Functional Requirements Elicitation for DAQ System

  • Kim, Kyung-Sik;Lee, Seok-Won
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.3
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    • pp.99-109
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    • 2021
  • In recent machine learning studies, in order to consider the quality and completeness of data, derivation of non-functional requirements for data has been proposed from the viewpoint of requirements engineering. In particular, requirements engineers have defined data requirements in machine learning. In this study, data requirements were derived at the data acquisition (DAQ) stage, where data is collected and stored before data preprocessing. Through this, it is possible to express the requirements of all data required in the existing DAQ system, the presence of tasks (functions) satisfying them, and the relationship between the requirements and functions. In addition, it is possible to elicit requirements and to define the relationship, so that a software design document can be produced, and a systematic approach and direction can be established in terms of software design and maintenance. This research using existing DAQ system cases, scenarios and use cases for requirements engineering approach are created, and data requirements for each case are extracted based on them, and the relationship between requirements, functions, and goals is illustrated through goal modeling. Through the research results, it was possible to extract the non-functional requirements of the system, especially the data requirements, from the DAQ system using requirements engineering.

Rainfall image DB construction for rainfall intensity estimation from CCTV videos: focusing on experimental data in a climatic environment chamber (CCTV 영상 기반 강우강도 산정을 위한 실환경 실험 자료 중심 적정 강우 이미지 DB 구축 방법론 개발)

  • Byun, Jongyun;Jun, Changhyun;Kim, Hyeon-Joon;Lee, Jae Joon;Park, Hunil;Lee, Jinwook
    • Journal of Korea Water Resources Association
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    • v.56 no.6
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    • pp.403-417
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    • 2023
  • In this research, a methodology was developed for constructing an appropriate rainfall image database for estimating rainfall intensity based on CCTV video. The database was constructed in the Large-Scale Climate Environment Chamber of the Korea Conformity Laboratories, which can control variables with high irregularity and variability in real environments. 1,728 scenarios were designed under five different experimental conditions. 36 scenarios and a total of 97,200 frames were selected. Rain streaks were extracted using the k-nearest neighbor algorithm by calculating the difference between each image and the background. To prevent overfitting, data with pixel values greater than set threshold, compared to the average pixel value for each image, were selected. The area with maximum pixel variability was determined by shifting with every 10 pixels and set as a representative area (180×180) for the original image. After re-transforming to 120×120 size as an input data for convolutional neural networks model, image augmentation was progressed under unified shooting conditions. 92% of the data showed within the 10% absolute range of PBIAS. It is clear that the final results in this study have the potential to enhance the accuracy and efficacy of existing real-world CCTV systems with transfer learning.

Spatial Conservation Prioritization Considering Development Impacts and Habitat Suitability of Endangered Species (개발영향과 멸종위기종의 서식적합성을 고려한 보전 우선순위 선정)

  • Mo, Yongwon
    • Korean Journal of Environment and Ecology
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    • v.35 no.2
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    • pp.193-203
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    • 2021
  • As endangered species are gradually increasing due to land development by humans, it is essential to secure sufficient protected areas (PAs) proactively. Therefore, this study checked priority conservation areas to select candidate PAs when considering the impact of land development. We determined the conservation priorities by analyzing four scenarios based on existing conservation areas and reflecting the development impact using MARXAN, the decision-making support software for the conservation plan. The development impact was derived using the developed area ratio, population density, road network system, and traffic volume. The conservation areas of endangered species were derived using the data of the appearance points of birds, mammals, and herptiles from the 3rd National Ecosystem Survey. These two factors were used as input data to map conservation priority areas with the machine learning-based optimization methodology. The result identified many non-PAs areas that were expected to play an important role conserving endangered species. When considering the land development impact, it was found that the areas with priority for conservation were fragmented. Even when both the development impact and existing PAs were considered, the priority was higher in areas from the current PAs because many road developments had already been completed around the current PAs. Therefore, it is necessary to consider areas other than the current PAs to protect endangered species and seek alternative measures to fragmented conservation priority areas.

Predicting Crime Risky Area Using Machine Learning (머신러닝기반 범죄발생 위험지역 예측)

  • HEO, Sun-Young;KIM, Ju-Young;MOON, Tae-Heon
    • Journal of the Korean Association of Geographic Information Studies
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    • v.21 no.4
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    • pp.64-80
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    • 2018
  • In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

Natural Language Processing Model for Data Visualization Interaction in Chatbot Environment (챗봇 환경에서 데이터 시각화 인터랙션을 위한 자연어처리 모델)

  • Oh, Sang Heon;Hur, Su Jin;Kim, Sung-Hee
    • KIPS Transactions on Computer and Communication Systems
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    • v.9 no.11
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    • pp.281-290
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    • 2020
  • With the spread of smartphones, services that want to use personalized data are increasing. In particular, healthcare-related services deal with a variety of data, and data visualization techniques are used to effectively show this. As data visualization techniques are used, interactions in visualization are also naturally emphasized. In the PC environment, since the interaction for data visualization is performed with a mouse, various filtering for data is provided. On the other hand, in the case of interaction in a mobile environment, the screen size is small and it is difficult to recognize whether or not the interaction is possible, so that only limited visualization provided by the app can be provided through a button touch method. In order to overcome the limitation of interaction in such a mobile environment, we intend to enable data visualization interactions through conversations with chatbots so that users can check individual data through various visualizations. To do this, it is necessary to convert the user's query into a query and retrieve the result data through the converted query in the database that is storing data periodically. There are many studies currently being done to convert natural language into queries, but research on converting user queries into queries based on visualization has not been done yet. Therefore, in this paper, we will focus on query generation in a situation where a data visualization technique has been determined in advance. Supported interactions are filtering on task x-axis values and comparison between two groups. The test scenario utilized data on the number of steps, and filtering for the x-axis period was shown as a bar graph, and a comparison between the two groups was shown as a line graph. In order to develop a natural language processing model that can receive requested information through visualization, about 15,800 training data were collected through a survey of 1,000 people. As a result of algorithm development and performance evaluation, about 89% accuracy in classification model and 99% accuracy in query generation model was obtained.

Development of Digital Games Based on Historical Material and its Design Components - With History Based Games of 5 Countries (역사소재 기반 디지털게임의 발전과정 및 기획요소 연구 - 동.서양 5개국의 역사소재 게임을 중심으로)

  • Moon, Man-Ki;Kim, Tae-Yong
    • Journal of Broadcast Engineering
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    • v.12 no.5
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    • pp.460-479
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    • 2007
  • When culture took large part in industrial area, every country has tried to utilize own cultural contents for educational or commercial purpose and the various cultures and histories are recognized as a main concept or subject so that a number of scholars who study history increase. In video game field, special characteristics of interface that audiences participate in the game to complete story-telling is considered as efficient material for learning process. As observed above, it is important to analyze the games that every country makes and export to the world in which the video games is understood as a play of human in general. This Paper has firstly analyzed the most favorite historical games developed in Korea, the USA, Japan, Taiwan and Germany from 1980 to 2005 and secondly, compared that wars and historical origin appears in game scenario, a world view and background story and finally after point out the preferable era and genre of the countries then propose the promising way of design for historical video games. In the process of analysis of a view and heroes in historical games, we compared the real persons, the real historical events and novel in which 11.8% only employed the real persons in 8 out of 68 games. Also the real history and background story are appeared in 37 games which is 54.4% of them. We discovered that the main material that is popular for each country is the historical backing rather than real persons where the favorite historical background is chosen at which they are proud of; 3-Throne era with strong ancient Gogurye for Korea, the 1st and 2nd World Wars and the Independence War for the USA, the tide of war around Middle age for Japan, ancient history of Europe for Germany. The favorite age for video games is Ancient times with 37 games for 54.4%, Middle Age with 7 games fer 10.3%, the prehistoric age with 5 games for 7.35%, remote age with 1 for 1.47%, while current historical games favor Ancient or Modern Age.

Future Prospects of Forest Type Change Determined from National Forest Inventory Time-series Data (시계열 국가산림자원조사 자료를 이용한 전국 산림의 임상 변화 특성 분석과 미래 전망)

  • Eun-Sook, Kim;Byung-Heon, Jung;Jae-Soo, Bae;Jong-Hwan, Lim
    • Journal of Korean Society of Forest Science
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    • v.111 no.4
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    • pp.461-472
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    • 2022
  • Natural and anthropogenic factors cause forest types to continuously change. Since the ratio of forest area by forest type is important information for identifying the characteristics of national forest resources, an accurate understanding of the prospect of forest type change is required. The study aim was to use National Forest Inventory (NFI) time-series data to understand the characteristics of forest type change and to estimate future prospects of nationwide forest type change. We used forest type change information from the fifth and seventh NFI datasets, climate, topography, forest stand, and disturbance variables related to forest type change to analyze trends and characteristics of forest type change. The results showed that the forests in Korea are changing in the direction of decreasing coniferous forests and increasing mixed and broadleaf forests. The forest sites that were changing from coniferous to mixed forests or from mixed to broadleaf forests were mainly located in wet topographic environments and climatic conditions. The forest type changes occurred more frequently in sites with high disturbance potential (high temperature, young or sparse forest stands, and non-forest areas). We used a climate change scenario (RCP 8.5) to establish a forest type change model (SVM) to predict future changes. During the 40-year period from 2015 to 2055, the SVM predicted that coniferous forests will decrease from 38.1% to 28.5%, broadleaf forests will increase from 34.2% to 38.8%, and mixed forests will increase from 27.7% to 32.7%. These results can be used as basic data for establishing future forest management strategies.