• Magazine of the Korean Society of Hazard Mitigation
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• v.11 no.1
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• pp.54-64
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• 2011

본 연구에서는 기상청 기상관측소 325개소(기상관서:61개소, 자동기상시스템(AWS):264개소)의 관측자료를 이용하여 우리나라 최근 10년간의 강수 현황, 평년대비 변화 추세, 그리고 전국적 가뭄년도의 강수 특성에 대한 분석을 수행하였다. 최근 10년간의 연강수량은 평년 대비 7.3% 증가하는 추세로서 7월의 경우 평년 대비 25% 증가하나 11월의 경우 34% 감소하여 가뭄과 홍수의 자연재해 발생 위험도가 높아지고 있음을 보여주고 있다. 최근 10년간의 언론보도 조사결과, 가뭄이 발생하지 않은 2003년을 제외하고 매년 지역적 강수부족으로 제한급수가 실시되어져 지역적 강수부족은 매년 존재하고 있음이 확인되었다. 가뭄년도와 비가뭄년도의 월별 강수 변화 분석결과, 최근 20년 전국적 가뭄이 발생한 연도에는 2-5월사이 10년 평균강수량에 못미치는 강수가 발생하고 있음이 나타났고 이 특성을 이용한 누적강수량의 지속적 감시는 가뭄 조기경보 가능성을 보여주고 있다고 사료된다. 이에 관하여 최근 90일 누적 강수량과 댐 수위 상황도를 산출하고 이를 사례분석하여 보았다. 이 방법을 사용할 때 현재(2011년 3월말) 강수부족이 지속된다면 낙동강 중 상류지역에 물부족이 발생 가능성이 높을 것으로 예상된다.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.6 no.1
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• pp.17-27
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• 2012

This study provides development information on Omni-Wheel Drive Rider Robot, futuristic electric scooters, with autonomous driving systems that are used for people including the disabled and senior. Also, it is meaningful in suggesting alternatives to replace motorized wheelchairs or electric scooters for the future. Prior to development of Omni-Wheel Drive Rider Robot with autonomous driving systems, it surveyed 49 people, including 18 people who own electric scooters and 31 senior people who have not. The summary of the survey is as follows. First, inconveniences during riding and exiting and short mileage due and safety driving to problems of recharging batteries are the most urgent task. For these problems, the study shows that charging time of batteries, mileage, armrests, footrests, angle of a seat are the primary considerations. Second, drivers prefer joystick over steering wheels because of convenience in one-handed driving against dangers from footrest and carriageways sloping roads, paving blocks. One-handed driving can reduce driving fatigues with automatic stop systems. Moreover, the study suggests many design factors related to navigation systems, obstacle avoidance systems, omni-wheels, automatic cover-opening systems in rainy.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.149-155
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• 2021

In this study, we intend to develop a defective road surface object recognition model that automatically detects road surface defects that restrict the movement of the transportation handicapped using electric mobile devices with deep learning. For this purpose, road surface information was collected from the pedestrian and running routes where the electric mobility aid device is expected to move in five areas within the city of Busan. For data, images were collected by dividing the road surface and surroundings into objects constituting the surroundings. A series of recognition items such as the detection of breakage levels of sidewalk blocks were defined by classifying according to the degree of impeding the movement of the transportation handicapped in traffic from the collected data. A road surface object recognition deep learning model was implemented. In the final stage of the study, the performance verification process of a deep learning model that automatically detects defective road surface objects through model learning and validation after processing, refining, and annotation of image data separated and collected in units of objects through actual driving. proceeded.

• Journal of Science and Technology Studies
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• v.18 no.1
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• pp.177-217
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• 2018

This study examines the case of living lab applied in the R&D initiatives for solving societal problems and challenges. It discusses how to use the living lab in national R&D projects. The analyzed cases are 'Develop portable fundus camera for eye disease screening test to resolve health inequalities' and 'Auto-sensing integrated system development in rural pedestrian crosswalk'. As a result of the analysis, both cases were designed as a user participatory R&D structure by utilizing living lab. In other words, living lab has operated as a system that evolves technology-products-services into an infrastructure. It can realize final demand specification, product, service improvement and demonstration through continuous interaction of end users. As a result of the case analysis, the following policy tasks can be derived. First, living lab is a new concept and it is in the early stage of implementation in Korea. Therefore, it is necessary to monitor and evaluate living lab experiments and build suitable models for Korean society by sharing cases and achievements. Second, the strategic niche management are necessary for the introduction of living lab. Third, living lab can be used as a tool to transform the existing technology acquisition centered innovation policy to the policy for customer needs and problem solving. Fourth, there is a need for flexibility and adaptability in strategy and system to correct errors that appear in the living lab processes.

• Journal of Internet Computing and Services
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• v.16 no.1
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• pp.83-90
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• 2015

This paper examines the rising value of social networked journalism and analyzes the case of a social news site based on the theory of networked journalism. Social networked journalism allows the public to be involved in every aspect of journalism production through crowd-sourcing and interactivity. The networking effect with the public is driving journalism to transform into a more open, more networked and more responsive venue. "wikitree" is a social networking news service on which anybody can write news and disseminate it via Facebook and Twitter. It is operated as an open sourced program which incorporates "Google Translate" to automatically convert all its content, enabling any global citizen with an Internet access to contribute news production and share either their own creative contents or generated contents from other sources. Since its inception, "wikitree global" site has been expanding its coverage rapidly with access points arising from 160 countries. Analyzing its international coverage by country and by news category as well as by the unique visit numbers via SNS, the results of the case study imply that networking with the global public can enhance news traffic to the social news site as well as to specific news items. The results also suggest that the utilization of Twitter and Facebook in social networked journalism can break the boundary between local and global public by extending news-gathering ability while growing audience's interest in the site, and engender a feasible business model for a local online journalism.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.166-172
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• 2022

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.