• 한국산학기술학회논문지
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• 제22권1호
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• pp.553-561
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• 2021

본 연구의 목적은 재난 취약계층 특히 장애아동을 전문으로 보육하는 어린이집의 피난 부하를 고려한 피난 모델 구축을 통해 장애아동의 피난 환경 개선 방안을 제시하는데 있다. 피난 부하 및 모델에 대한 이론적 고찰 및 장애아전문 어린이집 피난 훈련을 통해 장애아동의 피난 행태 및 방법을 분석하고, 이를 기초로 장애아전문 어린이집의 장애아동 피난 부하 모델을 제시하였다. 이상의 연구 결과 다음과 같은 결론을 도출하였다. 첫째, 장애아동의 피난은 장애 유형 및 수준, 피난 행태를 고려하여 피난 계획 수립의 필요성이 있으며, 피난시 피난 수단 및 방법의 특별한 고려의 필요성이 있다. 둘째, 재난시 장애아동의 피난 부하는 비장애인의 피난 부하와 상이한 조건을 보이며, 장애아동의 특별한 피난 행태(피난 방법 및 수단)를 반영하는 피난 통로 및 피난 부하 모델 구축의 필요성이 있다. 셋째, 휠체어 또는 뇌성마비 등 장애 유형 및 수준에 따라 수직 피난이 불가능한 경우, 장애아동의 안전한 피난을 위해서 2층 이상의 활동실에는 장애아동의 안전한 구난시까지 대기할 수 있는 피난안전구역을 설정할 필요성이 있다.

• 토지주택연구
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• 제9권4호
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• pp.10-18
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• 2018

As the volatility increasement of the number of tourist, there was been controversy over supply-demand imbalance in hotel market. The purpose of this study is to analysis on determinants of hotel occupancy rate in Jeju Island. The quantitative method is based on cointegrating regression, using an empirical dataset with hotel from 2000 to 2017. The primary results of research is briefly summarized as follows; First, there are high relationship between total hotel occupancy rate and hotel occupancy of foreign tourist. The volatility of hotel occupancy is caused by foreigner user than local tourists though local tourist high propotion of hotel occupancy in Jeju Island. Second, hotel occupancy of local tourist has not relationship with demand and supply variables. Because some hotel users are not local tourists but local resident, and effects to other variables of hotel consumer trend, accommodation such as Guest house, Airbnb. Third, there are high relationship between foreign hotel occupancy rate and demand-supply variables. These research imply that total management of supply-demand is very important to seek stability of hotel occupancy rate in Jeju Island. Also it can provide a useful solution regarding mismatch problem between supply-demand as well as development the systematic forecasting model for hotel market participants.

• 대한교통학회지
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• 제14권4호
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• pp.49-76
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• 1996

Until recently, the inductive loop detecters(ILD) have been used to collect a traffic information in a part of traffic manangment and control. The ILD is able to collect a various traffic data such as a occupancy time and non-occupancy time, traffic volume, etc. The occupancy time of these is very important information for traffic control algorithms, which is required a high accuracy. This accuracy may be improved by classifying a vehicle type with ILD. To classify a vehicle type based on a Analog Digital Converted data collect form ILD, this study used a typical and modifyed statistic method and General Learning Vector Quantization unsuperviser neural network model and a hybrid model of GLVQ and statistic method, As a result, the hybrid model of GLVQ neural network model is superior to the other methods.

• 한국항만학회지
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• 제14권1호
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• pp.66-75
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• 2000

From the traffic analysis, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results were obtained : ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy. ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period showed 10mph decrease when compared with the 24hours'average speed, but the speed did not show a big difference in the afternoon peak period. ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge section. ⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1999년도 추계학술대회논문집
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• pp.111-121
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• 1999

From the traffic analyses, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results obtained: ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy.ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period shown 10mph decrease when compared with the 24hours' average speed, but the speed did not show a big difference in the afternoon peak period.ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge sectionⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1999년도 추계학술대회논문집
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• pp.131-140
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• 1999

Today freeway is experiencing a severe congestion with incoming or outgoing traffic through freeway ramps during the peak periods. Thus, the objectives of this study is to identify the traffic characteristics, analyze the relationships between the traffic characteristics and finally construct the delay predictive models on the ramp junctions of freeway with 70mph speed limit. From the traffic analyses, and model constructions and verifications for delay prediction on the ramp junctions of freeway, the following results were obtained: ⅰ) Traffic flow showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy also showed a big difference depending on the time periods, and the downstream occupancy(Od) was especially shown to have a higher explanatory power for the delay predictive model construction on the ramp junction of freeway. ⅲ) The speed-occupancy curve showed a remarkable shift based on the occupancies observed ; Od < 9% and Od$\geq$9%. Especially, volume and occupancy were shown to be highly explanatory for delay prediction on the ramp junctions of freeway under Od$\geq$9%, but lowly for delay predicion on the ramp junctions of freeway under Od<9%. Rather, the driver characteristics or transportation conditions around the freeway were through to be a little higher explanatory for the delay perdiction under Od<9%. ⅳ) Integrated delay predictive models showed a higher explanatory power in the morning peak period, but a lower explanatory power in the non-peak periods.

• 대한교통학회지
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• 제26권5호
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• pp.73-80
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• 2008

ALINEA는 램프의 하류부에 설치된 검지기를 이용하여 최적의 차량점유율 상태를 유지하도록 유입램프의 교통량을 조절하는 방안으로 검지기를 이용한 차량의 점유율을 제어 변수로 이용하고 있다. 하지만, 현재 가장 널리 사용되고 있는 루프제어기 점유율의 정확도가 비교적 낮다는 점과 점유율은 검지기 길이의 함수로서 ALINEA의 적용 시 설치 지점마다, 그리고 검지기 길이마다 최적 점유율 보정과정이 필요한 점을 감안할 때 현재 사용 중인 ALINEA를 보완할 필요가 있다. 관리자와 이용자 측면에서 점유율이 사실상 인지하기 어려운 변수임을 감안할 때 쉽고 간편한 변수의 사용을 통한 모형개발이 의미를 가질 수 있을 것이다. 본 연구에서는 ALINEA 알고리즘의 기본 개념을 이용하되 제어변수인 점유율을 이용할 때의 불편한 점 및 단점을 일부 개선시킬 수 있는 속도 변수를 이용하여 ALINEA 모델을 보완하고자 한다.

• 방송공학회논문지
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• 제23권5호
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• pp.642-655
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• 2018

빌딩, 집에 설치되어 있는 점유 센서는 사람이 없으면 소등하고, 반대이면 점등한다. 현재는 주요 센서로 PIR(pyroelectric infra-red)이 널리 사용되고 있다. 최근에 비전 카메라 센서를 이용하여 사람 점유를 검출하는 연구가 진행되고 있다. 카메라 센서는 정지된 사람을 검출할 수 없는 PIR의 단점을 극복할 수 있는 장점이 있다. 이동 및 정지된 사람의 추적은 카메라 점유 센서의 주요 기능이다. 본 논문에서는 합성곱 신경망 모델과 자기 조직화 지도를 활용한 온라인 사람 추적 기법을 제안한다. 오프라인에 모델을 학습시키기 위해서는 많은 수의 훈련 샘플이 필요하다. 이러한 문제를 해결하기 위해, 학습되지 않은 모델을 사용하고, 실험 영상으로부터 직접 훈련 샘플을 수집하여 모델을 갱신한다. 오버헤드 카메라로 실내에서 촬영한 영상을 이용하여, 제안 방법이 효과적으로 사람을 추적하고 있음을 실험을 통해 증명하였다.

• 스마트미디어저널
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• 제13권1호
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• pp.24-35
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• 2024

The combination of Artificial Intelligence and the Internet of Things (AIoT) has gained significant popularity. Deep neural networks (DNNs) have demonstrated remarkable success in various applications. However, deploying complex AI models on embedded boards can pose challenges due to computational limitations and model complexity. This paper presents an AIoT-based system for smart parking lots using edge devices. Our approach involves developing a detection model and a decision tree for occupancy status classification. Specifically, we utilize YOLOv5 for car license plate (LP) detection by verifying the position of the license plate within the parking space.

• Journal of Preventive Medicine and Public Health
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• 제22권2호
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• pp.189-196
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• 1989

In January 1988, district medical insurance plan was executed on a national scale in Korea. We conducted an evaluation of the impact of execution of district medical insurance plan on number of hospital patients: number of outpatients; and occupancy rate. This study was carried out by Box-Jenkins time series analysis. We tested the statistical significance with intervention component added to ARIMA model. Results of our time series analysis showed that district medical insurance plan had a significant effect on the number of outpatients and occupancy rate. Due to this plan the number of outpatients had increased by 925 patients every month which is equivalent to 8.3 percents of average monthly insurance outpatients in 1987, and occupancy rate had also increased by 0.12 which is equivalent to 16 percents of that in 1987.