• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.12
• /
• pp.123-129
• /
• 2014

In accordance with the development of various convergence devices, cameras are being used in many types of the systems such as security system, driver assistance device and so on, and a lot of people are exposed to these system. Therefore the system should be able to recognize the human behavior and support some useful functions with the information that is obtained from detected human behavior. In this paper we use a machine learning approach based on 2D image and propose the human behavior pattern recognition methods. The proposed methods can provide valuable information to support some useful function to user based on the recognized human behavior. First proposed one is "phone call behavior" recognition. If a camera of the black box, which is focused on driver in a car, recognize phone call pose, it can give a warning to driver for safe driving. The second one is "looking ahead" recognition for driving safety where we propose the decision rule and method to decide whether the driver is looking ahead or not. This paper also shows usefulness of proposed recognition methods with some experiment results in real time.

• The Journal of Bigdata
• /
• v.6 no.1
• /
• pp.133-144
• /
• 2021

This paper derives the fire risk of buildings in Seoul through the prediction of property damage and the occurrence of fires. This study differs from prior research in that it utilizes variables that include not only a building's characteristics but also its affiliated administrative area as well as the accessibility of nearby fire-fighting facilities. We use Ensemble Voting techniques to merge different machine learning algorithms to predict property damage and fire occurrence, and to extract feature importance to produce fire risk. Fire risk prediction was made on 300 buildings in Seoul utilizing the established model, and it has been derived that with buildings at Level 1 for fire risks, there were a high number of households occupying the building, and the buildings had many factors that could contribute to increasing the size of the fire, including the lack of nearby fire-fighting facilities as well as the far location of the 119 Safety Center. On the other hand, in the case of Level 5 buildings, the number of buildings and businesses is large, but the 119 Safety Center in charge are located closest to the building, which can properly respond to fire.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.6
• /
• pp.703-710
• /
• 2018

Although various efforts have been made every year to reduce electric fire accidents such as accident analysis and inspection for electric fire accidents, there is no effective countermeasure due to lack of effective decision support system and existing cumulative data utilization method. The purpose of this study is to develop an algorithm for predicting electric fire based on data such as electric safety inspection data, electric fire accident information, building information, and weather information. Through the pre-processing of collected data for each institution such as Korea Electrical Safety Corporation, Meteorological Administration, Ministry of Land, Infrastructure, and Transport, Fire Defense Headquarters, convergence, analysis, modeling, and verification process, we derive the factors influencing electric fire and develop prediction models. The results showed insulation resistance value, humidity, wind speed, building deterioration(aging), floor space ratio, building coverage ratio and building use. The accuracy of prediction model using random forest algorithm was 74.7%.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.1
• /
• pp.127-134
• /
• 2021

In this study, an anomaly detection (AD) algorithm was implemented to detect the failure of a marine air compressor. A lab-scale experiment was designed to produce fault datasets (time-series electric current measurements) for 10 failure modes of the air compressor. The results demonstrated that the temporal pattern of the datasets showed periodicity with a different period, depending on the failure mode. An AD model with a convolutional autoencoder was developed and trained based on a normal operation dataset. The reconstruction error was used as the threshold for AD. The reconstruction error was noted to be dependent on the AD model and hyperparameter tuning. The AD model was applied to the synthetic dataset, which comprised both normal and abnormal conditions of the air compressor for validation. The AD model exhibited good detection performance on anomalies showing periodicity but poor performance on anomalies resulting from subtle load changes in the motor.

• Journal of Korean Society of Disaster and Security
• /
• v.15 no.2
• /
• pp.45-56
• /
• 2022

The countermeasure for the shortage of water during dry season and drought period has not been considered with return flowrate in detail. In this study, the outflow of STP was predicted through a data-based machine learning model, LSTM. As the first step, outflow, inflow, precipitation and water elevation were utilized as input data, and the distribution of variance was additionally considered to improve the accuracy of the prediction. When considering the variability of the outflow data, the residual between the observed value and the distribution was assumed to be in the form of a complex trigonometric function and presented in the form of the optimal distribution of the outflow along with the theoretical probability distribution. It was apparently found that the degree of error was reduced when compared to the case not considering where the variance distribution. Therefore, it is expected that the outflow prediction model constructed in this study can be used as basic data for establishing an efficient river management system as more accurate prediction is possible.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.5-5
• /
• 2023

제3차 우주개발 진흥 기본계획의 일환으로써 개발되는 차세대 중형위성 5호인 수자원위성은 수자원/수재해 감시 전용 위성으로 2025년 발사 예정이다. 수자원위성의 메인 센서인 C-band 영상레이더(Synthetic Aperture Radar, SAR)는 기상조건 및 주야 상관없이 지표면 관측이 가능한 센서로 급변하는 수재해 양상에 효과적으로 대응하기 위해 탑재된 센서이다. 본 연구사업은 차세대 수자원위성의 효과적 활용 방안 및 SAR 자료기반의 활용산출물 및 주제도 서비스를 위한 알고리즘 구조설계 및 표출시스템 시범개발을 목표로 하고 있으며, 홍수/가뭄/안전/환경모니터링을 주제로 수자원 및 원격탐사 분야의 다학제적 전문가들로 구성된 컨소시엄을 구성하여 추진하고 있다. 본 연구의 내용은 가뭄 모니터링을 위해 개발 중인 SAR 기반 토양수분과 농업적 가뭄지수 산정 알고리즘 개발 및 공간적 표출을 포함한다. 토양수분은 SAR 영상에서 지표피복별로 추출된 후방산란계수와 수문학적 개념의 융합을 통해 논/밭/산림에 대해 산정한다. 물리적 특성에 기반한 변화탐지모델을 활용해 토양수분량을 추출 후, 기계학습기법과 S C S - C N 방법에서 파생된 수문학적 개념 5일 선행강우량과 결합한 토양수분 산정 알고리즘을 개발하였다. 산정된 토양수분을 기반으로, 논 지역은 벼 재배에 따른 담수 시기를 고려한 토양의 포화/불포화상태, 밭 지역은 토양 종류에 따른 토양의 물리적 특성, 산림 지역은 수문학적 개념 및 식생지수를 활용하여 가뭄 판단 기준을 구축하고, 가뭄의 해갈 여부와 해갈되는 시점의 강우량을 산정 가능한 알고리즘을 개발하였다. 개발된 가뭄 모니터링 기법은 향후 고도화, 최적화 및 안정화를 통해 수자원위성의 핵심 활용기술로써 구현할 계획이다.

• Journal of Advanced Navigation Technology
• /
• v.28 no.4
• /
• pp.472-479
• /
• 2024

Half of the world's total population lives in cities, continuous urbanization is progressing, and the urban population is expected to exceed two-thirds of the total population by 2050. To resolve this phenomenon, the Korean government is focusing on building a new urban air mobility (UAM) industrial ecosystem. Airlines are also part of the UAM industry ecosystem and are preparing to improve efficiency in safe operations, passenger safety, aircraft operation efficiency, and punctuality. This study performs demand forecasting using time series data on the number of daily passengers on Korean Air's Gimpo to Jeju route from 2019 to 2023. For this purpose, statistical and machine learning models such as SARIMA, Prophet, CatBoost, and Random Forest are applied. Methods for effectively capturing passenger demand patterns were evaluated through various models, and the machine learning-based Random Forest model showed the best prediction results. The research results will present an optimal model for accurate demand forecasting in the aviation industry and provide basic information needed for operational planning and resource allocation.

• KIPS Transactions on Computer and Communication Systems
• /
• v.6 no.11
• /
• pp.445-452
• /
• 2017

Electric pole is a supporting beam used for power transmission/distribution which accelerometer are used for measuring a external force. The meteorological condition has various effects on the external forces of electric pole. One of them is the elasticity change of the aerial wire. It is very important to perform modelling. The acceleration sensor is converted into a pitch and a roll angle. The meteorological condition has a high correlation between variables, and selecting significant explanatory variables for modeling may result in the problem of over-fitting. We constructed high deviance explained model considering multicollinearity using the Generalized Additive Model which is one of the machine learning methods. As a result of the Variation Inflation Factor Test, we selected and fitted the significant variable as temperature, precipitation, wind speed, wind direction, air pressure, dewpoint, hours of daylight and cloud cover. It was noted that the Hours of daylight, cloud cover and air pressure has high explained value in explonatory variable. The average coefficient of determination (R-Squared) of the Generalized Additive Model was 0.69. The constructed model can help to predict the influence on the external forces of electric pole, and contribute to the purpose of securing safety on utility pole.

• The Journal of Engineering Geology
• /
• v.30 no.4
• /
• pp.649-661
• /
• 2020

Cut-slope management system (CSMS) has been investigated all slopes on the road of the whole country to evaluate risk rating of each slope. Based on this evaluation, the decision-making for maintenance can be conducted, and this procedure will be helpful to establish a consistent and efficient policy of safe road. CSMS has updated the database of all slopes annually, and this database is constructed based on a basic and detailed investigation. In the database, there are two type of data: first one is an objective data such as slopes' location, height, width, length, and information about underground and bedrock, etc; second one is subjective data, which is decided by experts based on those objective data, e.g., degree of emergency and risk, maintenance solution, etc. The purpose of this study is identifying an data application plan to utilize those CSMS data. For this purpose, logistic regression, which is a basic machine-learning method to construct a prediction model, is performed to predict a judging-type variable (i.e., subjective data) based on objective data. The constructed logistic model shows the accurate prediction, and this model can be used to judge a priority of slopes for detailed investigation. Also, it is anticipated that the prediction model can filter unusual data by comparing with a prediction value.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1195-1204
• /
• 2021

Recently, local torrential rain have become more frequent and severe due to abnormal climate conditions, causing a surge in human and properties damage including infrastructures along the river. In this study, water surface elevation prediction algorithm was developed using the LSTM (Long Short-term Memory) technique specialized for time series data among Machine Learning to estimate and prevent flooding of the facilities. The study area is Jamsu Bridge, the study period is 6 years (2015~2020) of June, July and August and the water surface elevation of the Jamsu Bridge after 3 hours was predicted. Input data set is composed of the water surface elevation of Jamsu Bridge (EL.m), the amount of discharge from Paldang Dam (m³/s), the tide level of Ganghwa Bridge (cm) and the number of tweets in Seoul. Complementary data were constructed by using not only structured data mainly used in precedent research but also unstructured data constructed through wordcloud, and the role of unstructured data was presented through comparison and analysis of whether or not unstructured data was used. When predicting the water surface elevation of the Jamsu Bridge, the accuracy of prediction was improved and realized that complementary data could be conservative alerts to reduce casualties. In this study, it was concluded that the use of complementary data was relatively effective in providing the user's safety and convenience of riverside infrastructure. In the future, more accurate water surface elevation prediction would be expected through the addition of types of unstructured data or detailed pre-processing of input data.