• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.293-302
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• 2016

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.63-63
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• 2022

기상 예보자료는 발생 가능한 재난의 예방 및 대비 차원에서 매우 중요한 자료로 활용되고 있다. 우리나라 기상청에서는 동네예보를 통해 5km 공간해상도의 1시간 간격 초단기예보와, 6시간 간격 정량강우예보(Quantitative Precipitation Forecast, QPF)의 단기예보 정보를 제공하고 있다. 그러나 이와 같은 예보자료는 강우량의 시·공간변화가 큰 집중호우와 같은 기상자료를 활용한 수문학적인 해석에는 한계가 있다. 예보자료를 수문학에 활용하기 위한 시·공간적 해상도 개선뿐만 아니라 방대한 기상 및 기후 자료의 예측성능을 개선하기 위한 다양한 연구가 진행되고 있다. 본 연구에서는 기상청이 제공하는 지역 앙상블 예측 시스템(Local ENsemble prediction System, LENS)와 종관기상관측시스템(ASOS) 및 방재기상관측시스템(AWS) 관측 데이터 및 동네예보에 기계학습 방법을 적용하여 수문학적 정량적 강수량 예측(Hydrological Quantitative Precipitation Forecast, HQPF) 정보를 생산하였다. 전처리 과정을 통해 모든 데이터의 시간해상도와 공간해상도를 동일한 해상도로 변환하였으며, 예측 변수의 인자 분석을 통해 기계학습의 예측 변수를 도출하였다. 기계학습 방법으로는 처리속도와 확장성을 고려하여 XGBoost(eXtreme Gradient Boosting) 방식을 적용하였으며, 집중호우에서의 예측정확도를 높이기 위해 확률매칭(PM) 방식을 적용하였다. 생산된 HQPF의 성능을 평가하기 위해 2020년에 발생한 14건의 호우 사상을 대상으로 태풍형과 비태풍형으로 구분하여 검증을 수행하였다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.27-31
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• 2011

차량의 대형화 및 고속화, 그리고 기존 교량의 노후화를 고려하였을 때, 교량의 건전성 평가는 매우 중요해지고 있다. 거동을 예측하는데 사용되는 유한요소 모델의 신뢰도는 이상적인 가정과 모델링 오차, 교량의 노후화 등에 의해 실제 거동을 반영하지 못하는 경우가 많다. 유한요소 모델의 신뢰도를 높이기 위해, 실제 교량의 거동을 계측하여, 이를 기반으로 물리적 의미를 가지는 변수들과 지점의 조건을 수정하는 모델의 개선이 주로 행해진다. 이러한 모델 개선은 최적화 기법을 통해 수행된다. 본 연구에서는 목적함수간 가중치에 의한 모델 개선 결과의 영향과 다중 목적 함수 최적화 기법을 통해, 가중치의 영향을 줄이고, 다양한 개선 모델들을 구하는데 적용하였다. 팔곡 3교의 실제 계측 데이터를 이용하여 단일 다중 목적 함수 기반의 모델 개선을 수행하였다. 단일 목적 함수의 경우, 정의되는 목적함수는 주로 고유진동수와 모드 형상에 관한 차이의 가중치 합으로 표현되어 지며, 이러한 가중치에 따라, 모델 개선의 결과에 영향을 가함을 확인하였다. 다중 목적 함수 기반의 모델 개선을 통해, 구해진 모델 개선 결과를 단일 목적 함수 기반 모델 개선의 결과들과 비교하였으며, 모델 개선에 대한 다중 목적 함수 최적화 적용을 분석하였다.

• Geotechnical Engineering
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• v.12 no.5
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• pp.103-116
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• 1996

Man-made vibrations from traffic and construction activities are important because they may cause damage to structures. The current literature provides that damages in the urban areas were not caused by direct transmission of vibration, but rather through subsequent settlement caused by soil densification. In this paper. prediction technique of ground borne vibration induced settlement was introduced on the basis of case studies. In situ application technique of the settlement prediction model developed in laboratary was described, and the predicted settlement was compared with the measured settlement from case studies. The settlement from case studies hlatched well with the settlement calculated from the model. The parametric studies of settlement in typical urban site conditions were performed to determine the sensitive parameters and to develop reliable vibration monitoring and interpretation schemes. These demonstrated the potential usefulness of the model for the evaluation and prediction of the vibration induced in-situ settlement of sands.

• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1232-1255
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• 2011

This paper proposes a model for demand forecasting that will require less effort in the process of utilizing the new product diffusion model while also allowing for more objective and timely application. Drawing upon the theoretical foundation provided by the hype cycle model and the consumer adoption model, this proposed model makes it possible to estimate the maximum market potential based solely on bibliometrics and the scale of the early market, thereby presenting a method for supplying the major parameters required for the Bass model. Upon analyzing the forecasting ability of this model by applying it to the case of the hybrid car market, the model was confirmed to be capable of successfully forecasting results similar in scale to the market potential deduced through various other objective sources of information, thus underscoring the potentials of utilizing this model. Moreover, even the hype cycle or the life cycle can be estimated through direct linkage with bibliometrics and the Bass model. In cases where the hype cycles of other models have been observed, the forecasting ability of this model was demonstrated through simple case studies. Since this proposed model yields a maximum market potential that can also be applied directly to other growth curve models, the model presented in the following paper provides new directions in the endeavor to forecast technology diffusion and identify promising technologies through bibliometrics.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.1-8
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• 2016

In the recently, violent crime and accidental crime has been generated continuously. Consequently, people anxiety has been heightened. The Closed Circuit Television (CCTV) has been used to ensure the security and evidence for the crimes. However, the video captured from CCTV has being used in the post-processing to apply to the evidence. In this paper, we propose a flexible multi-level models for estimating whether dangerous behavior and the environment and context for pedestrians. The situation analysis builds the knowledge for the pedestrians tracking. Finally, the decision step decides and notifies the threat situation when the behavior observed object is determined to abnormal behavior. Thereby, tracking the behavior of objects in a multi-region, it can be seen that the risk of the object behavior. It can be predicted by the behavior prediction of crime.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.5-10
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• 2022

Ground Subsidence has been continuously occurring in densely populated downtown. The main cause of ground subsidence is the damaged underground facility like sewer. Currently, ground subsidence is being dealt with by discovering cavities in ground using GPR. However, this consumes large amount of manpower and cost, so it is necessary to predict hazardous area for efficient operation of GPR. In this study, ◯◯city is divided into 500 m×500 m grids. Then, data set was constructed using the characteristics of the underground facility and ground subsidence in grids. Data set used to machine learning model for ground subsidence risk grade prediction. The purposed model would be used to present a ground subsidence risk map of target area.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.75-82
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• 2021

Mastitis in cows is a major factor that hinders dairy productivity of farms, and many attempts have been made to solve it. However, research on mastitis has been limited to diagnosis rather than prediction, and even this is mostly using a single sensor. In this study, a predictive model was developed using multivariate data including biometric data and environmental data. The data used for the analysis were collected from robot milking machines and sensors installed in farmhouses in Chungcheongnam-do, South Korea. The recurrent neural network model using three weeks of data predicts whether or not mastitis is diagnosed the next day. As a result, mastitis was predicted with an accuracy of 82.9%. The superiority of the model was confirmed by comparing the performance of various data collection periods and various models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2301-2306
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• 2015

The time to address the exabytes of data has come as the information age accelerates. Big data transfer technology is essential for processing large amounts of data. This paper posits to transfer big data in the optimal conditions by the proposed algorithm for predicting the optimal combination of Pipelining, Concurrency, and Parallelism (PCP), which are major functions of GridFTP. In addition, the author introduced a simple design process of Takagi-Sugeno-Kang (TSK) fuzzy model and designed a model for predicting transfer throughput with optimal combination of Pipelining, Concurrency and Parallelism. Hence, the author evaluated the model of the proposed algorithm and the TSK model to prove the superiority.

• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.3
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• pp.134-142
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• 2018

Developing model to predict the characteristic of vented gas was vented through an orifice is presented. Simulations with models which were developed with assumptions considering heat transfer inside the vessel were conducted. Also, representative pressure and temperature were measured from experiments with the pressure vessel which is applicable to a propulsion system. Developed model were verified with comparison between calculations and experiments.