• 한국컴퓨터정보학회논문지
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• 제21권5호
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• pp.91-98
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• 2016

We propose a method to train a model that can predict the probability of a crime being committed. CCTV data by matching criminal events are required to train the crime prediction model. However, collecting CCTV data appropriate for training is difficult. Thus, we collected actual criminal records and converted them to an appropriate format using variables by considering a crime prediction environment and the availability of real-time data collection from CCTV. In addition, we identified new specific crime types according to the characteristics of criminal events and trained and tested the prediction model by applying neural network partial least squares for each crime type. Results show a level of predictive accuracy sufficiently significant to demonstrate the applicability of CCTV to real-time crime prediction.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2009년도 세계 도시지반공학 심포지엄
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• pp.233-242
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• 2009

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in crystalline rocks like gneiss, granite, and so on, a prediction model was developed by the use of a decision tree model. The classification standard of the selected prediction model is composed of the slope angle, the coefficient of permeability and the void ratio in the order. The computer program, SHAPP ver. 1.0 for prediction of slope hazards around an important national facilities using GIS technique and the developed model. To prove the developed prediction model and the computer program, the field data surveyed from Jumunjin, Gangneung city were compared with the prediction result in the same site. As the result of comparison, the real occurrence location of slope hazards was similar to the predicted section. Through the continuous study, the accuracy about prediction result of slope hazards will be upgraded and the computer program will be commonly used in practical.

• 한국컴퓨터정보학회논문지
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• 제22권10호
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• pp.9-17
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• 2017

The student dropout prediction is an indispensable for many intelligent systems to measure the educational system and success rate of all university. Therefore, in this paper, we propose an intelligent dropout prediction system that minimizes the situation by adopting the proactive process through an effective model that predicts the students who are at risk of dropout. In this paper, the main data sets for students dropout predictions was used as questionnaires and university information. The questionnaire was constructed based on theoretical and empirical grounds about factor affecting student's performance and causes of dropout. University Information included student grade, interviews, attendance in university life. Through these data sets, the proposed dropout prediction model techniques was classified into the risk group and the normal group using statistical methods and Naive Bays algorithm. And the intelligence dropout prediction system was constructed by applying the proposed dropout prediction model. We expect the proposed study would be used effectively to reduce the students dropout in university.

• 한국항공운항학회지
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• 제25권4호
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• pp.124-129
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• 2017

Clouds are an important factor in aircraft flight. In particular, a significant impact on small aircraft flying at low altitude. Therefore, we have verified and characterized the low level cloud prediction data of the Unified Model(UM) - based Local Data Assimilation and Prediction System(LDAPS) operated by KMA in order to develop cloud forecasting service and contents important for safety of low-altitude aircraft flight. As a result of the low level cloud test for seven airports in Korea, a high correlation coefficient of 0.4 ~ 0.7 was obtained for 0-36 leading time. Also, we found that the prediction performance does not decrease as the lead time increases. Based on the results of this study, it is expected that model-based forecasting data for low-altitude aviation meteorology services can be produced.

• 신재생에너지
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• 제18권2호
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• pp.18-25
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• 2022

In this study, we propose a wind power generation prediction system that applies machine learning and data mining to predict wind power generation. This system increases the utilization rate of new and renewable energy sources. For time-series data, the data set was established by measuring wind speed, wind generation, and environmental factors influencing the wind speed. The data set was pre-processed so that it could be applied appropriately to the model. The prediction system applied the CNN (Convolutional Neural Network) to the data mining process and then used the LSTM (Long Short-Term Memory) to learn and make predictions. The preciseness of the proposed system is verified by comparing the prediction data with the actual data, according to the presence or absence of data mining in the model of the prediction system.

• Geomechanics and Engineering
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• 제18권4호
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• pp.373-389
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• 2019

It is important to study the problem of durability for tunnel structures. As a main influence on the durability of tunnel structures, carbonation-induced corrosion is studied. For the complicated environment of tunnel structures, based on the data samples from real engineering examples, the intelligent method (genetic programming) is used to construct the service life prediction model of tunnel structures. Based on the model, the prediction of service life for tunnel structures in carbonation environments is studied. Using the data samples from some tunnel engineering examples in China under carbonation environment, the proposed method is verified. In addition, the performance of the proposed prediction model is compared with that of the artificial neural network method. Finally, the effect of two main controlling parameters, the population size and sample size, on the performance of the prediction model by genetic programming is analyzed in detail.

• Computers and Concrete
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• 제24권6호
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• pp.555-560
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• 2019

During the application of shotcrete, a part of the concrete bounces back after hitting to the surface, the reinforcement or previously sprayed concrete. This rebound material is definitely not added to the mixture and considered as waste. In this study, a deep neural network model was developed to predict the rebound material during shotcrete application. The factors affecting rebound and the datasets of these parameters were obtained from previous experiments. The Long Short-Term Memory (LSTM) architecture of the proposed deep neural network model was used in accordance with this data set. In the development of the proposed four-tier prediction model, the dataset was divided into 90% training and 10% test. The deep neural network was modeled with 11 dependents 1 independent data by determining the most appropriate hyper parameter values for prediction. Accuracy and error performance in success performance of LSTM model were evaluated over MSE and RMSE. A success of 93.2% was achieved at the end of training of the model and a success of 85.6% in the test. There was a difference of 7.6% between training and test. In the following stage, it is aimed to increase the success rate of the model by increasing the number of data in the data set with synthetic and experimental data. In addition, it is thought that prediction of the amount of rebound during dry-mix shotcrete application will provide economic gain as well as contributing to environmental protection.

• 스마트미디어저널
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• 제13권2호
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• pp.9-15
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• 2024

본 논문에서는 신용 예측에서 발생하는 불균형 문제를 해결하기 위해 Diffusion Multi-step Classifier(DMC)를 제안한다. DMC는 Diffusion Model을 통해 신용 예측 데이터의 연속적인 수치형 데이터들을 생성하고 생성된 데이터들을 Multi-step Classifier로 구분하는 것으로 범주형 데이터를 생성한다. DMC를 통해 기존의 데이터를 생성하는 다른 알고리즘보다 실제 데이터와 유사한 분포를 가지는 데이터를 생성할 수 있었다. 이렇게 생성된 데이터를 사용하여 실험을 진행하였을 때 연체를 예측할 확률이 20%이상 상승하였으며, 전체적으로 예측 정확성은 약 4%정도 상승하였다. 이러한 연구 결과는 실제 금융기관에 적용 시 연체율 감소와 수익 증가에 큰 기여를 할 수 있을것으로 예상된다.

• 한국통신학회논문지
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• 제38C권3호
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• pp.263-270
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• 2013

최근 세계적으로 바다가 자원의 보고로 주목 받으면서 해양 환경 분석 및 예측 기술에 대한 연구가 활발히 진행 되고 있다. 자동화된 해양 환경 자료의 수집과 수집된 자료를 분석하여서 해양재해를 예측하면 기름 유출에 의한 해양오염의 피해, 적조에 의한 수산업의 피해, 해양환경 이변에 의한 수산업 및 재해 피해를 최소화하는데 기여할 수 있다. 그러나 국내 해양 환경에 대한 조사 및 분석 연구는 제한적이다. 본 논문은 국내의 원해 및 근 해역에서 수집된 해양 환경 자료를 분석하여 해양재해를 예측할 수 있는 시스템 모델을 연구한다. 이를 위해서 본 논문에서는 해양재해 예측 시스템을 위해서 통신시스템 모델, 해양환경 자료 수집 시스템 모델, 예측분석 시스템 모델, 상황전파시스템에 대한 모델을 제시하였다. 또한 예측분석 시스템을 위한 적조 예측 모델과 요약분석 모델을 제시하였다.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1256-1263
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• 2022

With the advent of the Fourth Industrial Revolution, the amount of energy used in buildings has been increasing due to changes in the energy use structure caused by the massive spread of information-oriented equipment, climate change and greenhouse gas emissions. For the efficient use of energy, it is necessary to have a plan that can predict and reduce the amount of energy use according to the type of energy source and the use of buildings. To address such issues, this study presents a model embedded in a digital twin that predicts energy use in buildings. The digital twin is a system that can support a solution of urban problems through the process of simulations and analyses based on the data collected via sensors in real-time. To develop the energy use prediction model, energy-related data such as actual room use, power use and gas use were collected. Factors that significantly affect energy use were identified through a correlation analysis and multiple regression analysis based on the collected data. The proof-of-concept prototype was developed with an exhibition facility for performance evaluation and validation. The test results confirm that the error rate of the energy consumption prediction model decreases, and the prediction performance improves as the data is accumulated by comparing the error rates of the model. The energy use prediction model thus predicts future energy use and supports formulating a systematic energy management plan in consideration of characteristics of building spaces such as the purpose and the occupancy time of each room. It is suggested to collect and analyze data from other facilities in the future to develop a general-purpose energy use prediction model.