• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.173-176
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• 1994

In this paper a closed-form predictive control which takes the intervalwise receding horizon strategy is presented and its stability properties are investigated. A slate-space form output predictor is derived which is composed of the one-step ahead optimal output prediction, input and output data of the system. A set of feedback gains are obtained using the dynamic programming algorithm so that they minimize a multi-stage quadratic cost function and they are used periodically.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.165-172
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• 2020

Recently, due to exponential growth of access information on the web, the importance of predicting a user's next web page use has been increasing. One of the methods that can be used for predicting user's next web page is deep learning. To predict next web page, web logs are analyzed by data preprocessing and then a user's next web page is predicted on the output of the analyzed web logs using a deep learning algorithm. In this paper, we propose a framework for web page prediction that includes methods for web log preprocessing followed by deep learning techniques for web prediction. To increase the speed of preprocessing of large web log, a Hadoop based MapReduce programming model is used. In addition, we present a web prediction system that uses an efficient deep learning technique on the output of web log preprocessing for training and prediction. Through experiment, we show the performance improvement of our proposed method over traditional methods. We also show the accuracy of our prediction.

• Journal of Environmental Science International
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• v.6 no.3
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• pp.205-211
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• 1997

For the prediction of multi-site rainfall with radar data and ground meteorological data, a rainfall prediction model was proposed, which uses the neural network theory, a kind of artifical Intelligence technique. The Input layer of the prediction model was constructed with current ground meteorological data, their variation, moving vectors of rain- fall field and digital terrain of the measuring site, and the output layer was constructed with the predicted rainfall up to 3 hours. In the application of the prediction model to the Pyungchang river basin, the learning results of neural network prediction model showed more Improved results than the parameter estimation results of an existing physically based model. And the proposed model comparisonally well predicted the time distribution of ralnfall.

• Journal of the Korean Burn Society
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• v.23 no.1
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• pp.1-6
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• 2020

Purpose: PL, creatinine and urine output are biomarkers of the suitability and prognosis of fluid therapy in severe burn patients. The purpose of this study is to evaluate the usefulness of predicting mortality by biomarkers and its change during initial fluid therapy for severe burn patients. Methods: A retrograde review was performed on 733 patients from January 2014 to December 2018 who were admitted as severe burn patients to our burn intensive care unit (BICU). Plasma lactate, serum creatinine and urine output were measured at the time of admission to the BICU and after 48 hours. ABSI score, Hangang score, APACHEII, revised Baux index and TBSA were collected after admission. Results: 733 patients were enrolled. PL was the most useful indicators for predicting mortality in burn patients at the time of admission (AUC: 0.813) and after 48 hours (AUC: 0.698). On the other hand, mortality prediction from initial fluid therapy for 48 hours showed different results. Only creatinine showed statistical differences (P<0.05) in mortality prediction. But there were no statistical differences in mortality prediction with PL and UO (P>0.05). Conclusion: In this study, PL was most useful predictor among biomarkers for predicting mortality. Improvement in creatinine levels during the first 48 hours is associated with improved mortality. Therefore, efforts are needed to improve creatinine levels.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.15-20
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• 2021

• Journal of the Korean Society of Safety
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• v.32 no.1
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• pp.115-120
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• 2017

The aim of this study is to develop construction safety and health management cost prediction model using support vector machine (SVM). To this end, theoretical concept of SVM is investigated to formulate the cost prediction model. Input and output variables have been selected by analyzing the balancing accounts for the completed construction project. In order to train and validate the proposed prediction model, 150 data sets have been gathered from field. Effects of SVM parameters on prediction accuracy are analyzed and from which the optimal parameter values have been determined. The prediction performance tests are conducted to confirm the applicability of the proposed model. Based on the results, it is concluded that the proposed SVM model can effectively be used to predict the construction safety and health management cost.

• Proceedings of the KSR Conference
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• 1998.05a
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• pp.211-218
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• 1998

The demand prediction becomes an essential mean to utilize efficiently finite traffic facilities and to provide the optimized schedules for transportation system. The demand prediction is one of the critical complex management schemes for distibuting resources of transportation service by means of computer system. The construction of a prediction model is based on data granulization, followed by processing the raw input data and evaluating the predicted output values. A large number of economic-social parameters are also to be implemented in conventional prediction models which are only based on a sequence of past data. The proposed prediction models are classified by static and dynamic characteristics and its performances are evaluated utilizing computer simulation.

• Korean Journal of Ecology and Environment
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• v.43 no.1
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1036-1043
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• 2014

The installation of PV system to the power distribution system is being increased as one of solutions for environmental pollution and energy crisis. Because the output efficiency of PV system is getting decreased because of the aging phenomenon and several operation obstacles, the technology development of output prediction and state diagnosis of PV modules are required in order to improve operation performance of PV modules. The conventional methods for output prediction by considering various parameters and standard test condition values of PV modules may have difficult and complex computation procedure and also their prediction values may produce large error. To overcome these problems, this paper proposes an optimal prediction algorithm and state diagnosis algorithm of PV modules by using least square methods of linear regression analysis. In addition, this paper presents a state diagnosis evaluation system of PV modules based on the proposed optimal algorithms of PV modules. From the simulation results of proposed evaluation system, it is confirmed that the proposed algorithms is a practical tool for state diagnosis of PV modules.

• New & Renewable Energy
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• v.2 no.2 s.6
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• pp.81-85
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• 2006

The output performance characteristics for surveyed sites were analyzed, using developed model. It consists of two main parts, the deciding flow duration characteristic of river and performance prediction model to estimate the output characteristics of small hydropower plants. As a result, It was found that the flowrate concerning with 25% of time ratio on flow duration curve can be selected to design flowrate of small hydropower plants, and the output characteristics of small hydropower plants having overflow dam are different from large scale hydropower plants.