• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.221-227
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• 2021

An early warning system was developed to help identify stock status as early as possible. For performance to improve, there needs to be a feature to predict the amount of stock that must be provided and a feature to estimate when to buy goods. This research was conducted to improve the inventory early warning system and optimize the Reminder Block's performance in minimum stock settings. The models used in this study are the single exponential smoothing (SES) method for prediction and the economic order quantity (EOQ) model for determining the quantity. The research was conducted by analyzing the Reminder Block in the early warning system, identifying data needs, and implementing the SES and EOQ mathematical models into the Reminder Block. This research proposes a new Reminder Block that has been added to the SES and EOQ models. It is hoped that this study will help in obtaining accurate information about the time and quantity of repurchases for efficient inventory management.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1335-1349
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• 2019

Crowdsensing technologies can improve the efficiency of smart parking system in comparison with present sensor based smart parking system because of low install price and no restriction caused by sensor installation. A lot of sensing data is necessary to predict parking lot saturation in real-time. However in real world, it is hard to reach the required number of sensing data. In this paper, we model a saturation predication combining a time-based prediction model and a sensing data-based prediction model. The time-based model predicts saturation in aspects of parking lot location and time. The sensing data-based model predicts the degree of saturation of the parking lot with high accuracy based on the degree of saturation predicted from the first model, the saturation information in the sensing data, and the number of parking spaces in the sensing data. We perform prediction model learning with real sensing data gathered from a specific parking lot. We also evaluate the performance of the predictive model and show its efficiency and feasibility.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1396-1403
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• 2011

It is not easy to analyze the strong nonlinear time series and effectively design a good prediction system especially due to the difficulties in handling the potential uncertainty included in data and prediction method. To solve this problem, a new design method for fuzzy prediction system is suggested in this paper. The proposed method contains the followings as major parts ; the first-order difference detection to extract the stable information from the nonlinear characteristics of time series, the fuzzy rule generation based on the hierarchically classifying clustering technique to reduce incorrectness of the system parameter identification, and the IT2TSK fuzzy logic system to reasonably handle the potential uncertainty of the series. In addition, the design of the multiple predictors is considered to reflect sufficiently the diverse characteristics concealed in the series. Finally, computer simulations are performed to verify the performance and the effectiveness of the proposed prediction system.

• ETRI Journal
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• v.42 no.3
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• pp.366-375
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• 2020

The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA-WNN. A comparative analysis of the experimental results shows that the performance of the IGSA-WNN-based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution; thus, it is a better smart substation network traffic prediction model.

• KIEAE Journal
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• v.10 no.1
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• pp.65-72
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• 2010

The use of natural light has the potential for improving both the energy efficiency and indoor environmental quality in buildings. A light-pipe system can introduce daylight to spaces that would otherwise not be able to benefit from the advantages of daylight penetration. For the light-pipe system to be widely used in Korea, it is important to quantify its daylighting performance with due consideration regarding the effects imposed by the local climate conditions. This paper presents the evaluation results of existing semi-empirical models to predict daylighting performance of a light-pipe system. The evaluation of the existing models was based on the monitoring data obtained from a underground parking lot in which the light-pipe system was installed. Comparisons were made between the predicted and the monitored data obtained from the study. The results indicated that semi-empirical models which was developed using the experimental data obtained under the Korean climatic conditions had a good prediction performance. We also quantified the effects caused by sky conditions, solar altitudes, room dimensions, and the aspect ratio of a light-pipe system on both the daylighting performance of the light-pipe system and the indoor illuminance distributions of the space using the semi-empirical model. Finally, this paper provides the design guideline of the light-pipe system for its application to an underground parking lot space.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.2
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• pp.101-115
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• 2024

The ocean heatwave is emerging as a major issue due to global warming, posing a direct threat to marine ecosystems and humanity through decreased food resources and reduced carbon absorption capacity of the oceans. Consequently, the prediction of ocean heatwaves in the vicinity of the Korean Peninsula is becoming increasingly important for marine environmental monitoring and management. In this study, an LSTM model was developed to improve the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system of the Korean Peninsula Ocean Prediction System. Based on the results of ocean heatwave predictions for the Korean Peninsula conducted in 2023, as well as those generated by the LSTM model, the performance of heatwave predictions in the East Sea, Yellow Sea, and South Sea areas surrounding the Korean Peninsula was evaluated. The LSTM model developed in this study significantly improved the prediction performance of sea surface temperatures during periods of temperature increase in all three regions. However, its effectiveness in improving prediction performance during periods of temperature decrease or before temperature rise initiation was limited. This demonstrates the potential of the LSTM model to address the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system during periods of enhanced stratification. It is anticipated that the utility of data-driven artificial intelligence models will expand in the future to improve the prediction performance of dynamical models or even replace them.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.8 no.2
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• pp.61-69
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• 2012

Geothermal heat pump(GHP) systems have been shown to be an environmentally-friendly, efficient alternative to traditional cooling and heating systems in both residential and commercial applications. Although some experimental work related to performance evaluation of GHP systems with vertical borehole ground heat exchangers for commercial buildings has been done, relatively little has been reported on the performance simulation of these systems. The aim of this study is to evaluate the cooling and heating performance of the GHP system with 30 borehole ground heat exchangers applied to an commercial building($1,210m^2$) in Seoul. For this purpose, a typical design procedure was involved with a combination of design parameters such as building loads, heat pump capacity, circulating pump, borehole diameter, and ground effective thermal properties, etc. The cooling and heating performance prediction of the system was conducted with different prediction methods and then each result is compared.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.652-657
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• 2004

As the real-time multimedia applications through Internet increase, the bandwidth available to TCP connections is oppressed by the UDP traffic, result in the performance of overall system is extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example meeting this necessity. The TCP-friendly (TFRC) is an UDP-based protocol that controls the transmission rate based on the available round transmission time (RTT) and the packet loss rate (PLR). In the data transmission processing, transmission rate is determined based on the conditions of the previous transmission period. If the one-step ahead predicted values of the control factors are available, the performance will be improved significantly. This paper proposes a prediction model of transmission rate control factors that will be used for the transmission rate control, which improves the performance of the networks. The model developed through this research is predicting one-step ahead variables of RTT and PLR. A multiplayer perceptron neural network is used as the prediction model and Levenberg-Marquardt algorithm is used for the training. The values of RTT and PLR were collected using TFRC protocol in the real system. The obtained prediction model is validated using new data set and the results show that the obtained model predicts the factors accurately.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1191-1205
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• 2022

Particulate matter(PM) among air pollutants with complex and widespread causes is classified according to particle size. Among them, PM_2.5 is very small in size and can cause diseases in the human respiratory tract or cardiovascular system if inhaled by humans. In order to prepare for these risks, state-centered management and preventable monitoring and forecasting are important. This study tried to predict PM_2.5 in Seoul, where high concentrations of fine dust occur frequently, using two ensemble models, random forest (RF) and extreme gradient boosting (XGB) using 15 local data assimilation and prediction system (LDAPS) weather-related factors, aerosol optical depth (AOD) and 4 chemical factors as independent variables. Performance evaluation and factor importance evaluation of the two models used for prediction were performed, and seasonal model analysis was also performed. As a result of prediction accuracy, RF showed high prediction accuracy of R² = 0.85 and XGB R² = 0.91, and it was confirmed that XGB was a more suitable model for PM_2.5 prediction than RF. As a result of the seasonal model analysis, it can be said that the prediction performance was good compared to the observed values with high concentrations in spring. In this study, PM_2.5 of Seoul was predicted using various factors, and an ensemble-based PM_2.5 prediction model showing good performance was constructed.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.203-223
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• 2014

This study proposes a novel context-aware recommender system, which is designed to recommend the items according to the customer's responses to the previously recommended item. In specific, our proposed system predicts the user's emotional state from his or her responses (such as facial expressions and movements) to the previous recommended item, and then it recommends the items that are similar to the previous one when his or her emotional state is estimated as positive. If the customer's emotional state on the previously recommended item is regarded as negative, the system recommends the items that have characteristics opposite to the previous item. Our proposed system consists of two sub modules-(1) emotion prediction module, and (2) responsive recommendation module. Emotion prediction module contains the emotion prediction model that predicts a customer's arousal level-a physiological and psychological state of being awake or reactive to stimuli-using the customer's reaction data including facial expressions and body movements, which can be measured using Microsoft's Kinect Sensor. Responsive recommendation module generates a recommendation list by using the results from the first module-emotion prediction module. If a customer shows a high level of arousal on the previously recommended item, the module recommends the items that are most similar to the previous item. Otherwise, it recommends the items that are most dissimilar to the previous one. In order to validate the performance and usefulness of the proposed recommender system, we conducted empirical validation. In total, 30 undergraduate students participated in the experiment. We used 100 trailers of Korean movies that had been released from 2009 to 2012 as the items for recommendation. For the experiment, we manually constructed Korean movie trailer DB which contains the fields such as release date, genre, director, writer, and actors. In order to check if the recommendation using customers' responses outperforms the recommendation using their demographic information, we compared them. The performance of the recommendation was measured using two metrics-satisfaction and arousal levels. Experimental results showed that the recommendation using customers' responses (i.e. our proposed system) outperformed the recommendation using their demographic information with statistical significance.