• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.98-109
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• 2006

Fire control system(FCS) errors can be classified as hardware errors, filter prediction errors, effective ballistic function errors, and aiming errors. Among these errors, the filter prediction errors are the most significant error sources. To reduce them, a target future position calculation method using the acceleration estimate is suggested and it is compared with the constant velocity target prediction method. Simulation results show that the suggested method has better performance than the constant velocity prediction method. Target tracking algorithm is established with multiple target tracking filters based on IMM structure.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.125-134
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• 2002

This paper proposes a method for identifying temporal pattern clusters to predict events in time series. Instead of predicting future values of the time series, the proposed method forecasts specific events that may be arbitrarily defined by the user. The prediction is defined by an event characterization function, which is the target of prediction. The events are predicted when the time series belong to temporal pattern clusters. To identify the optimal temporal pattern clusters, fuzzy goal programming is formulated to combine multiple objectives and solved by an adaptive differential evolution technique that can overcome the sensitivity problem of control parameters in conventional differential evolution. To evaluate the prediction method, five test examples are considered. The adaptive differential evolution is also tested for twelve optimization problems.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.11a
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• pp.59-60
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• 2018

The purpose of this study is to predict and classify the accident types based on the KOSHA (Korea Occupational Safety & Health Agency) and weather data. We also have an effort to suggest an important management method according to accident types by deriving feature importance. We designed two models based on accident data and weather data (model(a)) and only weather data (model(b)). As a result of random forest method, the model(b) showed a lack of accuracy in prediction. However, the model(a) presented more accurate prediction results than the model(b). Thus we presented safety management plan based on the results. In the future, this study will continue to carry out real time prediction to occurrence types to prevent safety accidents by supplementing the real time accident data and weather data.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.97-104
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• 2017

Location prediction has been successfully utilized to provide high quality of location-based services to customers in many applications. In its usual form, the conventional type of location prediction is to predict future locations based on user's past movement history. However, as location prediction needs are expanded into much complicated cases, it becomes necessary quite frequently to make inference on the locations that target user visited in the past. Typical cases include the identification of locations that infectious disease carriers may have visited before, and crime suspects may have dropped by on a certain day at a specific time-band. Therefore, primary goal of this study is to predict locations that users visited in the past. Information used for this purpose include user's demographic information and movement histories. Data mining classifiers such as Bayesian network, neural network, support vector machine, decision tree were adopted to analyze 6868 contextual dataset and compare classifiers' performance. Results show that general Bayesian network is the most robust classifier.

• The Journal of Society for e-Business Studies
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• v.9 no.4
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• pp.1-17
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• 2004

We guarantee the significance of the provided prediction model and predicted figures from the experts consulting group and we product the prediction figures of the domestic e-commerce market size in future by business subjects, BtoB, BtoG and BtoC. Besides, we do predict by the high raked 6 merchandises in the case of BtoC market size prediction. We use the KNSO(Korea National Statistical Office) BtoB, BtoG and BtoC data to ensure the significance of data.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.4
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• pp.101-109
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• 2018

In order to predict the future needs of the aircraft repair parts, each military group develops and applies various techniques to their characteristics. However, the aircraft and the equipped weapon systems are becoming increasingly advanced, and there is a problem in improving the hit rate by applying the existing demand prediction technique due to the change of the aircraft condition according to the long term operation of the aircraft. In this study, we propose a new prediction model based on the conventional time-series analysis technique to improve the prediction accuracy of aircraft repair parts by using machine learning model. And we show the most effective predictive method by demonstrating the change of hit rate based on actual data.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.295-302
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• 2022

This paper contains the development of a smart power device designed to collect load power data from industrial manufacturing machines, predict future variations in load power data, and detect abnormal data in advance by applying a machine learning-based prediction algorithm. The proposed load power data prediction model is implemented using a Long Short-Term Memory (LSTM) algorithm with high accuracy and relatively low complexity. The Flask and REST API are used to provide prediction results to users in a graphical interface. In addition, we present the results of experiments conducted to evaluate the performance of the proposed approach, which show that our model exhibited the highest accuracy compared with Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM) models. Moreover, we expect our method's accuracy could be improved by further optimizing the hyperparameter values and training the model for a longer period of time using a larger amount of data.

• International Journal of Advanced Culture Technology
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• v.11 no.2
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• pp.186-190
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• 2023

There are unimaginable possibilities ahead of us. As a result, it is difficult to predict the future, but the prediction itself is not meaningless. This is because it can have the flexibility to cope with contingencies by predicting various possibilities. This study was conducted to explore extreme events (X-event) in the Korean population sector. To this end, in-depth interviews were conducted with experts from the Korea Army Research Center for Future & Innovation and the Army College, and based on this, significant research results were derived that population problems such as population decline and aging can affect various fields such as economy. With this study, we hope that discussions on extreme events (X-event) that can occur in our society will be further activated.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.137-147
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• 2020

The Corona 19 pandemic is bringing a big change in the fields of politics, economy, society, culture, environment, ecosystem, science and technology, and business management activities and advertising. Therefore, in this study, after the Corona 19 pandemic, we predicted how the characteristics of future advertising and the types of future advertising will change, and we studied countermeasures. We used, a method of analyzing related literature and the Delphi Survey method predicting the future, as a research method. As expert Panel, the subject of the Delphi technique survey, we recruited 30 experts in the field of advertising and future fields with professional insight. We study to predict how the characteristics of future advertising and the types of future advertising will change according to changes in the advertising environment such as social changes, business changes, and consumer changes after the Corona 19 pandemic. In order to cope with these changes in future advertising, it is necessary to actively prepare the advertising industry and advertising experts. Therefore, we suggested countermeasures so that the advertising industry and advertising experts can understand and respond for future advertising changes.

• Water for future
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• v.8 no.2
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• pp.13-24
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• 1975

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