• Proceedings of the KSRS Conference
• /
• 2008.10a
• /
• pp.281-284
• /
• 2008

Due to the remarkable development of the GIS and spatial information technology, the information on the national land and scientific management are disseminated. According to the result of research for an efficient analysis of forest site, it presents distinguishing of satellite image and methodology of TPI (Topographic Position Index). The prediction of forest site distribution through this research, specified Gyeongju-si area, gives an effect to distinguishing honor system through Quickbird image with the resolution 0.6m. Furthermore it was carried out through TPI grid that is abstracted by DEM, slope of study area and type of topography, as well as it put its operation on analysis and verification of relativity between the result of prediction on forest site distribution and the field survey report. It distinguishes distribution of country rock that importantly effects to producing of soil, using 1: 5000 forest maps and grasping distribution type of soil using satellite image and TPI, it is supposed to provide a foundation of the result on prediction of forest site. With the GIS techniques of analysis, inclination of discussion, altitude, etc, and using high resolution satellite image and TPI, it is considered to be capable to provide more exact basis information of forest resources, management of forest management both in rational and efficient.

• Solar Energy
• /
• v.7 no.2
• /
• pp.37-53
• /
• 1987

This study has been prepared for the purpose of developing the system performance prediction method of natural circulation solar hot water system. The storage tank of the natural circulation solar hot water system equipped with flat-plate solar collector is located at higher elevation than the solar collectors. Therefor, the storage tank temperature distribution formed accordance with configuration of storage tank by flow rate of circulating fluid affect system collection efficiency. In this study measure the storage tank temperature distribution with various experimental system under real sun condition and present the theoretical prediction method of the storage tank temperature. Moreover measure the flow rate not only day-time but also night-time reverse flow rate with die injection visual flow meter. Main conclusion obtain from the present study is as follows; 1) The storage tank temperature distribution above the connecting pipe connection position is the same as that of the fully mixed tank and below the connection position is the same as that of stratified tank. 2) The system performance sensitive to the storage tank temperature distribution. Therefore detailed tank model is necessary. Average storage tank temperature can be calculate 3% and storage tank temperature profile can get less than 10% difference with this model system.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.11
• /
• pp.1551-1558
• /
• 2014

We propose a multi-rate sensor fusion of vision and radar using Kalman filter to solve problems of asynchronized and multi-rate sampling periods in object vehicle tracking. A model based prediction of object vehicles is performed with a decentralized multi-rate Kalman filter for each sensor (vision and radar sensors.) To obtain the improvement in the performance of position prediction, different weighting is applied to each sensor's predicted object position from the multi-rate Kalman filter. The proposed method can provide estimated position of the object vehicles at every sampling time of ECU. The Mahalanobis distance is used to make correspondence among the measured and predicted objects. Through the experimental results, we validate that the post-processed fusion data give us improved tracking performance. The proposed method obtained two times improvement in the object tracking performance compared to single sensor method (camera or radar sensor) in the view point of roots mean square error.

• Journal of Korea Multimedia Society
• /
• v.10 no.1
• /
• pp.139-146
• /
• 2007

Many researches had been gone about method to track moving object using wireless sensor network. We examined tradeoffs that exist between quantity of energy and correctness of tracking, and we confirmed that can get more energy sayings through improved motion prediction method. The consumed energy in the tracking is used by sensor node for sensing the object, and tracking correctness is a differ once of actual object position from calculated value by sensing. Some tracking methods and controlling parameters causes a variation of tracking correctness and energy consuming, we can get best energy effectiveness by motion prediction algorithm. Furthermore, we get better tracking quality and energy effectiveness through using a motion prediction algorithm that consider acceleration. By the simulation, we know that if we use an accurate motion prediction algorithm, node activation range that is used for target's predicted position should be restricted to sensing range of sensor is better.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.2
• /
• pp.84-90
• /
• 2019

Tremendous computation of full search or lossless motion estimation algorithms for video coding has led development of many fast motion estimation algorithms. We still need proper control of computation and prediction quality. In the paper, we suggest an algorithm that reduces computation effectively and controls computational amount and prediction quality, while keeping prediction quality as almost the same as that of the full search. The proposed algorithm uses multiple thresholds for partial block sum and times of counting unchanged minimum position for each step. It also calculates the partial block matching error, removes impossible candidates early, implements fast motion estimation by comparing times of keeping the position of minimum error for each step, and controls prediction quality and computation easily by adjusting the thresholds. The proposed algorithm can be combined with conventional fast motion estimation algorithms as well as by itself, further reduce computation while keeping the prediction quality as almost same as the algorithms, and prove it in the experimental results.

• Plant Journal
• /
• v.15 no.2
• /
• pp.56-61
• /
• 2019

In this study, the failure analysis of the internal tube occurred in the high pressure feedwater heater for power generation boiler of 500 MW supercritical pressure coal fired power plant was investigated. I suggested a prediction model that can diagnose internal tube failure by changing the position of level control valve on the shell side and the suction flow rate of the boiler feedwater pump. The suggested prediction model is demonstrated through additional cases of feedwater system unbalance. The simultaneous comparison of the shell side level control valve position and the suction flow rate of the boiler feedwater pump compared to the normal operating state value, even in the case of the high pressure feedwater heater for the power boiler, It can be a powerful prediction diagnosis.

• Nuclear Engineering and Technology
• /
• v.43 no.3
• /
• pp.301-308
• /
• 2011

The diametral creep of pressure tubes (PTs) in CANDU (CANada Deuterium Uranium) reactors is one of the principal aging mechanisms governing the heat transfer and hydraulic degradation of the heat transport system (HTS). PT diametral creep leads to diametral expansion, which affects the thermal hydraulic characteristics of the coolant channels and the critical heat flux (CHF). The CHF is a major parameter determining the critical channel power (CCP), which is used in the trip setpoint calculations of regional overpower protection (ROP) systems. Therefore, it is essential to predict PT diametral creep in CANDU reactors. PT diametral creep is caused mainly by fast neutron irradiation, temperature and applied stress. The objective of this study was to develop a bundle position-wise linear model (BPLM) to predict PT diametral creep employing previously measured PT diameters and HTS operating conditions. The linear model was optimized using a genetic algorithm and was devised based on a bundle position because it is expected that each bundle position in a PT channel has inherent characteristics. The proposed BPLM for predicting PT diametral creep was confirmed using the operating data of the Wolsung nuclear power plant in Korea. The linear model was able to predict PT diametral creep accurately.

• The Journal of the Korea Contents Association
• /
• v.22 no.5
• /
• pp.48-56
• /
• 2022

The importance of data analysis in baseball has been increasing after the success of MLB's Oakland which applied Billy Beane's money ball theory, and the 2020 KBO winner NC Dinos. Various studies using data in baseball has been conducted not only in the United States but also in Korea, In particular, the models using deep learning and machine learning has been suggested. However, in the previous studies using deep learning and machine learning, the focus is only on predicting the win or loss of the game, and there is a limitation in that it is difficult to interpret the results of which factors have an important influence on the game. In this paper, to investigate which factors is important by position, the prediction model for the Golden Glove award which is given for the best player by position is developed. To develop the prediction model, XGBoost which is one of boosting method is used, which also provide the feature importance which can be used to interpret the factors for prediction results. From the analysis, the important factors by position are identified.

• Journal of Korean Institute of Industrial Engineers
• /
• v.40 no.3
• /
• pp.257-266
• /
• 2014

The semiconductor manufacturing industry is managed by a number of parameters from the FAB which is the initial step of production to package test which is the final step of production. Various methods for prediction for the quality and yield are required to reduce the production costs caused by a complicated manufacturing process. In order to increase the accuracy of quality prediction, we have to extract the significant features from the large amount of data. In this study, we propose the method for extracting feature from the cell level data of probe test process using OPTICS which is one of the density-based clustering to improve the prediction accuracy of the quality of the assembled chips that will be placed in a package test. Two features extracted by using OPTICS are used as input variables of quality prediction model because of having position information of the cell defect. The package test progress for chips classified to the correct quality grade by performing the improved prediction method is expected to bring the effect of reducing production costs.

• Journal of the Korean Society of Visualization
• /
• v.20 no.2
• /
• pp.78-85
• /
• 2022

In these days, the rapid development in prediction technology using artificial intelligent is being applied in a variety of engineering fields. Especially, dimensionality reduction technologies such as autoencoder and convolutional neural network have enabled the classification and regression of high-dimensional data. In particular, pixel level prediction technology enables semantic segmentation (fine-grained classification), or physical value prediction for each pixel such as depth or surface normal estimation. In this study, the pressure distribution of the ship's surface was estimated at the pixel level based on the artificial neural network. First, a potential flow analysis was performed on the hull form data generated by transforming the baseline hull form data to construct 429 datasets for learning. Thereafter, a neural network with a U-shape structure was configured to learn the pressure value at the node position of the pretreated hull form. As a result, for the hull form included in training set, it was confirmed that the neural network can make a good prediction for pressure distribution. But in case of container ship, which is not included and have different characteristics, the network couldn't give a reasonable result.