• KIISE Transactions on Computing Practices
• /
• v.21 no.3
• /
• pp.247-256
• /
• 2015

Since the Gulf War, air power has played a key role. However, the effect of high-tech weapons, such as laser-guided bombs and electronic optical equipment, drops significantly if they do not match the weather conditions. So, aircraft that are assigned to carry laser-guided bombs must replace these munitions during bad weather conditions. But, there are no objective criteria for when weapons should be replaced. Therefore, in this paper, we propose an algorithm to predict the hit rate of laser-guided bombs using cloud image processing. In order to verify the accuracy of the algorithm, we applied the weather conditions that may affect laser-guided bombs to simulated flight equipment and executed simulated weapon release, then collected and analyzed data. Cloud images appropriate to the weather conditions were developed, and applied to the algorithm. We confirmed that the algorithm can accurately predict the hit rate of laser-guided bombs in most weather conditions.

• Nuclear Engineering and Technology
• /
• v.47 no.1
• /
• pp.74-84
• /
• 2015

If a transient occurs in a nuclear power plant (NPP), operators will try to protect the NPP by estimating the kind of abnormality and mitigating it based on recommended procedures. Similarly, operators take actions based on severe accident management guidelines when there is the possibility of a severe accident occurrence in an NPP. In any such situation, information about the occurrence time of severe accident-related events can be very important to operators to set up severe accident management strategies. Therefore, support systems that can quickly provide this kind of information will be very useful when operators try to manage severe accidents. In this research, the occurrence times of several events that could happen during a severe accident were predicted using support vector machines with short time variations of plant status variables inputs. For the preliminary step, the break location and size of a loss of coolant accident (LOCA) were identified. Training and testing data sets were obtained using the MAAP5 code. The results show that the proposed algorithm can correctly classify the break location of the LOCA and can estimate the break size of the LOCA very accurately. In addition, the occurrence times of severe accident major events were predicted under various severe accident paths, with reasonable error. With these results, it is expected that it will be possible to apply the proposed algorithm to real NPPs because the algorithm uses only the early phase data after the reactor SCRAM, which can be obtained accurately for accident simulations.

• Nuclear Engineering and Technology
• /
• v.40 no.7
• /
• pp.615-624
• /
• 2008

In this paper, the contribution of task types and error types involved in the human-related unplanned reactor trip events that have occurred between 1986 and 2006 in Korean nuclear power plants are analysed in order to establish a strategy for reducing the human-related unplanned reactor trips. Classification systems for the task types, error modes, and cognitive functions are developed or adopted from the currently available taxonomies, and the relevant information is extracted from the event reports or judged on the basis of an event description. According to the analyses from this study, the contributions of the task types are as follows: corrective maintenance (25.7%), planned maintenance (22.8%), planned operation (19.8%), periodic preventive maintenance (14.9%), response to a transient (9.9%), and design/manufacturing/installation (6.9%). According to the analysis of the error modes, error modes such as control failure (22.2%), wrong object (18.5%), omission (14.8%), wrong action (11.1 %), and inadequate (8.3%) take up about 75% of the total unplanned trip events. The analysis of the cognitive functions involved in the events indicated that the planning function had the highest contribution (46.7%) to the human actions leading to unplanned reactor trips. This analysis concludes that in order to significantly reduce human-induced or human-related unplanned reactor trips, an aide system (in support of maintenance personnel) for evaluating possible (negative) impacts of planned actions or erroneous actions as well as an appropriate human error prediction technique, should be developed.

• Plant Journal
• /
• v.8 no.3
• /
• pp.49-54
• /
• 2012

In this study, the way how we can find the defects of motor windings in advance will be discussed. The magnetic flux spectrum in the high frequency region of the large motor was analyzed based on the actual fault practices related with motor windings. In case of defective motor relative amplitude ratio of the stator slot frequency to its sideband was very high compared to that of healthy motor. And the defective signal related with motor windings was indicated in advance in the magnetic flux spectrum prior to over 1 month before failure. Considering this aspect it can be estimated that magnetic flux spectrum in the high frequency region has the excellent predictive diagnostic capability.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.14 no.5
• /
• pp.1264-1271
• /
• 1990

The vortex lattice method was adopted to predict the aerodynamic performance of a horizontal axis wind turbine. For this simulation. the rotor blade was divided into many panels both in chordwise and spanwise direction and then replaced by horseshoe vortices. The wake was divided into two parts of near wake and far wake : the near wake was assumed as helical vortex line elements and the far wake was modeled by semi-infinite circular vortex cylinder. The induced velocity components were calculated by the Biot-Savart law. By this way the power coefficient was obtained and represented as a function of the tip speed ratio. The numerical results obtained were compared with those of the other methods and experimental results and showed good agreement with experimental results.

• Korean Journal of Applied Biomechanics
• /
• v.17 no.1
• /
• pp.81-90
• /
• 2007

The effort identifying positioning information of the moving object in real time has been a issue not only in sport biomechanics but also other academic areas. In order to solve this issue, this study tried to track the movement of a pitched ball that might provide an easier prediction because of a clear focus and simple movement of the object. Machine learning has been leading the research of extracting information from continuous images such as object tracking. Though the rule-based methods in artificial intelligence prevailed for decades, it has evolved into the methods of statistical approach that finds the maximum a posterior location in the image. The development of machine learning, accompanied by the development of recording technology and computational power of computer, made it possible to extract the trajectory of pitched baseball from recorded images. We present a method of baseball tracking, based on object tracking methods in machine learning. We introduce three state-of-the-art researches regarding the object tracking and show how we can combine these researches to yield a novel engine that finds trajectory from continuous pitching images. The first research is about mean shift method which finds the mode of a supposed continuous distribution from a set of data. The second research is about the research that explains how we can find the mode and object region effectively when we are given the previous image's location of object and the region. The third is about the research of representing data into features that we can deal with. From those features, we can establish a distribution to generate a set of data for mean shift. In this paper, we combine three works to track baseball's location in the continuous image frames. From the information of locations from two sets of images, we can reconstruct the real 3-D trajectory of pitched ball. We show how this works in real pitching images.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.5
• /
• pp.153-160
• /
• 2020

Energy Consumption Predictions for Industries has a prominent role to play in the energy management and control system as dynamic and seasonal changes are occurring in energy demand and supply. This paper introduces and explores the steel industry's predictive models of energy consumption. The data used includes lagging and leading reactive power lagging and leading current variable, emission of carbon dioxide (tCO2) and load type. Four statistical models are trained and tested in the test set: (a) Linear Regression (LR), (b) Radial Kernel Support Vector Machine (SVM RBF), (c) Gradient Boosting Machine (GBM), and (d) Random Forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for calculating regression model predictive performance. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.2
• /
• pp.395-406
• /
• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.

• Journal of Radiation Protection and Research
• /
• v.29 no.2
• /
• pp.117-127
• /
• 2004

A dynamic compartment model was developed to evaluate the transport of accidently released radionuclides onto rice-fields. In the model, the surface water compartment and shoot-base absorption were introduced to account for the effect of irrigation, which is essential to a rice cultivation. The soil mixing by plough and irrigation before transplanting rice was also considered, and the rate of root-uptake and shoot-base absorption were modeled in terms of the function of biomass. In order to test the validation of the model, it was applied to the analysis of some simulated $^{137}Cs$ deposition experiments that were performed while cultivating rice in a greenhouse using soils sampled from rice-fields around Kori, Yonggwang and Ulchin nuclear power plants. The model prediction was generally agreed within about one order of magnitude with experimental data.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.5C
• /
• pp.302-308
• /
• 2011

In this paper we propose a coordinated beamforming(CBF) scheme considering the effects of feedback quantization and delay in time-varying multiple-input multiple-output(MIMO) broadcast channels. By equal power allocation per data stream, the proposed CBF scheme transmits multiple data streams per user terminals without additional feedback overhead when quantized feedback information is used. The proposed CBF scheme also applies a linear channel predictor to each user terminals to prevent errors due to feedback delays that are not evitable in practical wireless systems. Each user terminal utilizes Wiener filter to predict future channel responses and generates feedback information based on the predicted channels. Consequently the proposed CBF scheme adapting Wiener filter improves system performances compared with the conventional scheme using delayed feedback.