• Journal of the Korea Society of Computer and Information
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• v.22 no.10
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• pp.9-17
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• 2017

The student dropout prediction is an indispensable for many intelligent systems to measure the educational system and success rate of all university. Therefore, in this paper, we propose an intelligent dropout prediction system that minimizes the situation by adopting the proactive process through an effective model that predicts the students who are at risk of dropout. In this paper, the main data sets for students dropout predictions was used as questionnaires and university information. The questionnaire was constructed based on theoretical and empirical grounds about factor affecting student's performance and causes of dropout. University Information included student grade, interviews, attendance in university life. Through these data sets, the proposed dropout prediction model techniques was classified into the risk group and the normal group using statistical methods and Naive Bays algorithm. And the intelligence dropout prediction system was constructed by applying the proposed dropout prediction model. We expect the proposed study would be used effectively to reduce the students dropout in university.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.2198-2204
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• 2003

In order to make a match of the insufficient direct solar radiation, in this study, the target output is lowered to 9 kW smaller than 25 kW in former studies. It is also necessary to match the collector/receiver with engine/generator systems to accomplish the power level of a system. The simulation analyses of a dish solar power system with stirling engine are totally carried out to predict the system performance with the designed values. In addition, an influence of direct solar radiation on system performance and operation control is discussed in simulation. It is found that the diameter of concentrator could be made small to 8 m regardless of slope errors with 2.5 and 5.0 mrad radiation, and the operation range of mean pressure control. is wide even if the direct solar radiation is a quit low.

• MALSORI
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• no.57
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• pp.139-152
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• 2006

In this paper, we present a phrase break prediction method using CRF(Conditional Random Fields), which has good performance at classification problems. The phrase break prediction problem was mapped into a classification problem in our research. We trained the CRF using the various linguistic features which was extracted from POS(Part Of Speech) tag, lexicon, length of word, and location of word in the sentences. Combined linguistic features were used in the experiments, and we could collect some linguistic features which generate good performance in the phrase break prediction. From the results of experiments, we can see that the proposed method shows improved performance on previous methods. Additionally, because the linguistic features are independent of each other in our research, the proposed method has higher flexibility than other methods.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.5
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• pp.123-130
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• 2008

In order to clarify detailed mechanism of the flexible disk grinding system, workpiece length was introduced and its performance was evaluated. Flat zone ratio increased as the workpiece length increased. Increasing wheel speed and depth of cut also enhanced process performance by producing larger flat zone ratio. Neural network system was successfully applied to predict minimum depth of engagement and flat zone ratio. An additional input parameter as workpiece length to the neural network system enhanced the prediction performance by reducing error rate. By rearranging the Input combinations to the network, the workpiece length was precisely predicted with the prediction error rate lower than 2.8% depending on the network structure.

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

• Atmosphere
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• v.29 no.2
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• pp.149-164
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• 2019

The purpose of this study is to introduce the improvement of current operational climate prediction system of KMA and to compare previous and improved that. Whereas the previous system is based on GloSea5GA3, the improved one is built on GloSea5GC2. GloSea5GC2 is a fully coupled global climate model with an atmosphere, ocean, sea-ice and land components through the coupler OASIS. This is comprised of component configurations Global Atmosphere 6.0 (GA6.0), Global Land 6.0 (GL6.0), Global Ocean 5.0 (GO5.0) and Global Sea Ice 6.0 (GSI6.0). The compositions have improved sea-ice parameters over the previous model. The model resolution is N216L85 (~60 km in mid-latitudes) in the atmosphere and ORCA0.25L75 ($0.25^{\circ}$ on a tri-polar grid) in the ocean. In this research, the predictability of each system is evaluated using by RMSE, Correlation and MSSS, and the variables are 500 hPa geopotential height (h500), 850 hPa temperature (t850) and Sea surface temperature (SST). A predictive performance shows that GloSea5GC2 is better than GloSea5GA3. For example, the RMSE of h500 of 1-month forecast is decreased from 23.89 gpm to 22.21 gpm in East Asia. For Nino3.4 area of SST, the improvements to GloSeaGC2 result in a decrease in RMSE, which become apparent over time. It can be concluded that GloSea5GC2 has a great performance for seasonal prediction.

• Journal of Korea Multimedia Society
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• v.12 no.6
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• pp.876-881
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• 2009

Online game servers usually has been using the static thread pool system. But this system is not fit for huge online game server because the overhead is always up-and-down. Therefore, in this paper, we suggest the new algorithm for huge online game server. This algorithm is based on the prediction-based dynamic thread pool system. But it was developed for web servers and every 0.1 seconds the system prediction the needed numbers of threads and determine the thread pool size. Some experimental results show that the check time of 0.4 seconds is the best one for online game server and if the number of worker threads do not excess or lack to the given threshold then we do not predict and keep the current state. Otherwise we apply the prediction algorithm and change the number of threads. Some experimental results shows that this proposed algorithm reduce the overhead massively and make the performance of huge online game server improved in comparison to the static thread pool system.

• Journal of Power System Engineering
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• v.18 no.6
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• pp.13-20
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• 2014

The steady-state, incompressible and three-dimensional numerical analysis was carried out to evaluate turbulent models on the aerodynamic performance of a small-size axial fan(SSAF). The prediction performance on the static pressure of all turbulent models is going downhill at the high static pressure and low flowrate region, but has improved at the axial flow region. In consequence, all turbulent models predict the static pressure coefficient with an error performance less than about 4% after the region of the flowrate coefficient of about 0.14. Especially, the turbulent model of SST $k-{\omega}$ shows the best prediction performance equivalent to an error performance less than about 2% on the static pressure.

• Journal of Power System Engineering
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• v.16 no.5
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• pp.55-62
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• 2012

Centrifugal compressors are widely used and each operating condition is different. However, it cannot be manufactured according to the every operating condition. In the this study, performance of compressor was evaluated with various rotational speeds of impeller and various stagger angles of diffuser in order to apply a typical model widely. A centrifugal compressor was designed and manufactured based on the design point. On this machines, an experiment was conducted and the performance was predicted at off-design point. The performance prediction was validated with the experimental result and the numerical result. Although the isentropic efficiency on the prediction was slightly lower than that on the experimental result due to the heat loss in the experiment, the pressure ratio was predicted well and also the predicted results were matched well with the numerical results. When the rotational speed of the impeller and the stagger angle of the diffuser were changed together, the compressor can be worked in the high efficiency region and avoided operating in the stall region.

• International Journal of Highway Engineering
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• v.4 no.4 s.14
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• pp.1-12
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• 2002

Pavement Management System has a special purpose that the rehabilitation strategy applied on pavement should be executable in view of technical and economical point after new pavement open to the traffic. To achieve that purpose, a reliable pavement performance prediction model should be embeded in the system. The object of this study is to develop a probabilistic pavement performance prediction model for evaluating asphalt pavements based on the Markov chain concept. In this paper, methodology of the Markov chain modeling principle is explained, and the application of this model to asphalt pavement is described. As the results, transition matrics for predicting asphalt pavement performance are obtained, and also performance life is estimated quantitatively by this system.