• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1171-1176
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• 2023

In this paper, we predicts the estimated price using the DNBP (Deep learning Network to predict Budget Price) model with bidding data obtained from the bidding websites, ElecNet and OK EMS. We use the DNBP model to predict four lottery preliminary price, calculate their arithmetic mean, and then estimate the expected budget price ratio. We evaluate the model's performance by comparing it with the actual expected budget price ratio. We train the DNBP model by removing some of the 15 input nodes. The prediction results showed the lowest RMSE of 0.75788% when the model had 6 input nodes (a, g, h, i, j, k).

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• The Journal of Information Systems
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• v.14 no.2
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• pp.277-292
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• 2005

This paper uses a data mining approach to develop bankruptcy prediction models suitable for traditional (off-line) companies and electronic (on-line) companies. It observes the differences in the composition prediction models between these two types of companies and provides interpretation of bankruptcy classifications. The bankruptcy prediction models revealed the major variables in predicting bankruptcy to be 'cash flow to total assets' and 'gross value-added to net sales' for traditional off-line companies while 'cash flow to liabilities','gross value-added to net sales', and 'current ratio' for electronic companies. The accuracy rates of final prediction models for traditional off-line and electronic companies were found to be $84.7\%\;and\;82.4\%$, respectively. When the model for traditional off-line companies was applied for electronic companies, prediction accuracy dropped significantly in the case of bankruptcy classification (from $70.4\%\;to\;45.2\%$) at the level of a blind guess ($41.30\%$). Therefore, the need for different models for traditional off-line and electronic companies is justified.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.

• Journal of Environmental Impact Assessment
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• v.15 no.6
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• pp.373-383
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• 2006

The spatial mapping of risk is very useful data in planning for disaster preparedness. This research presents a methodology for making the landslide life risk map in the Boeun area which had considerable landslide damage following heavy rain in August, 1998. We have developed a three-stage procedure in spatial data analysis not only to estimate the probability of the occurrence of the natural hazardous events but also to evaluate the uncertainty of the estimators of that probability. The three-stage procedure consists of: (i)construction of a hazard prediction map of "future" hazardous events; (ii) validation of prediction results and estimation of the probability of occurrence for each predicted hazard level; and (iii) generation of risk maps with the introduction of human life factors representing assumed or established vulnerability levels by combining the prediction map in the first stage and the estimated probabilities in the second stage with human life data. The significance of the landslide susceptibility map was evaluated by computing a prediction rate curve. It is used that the Bayesian prediction model and the case study results (the landslide susceptibility map and prediction rate curve) can be prepared for prevention of future landslide life risk map. Data from the Bayesian model-based landslide susceptibility map and prediction ratio curves were used together with human rife data to draft future landslide life risk maps. Results reveal that individual pixels had low risks, but the total risk death toll was estimated at 3.14 people. In particular, the dangerous areas involving an estimated 1/100 people were shown to have the highest risk among all research-target areas. Three people were killed in this area when landslides occurred in 1998. Thus, this risk map can deliver factual damage situation prediction to policy decision-makers, and subsequently can be used as useful data in preventing disasters. In particular, drafting of maps on landslide risk in various steps will enable one to forecast the occurrence of disasters.

• Journal of the Korean Solar Energy Society
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• v.37 no.1
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• pp.81-90
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• 2017

We have analyzed the performance of 58 kWp photovoltaic (PV) power systems installed in Jeddah, Saudi Arabia. Performance ratio (PR) of 3 PV systems with 3 desert-type PV modules using monitoring data for 1 year showed 85.5% on average. Annual degradation rate of 5 individual modules achieved 0.26%, the regression model using monitoring data for the specified interval of one year showed 0.22%. Root mean square error (RMSE) of 6 big data analysis models for power output prediction in May 2016 was analyzed 2.94% using a support vector regression model.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2001.10a
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• pp.134-137
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• 2001

In this paper on-line model is derived from investigating via series of finite element process simulation. Some variables that little affect on non-dimensional parameters. ie. forward slip and torque factor. is extracted from composing on-line model Especially, this research focused on deriving on-line model which exactly predict roll force and roll power in the roughing mill process under small shape factor and small reduction ratio. The prediction accuracy of the proposed model is examined through comparison with predictions from a finite element process model

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.3
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• pp.52-56
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• 1980

The Signal -to- Noise Ratio of delta modulation for a fi rEt -order Gauss -Markov signal is derived and an approximate expreession of SND is discussed, in the case that only granular noise arises. Cross covariance of input and error signals are negligible when the adjacent correlation of input signal is larger than the difference between the adjacent correlation and the prediction coefficient of local decoder. The approximately derived SNR is available for any value of adjacent correlation.

• Transactions of the Korean Society of Mechanical Engineers
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• v.7 no.4
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• pp.451-460
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• 1983

Computational four-equation turbulence model is developed and is applied to predict twodimensional unsteady thermal surface discharge into a reservoir. Turbulent stresses and heat fluxes in the momentum and energy equations are determined from transport equations for the turbulent kinetic energy (R), isotropic rate of kinetic energy dissipation (.epsilon.), mean square temperature variance (theta. over bar $^{2}$), and rate of destruction of the temperature variance (.epsilon. $_{\theta}$). Computational results by four-equation model are favorably compared with those obtained by an extended two-equation model. Added advantage of the four-equation model is that it yields quantitative information about the ratio between the velocity time scale and the thermal time scale and more detailed information about turbulent structure. Predicted time scale ratio is within experimental observations by others. Although the mean velocity and temperature fields are similarly predicted by both models, it is found that the four-equation model is preferably candidate for prediction of highly buoyant turbulent flows.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.12
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• pp.3326-3334
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• 1994

To theoretically predict knock occurrence in S. I. engine as a function of engine design and operating parameters, transient local temperature and pressure, mixture density of flame front in combustion period are calculated. We next determined normal combustion period and auto ignition period of end gas using the prediction method on occurrence of spark knock which we suggested. We predict knock occurrence in S. I. engine by comparing consecutively normal combustion period with the auto ignition period of end gas in combustion period. Engine design and operating parameters such as compression ratio, engine speed, spark timing, inlet temperature and pressure are taken into account in this calculations. The predicted result are well matched with the experimental results in turbocharged engine. Therefore, this method will provide the systematic guideline for designing engines in view of knocking limits.