• Annual Conference of KIPS
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• 2001.10a
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• pp.535-538
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• 2001

Since 1990s, many literatures have shown that connectionist models, such as back propagation, recurrent network, and RBF (Radial Basis Function) outperform the traditional models, MA (Moving Average), AR (Auto Regressive), and ARIMA (Auto Regressive Integrated Moving Average) in time series prediction. Neural based approaches to time series prediction require the enough length of historical measurements to generate the enough number of training patterns. The more training patterns, the better the generalization of MLP is. The researches about the schemes of generating artificial training patterns and adding to the original ones have been progressed and gave me the motivation of developing VTG schemes in 1996. Virtual term is an estimated measurement, X(t+0.5) between X(t) and X(t+1), while the given measurements in the series are called actual terms. VTG (Virtual Tern Generation) is the process of estimating of X(t+0.5), and VTG schemes are the techniques for the estimation of virtual terms. In this paper, the alternative VTG schemes to the VTG schemes proposed in 1996 will be proposed and applied to multivariate time series prediction. The VTG schemes proposed in 1996 are called deterministic VTG schemes, while the alternative ones are called stochastic VTG schemes in this paper.

• International Journal of Contents
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• v.13 no.4
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• pp.23-28
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• 2017

Of the total economic loss caused by disasters, 40% are due to floods and floods have a severe impact on human health and life. So, it is important to monitor the water level of a river and to issue a flood warning during unfavorable circumstances. In this paper, we propose a modified error function to improve a hydrological modeling using a multi-layer perceptron (MLP) neural network. When MLP's are trained to minimize the conventional mean-squared error function, the prediction performance is poor because MLP's are highly tunned to training data. Our goal is achieved by preventing overspecialization to training data, which is the main reason for performance degradation for rare or test data. Based on the modified error function, an MLP is trained to predict the water level with rainfall data at upper reaches. Through simulations to predict the water level of Nakdong River near a UNESCO World Heritage Site "Hahoe Village," we verified that the prediction performance of MLP with the modified error function is superior to that with the conventional mean-squared error function, especially maximum error of 40.85cm vs. 55.51cm.

• Food Science of Animal Resources
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• v.42 no.4
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• pp.655-671
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• 2022

There are differences of spectral characteristics between different types of meat cut, which means the model established using only one type of meat cut for meat quality prediction is not suitable for other meat cut types. A novel portable visible and near-infrared (Vis/NIR) optical system was used to simultaneously predict multiple quality indicators for different commercial meat cut types (silverside, back strap, oyster, fillet, thick flank, and tenderloin) from Small-tailed Han sheep. The correlation coefficients of the calibration set (R_c) and prediction set (R_p) of the optimal prediction models were 0.82 and 0.81 for pH, 0.88 and 0.84 for L^*, 0.83 and 0.78 for a^*, 0.83 and 0.82 for b^*, 0.94 and 0.86 for cooking loss, 0.90 and 0.88 for shear force, 0.84 and 0.83 for protein, 0.93 and 0.83 for fat, 0.92 and 0.87 for moisture contents, respectively. This study demonstrates that Vis/NIR spectroscopy is a promising tool to achieve the predictions of multiple quality parameters for different commercial meat cut types.

• The Korean Journal of Financial Management
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• v.27 no.1
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• pp.121-156
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• 2010

Back-door-listing can be viewed both as M&A and an alternative to IPO. If IPO is an access to the capital market through regulations, back-door-listing would be the way of entering the market through trading. Back-door-listing can be a better choice considering the common wisdom that regulations hinder the functioning of free market system. One would, however, prefer IPO, for the informational asymmetry isless severe in case of IPO. This paper examines if IPO is superior to back-door-listing as to the informational efficiency. The excess buy-and-hold returns of the Kosdaq back-door-listing firms are estimated over the three-year-period since the event. They are compared against the excess buy-and-hold returns of the Kosdaq IPO firms over the same period of time. The results confirm this paper's prediction that IPO should be more information-efficient. Both IPO and back-door-listing firms start with high short-term excess returns and end up with long-term under-performance. However, back-door-listing firms show more significantly damaging long-term results. Furthermore, back-door-listing firms record poorer accounting results over the research period. These results imply that there exists fad at the time of both events and, in case of back-door-listing, this fad is reinforced by the possibility of window dressing.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.33-40
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• 2003

Criticality prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development costs because the problems in early phases largely affect the quality of the late products. Real-time systems such as telecommunication systems are so large that criticality prediction is mere important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing causes of the prediction results and low extendability. This paper builds a new prediction model, GAM, based on Genetic Algorithm. GAM is different from other models because it produces a criticality function. So GAM can be used for comparison between entities by criticality. GAM is implemented and compared with a well-known prediction model, BackPropagation neural network Model(BPM), considering Internal characteristics and accuracy of prediction.

• Asian-Australasian Journal of Animal Sciences
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• v.7 no.3
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• pp.441-448
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• 1994

Postweaning performance data were obtained on 187 group fed purebred Angus calves from 12 selected sires (six high and six low feed conversion sires) in 1985 and 1986. The objective of this portion of the study was to develop prediction equations for feed conversion from a stepwise regression analysis. Variables measured were on-test weight (ONTSTWT), on-test age (ONTSTAG), five weights by 28-d periods, seven linear body measurements: heart girth (HG), hip height (HH), head width (HDW), head length (HDL), muzzle circumference (MC), length between hooks and pins (HOPIN) and length between shoulder and hooks (SHHO), and backfat thickness (BF). Stepwise regressions for maintenance adjusted feed conversion (ADJFC) and unadjusted feed conversion (UNADFC) over the first 140 d of the test, and total feed conversion (FC) until progeny reached 8.89 mm of back fat were obtained separately by conversion groups and sexes and for combined feed conversion groups and sexes. In general, weights were more important than linear body measurements in prediction of feed utilization. To some extent this was expected as weight is related directly to gain which is a component of feed conversion. Weight at 112 d was the most important variable in prediction of feed conversion when data from both feed conversion groups and sexes were combined. Weights at 84 and 140 d were important variables in prediction of UNADFC and FC, respectively, of bulls. ONTSTWT and weight at 140 d had the highest standardized partial regression coefficients for UNADFC and ADJFC, respectively, of heifers. Results indicated that linear measurements, such as MC, HDL and HOPIN, are useful in prediction of feed conversion when feed in takes are unavailable.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.5
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• pp.502-507
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• 2009

Curvature extrusion process has several advantages in comparison to the conventional extrusion and bending process. In the curvature extrusion, the extruded part is directly bent during extrusion. Therefore, it does not need additional bending process after extrusion. In the curvature extrusion process, it is possible to produce curved extruded products that have a constant or various curvatures. It is essential that we predict the curvatures of the extruded product to meet the required curvatures. This paper proposed a theoretical model that can predict the curvature of extruded product produced by the curvature extrusion process. Using the proposed model the movement of guide tool that causes the bending of extruded product was controlled to produce the required curved automotive Al bumper back beam. The effectiveness of the proposed prediction model and the movement of guide tool were verified by the FE analysis and curved extrusion experiment.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.4
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• pp.273-279
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• 2017

In sheet metal forming numerical analysis, the strain hardening equation has a significant effect on calculation results, especially in the field of spring-back. This study introduces the Kim-Tuan strain hardening model. This model represents sheet material behavior over the entire strain hardening range. The proposed model is compared to other well known strain hardening models using a series of uniaxial tensile tests. These tests are performed to determine the stress-strain relationship for Al6016-T4, DP980, and CP Ti sheets. In addition, the Kim-Tuan model is used to integrate the CP Ti sheet strain hardening equation in ABAQUS analysis to predict spring-back amount in a bending test. These tests highlight the improved accuracy of the proposed equation in the numerical field. Bending tests to evaluate prediction accuracy are also performed and compared with numerical analysis results.

• Journal of the Korean Society of Visualization
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• v.10 no.1
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• pp.47-54
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• 2012

Single Stage To Orbit (SSTO) missions which require its engines to be operated at varying back pressure conditions, use engines operate at high combustion chamber pressures (more than 100bar) with moderate area ratios (AR 70~80). This ensures that the exhaust jet flows full during most part of the operational regimes by optimal expansion at each altitude. Aero-spike nozzle is a kind of altitude adaptation nozzle where requirement of high combustion chamber pressures can be avoided as the flow is adapted to the outside conditions by the virtue of the nozzle configuration. However, the thrust prediction using the conventional thrust equations remains to be a challenge as the nozzle plume shapes vary with the back pressure conditions. In the present work, the performance evaluation of a new aero-spike nozzle is being carried out. Computational studies are carried out to predict the thrust generated by the aero-spike nozzle in varying back pressure conditions which requires the unsteady pressure boundary conditions in the computational domain. Schlieren pictures are taken to validate the computational results. It is found that the flow in the aero-spike nozzle is mainly affected by the base wall pressure variation. The aerospike nozzle exhibits maximum performance in the properly expanded flow regime due to the open wake formation.

• Journal of Ship and Ocean Technology
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• v.4 no.3
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• pp.1-12
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• 2000

A number of numerical methods like Computational Fluid Dynamics(CFD) have been developed to predict the flow fields of a vessel but the present study is developed to infer the wake fields on propeller plane by Statistical Fluid Dynamics(SFD) approach which is emerging as a new technique over a wide range of industrial fields nowadays. Neural network is well known as one prospective representative of the SFD tool and is widely applied even in the engineering fields. Further to its stable and effective system structure, generalization of input training patterns into different classification or categorization in training can offer more systematic treatments of input part and more reliable result. Because neural network has an ability to learn the knowledge through the external information, it is not necessary to use logical programming and it can flexibly handle the incomplete information which is not easy to make a definition clear. Three dimensional stern hull forms and nominal wake values from a model test are structured as processing elements of input and output layer respectively and a neural network is trained by the back-propagation method. The inferred results show similar figures to the experimental wake distribution.