• Asian-Australasian Journal of Animal Sciences
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• v.15 no.4
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• pp.591-595
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• 2002

Three hundred thirty five progeny testing steers of Korean beef cattle were evaluated ultrasonically for back fat thickness (BFT), longissimus muscle area (LMA) and intramuscular fat (IF) before slaughter. Class measurements associated with the Korean yield grade and quality grade were also obtained. Residual standard deviation between ultrasonic estimates and carcass measurements of BFT, LMA were 1.49 mm and $0.96cm^2$. The linear correlation coefficients (p<0.01) between ultrasonic estimates and carcass measurements of BFT, LMA and IF were 0.75, 0.57 and 0.67, respectively. Results for improving predictions of yield grade by four methods-the Korean yield grade index equation, fat depth alone, regression and decision tree methods were 75.4%, 79.6%, 64.3% and 81.4%, respectively. We conclude that the decision tree method can easily predict yield grade and is also useful for increasing prediction accuracy rate.

• Journal of Information Technology Services
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• v.10 no.2
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• pp.235-245
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• 2011

Early criticality prediction models that determine whether a design entity is fault-prone or not are becoming more and more important as software development projects are getting larger. Effective predictions can reduce the system development cost and improve software quality by identifying trouble-spots at early phases and proper allocation of effort and resources. Many prediction models have been proposed using statistical and machine learning methods. This paper builds a prediction model using Support Vector Machine(SVM) which is one of the most popular modern classification methods and compares its prediction performance with a well-known prediction model, BackPropagation neural network Model(BPM). SVM is known to generalize well even in high dimensional spaces under small training data conditions. In prediction performance evaluation experiments, dimensionality reduction techniques for data set are not used because the dimension of input data is too small. Experimental results show that the prediction performance of SVM model is slightly better than that of BPM and polynomial kernel function achieves better performance than other SVM kernel functions.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.16-19
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• 2002

Random and/or periodic defects occur in all spun yarns. These irregularities can often lead to defects in finished fabric. Yarn evenness tests are used to obtain statistical data about yarn properties, such as CV%, which is useful in comparing several sets of similar data that differ in mean value but may have some commonality in relative variation. Although this statistical data is helpful in determining relative yarn Quality, accurate predictions of how the yarn will appear in fabric form are still difficult to obtain. As an promising alterative, wavelet analysis has been employed to localize yam defect so as to predict the visual qualifies of the fabrics.

• Science of Emotion and Sensibility
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• v.5 no.3
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• pp.35-38
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• 2002

Random and/or periodic defects occur in all spun yarns. These irregularities can often lead to defects in finished fabric. Yarn evenness tests are used to obtain statistical data about yarn properties, such as CV%, which is useful in comparing several sets of similar data that differ in mean value but may have some commonality in relative variation. Although this statistical data is helpful in determining relative yam quality, accurate predictions of how the yarn will appear in fabric form are still difficult to obtain. As an promising alterative, wavelet analysis has been employed to localize yarn defect so as to predict the visual qualities of the fabrics.

• Journal of Environmental Health Sciences
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• v.28 no.2
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• pp.61-69
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• 2002

Oship stream is located nearby south eastern coasts. This study was performed to find out waters quality modeling and then to predict water quality of Oship stream. Based on survey data, BOD, T-N, T-P calibration and verification result were in good agreement with measured value within mean coefficient variance(MSE) value, which were 13.9%, 9.0%, 26.5% and 19.5%, 12.0%, 16.5%, respectively. Sectional water quality predictions of the main stream of Oship stream are executed on the basis of the following cases 1) with sewage treatment of Dogye-eup 2) reduction of mine wastewater treatment of 80% in th basin. As a result, BOD, T-P improvement rates at down stream of Oship stream, case 1) were appeared 12.2%, 22.2%, case 2) maximum sulfate ion and conductivity reduction removal rate of Oship stream were 58%, 68%. The main pollution sources of Oship-stream were almost domestic wastewater and mine wastewater discharged from Dogye-eup which located in uppers stream. The large effects will appear after the construction of Dogye sewage water treatment plant which remove the organic matter and nutrients in these sewage water. The waste water from mine can not easily to treat for characteristics of effluence and economic problems. However, to achieve the goal of water quality in Oship-stream water system, treatments of those are necessary.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.558-573
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• 2024

Considering factors such as illumination, camera quality variations, and background-specific variations, identifying a face using a smartphone-based facial image capture application is challenging. Face Image Quality Assessment refers to the process of taking a face image as input and producing some form of "quality" estimate as an output. Typically, quality assessment techniques use deep learning methods to categorize images. The models used in deep learning are shown as black boxes. This raises the question of the trustworthiness of the models. Several explainability techniques have gained importance in building this trust. Explainability techniques provide visual evidence of the active regions within an image on which the deep learning model makes a prediction. Here, we developed a technique for reliable prediction of facial images before medical analysis and security operations. A combination of gradient-weighted class activation mapping and local interpretable model-agnostic explanations were used to explain the model. This approach has been implemented in the preselection of facial images for skin feature extraction, which is important in critical medical science applications. We demonstrate that the use of combined explanations provides better visual explanations for the model, where both the saliency map and perturbation-based explainability techniques verify predictions.

• 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.

• Journal of Biosystems Engineering
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• v.34 no.1
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• pp.37-43
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• 2009

Hyperspectral reflectance and fluorescence scattering have been researched recently for measuring fruit post-harvest quality and condition. And they are promising for nondestructive detection of fruit quality. The objective of this research was to develop a model, which measure the quality of apple by using hyperspectral reflectance and fluorescence. A violet laser (408 nm) and a quartz tungsten halogen light were used as light sources for generating laser induced fluorescence and reflectance scattering in apples, respectively. The laser induced fluorescence and reflectance of 'Golden Delicious' apples were measured by using a hyperspectral imaging system. Fruit firmness, soluble solids and acid content were measured using standard destructive methods. Principal component analyses were performed to extract critical information from both hyperspectral reflectance and fluorescence data and this information was then related to fruit quality indexes. The fluorescence models had poorer predictions of the three quality indexes than the reflectance models. However, the prediction models of integrating fluorescence and reflectance performed consistently better than the individual models of either reflectance or fluorescence. The correlation coefficient for fruit firmness, soluble solid content, and tillable acidity from the integrated model was 0.86, 0.75, and 0.66 respectively. Also the standard errors were 6.97 N, 1.05%, and 0.07% respectively.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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• pp.77-81
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• 1999

The predictions of metal flow forging load optimal die angle and preform size are not so easy in turbine blade forging. First of all the quality of final product is influenced by side force which is one of the significant factors. in this study slab method is applied to determine optimal die angle minimizing side force and the position of preform Finally drawing of die design is obtained in optimal die angle with developing tool that is composed of Visual Basic.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.25-30
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• 2018

This paper is about wine quality classification with multilayer perceptron using the deep neural network. Wine complexity is an issue when predicting the quality. And the deep neural network is considered when using complex dataset. Wine Producers always aim high to get the highest possible quality. They are working on how to achieve the best results with minimum cost and efforts. Deep learning is the possible solution for them. It can help them to understand the pattern and predictions. Although there have been past researchers, which shows how artificial neural network or data mining can be used with different techniques, in this paper, rather not focusing on various techniques, we evaluate how a deep learning model predicts for the quality using two different activation functions. It will help wine producers to decide, how to lead their business with deep learning. Prediction performance could change tremendously with different models and techniques used. There are many factors, which, impact the quality of the wine. Therefore, it is a good idea to use best features for prediction. However, it could also be a good idea to test this dataset without separating these features. It means we use all features so that the system can consider all the feature. In the experiment, due to the limited data set and limited features provided, it was not possible for a system to choose the effective features.