• Design & Manufacturing
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• v.2 no.1
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• pp.11-14
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• 2008

we can save a development cost and time as computer was used in tool and die design of car fields in die manufacture process. Dimension accuracy errors such as springback, springgo, overcrown and twist were reduced product accuracy and caused trouble to assembly each parts of car. In this paper, CADCEUS was used to modify and optimize results of deflection for a tail gate panel of car parts in order to reduce dimension accuracy errors by springback in sheet metal forming. As CADCEUS was used to apply for a tail gate panel, the time for quality to improve was reduced to 30%.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.10a
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• pp.338-341
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• 2004

The dimension of forged part is different with the die dimension by the various effects, such as, elastic deformation and thermal effect. And, the difference amounts are not same according to the forging conditions, for example, forging mode, flow stress, etc. Therefore, the use of FEA is effective to predict and update the required die dimension. However, the variables for FE simulation are also as many as variables in the experiment. The variables give very much effect to the accuracy of FE results. At first, the material model is very deeply affected to the estimated dimension of forged part. And the considering of loading and ejecting stages is also important to increase the dimensional accuracy. The experiment and FEA are performed to investigate the dimensional changes and accuracy in cold forging. Two types of upsetting are used to survey the effects of forging mode and stages.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.10a
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• pp.34-38
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• 2003

In metal working, cold forging that has profit to satisfy dimension accuracy is using in various manufacturing products. Recently, most of the interest thing is precision forging of gear. Gear forging product is more strength than broaching gear, and it has many advantages with reduction of factory expenses. The reason of difficulty to improve accuracy of gear dimension compare to another products is the dimension accuracy is very high, approximately 10$\mu\textrm{m}$, and because die of involute teeth and elastic strain of forged tool differ from standard curve. This paper represent quantitative analysis of die and teeth of forged tool, namely difference of curves, with experiments and analyze the factor of dimension gap, finally, will design compensated involute curve.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.05a
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• pp.44-48
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• 2003

In metal working, cold forging that has profit to satisfy dimension accuracy is using in various manufacturing products. Recently, most of the interest thing is precision forging of gear, Gear forging product is more strength than broaching gear, and it has many advantages with reduction of factory expenses. The reason of difficulty to improve accuracy of gear dimension compare to another products is the dimension accuracy is very high, approximately 10$\mu\textrm{m}$, and because die of involute teeth and elastic strain of forged tool differ from standard curve. This paper represent quantitative analysis of die and teeth of forged tool, namely difference of curves, with experiments and analyze the factor of dimension gap, finally, will design compensated involute curve.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.213-234
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• 2024

In recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be competitive for datasets with time-series data. Long short term memory (LSTM) further improves RNN by providing an alternative approach to the gradient loss problem. LSTM has its own advantage in predictive accuracy by retaining memory for a longer time. In this paper, we combine both supervised and unsupervised dimension reduction methods with LSTM to enhance the forecasting performance and refer to this as a dimension reduction based LSTM (DR-LSTM) approach. For a supervised dimension reduction method, we use methods such as sliced inverse regression (SIR), sparse SIR, and kernel SIR. Furthermore, principal component analysis (PCA), sparse PCA, and kernel PCA are used as unsupervised dimension reduction methods. Using datasets of real stock market index (S&P 500, STOXX Europe 600, and KOSPI), we present a comparative study on predictive accuracy between six DR-LSTM methods and time series modeling.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.05a
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• pp.129-134
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• 2009

The dimension of forged part is different from that of die. Therefore, a more precise die dimension is necessarys to produce the precise part, considering the dimensional changes from forging die to final part. In this paper, both experimental and FEM analysis are performed to investigate the effect of several features including die dimension at each forging step and heat-treatment on final part accuracy in the closed-die upsetting. The dimension of forged part is checked at each stage as machined die, cold forged, and post-heat-treatment steps. The elastic characteristics and thermal influences on forging stage are analyzed numerically by the DEFORM-$2D^{TM}$. The effect of residual stress after heat-treatment on forged part could be considered successfully by using DEFOAM-$HT^{TM}$.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.269-276
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• 2012

The objective of this short communication is to introduce several evaluation methods to crop model users because the evaluation of crop model performance is an important step to develop or select crop model. In this paper, mean error, mean absolute error, index of agreement, root mean square error, efficiency of model, accuracy factor and bias factor were explained and compared in terms of dimension and observed number. Efficiency of model and index of agreement are dimensionless and independent of number of observation. Relative root mean square, accuracy factor and bias factor are dimensionless and not independent of number of observation. Mean error and mean absolute error are affected by dimension and number of observation.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.492-499
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• 2022

This study experimentally investigates the effect of dimensionality reduction of vibration signal on fault diagnosis of a marine engine. By using the principal component analysis, a vibration signal having the dimension of 513 is converted into a low-dimensional signal having the dimension of 1 to 15, and the variation in fault diagnosis accuracy according to the dimensionality change is observed. The vibration signal measured from a full-scale marine generator diesel engine is used, and the contribution of the dimension-reduced signal is quantitatively evaluated using two kinds of variable importance analysis algorithms which are the integrated gradients and the feature permutation methods. As a result of experimental data analysis, the accuracy of the fault diagnosis is shown to improve as the number of dimensions used increases, and when the dimension approaches 10, near-perfect fault classification accuracy is achieved. This shows that the dimension of the vibration signal can be considerably reduced without degrading fault diagnosis accuracy. In the variable importance analysis, the dimension-reduced principal components show higher contribution than the conventional statistical features, which supports the effectiveness of the dimension-reduced signals on fault diagnosis.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.80-87
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• 2024

This paper focuses on improving accuracy in constrained computing settings by employing the ReLU (Rectified Linear Unit) activation function. The research conducted involves modifying parameters of the ReLU function and comparing performance in terms of accuracy and computational time. This paper specifically focuses on optimizing ReLU in the context of a Multilayer Perceptron (MLP) by determining the ideal values for features such as the dimensions of the linear layers and the learning rate (Ir). In order to optimize performance, the paper experiments with adjusting parameters like the size dimensions of linear layers and Ir values to induce the best performance outcomes. The experimental results show that using ReLU alone yielded the highest accuracy of 96.7% when the dimension sizes were 30 - 10 and the Ir value was 1. When combining ReLU with the Adam optimizer, the optimal model configuration had dimension sizes of 60 - 40 - 10, and an Ir value of 0.001, which resulted in the highest accuracy of 97.07%.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.10b
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• pp.26-30
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• 2003

Since the dimension of cold forged part is larger than the cavity size of forging die, the difference results from the various features, such as, the elastic characteristics of die and workpiece, thermal influences, and machine-elasticity. All of these factors should be considered to get more accurate prediction of the dimension of forged part. In this paper, severe FE techniques are proposed to improve the prediction accuracy of dimension for cold forged part. To validate the importance of the above mentioned factors, and the estimated results are compared with the experimental results. The used model is a closed die upsetting of cylindrical billet. The calculated dimensions are well coincided with .the measured values based on the proposed techniques. The proposed techniques have put two simple but important points into Fe simulation. One is the separation of forging stages into 3 steps, from a loading through punch retraction to ejecting stage. The other is the dimensional change, according to the temperature changes due to the deformation. The FE analysis could predict the dimension of cold forged part within the $10{\mu}m$, based on the more realistic consideration.