• Journal of KIISE
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• v.44 no.10
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• pp.1026-1033
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• 2017

In this paper a Bayesian modeling and duration-based prediction method is proposed for health clinic time series data using the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). HDP-HMM is a Bayesian extension of HMM which can find the optimal number of health states, a number which is highly uncertain and even difficult to estimate under the context of health dynamics. Test results of HDP-HMM using simulated data and real health clinic data have shown interesting modeling behaviors and promising prediction performance over the span of up to five years. The future of health change is uncertain and its prediction is inherently difficult, but experimental results on health clinic data suggests that practical long-term prediction is possible and can be made useful if we present multiple hypotheses given dynamic contexts as defined by HMM states.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.484-492
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• 2016

The High efficiency video coding (HEVC) is the newest video coding standard that achieves coding efficiency higher than previous video coding standards such as H.264/AVC. In intra prediction, the prediction units (PUs) are derived from a large coding unit (LCU) which is partitioned into smaller coding units (CUs) sizing from 8x8 to 64x64 in a quad-tree structure. As they are divided until having the minimum depth, Optimum CU splitting is selected in RDO (Rate Distortion Optimization) process. In this process, HEVC demands high computational complexity. In this paper, to reduce the complexity of HEVC, we propose a fast CU mode decision (FCDD) for intra prediction by using FAST (Features from Accelerated Segment Test) corner detection. The proposed method reduces computational complexity with 53.73% of the computational time for the intra prediction while coding performance degradation with 0.7% BDBR is small compared to conventional HEVC.

• Journal of Applied Reliability
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• v.16 no.2
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• pp.98-103
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• 2016

Purpose: The purpose of this paper is to get a meaningful information for improving manufacturing quality of the products before they are produced in client's manufacturing process. Methods: A variety of data mining techniques have been being used for wide range of industries from process data in manufacturing factories for quality improvement. One application of those is to get meaningful information from process data in manufacturing factories for quality improvement. In this paper, the failure rate at client's manufacturing process is predicted by using the parameters of the characteristics of the product based on PCA (Principle Component Analysis) and regression analysis. Results: Through a case study, we proposed the predicting methodology and regression model. The proposed model is verified through comparing the failure rates of actual data and the estimated value. Conclusion: This study can provide the guidance for predicting the failure rate on the manufacturing process. And the manufacturers can prevent the defects by confirming the factor which affects the failure rate.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1996.10a
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• pp.122-130
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• 1996

During the cold forming, due to high working pressure acting on the die surface, failure mechanics must be considered before die design. One of the main reasons of die failure in industrial application of metal forming technologies is wear. Die wear affects the tolerances of formed parts, metal flow and costs of process etc. The only way to control these failures is to develop methods which allow prediction of die wear and costs of process etc. The only way to control these failures is to develop methods which allow prediction of die wear and which are suited to be used in the design state in order to optimize the process. In this paper, the wear experiments to abtain the wear coefficients and the upsetting processes was accomplished to observe the wear phenomenon during the cold forming process. The analysis of upsetting processes was accomplished to observe the wear phenomenon during the cold forming process. The analysis of upsetting processes was accomplished by the rigid-plastic finite element method. The result from the deformation analysis was used to analyse the die wear during the processes and the predicted die wear profiles were compared with the measured die wear profiles.

• Journal of Welding and Joining
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• v.32 no.1
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• pp.53-60
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• 2014

The automatic arc welding is generally accepted as the preferred joining technique and commonly chosen for assembly of large metal structures such as in areas of automotive, aircraft and shipbuilding due to its joint strength, reliability, and low cost compared to other joint processes. Recently, several mathematical models have been developed and studied for control and monitoring welding quality, productivity, microstructure and weld properties in arc welding processes. This study indicates the prediction of process parameters for the expected welding quality with accordance to the adaptive GTA welding process. Furthermore, the mathematical models is also develop to aid the selection of an optimal welding process as the generation of process controls to predict the bead geometry as a function output parameters in the GTA welding process. The developed models through this study showed comparatively excellent predicted results, and will extend to other welding processes to integrate an optimized system for the robotic welding process.

• Proceedings of the KSRS Conference
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• v.2
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• pp.635-638
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• 2006

Regression of conventional prediction techniques in data mining uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to time series, the rate of prediction accuracy will be decreased. This paper proposes an incremental regression for time series prediction like typhoon track prediction. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of typhoon track prediction experiment are performed by the proposed technique IMLR(Incremental Multiple Linear Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.75-82
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• 2017

Correct prediction of emotion is essential for developing advanced health devices. For this purpose, neural network has been successfully used. However, interpretation of how a certain emotion is predicted through the emotion prediction neural network is very tough. When interpreting mechanism about how emotion is predicted by using the emotion prediction neural network can be developed, such mechanism can be effectively embedded into highly advanced health-care devices. In this sense, this study proposes a novel approach to interpreting how the emotion prediction neural network yields emotion. Our proposed mechanism is based on HRV (heart rate variability) measurements, which is based on calculating physiological data out of ECG (electrocardiogram) measurements. Experiment dataset with 23 qualified participants were used to obtain the seven HRV measurement such as Mean RR, SDNN, RMSSD, VLF, LF, HF, LF/HF. Then emotion prediction neural network was modelled by using the HRV dataset. By applying the proposed mechanism, a set of explicit mathematical functions could be derived, which are clearly and explicitly interpretable. The proposed mechanism was compared with conventional neural network to show validity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.145-148
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• 2016

In this paper, we propose a design of Intra Prediction angular mode decision for high-performance HEVC encoder. Intra Prediction works by performing all 35 modes for efficient encoding. However, in order to process all of the 35 modes, the computational complexity and operational time required are high. Therefore, this paper proposes comparing the difference in the value of the original image pixel, using an algorithm that determines Angular mode efficiently. This new algorithm reduces the Hardware size. The hardware which is proposed was designed using Verilog HDL and was implemented in 65nm technology. Its gate count is 14.9k and operating speed is 2GHz.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.171-176
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• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

• Transactions of Materials Processing
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• v.7 no.2
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• pp.150-157
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• 1998

Recently the SPPC technology is being developed in steel rolling industries for the purpose of enhancing mechanical properties of rolled sheets. The technology is to produce steel sheets with finer and more uniformly distributed grains by prediction of recrystallization behaviors and on-line control of rolling parameters during hot rolling process. In this study a finish rolling process was analyzed by a three-dimensional rigid-thermoviscoplastic finite element method and recrystallization behaviors of several locations in the sheet were predicted by Sellars equations. As a result it was found that the initial grain size of 84 ${\mu}m$ became $21-23\;{\mu}m\;20-22{\mu}m\;and\;18-20{\mu}m$ at front middle and end portions of the sheet respectively. It was also found that variations of the grain size became $$0.6{\sim}2{\mu}m\;and\;10{\mu}\mum$$ in thickness and width directions respectively.