• Textile Coloration and Finishing
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• v.20 no.6
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• pp.69-74
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• 2008

In this paper, a comfort evaluation system based on a product-focused process design (PFPD) has been proposed for high comforts interior seat covers. Correlations between comforts properties and physical/thermal properties of interior seat covers were examined by combining traditional regression analysis and data mining techniques. A skin sensorial comfort of leather samples was evaluated by only human tactile sensation. The adjectives of leather car seat covers are 'Soft', 'Sticky' and 'Elastic'. Thermo-physiological comfort properties of leather samples were evaluated by only human tactile sensation. The adjectives of leather car seat covers are 'Coolness to the touch' and 'Thermal and humid'. Skin sensorial comforts of cloth samples were evaluated by only human tactile sensation. The adjectives of cloth car seat covers are 'Soft', 'Smooth', 'Voluminous' and 'Elastic'. Thermo-physiological comforts of cloth samples were evaluated by only human tactile sensation. The adjectives of cloth car seat covers are 'Coolness to the touch' and 'Thermal and humid'.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4367-4385
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• 2016

With millions of people across the globe suffering from Parkinson's disease (PD), an objective, confirmatory test for the same is yet to be developed. This research aims to develop a system which can assist the doctor in objectively saying whether the patient is normal or under risk of PD. The proposed work combines the eye-hand co-ordination behaviour with the DaTscan images in order to determine the risk of this disorder. Initially, eye-hand coordination level of the patient is assessed through a hardware module. Then, the DaTscan image is analysed and used to extract certain geometrical parameters which shall indicate the presence of PD. These parameters are then finally fed into a Multi-Layer Perceptron Neural Network using Levenberg-Marquardt (LM) Back propagation training algorithm. Experimental results indicate that the proposed system exhibits an accuracy of around 93%.

• Near Infrared Analysis
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• v.1 no.1
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• pp.1-7
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• 2000

A convenient algorithm for optimizing wavelength selection in multiple linear regression (MLR) has been developed. MOP (MLP Optimization Program) has been developed to test all possible MLR calibration models in a given spectral range and finally find an optimal MLR model with external validation capability. MOP generates all calibration models from all possible combinations of wavelength, and simultaneously calculates SEC (Standard Error of Calibration) and SEV (Standard Error of Validation) by predicting samples in a validation data set. Finally, with determined SEC and SEV, it calculates another parameter called SAD (Sum of SEC, SEV, and Absolute Difference between SEC and SEV: sum(SEC+SEV+Abs(SEC-SEV)). SAD is an useful parameter to find an optimal calibration model without over-fitting by simultaneously evaluating SEC, SEV, and difference of error between calibration and validation. The calibration model corresponding to the smallest SAD value is chosen as an optimum because the errors in both calibration and validation are minimal as well as similar in scale. To evaluate the capability of MOP, the determination of benzene content in unleaded gasoline has been examined. MOP successfully found the optimal calibration model and showed the better calibration and independent prediction performance compared to conventional MLR calibration.

• BMB Reports
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• v.32 no.6
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• pp.521-528
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• 1999

The folding of recombinant hepatitis B virus X-protein (rHBx) solubilized from Escherichia coli inclusion bodies was investigated. By sequential dialysis of urea, rHBx was folded into its native structure, which was demonstrated by the efficacy of its transcriptional activation of the adenovirus major late promoter (MLP), fluorescence spectroscopy, and circular dichroism (CD) analysis. The decrease in CD values at 220 nm and a corresponding blue shift of the intrinsic fluorescence emission confirmed the ability of rHBx to refold in lower concentrations of urea, yielding the active protein. Equilibrium and kinetic studies of the refolding of rHBx were carried out by tryptophan fluorescence measurements. From the biphasic nature of the fluorescence curves, the existence of stable intermediate states in the renaturation process was inferred. Reverse phase-high performance liquid chromatography (RP-HPLC) analysis further demonstrated the existence of these intermediates and their apparent compactness.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.735-742
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• 2004

We present a new use of common graphics hardware to perform a faster artificial neural network. And we examine the use of GPU enhances the time performance of the image processing system using neural network, In the case of parallel computation of multiple input sets, the vector-matrix products become matrix-matrix multiplications. As a result, we can fully utilize the parallelism of GPU. Sigmoid operation and bias term addition are also implemented using pixel shader on GPU. Our preliminary result shows a performance enhancement of about thirty times faster using ATI RADEON 9800 XT board.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.2
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• pp.147-161
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• 2002

In novelty detection, one attempts to discriminate abnormal patterns from normal ones. Novelty detection is quite difficult since, unlike usual two class classification problems, only normal patterns are available for training. Auto-Associative Multi-Layer Perceptron (AAMLP) has been shown to provide a good performance based upon the property that novel patterns usually have larger auto-associative errors. In this paper, we give a mathematical analysis of 2-layer AAMLP's output characteristics and empirical results of 2-layer and 4-layer AAMLPs. Various activation functions such as linear, saturated linear and sigmoid are compared. The 2-layer AAMLPs cannot identify non-linear boundaries while the 4-layer ones can. When the data distribution is multi-modal, then an ensemble of AAMLPs, each of which is trained with pre-clustered data is required. This paper contributes to understanding of AAMLP networks and leads to practical recommendations regarding its use.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2365-2370
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• 2010

The method of cancer classification based on microarray could contribute to being accurate cancer classification by finding differently expressing gene pattern statistically according to a cancer type. Therefore, the process to select a closely related informative gene with a particular cancer classification to classify cancer using present microarray technology with effect is essential. In this paper, the system can detect marker genes to likely express the most differentially explaining the effects of cancer using ovarian cancer microarray data. And it compare and analyze a performance of classification of the proposed system with it of established microarray system using multi-perceptron neural network layer. Microarray data set including marker gene that are selected using ANOVA method represent the highest classification accuracy of 98.61%, which show that it improve classification performance than established microarray system.

• Journal of KIISE
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• v.42 no.6
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• pp.723-733
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• 2015

The OTT service that provides multimedia video has spread over the Internet for terminals with a variety of resolutions. The terminals are in communication via a networks such as 3G, LTE, VDSL, ADSL. The service of the network has been increased for a variety of terminals giving rise to the need for a new way of encoding multimedia is increasing. SVC is an encoding technique optimized for OTT services. We proposed an efficient multimedia management system for the SVC encoded multimedia data. The I/O trace was generated using a zipf distribution, and were comparatively evaluated for performance with the existing system.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.5
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• pp.1-10
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• 2016

In this paper, a car model recognition system using image processing and machine learning is proposed and it's performance is also evaluated. The system recognizes the front of car because the front of car is different for every car model and manufacturer, and difficult to remodel. The proposed method extracts HOG features from training data set, then builds classification model by the HOG features. If user takes photo of the front of car, then HOG features are extracted from the photo image and are used to determine the model of car based on the trained classification model. Experimental results show a high average recognition rate of 98%.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.3
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• pp.59-71
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• 2018

Social media has immense popularity among all services today. Data from social network services (SNSs) can be used for various objectives, such as text prediction or sentiment analysis. There is a great deal of Korean and English data on social media that can be used for sentiment analysis, but handling such huge amounts of unstructured data presents a difficult task. Machine learning is needed to handle such huge amounts of data. This research focuses on predicting Korean and English sentiment using deep forward neural network with a deep learning architecture and compares it with other methods, such as LDA MLP and GENSIM, using logistic regression. The research findings indicate an approximately 75% accuracy rate when predicting sentiments using DNN, with a latent Dirichelet allocation (LDA) prediction accuracy rate of approximately 81%, with the corpus being approximately 64% accurate between English and Korean.