• Genomics & Informatics
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• v.18 no.1
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• pp.11.1-11.3
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• 2020

In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.33-36
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• 2003

Principal component analysis (PCA) was used to improve image classification by the unsupervised classification techniques, the K-means. To do this, I selected a Landsat TM scene of Jeju Island, Korea and proposed two methods for PCA： unstandardized PCA (UPCA) and standardized PCA (SPCA). The estimated accuracy of the image classification of Jeju area was computed by error matrix. The error matrix was derived from three unsupervised classification methods. Error matrices indicated that classifications done on the first three principal components for UPCA and SPCA of the scene were more accurate than those done on the seven bands of TM data and that also the results of UPCA and SPCA were better than those of the raw Landsat TM data. The classification of TM data by the K-means algorithm was particularly poor at distinguishing different land covers on the island. From the classification results, we also found that the principal component based classifications had characteristics independent of the unsupervised techniques (numerical algorithms) while the TM data based classifications were very dependent upon the techniques. This means that PCA data has uniform characteristics for image classification that are less affected by choice of classification scheme. In the results, we also found that UPCA results are better than SPCA since UPCA has wider range of digital number of an image.

• Journal of Arbitration Studies
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• v.33 no.2
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• pp.109-137
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• 2023

This paper reviews legal framework, characteristics and main contents of the 'Optional Rules for the Arbitration of Disputes Relating to Outer Space Activity' enacted by the Permanent Court of Arbitration (PCA) in 2011. Space activities, which began in the 1950s, are undergoing significant changes according to the international characteristics and development of science and technology. New Space and the space business will be the key factors driving these changes. However, the diversity of disputes caused by New Space space activities and the characteristics of each type of dispute must be considered together. This is because the space business can be maintained and developed by securing the effectiveness of dispute resolution. This paper identifies that the PCA Space Dispute Arbitration Rules have important legislative and policy significance in this respect. Specifically, in this paper, the international space law system, the draft convention of the International Law Association, and the PCA arbitration rules were introduced in an overview of the international dispute settlement system related to space activities. Afterwards, it examines that the systematic structure and some major contents of the PCA Space Dispute Arbitration Rules in detail. Based on this, the paper suggests some points of application of the PCA Arbitration Rules and the legislative policy implications.

• Clinics in Shoulder and Elbow
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• v.18 no.4
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• pp.237-241
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• 2015

Background: We investigated the effectiveness of pain management and the adverse events of intravenous (IV) patient-controlled analgesia (PCA) after orthopedic surgery. Methods: From September 2014 and August 2015, we performed a retrospective analysis of 77 patients who underwent orthopedic surgery of the shoulder or the elbow in our hospital. The composition of the intravenous PCA administered to the patients was as follows: 250 mg of dexketoprofen trametamol, 70 mg of oxycodone, and 0.6 mg of ramosetron, which were made up to 79 ml of normal saline. We evaluated and statistically analyzed the difference in the visual analogue scale (VAS) scores for pain at immediate postoperation, at 24 hours of PCA, at 72 hours of PCA, and after discontinuation of PCA and in the incidence of adverse events. Results: We found that VAS score decreased for 3 postoperative days and that with discontinuation of IV PCA a meaningful change in VAS score was no longer seen. Of the 77 patients, 22 presented with adverse events (28.6%). We terminated IV PCA temporarily in the 21 patients who presented with adverse events; we terminated analgesia permanently in one patient (1.2%). Consequently, 76 of 77 patients carried out IV PCA till the designated period. Conclusions: Intravenous PCA after orthopedic surgery of the shoulder or the elbow may be accompanied with adverse events. Careful assessment of the patient and treatment of the adverse outcomes are key to a successful maintenance of PCA and to a successful management of postoperative pain.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.922-929
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• 2005

In this paper, we propose the MRS p:Ittern classification techniques by the fusion scheme based on the SpPCA and MLP. A conventional PCA teclulique for the dimension reduction has the problem that it can't find a optimal transformation matrix if the property of input data is nonlinear. To overcome this drawback we extract features by the SpPCA technique which use the local patterns rather than whole patterns. In a next classification step, individual classifier based on MLP calculates the similarity of each class for local features. Finally, MRS patterns is classified by the fusion scheme to effectively combine the individual information. As the simulation results to verify the effectiveness, the proposed method showed more improved classification results than conventional methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.383-388
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• 2006

SBR is one of the most general sewage/wastewater treatment processes and, particularly, has an advantage in high concentration wastewater treatment like sewage wastewater. A Kernel PCA based fault diagnosis system for biological reaction in full-scale wastewater treatment plant was proposed using only common bio-chemical sensors such as ORP(Oxidation-Reduction Potential) and DO(Dissolved Oxygen). During the SBR operation, the operation status could be divided into normal status and abnormal status such as controller malfunction, influent disturbance and instrumental trouble. For the classification and diagnosis of these statuses, a series of preprocessing, dimension reduction using PCA, LDA, K-PCA and feature reduction was performed. Also, the diagnosis result using differential data was superior to that of raw data, and the fusion data show better results than other data. Also, the results of combination of K-PCA and LDA were better than those of LDA or (PCA+LDA). Finally, the fault recognition rate in case of using only ORP or DO was around maximum 97.03% and the fusion method showed better result of maximum 98.02%.

• The Journal of the Acoustical Society of Korea
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• v.22 no.6
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• pp.452-456
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• 2003

We discuss how to reduce the number of inverse matrix and its dimensions requested in MLLR framework for speaker adaptation. To find a smaller set of variables with less redundancy, we adapt PCA (principal component analysis) and ICA (independent component analysis) that would give as good a representation as possible. The amount of additional computation when PCA or ICA is applied is as small as it can be disregarded. 10 components for ICA and 12 components for PCA represent similar performance with 36 components for ordinary MLLR framework. If dimension of SI model parameter is n, the amount of computation of inverse matrix in MLLR is proportioned to O(n⁴). So, compared with ordinary MLLR, the amount of total computation requested in speaker adaptation is reduced by about 1/81 in MLLR with PCA and 1/167 in MLLR with ICA.

• The Korean Journal of Food And Nutrition
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• v.11 no.2
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• pp.228-236
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• 1998

Egg is an important foods containing many good proteins. But it is well known that egg protein has a lot of allergenicity. The purpose of this study is to develop the methods to reduce the allergenicity of egg. I tried various experimental methods ; For example, heat treatment, irradiation with ultraviolet and microwaves, treatment with polyphosphate, enzyme hydrolysis and PCA inhibition test using guinea pigs and degrees of hydrolysis. The results obtained were as follows ; 1. Heat treatment reduced allergenicity of egg protein. The longer the heat time, the better the effect. 2. Irradiating with ultraviolet and microwave increased both the degree of protein hydrolysis and PCA inhibition reduced the allergenicity. Ultraviolet was more effective than microwaves on egg protein. Fertilized eggs did not reduce allergenicity. 3. Enzyme treatment increased the degree of hydrolysis and PCA inhibition, and reduced allergenicity considerably. Alcalase was more effective than neutrase. 4. Adding polyphosphate did not induced protein hydrolysis, but increased PCA inhibition and reduced allergenicity. 5. The picture of various treatments of egg gel by SEM showed a light surface which indicated that protein was desolved. Neutrase was lighter than alcalase, and the longer the heating time, the lighter the surface became. 6. Measurements of the hardness of egg gel by Instron showed that the longer the reaction time with enzyme, the softer it became.

• Elastomers and Composites
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• v.44 no.4
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• pp.401-407
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• 2009

Commercial grades of solution styrene-butadiene rubbers extended with high aromatic oils having high polycyclic aromatic compounds (PCA) and low PCA oils were used to study the effect of the processing oil particularly on the crack propagation resistance and frictional wear resistance of the vulcanizates. The aromatic oil based vulcanizates exhibited superior fracture behavior over the low PCA oil extended vulcanizates based on tensile and trouser tear tests. Compounds with aromatic oil showed superior crack propagation resistance compared with those containing low PCA oil, especially at the lower ranges of tearing energy. In terms of frictional wear resistance, the aromatic oil extended compounds showed superior performance particularly in the lower frictional work ($W_f$) range but in the higher $W_f$ range the low PCA oil extended vulcanizates performed better.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.213-223
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• 2004

In this paper, we propose a new face detection method for real-time applications. It is based on the template-matching and appearance-based method. At first, we apply Min-max normalization with histogram equalization to the input image according to the variation of intensity. By applying the PCA transform to both the input image and template, PC components are obtained and they are applied to the LDA transform. Then, we estimate the distances between the input image and template, and we select one region which has the smallest distance. SVM is used for final decision whether the candidate face region is a real face or not. Since we detect a face region not the full region but within the $\pm$12 search window, our method shows a good speed and detection rate. Through the experiments with 6 category input videos, our algorithm shows the better performance than the existing methods that use only the PCA transform. and the PCA and LDA transform.