• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.6
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• pp.1461-1470
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• 2016

In side channel analysis, signal processing techniques can be used as preprocessing to enhance the efficiency and performance of analysis by reducing the noise or compressing the dimension. As signal processing techiniques using singular value decomposition can increase the information of main signal and reduce the noise by using the variance and tendency of signal, it is a great help to improve the performance of analysis. Typical techniques of that are PCA(Principal Component Analysis), LDA(Linear Discriminant Analysis) and SSA(Singular Spectrum Analysis). PCA and LDA can compress the dimension with increasing the information of main signal, and SSA reduces the noise by decomposing the signal into main siganl and noise. When applying each one or combination of these techniques, it is necessary to compare the performance. Therefore, it needs to suggest methodology of that. In this paper, we compare the performance of the three technique and propose using Sinal-to-Noise Ratio(SNR) as the methodology. Through the proposed methodology and various experiments, we confirm the performance and efficiency of each technique. This will provide useful information to many researchers in the field of side channel analysis.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.27-32
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• 1997

Continual monitoring of abnormal operating conditions i a key issue in maintaining high product quality and safe operation, since the undetected process abnormality may lead to the undesirable operations, finally producing low quality products, or breakdown of equipment. The statistical projection method recently highlighted has the advantage of easily building reference model with the historical measurement data in the statistically in-control state and not requiring any detailed mathematical model or knowledge-base of process. As the complexity of process increases, however, we have more measurement variables and recycle streams. This situation may not only result in the frequent occurrence of process Perturbation, but make it difficult to pinpoint trouble-making causes or at most assignable source unit due to the confusing candidates. Consequently, an ad hoc skill to monitor and diagnose in plat-wide scale is needed. In this paper, we propose a hierarchical plant-wide monitoring methodology based on hierarchical decomposition and principal component analysis for handling the complexity and interactions among process units. This have the effect of preventing special events in a specific sub-block from propagating to other sub-blocks or at least delaying the transfer of undesired state, and so make it possible to quickly detect and diagnose the process malfunctions. To prove the performance of the proposed methodology, we simulate the Tennessee Eastman benchmark process which is operated continuously with 41 measurement variables of five major units. Simulation results have shown that the proposed methodology offers a fast and reliable monitoring and diagnosis for a large scale chemical plant.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.38-46
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• 2012

Atrial fibrillation is needed to be detected at paroxysmal stage and to be treated. But, paroxysmal atrial fibrillation ECG is hardly obtained with 12-lead electrocardiographs but Holter systems. Presently, the averaged beat subtraction(ABS) method is solely used to estimate atrial fibrillatory waves even with somewhat large residual error. As an alternative, in this study, we suggested an ESAF(event-synchronous adaptive filter) based algorithm, in which the AF ECG was treated as a primary input and event-synchronous impulse train(ESIT) as a reference. And, ESIT was generated so to be synchronized with the ventricular activity by detecting QRS complex. We tested proposed algorithm with simulated AF ECGs and real AF ECGs. As results, even with low computational cost, this ESAF based algorithm showed better performance than the ABS method and comparable performance to algorithm based on PCA(principal component analysis) or SVD(singular value decomposition). We also proposed an expanded version of ESAF for some AF ECGs with multi-morphologic ventricular activities and this also showed reasonable performance. Ultimately, with Holter systems including our proposed algorithm, atrial activity signal can be precisely estimated in real-time so that it will be possible to calculate atrial fibrillatory rate and to evaluate the effect of anti-arrhythmic drugs.

• Korean Journal of Environment and Ecology
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• v.25 no.3
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• pp.307-317
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• 2011

Ground beetle fauna of Mt. Bangtaesan in Inje-gun, Gangwon-do was investigated from June to October in 2010. Ground beetles were collected by pitfall trapping. A total of 34 species of 18 genera belonging to 7 families were identified from 1,041 collected ground beetles. Species richness was high in Pterostichinae (15 species, 44.1%), Carabinae (7 species, 20.6%), Harpalinae (4 species, 11.8%), Nebriinae (3 species, 8.8%) and others (5 species, 14.7%). Dominant species were Synuchus spp. (260 individuals, 25.0%), Aulonocarabus semiopacus (218 individuals, 20.9%), and Pterostichus audax (205 individuals, 19.7%) in order. Korean endemic species were collected 564 individuals belonging to 10 species. By principal component analysis (PCA), species distribution and abundance of ground beetles were different depending on habitat characteristics of vegetation, surrounding environment and feeding habitat. Carabinae and Pterostichinae which live in the forest were preferred in hardwoods and coniferous in the Bangtansan Natural Recreation Forest, while Callistinae and Harpalinae were collected in forest adjacent to grasslands. Overall, differences of habitat environments within forest are important factors associated with distribution of ground beetles. This result will provide useful informations with establishment of conservation program and long-term monitoring against environmental change within mountain by using ground beetles.

• Imaging Science in Dentistry
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• v.42 no.3
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• pp.175-182
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• 2012

Purpose: The aim of this study was to provide sex-matched three-dimensional (3D) statistical shape models of the mandible, which would provide cephalometric parameters for 3D treatment planning and cephalometric measurements in orthognathic surgery. Materials and Methods: The subjects used to create the 3D shape models of the mandible included 23 males and 23 females. The mandibles were segmented semi-automatically from 3D facial CT images. Each individual mandible shape was reconstructed as a 3D surface model, which was parameterized to establish correspondence between different individual surfaces. The principal component analysis (PCA) applied to all mandible shapes produced a mean model and characteristic models of variation. The cephalometric parameters were measured directly from the mean models to evaluate the 3D shape models. The means of the measured parameters were compared with those from other conventional studies. The male and female 3D statistical mean models were developed from 23 individual mandibles, respectively. Results: The male and female characteristic shapes of variation produced by PCA showed a large variability included in the individual mandibles. The cephalometric measurements from the developed models were very close to those from some conventional studies. Conclusion: We described the construction of 3D mandibular shape models and presented the application of the 3D mandibular template in cephalometric measurements. Optimal reference models determined from variations produced by PCA could be used for craniofacial patients with various types of skeletal shape.

• The Journal of the Acoustical Society of Korea
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• v.22 no.5
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• pp.375-382
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• 2003

Lip-reading technique based on bimodal is to enhance speech recognition rate in noisy environment. It is most important to detect the correct lip-image. But it is hard to estimate stable performance in dynamic environment, because of many factors to deteriorate Lip-reading's performance. There are illumination change, speaker's pronunciation habit, versatility of lips shape and rotation or size change of lips etc. In this paper, we propose the IIR filtering in time-domain for the stable performance. It is very proper to remove the noise of speech, to enhance performance of recognition by digital filtering in time domain. While the lip-reading technique in whole lip image makes data massive, the Principal Component Analysis of pre-process allows to reduce the data quantify by detection of feature without loss of image information. For the observation performance of speech recognition using only image information, we made an experiment on recognition after choosing 22 words in available car service. We used Hidden Markov Model by speech recognition algorithm to compare this words' recognition performance. As a result, while the recognition rate of lip-reading using PCA is 64%, Time-domain filter applied to lip-reading enhances recognition rate of 72.4%.

• Resources Recycling
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• v.26 no.1
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• pp.69-77
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• 2017

Used small household appliances(small e-waste) consists of a variety of complex materials and components. The small e-waste is mainly composed of plastics and an important potential source of waste plastic. The black plastics, particularly are very difficult to separate by resin type and therefore these are mainly recycled in the form of a mixtures. In the present study, the sorting technologies such as gravity and electro static separation, near-infrared ray(NIR) and IR/Raman optical sorting separation on mixture of black plastics were analyzed and their limitations on sorting process were also investigated. The Laser Induced Breakdown Spectroscopy(LIBS) spectrum of each black plastics was used for identification of black plastics by resin type, and after analyzing the normalization operation, Principal Component Analysis(PCA) was carried out. The spectrum data was optimized through PCA process. In order to improve the identification accuracy and sorting efficiency of black plastics, it is necessary to design a classifier with high efficiency and to improve the performance and reliability of the classifier by applying the field of intelligent algorithms.

• Journal of Pharmaceutical Investigation
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• v.38 no.1
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• pp.23-29
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• 2008

Red ginseng is made of white ginseng through the steaming and drying procedure. In this process, the amounts of toxic elements of ginseng are decreased and those of effective components, ginsenosides are increased. In order to identify the components alteration of white ginseng by processing time, we applied HPLC-based metabolomics approach combined with the principal component analysis (PCA) multivariate analysis. White ginsengs were steamed at 0, 1, 2, 4, 8 and 16 h, respectively and followed by drying process at moderate temperature. Then the steamed ginsengs and the commercial red ginsengs were analyzed by HPLC. On the basis of HPLC results, PCA multivariate analysis was applied for evaluating the quality of red ginseng, which showed the processed ginsengs are grouped by processed time because less polar ginsenosides were increased in proportion as the steaming time was increased. The purchased red ginsengs were distributed in the range of $0{\sim}1$ hour steaming time. This pilot experiment suggests that HPLC-based metabolomics approach is able to allow the quality of herbal medicines to be controlled with a simple and economic method.

• Korean Journal of Ecology and Environment
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• v.43 no.4
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• pp.459-470
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• 2010

In this study, we analyzed fish tolerance guilds in mainstems and tributaries of 65 streams and rivers arid their relations to water quality using dataset sampled from April to November, 2009. For the study, water quality parameters including biochemical oxygen demand (BOD), electric conductivity (EC), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen ($NH_3$-N), nitrate nitrogen ($NO_3$-N) and phosphate phosphorus ($PO_4$-P) were analyzed in the laboratory and also tolerance ranges in 3 category fishes of sensitive, intermediate, and tolerant species with high abundance were analyzed. According to fish guild analysis, tolerant species was 58% of the total community and the proportion of omnivore species was 63% of the total, indicating a degradation of habitats and water quality. Water quality was shown typical longitudinal gradients from the headwater to the down-river; TN and TP increased toward the down-rivers except for the big point-source area and ionic contents, based on, electric conductivity showed same pattern. Tolerance guild analysis of 9 major species with high abundance indicated that sensitive groups had narrower tolerance range in the water quality than the groups of intermediate and tolerant species. In contrast, tolerant groups including Zacco platypus, Carassius auratus, and Opsarichthys uncirostris amurensis had wider tolerance ranges than the groups of sensitive and intermediate species. Thus, each group was evidently segregated from the tolerance levels. Principal Component Analysis (PCA) employed for the relations of water quality to fish species in each groups suggests that water quality had highest eigenvalues with fish species in the 1st axis of the PCA and nitrogen (TN, $NH_3$-N, $NO_3$-N) and phosphorus (TP) were key components differentiating three groups of sensitive, intermediate and tolerance guilds.

• International Journal of Industrial Entomology and Biomaterials
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• v.14 no.1
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• pp.23-32
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• 2007

The nature of genetic variation and diversity among the 65 multivoltine silkworm genotypes was evaluated for 16 post cocoon characters. The components of genetic variation revealed higher PCV (60.487%) and GCV (44.56%) for evenness (variation 1) followed by cohesion (PCV=55.38%, GCV=40.36%) and non-broken filament length (PCV=32.05%, GCV=31.28%). The higher heritability ($h^2$ in broad sense) was observed for boil-off loss (95.6%) followed by non-broken filament length (95.22%). The both genotypic and phenotypic correlation indicated significant positive correlation of filament length with non-broken filament length, silk recovery, raw silk, neatness, and low neatness; and negative correlation with denier, renditta and silk waste. The principal component analysis (PCA) revealed 75.381 % of total variance from the five principal components extracted. On the basis of Mahalonobis' $D^2$ values (Ward's minimum variance), the sixty-five multivoltine silkworm genotypes were classified in to 9 clusters with substantial inter and intra cluster distances. Number of genotypes included in different clusters varied from 3 to 17. The results indicated that the optimum distance obtained in cluster VII (15.059) along with higher cluster mean values especially for filament length, non broken filament length, renditta, silk recovery, silk waste, and raw silk emphasized the utilization of these genotypes in the conventional silkworm breeding programme for improvement of multivoltine silk fibre quality. The possibility of exploiting genetic variation in post cocoon traits for efficient breeding programme is discussed.