• Journal of Korea Multimedia Society
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• v.14 no.8
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• pp.1010-1019
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• 2011

This was a preliminary research for the goal of understanding between internal structure of Osteogenesis Imperfecta Murine (OIM) bone and its fragility. 54 hyperspectral bone data sets were captured by using JASCO 2000 Raman spectrometer at UMKC-CRISP (University of Missouri-Kansas City Center for Research on Interfacial Structure and Properties). Each data set consists of 1,091 data points from 9 OIM bones. The original captured hyperspectral data sets were noisy and base-lined ones. We removed the noise and corrected the base-lined data for the final efficient classification. High dimensional Raman hyperspectral data on OIM bones was reduced by Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) and efficiently classified for the first time. We confirmed OIM bones could be classified such as strong, middle and weak one by using the coefficients of their PCA or LDA. Through experiment, we investigated the efficiency of classification on the reduced OIM bone data by the Bayesian classifier and K -Nearest Neighbor (K-NN) classifier. As the experimental result, the case of LDA reduction showed higher classification performance than that of PCA reduction in the two classifiers. K-NN classifier represented better classification rate, compared with Bayesian classifier. The classification performance of K-NN was about 92.6% in case of LDA.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.250-255
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• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

• Convergence Security Journal
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• v.23 no.1
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• pp.117-123
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• 2023

To detect advanced ransomware attacks with machine learning-based models, the classification model must train learning data with high-dimensional feature space. And in this case, a 'curse of dimension' phenomenon is likely to occur. Therefore, dimensionality reduction of features must be preceded in order to increase the accuracy of the learning model and improve the execution speed while avoiding the 'curse of dimension' phenomenon. In this paper, we conducted classification of ransomware by applying three machine learning models and two feature extraction techniques to two datasets with extremely different dimensions of feature space. As a result of the experiment, the feature dimensionality reduction techniques did not significantly affect the performance improvement in binary classification, and it was the same even when the dimension of featurespace was small in multi-class clasification. However, when the dataset had high-dimensional feature space, LDA(Linear Discriminant Analysis) showed quite excellent performance.

• Communications for Statistical Applications and Methods
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• v.30 no.1
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• pp.75-94
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• 2023

The growing trend of cyber risk has put forward the importance of cyber risk management. Cyber risk is defined as an accidental or intentional risk related to information and technology assets. Although cyber risk is a subset of operational risk, it is reported to be handled differently from operational risk due to its different features of the loss distribution. In this study, we aim to detect the characteristics of cyber loss and find a suitable model by measuring value at risk (VaR). We use the loss distribution approach (LDA) and the time series model to describe cyber losses of financial and non-financial business sectors, provided in SAS^® OpRisk Global Data. Peaks over threshold (POT) method is also incorporated to improve the risk measurement. For the financial sector, the LDA and GARCH model with POT perform better than those without POT, respectively. The same result is obtained for the non-financial sector, although the differences are not significant. We also build a two-dimensional model reflecting the dependence structure between financial and non-financial sectors through a bivariate copula and check the model adequacy through VaR.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.170-185
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• 2023

The advent of big data has brought about the need for analytics. Natural language processing (NLP), a field of big data, has received a lot of attention. Topic modeling among NLP is widely applied to identify key topics in various academic journals. The Korean Society of Industrial and Systems Engineering (KSIE) has published academic journals since 1978. To enhance its status, it is imperative to recognize the diversity of research domains. We have already discovered eight major research topics for papers published by KSIE from 1978 to 1999. As a follow-up study, we aim to identify major topics of research papers published in KSIE from 2000 to 2022. We performed topic modeling on 1,742 research papers during this period by using LDA and BERTopic which has recently attracted attention. BERTopic outperformed LDA by providing a set of coherent topic keywords that can effectively distinguish 36 topics found out this study. In terms of visualization techniques, pyLDAvis presented better two-dimensional scatter plots for the intertopic distance map than BERTopic. However, BERTopic provided much more diverse visualization methods to explore the relevance of 36 topics. BERTopic was also able to classify hot and cold topics by presenting 'topic over time' graphs that can identify topic trends over time.

• International Journal of Naval Architecture and Ocean Engineering
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• v.5 no.1
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• pp.81-100
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• 2013

During the final quarter of the last century considerable efforts have been spent to reduce the hull pressure fluctuations caused by unsteady propeller cavitation. This has resulted in further changes in propeller design characteristics including increased skew, tip unloading and introduction of "New Blade Sections" (NBS) designed on the basis of the so-called Eppler code. An experimental study was carried out to investigate flow characteristics of alternative two-dimensional (2-D) blade sections of rectangular planform, one of which was the New Blade Section (NBS) developed in Newcastle University and other was based on the well-known National Advisory Committee for Aeronautics (NACA) section. The experiments comprised the cavitation observations and the measurements of the local velocity distribution around the blade sections by using a 2-D Laser Doppler Anemometry (LDA) system. Analysis of the cavitation tests demonstrated that the two blade sections presented very similar bucket shapes with virtually no width at the bottom but relatively favourable buckets arms at the suction and pressure sides for the NACA section. Similarly, pressure analysis of the sections displayed a slightly larger value for the NBS pressure peak. The comparative overall pressure distributions around the sections suggested that the NBS might be more susceptible to cavitation than the NACA section. This can be closely related to the fundamental shape of the NBS with very fine leading edge. Therefore a further investigation into the modification of the leading edge should be considered to improve the cavitation behaviour of the NBS.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.8
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• pp.1552-1565
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• 1992

Three dimensional numerical calculations were carried out for two different combustion chambers with the offset valve in order to investigate the swirl and the squish effects on the flow fields. The modified K-.epsilon. turbulence model considering the change of the density under the condition of the rapid compression and expansion of the pistion was used. During the compression process, it was found that the squish flow which controls the subsequent combustion process was produced due to the piston bowl in the bowl piston type combustion chambers but not for the flat piston type. The swirl velocity close to the solid body rotation was maintained in the flat piston type combustion chambers, but for the bowl piston type a resulting from the change of the solid body rotation was generated in the radial-circumferential plane. For the swirl ratio effect, as the swirl ratio increases, it was found that a large and strong vortex was generated in the radial-circumferential plane of bowl piston type combustion chambers because of the strong inward flows from the combustion chamber wall. These computational results were compared with the results of LDA measurement.