• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1145-1156
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• 2002

We propose a web document transcoding technique that translates existing web pages designed for desktop computers into an appropriate form for hand-held devices connected to the wireless internet. By defining a content block based on a visual separation and using it as a minimum unit for analyzing and converting processes, we can get web pages converted more exactly. We also apply the reallocation of the content block and the generation of new index in order to provide convenient interface without left-right scrolling in small screen devices. These methods, compared with existing ways such as text level summary or partial extraction method, can provide efficient navigation and a full recognition of web documents. To gain those transcoding benefits, we propose the Layout-Forming Tag Analysis Algorithm that analyzes structural tags, which motivate visual separation and the Component Grouping Algorithm that extracts the content block. We also classify and rearrange the content block and generate the new index to produce an appropriate form of web pages for small display devices. We have designed and implemented our transcoding system in a proxy server and evaluated the methods and the algorithms through an analysis of transcoded results. Our transcoding system showed a good result on most of popular web pages that have complicated structures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.728-735
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• 2017

With the proliferation of digital devices, the devices have generated considerable additive white Gaussian noise while acquiring digital images. The most well-known denoising methods focused on eliminating the noise, so detailed components that include image information were removed proportionally while eliminating the image noise. The proposed algorithm provides a method that preserves the details and effectively removes the noise. In this proposed method, the goal is to separate meaningful detail information in image noise environment using the edge strength and edge connectivity. Consequently, even as the noise level increases, it shows denoising results better than the other benchmark methods because proposed method extracts the connected detail component information. In addition, the proposed method effectively eliminated the noise for various noise levels; compared to the benchmark algorithms, the proposed algorithm shows a highly structural similarity index(SSIM) value and peak signal-to-noise ratio(PSNR) value, respectively. As shown the result of high SSIMs, it was confirmed that the SSIMs of the denoising results includes a human visual system(HVS).

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.191-196
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• 2021

This paper proposes to strengthen the technological capabilities of small and medium enterprises on the establishment of a component standardization system and the localization of parts, which is the basis of the marine wind industry. The wind industry is a natural energy industry that countries around the world are paying attention to, and continues to invest and research and development. In particular, most companies are focusing on research and investment in component development, the smallest unit. Therefore, it is believed that we should focus on the three most fundamental and underlying wind industry, an eco-friendly energy industry that could determine the fate of the nation in the future. First, an understanding of the roadmap for standardization should be prioritized. Second, it is necessary to establish a domestic standardization of international standards according to domestic conditions. Third, localization of high value-added single products and components should be achieved by lowering dependence on overseas imports. In the future, it is hoped that the wind industry, centered on small and medium-sized enterprises, will become a solid-based national industry and be completed as a national infrastructure leading the global wind market.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.1-13
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• 2012

This study aims to analyze actual conditions of the present national defense information network operation, the structure and management of the system, communication lines, security equipments for the lines, the management of network and software, stored data and transferred data and even general vulnerable factors of our army tactical C4I system. Out of them, by carrying out an extensive analysis of the army tactical C4I system, likely to be the core of future information warfare, this study suggested plans adaptive to better information security, based on the vulnerable factors provided. Firstly, by suggesting various information security factor technologies, such as VPN (virtual private network), IPDS (intrusion prevention & detection system) and firewall system against virus and malicious software as well as security operation systems and validation programs, this study provided plans to improve the network, hardware (computer security), communication lines (communication security). Secondly, to prepare against hacking warfare which has been a social issue recently, this study suggested plans to establish countermeasures to increase the efficiency of the army tactical C4I system by investigating possible threats through an analysis of hacking techniques. Thirdly, to establish a more rational and efficient national defense information security system, this study provided a foundation by suggesting several priority factors, such as information security-related institutions and regulations and organization alignment and supplementation. On the basis of the results above, this study came to the following conclusion. To establish a successful information security system, it is essential to compose and operate an efficient 'Integrated Security System' that can detect and promptly cope with intrusion behaviors in real time through various different-type security systems and sustain the component information properly by analyzing intrusion-related information.

• Journal of Plant Biotechnology
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• v.43 no.2
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• pp.213-222
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• 2016

This study aims to establish a system for the rapid discrimination of Zoysia species using metabolite fingerprinting of FT-IR spectroscopy combined with multivariate analysis. Whole cell extracts from leaves of 19 identified Zoysia japonica, 6 identified Zoysia sinica, and 38 different unidentified Zoysia species were subjected to Fourier transform infrared spectroscopy (FT-IR). PCA (principle component analysis) and PLS-DA (partial least square discriminant analysis) from FT-IR spectral data successfully divided the 25 identified turf grasses into two groups, representing good agreement with species identification using molecular markers. PC (principal component) loading values show that the $1,100{\sim}950cm^{-1}$ region of the FT-IR spectra are important for the discrimination of Zoysia species. A dendrogram based on hierarchical clustering analysis (HCA) from the PCA and PLS-DA data of turf grasses showed that turf grass samples were divided into Zoysia japonica and Zoysia sinica in a species-dependent manner. PCA and PLS-DA from FT-IR spectral data of Zoysia species identified and unidentified by molecular markers successfully divided the 49 turf grasses into Z. japonica and Z. sinica. In particular, PLS-DA and the HCA dendrogram could mostly discriminate the 47 Z. japonica grasses into two groups depending on their origins (mountainous areas and island area). Considering these results, we suggest that FT-IR fingerprinting combined with multivariate analysis could be applied to discriminate between Zoysia species as well as their geographical origins of various Zoysia species.

• Clean Technology
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• v.27 no.1
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• pp.9-16
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• 2021

Since the active matrix organic light-emitting diode (AMOLED) encapsulation process is very vulnerable to moisture and oxygen, high-purity nitrogen with minimal moisture and oxygen must be used. In this study, a copper-based catalyst used to remove oxygen from nitrogen in the AMOLED encapsulation process was optimized. Two-component and three-component catalysts composed of CuO, Al2O3, or ZnO were prepared through a co-precipitation method. The prepared catalysts were characterized by using BET, XRD, TPR, and XRF analysis. In order to verify the oxygen removal performance of the catalyst, several catalytic reactions were conducted in a fixed bed reactor, and the corresponding oxygen contents were measured through an oxygen analyzer. In addition, reusability of the catalysts was proven through repetitive regeneration. The properties and oxygen removal capacity of the catalysts prepared with CuO and Al2O3 ratios of 6 : 4, 7 : 3, and 8 : 2 were compared. The number of active sites of the catalyst with a ratio of CuO and Al2O3 of 8 : 2 was the highest among the 2-component catalysts. Moreover, the reducibility of the catalyst with a ratio of CuO and Al2O3 of 8 : 2 was the best as it had the highest CuO dispersion. As a result, the oxygen removal ability of the catalyst with a ratio of CuO and Al2O3 of 8 : 2 was the best among the 2-component catalysts. The best oxygen removal capacity was obtained when 2wt% of ZnO was added to the sub-optimized catalyst (i.e., CuO : Al2O3 = 8 : 2) probably due to its outstanding reducibility. Furthermore, the optimized catalyst kept its performance during a couple of regeneration tests.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.37-52
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• 2011

Supply chain management is a strategic thinking which enhances the value of supply chain and adapts more promptly for the changing environment. For the seamless partnership and value creation in supply chains, information and knowledge sharing and proper partner selection criteria must be applied. Thus, the partner selection criteria are critical to maintain product quality and reliability. Each part of a product is supplied by an appropriate supply partner. The criteria for selecting partners are technological capability, quality, price, consistency, etc. In reality, the criteria for partner selection may change according to the characteristics of the components. When the part is a core component, quality factor is the top priority compared to the price. For a standardized component, lower price has a higher priority. Sometimes, unexpected case occurs such as emergency order in which the preference may shift on the top. Thus, SCM partner selection criteria must be determined dynamically according to the characteristics of part and its context. The purpose of this research is to develop an OWL model for the supply chain partnership depending on its context and characteristics of the parts. The uncertainty of variable is tackled through fuzzy logic. The parts with preference of numerical value and context are represented using OWL. Part preference is converted into fuzzy membership function using fuzzy logic. For the ontology reasoning, SWRL (Semantic Web Rule Language) is applied. For the implementation of proposed model, starter motor of an automobile is adopted. After the fuzzy ontology is constructed, the process of selecting preference-based supply partner for each part is presented.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.

• Journal of KIISE:Software and Applications
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• v.35 no.2
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• pp.70-80
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• 2008

Today, businesses have to respond with flexibility and speed to ever-changing customer demand and market opportunities. Service-oriented architecture (SOA) is the best methodology for minimizing the complexity and the cost of enterprise-level infrastructure and for maximizing the productivity and the flexibility of an enterprise. Most of the enterprise-level SOA delivery strategies deal with the top-down approach, which organization has to define the business processes, to model business services, and to find the required services or to develop new services. However, a lot of peoples want to maximally reuse legacy component-based systems as well as to deliver SOA into their organizations. In this paper, we propose a bottom-up approach for identifying business services with proper granularity. It can improve the reusability and maintenance of services by considering not data I/O of components of legacy applications but GUI event patterns. Our proposed method is applied to MIS with 129 GUIs and 13 components. As a result, the valiance of the coupling value of components is increased five times and three business services are distinctly exposed. It also provides a 49% improvement in reducing the relationship problems between services over a service identification method using only partitioning information of components.