• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1209-1217
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• 2008

In this paper, we present a new bit-parallel multiplier for performing the bit-parallel multiplication of two polynomials in the finite fields $GF(2^m)$. Prior to construct the multiplier circuits, we consist of the vector code generator(VCG) to generate the result of bit-parallel multiplication with one coefficient of a multiplicative polynomial after performing the parallel multiplication of a multiplicand polynomial with a irreducible polynomial. The basic cells of VCG have two AND gates and two XOR gates. Using these VCG, we can obtain the multiplication results performing the bit-parallel multiplication of two polynomials. Extending this process, we show the design of the generalized circuits for degree m and a simple example of constructing the multiplier circuit over finite fields $GF(2^4)$. Also, the presented multiplier is simulated by PSpice. The multiplier presented in this paper use the VCGs with the basic cells repeatedly, and is easy to extend the multiplication of two polynomials in the finite fields with very large degree m, and is suitable to VLSI.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.346-349
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• 2011

In this paper, we design a perceptually invisible dot pattern layout and its recognition scheme, and we apply the recognition scheme into a smart self learning aid for interactive learning aid. To increase maximum information capacity and also increase robustness to the noises, we design a ECC (error correcting code) based dot pattern with directional vector indicator. To make a smart self-learning aid, we embed the micro dot pattern (20 information bit + 15 ECC bits + 9 layout information bit) using K ink (CMYK) and extract the dot pattern using IR (infrared) LED and IR filter based camera, which is embedded in the smart pen. The reason we use K ink is that K ink is a carbon based ink in nature, and carbon is easily recognized with IR even without light. After acquiring IR camera images for the dot patterns, we perform layout adjustment using the 9 layout information bit, and extract 20 information bits from 35 data bits which is composed of 20 information bits and 15 ECC bits. To embed and extract information bits, we use topology based dot pattern recognition scheme which is robust to geometric distortion which is very usual in camera based recognition scheme. Topology based pattern recognition traces next information bit symbols using topological distance measurement from the pivot information bit. We implemented and experimented with sample patterns, and it shows that we can achieve almost 99% recognition for our embedding patterns.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.221-241
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• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Microbiology and Biotechnology Letters
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• v.31 no.2
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• pp.117-123
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• 2003

For screening of autoregulator receptor gene from Saccharopolyspora erythraea, PCR was performed with primers of receptor gene designed on the basis of amino acid sequences of autoregulator receptor proteins with known function. PCR products were subcloned into the BamHI site of pUC19 and transformed into the E. coli DH5$\alpha$. The isolated plasmid from transformant contained the fragment of 120 bp, which was detected on 2% gel after BamHI treatment. The insert, 120 bp PCR product, was confirmed as the expected internal segment of gene encoding autoregulator receptor protein by sequencing. Southern and colony hybridization using Saccha. erythraea chromosomal DNA were performed with the insert as probe. The plasmid (pEsg) having 3.2 kbp SacI DNA fragment from Saccha. erythraea is obtained. The 3.2 kbp SacI DNA fragment was sequenced by the dye terminator sequencing. The nucleotide sequence data was analyzed with GENETYX-WIN (ver 3.2) computer program and DNA database. frame analyses of the nucleotide sequence revealed a gene encoding autoregulator receptor protein which is a region including KpnI and SalI sites on 3.2 kbp SacI DNA fragment. The autoregulator receptor protein consisting of 205 amino acid was named EsgR by author. In comparison with known autoregulator receptor proteins, homology of EsgR showed above 30%.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1496-1509
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• 2005

In this paper, an adaptive license plate recognition system which detects and recognizes license plate at real-time by using projected plane convolution and Decision Tree Classifier is proposed. And it was tested in circumstances which presence of complex background. Generally, in expressway tollgate or gateway of parking lots, it is very difficult to detect and segment license plate because of size, entry angle and noisy problem of vehicles due to CCD camera and road environment. In the proposed algorithm, we suggested to extract license plate candidate region after going through image acquisition process with inputted real-time image, and then to compensate license size as well as gradient of vehicle with change of vehicle entry position. The proposed algorithm can exactly detect license plate using accumulated edge, projected convolution and chain code labeling method. And it also segments letter of license plate using adaptive binary method. And then, it recognizes license plate letter by applying hybrid pattern vector method. Experimental results show that the proposed algorithm can recognize the front and rear direction license plate at real-time in the presence of complex background environments. Accordingly license plate detection rate displayed $98.8\%$ and $96.5\%$ successive rate respectively. And also, from the segmented letters, it shows $97.3\%$ and $96\%$ successive recognition rate respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.11
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• pp.2594-2603
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• 1997

A novel method of adaptive beam forming is presented in this paper. The proposed technique provides for a suboptimal beam pattern that increases the Signal to Noise/Interference Ratio (SNR/SIR), thus, eventually increases the capacity of the communication channel, under an assumption that the desired signal is dominant compared to each component of interferences at the receiver, which is precoditionally achieved in Code Division Multiple Access (CDMA) mobile communications by the chip correlator. The main advantages of the new technique are:(1)The procedure requires neither reference signals nor training period, (2)The signal interchoerency does not affect the performance or complexity of the entire procedure, (3)The number of antennas does not have to be greater than that of the signals of distinct arrival angles, (4)The entire procedure is iterative such that a new suboptimal beam pattern be generated upon the arrival of each new data of which the arrival angle keeps changing due tot he mobility of the signal source, (5)The total amount of computation is tremendously reduced compared to that of most conventional beam forming techniques such that the suboptimal beam pattern be produced at vevery snapshot on a real-time basis. The total computational load for generating a new set of weitht including the update of an N-by-N(N is the number of antenna elements) autocovariance matrix is $0(3N^2 + 12N)$. It can further be reduced down to O(11N) by approximating the matrix with the instantaneous signal vector.

• Food Science and Preservation
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• v.17 no.1
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• pp.16-22
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• 2010

To maintain the storage quality of agricultural products, temperature uniformity during cold storage, which is affected by fan flow rate and product arrangement, is important. We simulated and validated a CFD (Computational Fluid Dynamics) model that can predict both airflow and temperature distribution in a cold storage environment. Computations were based on a commercial code (FLUENT 6.2) and two turbulence models. The standard k-$\varepsilon$ model and the Reynolds stress model (RSM) were chosen to improve the accuracy of CFD prediction. To obtain comparative data, the temperature distribution and velocity vector profiles were measured in a full-scale cold storage facility and in a 1/5 scale model. The agricultural products domain in cold storage was modeled as porous for economical computation. The RSM prediction showed good agreement with experimental data. In addition, temperature distribution was simulated in the cold storage rooms to estimate the uniformity of temperature distribution using the validated model.

• Korean journal of applied entomology
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• v.32 no.3
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• pp.300-306
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• 1993

The CryIIA gene encoding the insecticidal crystal protein of Bacillus thuringiens!s subsp. kurstalri HD-l has been cloned in Escherichia col!, and its nucleotide sequences were determined completely. 5kb Hindlli fragment harboring CryIIA gene was screened in the large ca. 225kb plasmid DNA by southern blot. HindlIT digested 5kb fragment was ligated into pUC19 and transformed in E. coli. The 4kb BamHI-HindlIT fragment containing the CryIIA gene was subcloned and named pSKIIA. DNA sequence analysis demonstrates that pSKIIA is the gene of an operon which is comprised of Lhree open reading frames (designated orn, orf2 and or£3). The CrylIA gene is composed of 3,952bp-long BamHI-Hindill DNA restriction fragment. The orf3 code for a polypeptide of 633 amino acid residues. The protoxin protein has a predicted molecular weight of 70,780. The E. coli derived protoxin gene product is biologICally active against three species of Lepidopteran (Plu.lelia maculipennis, He/iolhis assulta, Spodoptera litura) and a species of Dip Leran( Culex pipines) larvae in bioassay.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.675-680
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• 2017

Considering that most sewage treatment facilities have a water head of less than 2.0 m and a constant flow rate, the development of a small hydro power generation device capable of maintaining stable power generation and efficiency is urgently needed. In this study, a numerical analysis using the CFD code was carried out to develop a drag force type vertical axis hydro turbine for the improvement of the production efficiency of small-scale hydro energy underlow flow velocity conditions. The blade pressure changes and internal flows were analyzed in the presence or absence of hydro turbine blade holes at a flow velocity of less than 2.0 m/s. The pressure distribution of the hydro turbine blades with holes was found to be about 5.1 % lower than that of the hydro turbine blades without holes. The analysis of the internal flow around the water tank and hydro turbine blade revealed that the flow velocity varied with the vector distribution and that the flow velocity of the hydro turbine blades with holes was 5.6 % less than that of the hydro turbine blades without holes. It is believed that forming a hole in the blade may be helpful for its structural safety.