• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.2
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• pp.3-10
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• 2008

The choice of basis for representation of element in $GF(2^m)$ affects the efficiency of a multiplier. Among them, a multiplier using redundant representation efficiently supports trade-off between the area complexity and the time complexity since it can quickly carry out modular reduction. So time of a previous multiplier using redundant representation is faster than time of multiplier using others basis. But, the weakness of one has a upper space complexity compared to multiplier using others basis. In this paper, we propose a new efficient multiplier with consideration that polynomial exponentiation operations are frequently used in cryptographic hardwares. The proposed multiplier is suitable fer left-to-right exponentiation environment and provides efficiency between time and area complexity. And so, it has both time delay of $T_A+({\lceil}{\log}_2m{\rceil})T_x$ and area complexity of (2m-1)(m+s). As a result, the proposed multiplier reduces $2(ms+s^2)$ compared to the previous multiplier using equally-spaced polynomials in area complexity. In addition, it reduces $T_A+({\lceil}{\log}_2m+s{\rceil})T_x$ to $T_A+({\lceil}{\log}_2m{\rceil})T_x$ in the time complexity.($T_A$:Time delay of one AND gate, $T_x$:Time delay of one XOR gate, m:Degree of equally spaced irreducible polynomial, s:spacing factor)

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.193-200
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• 2022

In this study, the finite-failure NHPP software reliability model was applied to the software development model based on the Weibull lifetime distribution (Goel-Okumoto, Rayleigh, Type-2 Gumbe), which is widely used in the software reliability field, and then the cost attributes were compared and evaluated. For this study, failure time data detected during normal operation of the software system were collected and used, the most-likelihood estimation (MLE) method was applied to the parameter estimation of the proposed model, and the calculation of the nonlinear equation was solved using the binary method. As a result, first, in the software development model, when the cost of testing per unit time and the cost of removing a single defect increased, the cost increased but the release time did not change, and when the cost of repairing failures detected during normal system operation increased, the cost increased and the release time was also delayed. Second, as a result of comprehensive comparative analysis of the proposed models, it was found that the Type-2 Gumble model was the most efficient model because the development cost was lower and the release time point was relatively faster than the Rayleigh model and the Goel-Okumoto basic model. Third, through this study, the development cost properties of the Weibull distribution model were newly evaluated, and the analyzed data is expected to be utilized as design data that enables software developers to explore the attributes of development cost and release time.

• Journal of Practical Agriculture & Fisheries Research
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• v.21 no.2
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• pp.103-114
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• 2019

In this study we examined the preferences of college life factors for students at Korea National College of Agriculture and Fisheries(KNCAF). Analytical techniques of unstructured data used opinion mining and text mining techniques, and the results of text mining were visualized as word cloud. And those results were used for statistical analysis of the students' willingness to farm after graduation. The items of the favorable survey consisted of 10 items in 5 areas including university image, self-capacity, dormitory, education system, and future vision. After classifying the emotions of positive and negative in the collected questionnaire, a dictionary of positive and negative was created to evaluate the preference. The items of 'college image' at the time of university support, 'self after 10 years' after graduation, 'self-capacity' and 'present KNCAF' showed high positive emotion. On the other hand, positive emotion was low in the items of 'college dormitory', 'educational course', 'long-term field practice' and 'future of Korean agriculture'. In the cross-analysis of the difference in the will to farming according to gender, farming base, and entrance motivation, the will to farm according to gender and entrance motivation showed statistically significant results, but it was not significant in farming base. Also in binary logistic regression analysis on the will to farming, the statistically significant variable was found to be 'motivation for admission'

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.841-853
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• 2024

This paper proposes an optimization method for the GF(2)[x] multiplication operation in HQC on AVX2. HQC is a candidate in NIST PQC standardization round 4 and is a binary code-based key exchange algorithm. The multiplication operation is one of the most time-complex operations in HQC, accounting for about 30% of the total clock cycles in the AVX2 environment. For the optimization, we used Karatsuba and Toom-Cook algorithms. Both algorithms are based on divide-and-conquer methods, which require multiplications of smaller order within them. We propose a method to optimize polynomial multiplication in HQC by finding the most efficient combination of Karatsuba and Toom-Cook algorithms, and compare the performance of the proposed method based on the implementation submitted to the PQC standardization. The results of the comparison demonstrate a performance improvement of 4.5%, 2.5%, and 30.3% over the GF(2)[x] multiplications of original hqc-128, -192, and -256. When applied to key generation, encapsulation, and decapsulation, the performance improvement over the original HQC is 2.2%, 2.4%, and 2.3% for hqc-128, 1.6%, 4.2%, and 2.6% for hqc-192, and 13.3%, 14.7%, and 13.3% for hqc-256, respectively.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.135-151
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• 2021

This research attempts to analyze the factors influencing the entrepreneurial intention of employees in manufacturing field. In particular, key factors of entrepreneurship and personal characteristics explain a significant association with the intention to start-up. And study whether R&D support from public enterprise adjusts intention to entrepreneurial Intention. This study conducted a online survey on 292 small and medium-sized enterprise manufacturing employees in May 2020. Using linear regression model and binary logistic model. The main study results are the following: First, among the key factors(innovativeness, proactiveness, risk-taking) of entrepreneurship, proactiveness hardly influenced the opportunity competency. Second, among the factors(risk-taking propensity, locus of control, tolerance for ambiguity) of personal characteristics, locus of control hardly influenced the opportunity competency. Third, opportunity competency(opportunity recognition and opportunity evaluation) had positive influence to entrepreneurial intention. Fourth, the study investigated the mediated effect of opportunity competency. The result showed that among the factors of entrepreneurship and personal characteristics, only two factors that are proactiveness and locus of control were not mediated by opportunity competency. and opportunity evaluation was acted as a mediator between proactiveness and entrepreneurial Intention, compared with opportunity recognition. Lastly, public enterprise's R&D supporting moderated the entrepreneurial intention). Based on the result, the study showed that first, the key factors of entrepreneurship except for proactiveness and personal characteristics(risk-taking propensity, locus of control, tolerance for ambiguity) except for locus of control affect the intention to start-up, repeatedly. This results are explained that employees have not started a business yet. Second, research on start-up suggests the need to analyze factors differentiated before and after the start-ups. Based on the results, entrepreneurship and personal characteristics show that study on the effects of start-up intentions should be carried out before and after the actual start-up takes place, and can be used as effective data in policies to promoting start-ups in manufacturing field.

• Korean Chemical Engineering Research
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• v.55 no.3
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• pp.322-331
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• 2017

District heating was first introduced in Korea in 1985. As the service life of the underground thermal piping network has increased for more than 30 years, the maintenance of the underground thermal pipe has become an important issue. A variety of complex technologies are required for periodic inspection and operation management for the maintenance of the aged thermal piping network. Especially, it is required to develop a model that can be used for decision making in order to derive optimal maintenance and replacement point from the economic viewpoint in the field. In this study, the analysis was carried out based on the repair history and accident data at the operation of the thermal pipe network of five districts in the Korea District Heating Corporation. A failure probability model was developed by introducing statistical techniques of qualitative analysis and binomial logistic regression analysis. As a result of qualitative analysis of maintenance history and accident data, the most important cause of pipeline damage was construction erosion, corrosion of pipe and bad material accounted for about 82%. In the statistical model analysis, by setting the separation point of the classification to 0.25, the accuracy of the thermal pipe breakage and non-breakage classification improved to 73.5%. In order to establish the failure probability model, the fitness of the model was verified through the Hosmer and Lemeshow test, the independent test of the independent variables, and the Chi-Square test of the model. According to the results of analysis of the risk of thermal pipe network damage, the highest probability of failure was analyzed as the thermal pipeline constructed by the F construction company in the reducer pipe of less than 250mm, which is more than 10 years on the Seoul area motorway in winter. The results of this study can be used to prioritize maintenance, preventive inspection, and replacement of thermal piping systems. In addition, it will be possible to reduce the frequency of thermal pipeline damage and to use it more aggressively to manage thermal piping network by establishing and coping with accident prevention plan in advance such as inspection and maintenance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.12C
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• pp.1288-1298
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• 2002

To implement elliptic curve cryptosystem in GF(2$\^$m/) at high speed, a fast divider is required. Although bit-parallel architecture is well suited for high speed division operations, elliptic curve cryptosystem requires large m(at least 163) to support a sufficient security. In other words, since the bit-parallel architecture has an area complexity of 0(m$\^$m/), it is not suited for this application. In this paper, we propose a new serial-in serial-out systolic array for computing division operations in GF(2$\^$m/) using the standard basis representation. Based on a modified version of tile binary extended greatest common divisor algorithm, we obtain a new data dependence graph and design an efficient bit-serial systolic divider. The proposed divider has 0(m) time complexity and 0(m) area complexity. If input data come in continuously, the proposed divider can produce division results at a rate of one per m clock cycles, after an initial delay of 5m-2 cycles. Analysis shows that the proposed divider provides a significant reduction in both chip area and computational delay time compared to previously proposed systolic dividers with the same I/O format. Since the proposed divider can perform division operations at high speed with the reduced chip area, it is well suited for division circuit of elliptic curve cryptosystem. Furthermore, since the proposed architecture does not restrict the choice of irreducible polynomial, and has a unidirectional data flow and regularity, it provides a high flexibility and scalability with respect to the field size m.

• Journal of the Korean Society of Radiology
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• v.18 no.5
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• pp.531-540
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• 2024

Recently, research and development on automatic diagnosis solutions in the medical imaging field using deep learning are actively underway. In this study, we sought to find a fast and accurate classification deep learning modeling for classification of pneumonia in chest images using Inception V3, a deep learning model based on a convolutional artificial neural network. For this reason, after applying the optimization algorithms AdaGrad, RMS Prop, and Adam to deep learning modeling, deep learning modeling was implemented by selectively applying learning rates of 0.01 and 0.001, and then the performance of chest X-ray image pneumonia classification was compared and evaluated. As a result of the study, in verification modeling that can evaluate the performance of the classification model and the learning state of the artificial neural network, it was found that the performance of deep learning modeling for classification of the presence or absence of pneumonia in chest X-ray images was the best when applying Adam as the optimization algorithm with a learning rate of 0.001. I was able to. And in the case of Adam, which is mainly applied as an optimization algorithm when designing deep learning modeling, it showed excellent performance and excellent metric results when selectively applying learning rates of 0.01 and 0.001. In the metric evaluation of test modeling, AdaGrad, which applied a learning rate of 0.1, showed the best results. Based on these results, when designing deep learning modeling for binary-based medical image classification, in order to expect quick and accurate performance, a learning rate of 0.01 is preferentially applied when applying Adam as an optimization algorithm, and a learning rate of 0.01 is preferentially applied when applying AdaGrad. I recommend doing this. In addition, it is expected that the results of this study will be presented as basic data during similar research in the future, and it is expected to be used as useful data in the health and bio industries for the purpose of automatic diagnosis of medical images using deep learning.