• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.27-35
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• 2021

Generally, a proper evaluation method of the aging phenomenon of PV modules is required as the electrical performance and lifespan of PV modules can degrade significantly due to several environmental factors, while they are generally known as devices that are used semi-permanently for more than 20 years. On the other hand, there is a lack of objectivity in the existing evaluation method of the aging phenomenon, which compares the adjusted PV output based on STC with the initial PV module specifications due to the data distortion while adjusting the measured data. Therefore, this study implemented a test device for an aging diagnosis to measure and collect actual data from a PV module section and modeled the data for aging using MATLAB S/W to minimize the variability of the PV output, communication error, and delay. Furthermore, this study confirmed the usefulness of the presented test device for aging diagnosis of the PV modules by diagnosing the total period and yearly-basis degradation rate of aging PV modules as 25.73% and 1.55%, respectively, according to the on-site output characteristics of the PV modules by season.

• International Area Studies Review
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• v.22 no.3
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• pp.255-292
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• 2018

This paper evaluates the new food policy adopted by the European Union to enhance the food safety after the mad cow crisis occurred in 1990's. Newly introduced rules at the EU level are characterized by two features. Firstly, an important part of them have the form of Regulation which is a binding legislative to all member countries. Secondly, most of them are horizontally applied to the whole food industry, irrespective of their kinds of performance, hygiene or labelling. According to theoretical studies on this topic, any food safety regulation for solving adverse selection problem or reducing negative externality in food consumption should be fine-tuning depending on the concrete demand and costs conditions of the food sector concerned. In this theoretical perspective, the food safety laws introduced at EU level after mad cow crisis have been over-regulated for improving social welfare. The true motivation for the transfer of the policy competence on food safety to the Union level is political rather than economic. Our analysis with a political economic perspective shows that how the EU food regulations have been embraced not only by the governments of member countries, but also by diverse interest groups like food processor & distributors, consumers and agro-livestock groups, and that they have been used as protectionist purpose specially against non-member developing countries. Taking into account the fact that the basic aim to form the Union is to establish a single market to enhance economic efficiency at the Union level, the EU is required to adopt some policy actions to reduce negative effects of too restrictive food safety regulations.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.1
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• pp.43-51
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• 2009

In 1998, Kocher et al. introduced Differential Power Attack on block ciphers. This attack allows to extract secret key used in cryptographic primitives even if these are executed inside tamper-resistant devices such as smart card. At FSE 2003 and 2004, Akkar and Goubin presented several masking methods, randomizing the first few and last few($3{\sim}4$) rounds of the cipher with independent random masks at each round and thereby disabling power attacks on subsequent inner rounds, to protect iterated block ciphers such as DES against Differential Power Attack. Since then, Handschuh and Preneel have shown how to attack Akkar's masking method using Differential Cryptanalysis. This paper presents how to combine Truncated Differential Cryptanalysis and Power Attack to extract the secret key from intermediate unmasked values and shows how much more efficient our attacks are implemented than the Handschuh-Preneel method in term of reducing the number of required plaintexts, even if some errors of Hamming weights occur when they are measured.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.6
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• pp.23-35
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• 2009

Password-Authenticated Key Exchange (PAKE) Protocol is a useful tool for secure communication conducted over open networks without sharing a common secret key or assuming the existence of the public key infrastructure (PKI). It seems difficult to design efficient PAKE protocols using RSA, and thus many PAKE protocols are designed based on the Diffie-Hellman key exchange (DH-PAKE). Therefore it is important to design an efficient PAKE based on RSA function since the function is suitable for designing a PAKE protocol for imbalanced communication environment. In this paper, we propose a computationally-efficient key exchange protocol based on the RSA function that is suitable for low-power devices in imbalanced environment. Our protocol is more efficient than previous RSA-PAKE protocols, required theoretical computation and experiment time in the same environment. Our protocol can provide that it is more 84% efficiency key exchange than secure and the most efficient RSA-PAKE protocol CEPEK. We can improve the performance of our protocol by computing some costly operations in offline step. We prove the security of our protocol under firmly formalized security model in the random oracle model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.135-141
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• 2011

Recent advances in mobile devices and development of various mobile applications dealing with private information of users made user authentication in mobile devices a very important issue. This paper presents a new user authentication method based on touch screen interfaces. This method uses for authentication the PIN digits as well as the exact locations the user touches to input these digits. Our method is fully compatible with the regular PIN entry method which uses numeric keypads, and it provides better usability than the behavioral biometric schemes because its PIN registration process is much simpler. According to our experiments, our method guarantees EERs of 12.8%, 8.3%, and 9.3% for 4-digit PINs, 6-digit PINs, and 11-digit cell phone numbers, respectively, under the extremely conservative assumption that all users have the same PIN digits and cell phone numbers. Thus we can guarantee much higher performance in identification functionality by applying this result to a more practical situation where every user uses distinct PIN and sell phone number. Finally, our method is far more secure than the regular PIN entry method, which is verified by our experiments where attackers are required to recover a PIN after observing the PIN entry processes of the regular PIN and our method under the same level of security parameters.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.58-73
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• 2020

The purpose of this study is to empirically analyze the relationship between organizational commitment, job satisfaction, education and training, human resource development, and communication in Korea's financial industry. In particular, the mediating relationships between organizational commitment and job satisfaction, education and training, human resource development, and communication were examined in order to provide a better understanding of organizational commitment. A structural equation model was used for the empirical analysis of this study. As a result of the study, it was confirmed that education and training, human resource development, communication, organizational commitment, and job satisfaction are correlated within the Korean financial industry. Furthermore, organizational commitment was found to have mediating effects on education and training, human resource development, communication, and job satisfaction. Based on these results, this study emphasizes the importance of education and training for organizational commitment in the Korean financial industry. In particular, this study establishes the importance of a culture of trust within organizations through human resource development programs, communication for job satisfaction, and organizational performance to face changes in the post-COVID era. In the future, more in-depth qualitative studies are required to derive factors related to the employees of financial companies and to conduct comparison analyses with companies in other industries.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.541-551
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• 2021

Among the numerous duties of a librarian in a library, the work of arranging books is a job that the librarian has to do one by one. Thus, the cost of labor and time is large. In order to solve this problem, the interest in book-arranging robots based on artificial intelligence has recently increased. In this paper, we propose the K-ACO algorithm, which is the shortest path algorithm for multi-stops that can be applied to the library book arrangement robots. The proposed K-ACO algorithm assumes multiple robots rather than one robot. In addition, the K-ACO improves the ANT algorithm to create K clusters and provides the shortest path for each cluster. In this paper, the performance analysis of the proposed algorithm was carried out from the perspective of book arrangement time. The proposed algorithm, the K-ACO algorithm, was applied to a university library and compared with the current book arrangement algorithm. Through the simulation, we found that the proposed algorithm can allocate fairly, without biasing the work of arranging books, and ultimately significantly reduce the time to complete the entire work. Through the results of this study, we expect to improve quality services in the library by reducing the labor and time costs required for arranging books.

• Knowledge Management Research
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• v.22 no.2
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• pp.247-268
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• 2021

Investors mainly use PER and PBR among financial ratios for valuation and investment decision-making. I conduct an analysis of two basic financial ratios from a statistical perspective. Financial ratios contain key accounting numbers which reflect firm fundamentals and are useful for valuation or risk analysis such as enterprise credit evaluation and default prediction. The distribution of financial data tends to be extremely heavy-tailed, and PER and PBR show exceedingly high level of kurtosis and their extreme cases often contain significant information on financial risk. In this respect, Extreme Value Theory is required to fit its right tail more precisely. I introduce not only GPD but exGPD. GPD is conventionally preferred model in Extreme Value Theory and exGPD is log-transformed distribution of GPD. exGPD has recently proposed as an alternative of GPD(Lee and Kim, 2019). First, I conduct a simulation for comparing performances of the two distributions using the goodness of fit measures and the estimation of 90-99% percentiles. I also conduct an empirical analysis of Information Technology firms in Korea. Finally, exGPD shows better performance especially for PBR, suggesting that exGPD could be an alternative for GPD for the analysis of financial ratios.

• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.53-66
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• 2021

Velocity model building is an essential procedure in seismic data processing. Conventional techniques, such as traveltime tomography or velocity analysis take longer computational time to predict a single velocity model and the quality of the inversion results is highly dependent on human expertise. Full-waveform inversions also depend on an accurate initial model. Recently, deep neural network techniques are gaining widespread acceptance due to an increase in their integration to solving complex and nonlinear problems. This study investigated cases of seismic velocity model building using deep neural network techniques by classifying items according to the neural networks used in each study. We also included cases of generating training synthetic velocity models. Deep neural networks automatically optimize model parameters by training neural networks from large amounts of data. Thus, less human interaction is involved in the quality of the inversion results compared to that of conventional techniques and the computational cost of predicting a single velocity model after training is negligible. Additionally, unlike full-waveform inversions, the initial velocity model is not required. Several studies have demonstrated that deep neural network techniques achieve outstanding performance not only in computational cost but also in inversion results. Based on the research results, we analyzed and discussed the characteristics of deep neural network techniques for building velocity models.

• Journal of IKEEE
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• v.25 no.2
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• pp.273-279
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• 2021

In this paper, we propose an algorithm that shortens the learning time by performing individual learning using partitioning the deep learning structure. The proposed algorithm consists of four processes: network classification origin setting process, feature vector extraction process, feature noise removal process, and class classification process. First, in the process of setting the network classification starting point, the division starting point of the network structure for effective feature vector extraction is set. Second, in the feature vector extraction process, feature vectors are extracted without additional learning using the weights previously learned. Third, in the feature noise removal process, the extracted feature vector is received and the output value of each class is learned to remove noise from the data. Fourth, in the class classification process, the noise-removed feature vector is input to the multi-layer perceptron structure, and the result is output and learned. To evaluate the performance of the proposed algorithm, we experimented with the Extended Yale B face database. As a result of the experiment, in the case of the time required for one-time learning, the proposed algorithm reduced 40.7% based on the existing algorithm. In addition, the number of learning up to the target recognition rate was shortened compared with the existing algorithm. Through the experimental results, it was confirmed that the one-time learning time and the total learning time were reduced and improved over the existing algorithm.