• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.1
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• pp.123-136
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• 2004

In this paper, complexity and regularity of polynomial multiplication over $GF({2^n})$ are defined by using Hamming weight of rows and columns of the matrix ever GF(2) which represents polynomial multiplication. It is shown experimentally that in order to construct the block cipher robust against differential cryptanalysis, polynomial multiplication of substitution layer and the permutation layer should have high complexity and high regularity. With result of the experiment, a way of constituting S box and G function is suggested in the block cipher whose structure is similar to SEED, which is KOREA standard of 128-bit block cipher. S box can be formed with a nonlinear function and an affine transform. Nonlinear function must be strong with differential attack and linear attack, and it consists of an inverse number over $GF({2^8})$ which has neither a fixed pout, whose input and output are the same except 0 and 1, nor an opposite fixed number, whose output is one`s complement of the input. Affine transform can be constituted so that the input/output correlation can be the lowest and there can be no fixed point or opposite fixed point. G function undergoes linear transform with 4 S-box outputs using the matrix of 4${\times}$4 over $GF({2^8})$. The components in the matrix of linear transformation have high complexity and high regularity. Furthermore, G function can be constituted so that MDS(Maximum Distance Separable) code can be formed, SAC(Strict Avalanche Criterion) can be met, and there can be no weak input where a fixed point an opposite fixed point, and output can be two`s complement of input. The primitive polynomials of nonlinear function affine transform and linear transformation are different each other. The S box and G function suggested in this paper can be used as a constituent of the block cipher with high security, in that they are strong with differential attack and linear attack with no weak input and they are excellent at diffusion.

• Journal of vocational education research
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• v.30 no.4
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• pp.23-49
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• 2011

Faculty performance evaluation system has been settled down as an uncomfortable but unavoidable system, and it is one of the most important factors to grow the college competitiveness up. In this study, we selected and surveyed faculty evaluation models of several universities and colleges in Korea, and analyzed by comparing each evaluation areas of educational achievement, college-industry collaboration, research, and service. We also identified the properties of the current faculty evaluation models of the junior colleges, and derived several problems from these models such as an imitation of four-year university model, a disorders of job evaluation with respect to the attributes of classified jobs, a large variation of individual item weights, and an insufficient reflection of major characteristics. Based on these surveys and analysis, an improved faculty evaluation model for the junior college is proposed in this study. This model proposed four basic areas-educational achievement, college-industry collaboration, research, and service by considering the importance of the college-industry collaboration in the junior college-as well as the team evaluation area. Weights of the SCI-class paper was selected as a criterion for the arrangement of objective comparison of each evaluation items. We showed the integration method of several different evaluation model with respect to the attributes of classified jobs of each faculties, and evaluation plan of variational characteristics according to the majors of individuals in this model. Finally, we introduced an area fail and rating system to operate efficiently the proposed faculty evaluation model.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.2
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• pp.89-100
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• 2021

This study evaluated the degree of interaction between the people and the environment using perceived naturalness measure. The seventh-grade index of Hemeroby was divided into subclasses of land cover according to degrees of human influence. The grade was standardized for each indicator to evaluate the current state of urban parks in Seoul by applying probability density function and weight. User evaluation was conducted on six distinctive parks selected. In the results, three implications were found between spatial evaluation according to the perceived naturalness. First, park users evaluated highly for the spaces such as broad-leaved forest, coniferous forest and mixed forest evaluated highly in the Hemeroby grade index. Park users generally recognized that various types of trees in the area had high naturalness. The density of trees is one of the factors in perceived naturalness. Second, water spaces were highly evaluated for naturalness in the Hemeroby grade index. However, the perceived naturalness of water spaces such as inland wetlands, pond and reservoir evaluated in various ways depending on environmental conditions around the park. Third, perceived naturalness is easily evaluated through vertical landscape elements such as trees rather than horizontal landscapes such as grassland. The perceived naturalness is similar to the naturalness evaluation using land cover. However the study found the perceived naturalness for a specific space was different from the Hemeroby index. Perceived naturalness by the user includes the content that the individual sees, hears, and experiences. Park users are usually structuring naturalness through evaluating the value of urban green spaces based on personal perception. Therefore there is no absolute standard criterion for evaluating the naturalness of urban green spaces. A deeper study is needed that considers user bundles or user groups with conflicting interests on the perceived naturalness in urban parks. These studies will be essential data on the direction of naturalness urban park service should provide.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.135-154
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• 2020

This paper analyzes the 5,521 samples of the small and medium-sized businesses(SMBs) obtained from the Korea Credit Guarantee Fund. From January 2014 to September 2019, 85% of the SMBs have 5 or fewer full-time employees. The proportion of SMBs is overwhelmed by the elderly men, and most founders are the CEO. Also, about 87% of the workplace types are rented, while 64% of the CEO's residence types are owner-occupation. 47% of the financial grade score is less than 10 points out of 100 and 80% of SMBs have less than 200 million won of the loan guarantee. In particular, the total guarantee loan amount or the days of net guarantee have significantly positive relations with the working period of the CEO in the same industry, the number of employees, the operation period of SMBs, and the corporate business type. In the case of the financial grading score which has the highest weight in overall credit rating gets higher with the higher number of employees, the longer the operation period, and the corporate business type. However, the quantified non-financial grading score has no significant relationship with other explanatory variables, except for the corporate business type. This implies that a non-financial grade score is measured by other determinants that are not observed by the Korea credit guarantee fund. The pure non-financial grade score has positive relations with the working period of the CEO. Overall, this paper would help Korean SMBs upgrade their credit ratings and expand the money supply when there is no standardized credit rating model or no publicly available evaluation criteria for SMBs. We expect this paper provides important insights for further research and policy-makers for SMBs. In particular, to address the financial needs of thin-filers such as SMBs, technology-based financial services (TechFin) would use alternative data to evaluate the financial capabilities of thin-filers and to develop new financial services.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.2
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• pp.23-40
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• 2022

Many constructed wetlands have been installed in dam reservoirs nationwide for ecological purification of watershed pollutants, but aging and reduced efficiency are becoming issues. To improve the management of constructed wetlands, an objective evaluation of structural suitability is required. This study evaluated 39 constructed wetlands of 15 dams. First, through fogus group interview(FGI), survey analysis, and analytic hierarchy process(AHP), eight evaluation items in the physical and vegetative aspects were selected and the evaluation criteria applied with weights were prepared. Second, as a result of the structural suitability evaluation, the average score of the overall constructed wetlands was 80.8, with 10 sites rated as 'good grade(91~100)', 22 sites rated as 'normal grade(71~90)' and 7 sites rated as 'poor grade(70 or less)'. The average score of physical structure evaluation was 52.6, with 14 sites rated as 'good', 21 sites as 'normal' and 4 sites as 'poor'. The suitability of location was good level in most constructed wetlands, but the water supply system, depth of water, ratio of length-to-width, and slope of flow channel were evaluated as 'normal' or less in constructed wetlands of 50% or more. Therefore, it was found that overall improvement was necessary for stable flow supply and flow improvement in the constructed wetland. The average score of vegetative structure evaluation was 28.2, and about 84% of them were identified as 'normal' or lower. As a result of analyzing the Spearman's correlation coefficient between the physical structure evaluation score and the vegetation structure evaluation score, there was a significant correlation(r = 0.728, p < 0.001), and it was found that each evaluation factor also influences each other. As a result of the case review of 6 constructed wetlands, the appropriateness of the evaluation results was confirmed, and it was found that the location, flow rate supply, and type of wetland had a great influence on the efficiency and operation of the wetland. Through this study, it will be possible to derive structural weaknesses of constructed wetlands in dam reservoirs as a nature-based solution, to prepare types and practical alternatives for improved management of each constructed wetland in the future, and to contribute to enhancing various environmental functions.

• Journal of the Korea Institute of Building Construction
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• v.22 no.5
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• pp.473-483
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• 2022

BIM(Building Information Modeling) is a technology that can manage information throughout the entire life cycle of the construction industry and serves as a platform for improving productivity and integrating the entire construction industry. Currently, BIM is actively applied in developed countries, and its use at various overseas construction sites is increasing This is unclear. due to air shortening and budget savings. However, there is still a lack of institutional basis and technical limitations in the domestic construction sector, which have led to the lack of utilization of BIM. Various activation measures and institutional frameworks will need to be established for the early establishment of these productive BIMs in Korea. Therefore, as part of the research for the domestic settlement and revitalization of BIM, this study derived a number of key factors necessary for the development of the construction industry through brainstorming and expert surveys using AHP techniques and analyzed the relative importance of each factor. In addition, prior surveys by a group of experts resulted in 1, 3 items in level, 2, 9 items in level, and 3, 27 items in level, and priorities analysis was performed through pairwise comparisons. As a result of the AHP analysis, it was found that the relative importance weight of policy aspects was highest in level 1, and the policy factors in level 2 and the cost-based and incentive system introduction factors were considered most important in level 3. These findings show that the importance of the policy guidance or institutions underlying the activation of BIM rather than research and development or corporate innovation is relatively high, and that the preparation of policy plans by public institutions should be the first priority. Therefore, it is considered that the development of a policy system or guideline must be prioritized before it can be advanced to the next activation stage. The use of BIM technologies will not only contribute to improving the productivity of the construction industry, but also to the overall development of the industry and the growth of the construction industry. It is expected that the results of this study can provide as useful information when establishing policies for activating BIM in central government, relevant local governments, and related public institutions.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.17-32
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• 2023

In recent years, the need for social ventures that aim to grow while solving social problems through the efficiency and effectiveness of commercial organizations in the market has increased, while there is a limit to how much the government and the public can do to solve social problems. Against this background, the number of social venture startups is increasing in the domestic startup ecosystem, and interest in impact investors, which are investors in social ventures, is also increasing. Therefore, this research utilized judgment analysis technology to objectively analyze the validity and weight of judgment information based on the cognitive process and decision-making environment in the investment decision-making of impact investors. We proceeded with the research by constructing three classifications; first, investment priorities at the initial investment stage for financial benefit and return on investment as an investor, second, the political skills of the entrepreneurs (teams) for the social impact and ripple power, and social venture coexistence and solidarity, third, the social mission of a social venture that meets the purpose of an impact investment fund. As a result of this research, first of all, the investment decision-making priorities of impact investors are the expertise of the entrepreneur (team), the potential rate of return when the entrepreneur (team) succeeds, and the social mission of the entrepreneur (team). Second, impact investors do not have a uniform understanding of the investment decision-making factors, and the factors that determine investment decisions are different, and there are differences in the degree of the weighting. Third, among the various investment decision-making factors of impact investment, "entrepreneur's (team's) networking ability", "entrepreneur's (team's) social insight", "entrepreneur's (team's) interpersonal influence" was relatively lower than the other four factors. The practical contribution through this research is to help social ventures understand the investment determinant factors of impact investors in the process of financing, and impact investors can be expected to improve the quality of investment decision-making by referring to the judgment cases and analysis of impact investors. The academic contribution is that it empirically investigated the investment priorities and weighting differences of impact investors.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.