• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.285-288
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• 2010

Chinese hackers hack the e-commerce site by bypass South Korea IP to connect to the third country, finance damaging a violation incident that fake account. 7.7.DDoS attack was the case of a hacker attack that paralyzed the country's main site. In this paper, the analysis is about vulnerabilities that breaches by hackers and DDoS attacks. Hacker's attacks and attacks on the sign of correlation analysis is share the risk rating for in real time, Red, Orange, Yellow, Green. Create a blacklist of hackers and real-time attack will be studied security and air conditioning systems that attacks and defend. By studying generate forensic data and confirmed in court as evidence of accountability through IP traceback and detection about packet after Incident, contribute to the national incident response and development of forensic techniques.

• Journal of Ocean Engineering and Technology
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• v.35 no.5
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• pp.382-392
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• 2021

In recent years, floating offshore wind turbines have attracted more attention as a new renewable energy resource while bottom-fixed offshore wind turbines reach their limit of water depth. Various projects have been proposed with the rapid increase in installed floating wind power capacity, but the economic aspect remains as a biggest issue. To figure out sensible approaches for saving costs, a comparison analysis of the levelized cost of electricity (LCOE) between floating and bottom-fixed offshore wind turbines was carried out. The LCOE was reviewed from a social perspective and a cost breakdown and a literature review analysis were used to itemize the costs into its various components in each level of power plant and system integration. The results show that the highest proportion in capital expenditure of a floating offshore wind turbine results in the substructure part, which is the main difference from a bottom-fixed wind turbine. A floating offshore wind turbine was found to have several advantages over a bottom-fixed wind turbine. Although a similarity in operation and maintenance cost structure is revealed, a floating wind turbine still has the benefit of being able to be maintained at a seaport. After emphasizing the cost-reduction advantages of a floating wind turbine, its LCOE outlook is provided to give a brief overview in the following years. Finally, some estimated cost drivers, such as economics of scale, wind turbine rating, a floater with mooring system, and grid connection cost, are outlined as proposals for floating wind LCOE reduction.

• Journal of Korean Society of Disaster and Security
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• v.16 no.3
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• pp.45-56
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• 2023

In this study, the application of ANC (Active Noise Control) technology to address inter-floor noise was explored. To achieve this, an ANC system was developed to manage the heavy impact sound within the frequency range of 40 to 500 Hz. The ANC system utilized an adaptive filter employing a feedforward approach based on the Fx-LMS algorithm. To set up the ANC system, a comprehensive analysis of various variables within the system was performed using computational simulations. This process enabled the identification of optimal filter settings and system configuration arrangements. In addition, the ANC system was implemented in the inter-floor noise test room at the Korea Conformity Laboratories (KCL). Through a certified standard testing procedure, it was confirmed that the ANC system led to a 4 dB reduction in inter-floor noise when the system was activated compared to when it was turned off. The results of this study indicate that the developed ANC system has an effect significant enough to elevate the rating criteria by one level for heavy impact sound.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.69-78
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• 2024

Recently, consumers have shown an increasing tendency to seek information related to environmental, social, and governance (ESG) aspects in order to choose products with higher social value and environmental friendliness. In this paper, we proposes a product recommendation system applying ESG indicators tailored to the recent consumer trend of value-based consumption, utilizing a model called MultiSAGE that combines GraphSAGE and GAT. To achieve this, ESG rating data for 1,033 companies in 2022 collected from the Korea ESG Standard Institute and actual product data from N companies were transformed into a Heterogeneous Graph format through a data processing pipeline. The MultiSAGE model was then applied in machine learning to implement a recommendation system that, given a specific product, suggests eco-friendly alternatives. The implementation results indicate that consumers can easily compare and purchase products with ESG indicators applied, and it is anticipated that this system will be utilized in recommending products with social value and environmental friendliness.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.6
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• pp.632-638
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• 2014

Nowadays, as part of an effort to increase the efficiency of propulsion shafting system, the revolution of the main diesel engine in CMCR(Contract Maximum Continuous Rating) is reduced whereas the stiffness of hull structure supporting the main diesel engine is relatively flexible. However, vibration problems related with resonant response of main diesel engine are increasing although top bracing is installed between the main diesel engine and the hull structures to increase natural frequency of engine body above CMCR to avoid resonant phenomenon. In this study, the dynamic characteristic of top bracing is reviewed by analyzing measuring results of general cargo ships which apply the hydraulic type instead of the friction type to control the natural frequency and the vibration of the engine body. Moreover, considering the vibration characteristic of the engine body and the hydraulic type of the top bracing by varying the number of top bracing, authors suggest the more effective way to control the vibration of the engine body despite of lower stiffness of the hull structure than in the past when the hydraulic type of top bracing is used.

• Journal of radiological science and technology
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• v.20 no.2
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• pp.49-55
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• 1997

This study was carried out to investigate radiographical and operating conditions of X-ray units and exposure doses to patients during chest radiography, so that the results could provide basic data used for reducing the exposure dose and for providing the diagnostic information with better quality. The conditions and exposure doses of 100 X-ray units mainly used for chest radiography were examined and also 100 radiological technologists mainly handling those apparatus at 76 medical facilities in Pusan were surveyed using a questionnaire from October 1 to December 31 in 1995. The following results were obtained from the study : 1. It was found that most units were capable of taking a high tube voltage radiography by showing 67% of the units equipped with the maximum tube voltage of 150 kV, 94% with more than 500 mA for the rating capacity and 85% with the full wave type of a signal phase. 2. For actual chest radiographical conditions, however, 80% of the units were operated at $60{\sim}100\;kVp$ and only 14% at 100 kVp and over for the high tube voltage. 3. The average exposure time was less than 0.1 second, and eighty four percent of the units adapted the X-ray tube currents ranging from 200 to 300 mA, 80% the focus-film distances between 180 and 210 cm, and 63% the focus sizes of more than 2.0 mm. 4. Most units(98%) employed additional filters made of aluminum, 75% the thickness of filters less than 2.0 mm, and only 2 units the compound filters. 5. Ortho chromatic system was only adopted in 13% of screen film system for the units, and 73% used the grid ratio at 8 : 1 for the low tube voltage during chest radiography. 6. The average exposure dose of all X-ray units during chest radiography was $371\;{\mu}Sv$ with a difference of about 16 times between the minimum to the maximum, and $386\;{\mu}Sv$ both at hospitals and at health centers, followed by $380\;{\mu}Sv$ at general hospitals and $263\;{\mu}Sv$ at university hospitals without showing any statistically significant differences. In conclusion, since patients during chest radiography at medical facilities in Pusan exposed to high levels of radiation, it is recommended that appropriate added filters and grids necessary for the high tube voltage radiography and high-speed screen systems should be adopted and used as soon as possible in order to reduce exposure dose to the patients.