• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.135-145
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• 2022

This paper presented a method to build a predictable smart ammunition logistics system using the 4th industrial technology for ammunition logistics, which is the core functions in the field of defense and logistics. We have analyzed the current level of ammunition logistics with various perspectives such as domestic and overseas logistics policies, technology trends, ammunition logistics characteristics, the smart logistics certification measures by Ministry of Land, Infrastructure and Transport. As a result it is considered that the current ammunition logistics needs needs improvement. To improve this, we presented a direction based on the implications derived after analyzing various ongoing programs such as wired/wireless-based automation, smart ammunition depots, and logistics innovation of the army, navy, and air force that can be applied to the ammunition logistics. In order to implement a data-based smart ammunition logistics management system that can achieve innovation and efficiency of total life cycle while meeting changes in the battlefield environment, we presented 4 objectives such as "automation and modernization of field work", "3D-based storage management & improvement of issuing at war," and "data management for prediction-oriented ammunition management". it is expected that there will be benefits such as improvement of operational continuity, guarantee of ammunition reliability, budget reduction, improvement of inefficiencies such as delay, waiting, and double work, and reduction of accidents.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.209-235
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• 2022

Starting from 2010, blockchain technology, along with the development of artificial intelligence, has been in the spotlight as the latest technology to lead the 4th industrial revolution. Furthermore, previous research regarding blockchain's technological applications has been ongoing ever since. However, few studies have been examined the standards for classifying the blockchain economic ecosystem from a capital market perspective. Our study is classified into a collection of interviews of software developers, entrepreneurs, market participants and experts who use blockchain technology to utilize the blockchain economic ecosystem from a capital market perspective for investing in stocks, and case study methodologies of blockchain economic ecosystem according to application fields of blockchain technology. Additionally, as a way that can be used in connection with equity investment in the capital market, the blockchain economic ecosystem classification methodology was established to form an investment universe consisting of global blue-chip stocks. It also helped construct an intelligent portfolio through quantitative and qualitative analysis that are based on quant and artificial intelligence strategies and evaluate its performances. Lastly, it presented a successful investment strategy according to the growth of blockchain economic ecosystem. This study not only classifies and analyzes blockchain standardization as a blockchain economic ecosystem from a capital market, rather than a technical, point of view, but also constructs a portfolio that targets global blue-chip stocks while also developing strategies to achieve superior performances. This study provides insights that are fused with global equity investment from the perspectives of investment theory and the economy. Therefore, it has practical implications that can contribute to the development of capital markets.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.135-155
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• 2022

This study analyzes patent trends by deriving sub-technical fields of Organic Light-Emitting Diodes (OLEDs) industry, and analyzing technology value, originality, and diversity for each sub-technical field. To collect patent data, a set of international patent classification(IPC) codes related to OLED technology was defined, and OLED-related patents applied from 2005 to 2017 were collected using a set of IPC codes. Then, a large number of collected patent documents were classified into 12 major technologies using the Latent Dirichlet Allocation(LDA) topic model and trends for each technology were investigated. Patents related to touch sensor, module, image processing, and circuit driving showed an increasing trend, but virtual reality and user interface recently decreased, and thin film transistor, fingerprint recognition, and optical film showed a continuous trend. To compare the technological value, the number of forward citations, originality, and diversity of patents included in each technology group were investigated. From the results, image processing, user interface(UI) and user experience(UX), module, and adhesive technology with high number of forward citations, originality and diversity showed relatively high technological value. The results provide useful information in the process of establishing a company's technology strategy.

• Clean Technology
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• v.28 no.3
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• pp.232-237
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• 2022

Biorefineries, in which renewable resources are utilized, are an eco-friendly alternative based on biomass feedstocks. Alginic acid, a major component of brown algae, which is a type of marine biomass, is widely used in various industries and can be converted into value-added chemicals such as sugars, sugar alcohols, furans, and organic acids via catalytic hydrothermal decomposition under certain conditions. In this study, ruthenium-supported activated carbon and magnesium oxide were mixed and applied to the depolymerization of alginic acid in a batch reactor. The addition of magnesium oxide as a basic promoter had a strong influence on product distribution. In this heterogeneous catalytic system, the separation and purification processes are also simplified. After the reaction, low molecular weight alcohols and organic acids with 5 or fewer carbons were produced. Specifically, under the optimal reaction conditions of 30 mL of 1 wt% alginic acid aqueous solution, 100 mg of ruthenium-supported activated carbon, 100 mg of magnesium oxide, 210 ℃ of reaction temperature, and 1 h of reaction time, total carbon yields of 29.8% for alcohols and 43.8% for a liquid product were obtained. Hence, it is suggested that this catalytic system results in the enhanced hydrogenolysis of alginic acid to value-added chemicals.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.565-575
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• 2022

Recently, in the field of water resource engineering, interest in predicting time series water levels and flow rates using deep learning technology that has rapidly developed along with the Fourth Industrial Revolution is increasing. In addition, although water-level and flow-rate prediction have been performed using the Long Short-Term Memory (LSTM) model and Gated Recurrent Unit (GRU) model that can predict time-series data, the accuracy of flow-rate prediction in rivers with rapid temporal fluctuations was predicted to be very low compared to that of water-level prediction. In this study, the Paldang Bridge Station of the Han River, which has a large flow-rate fluctuation and little influence from tidal waves in the estuary, was selected. In addition, time-series data with large flow fluctuations were selected to collect water-level and flow-rate data for 2 years and 7 months, which are relatively short in data length, to be used as training and prediction data for the LSTM and GRU models. When learning time-series water levels with very high time fluctuation in two models, the predicted water-level results in both models secured appropriate accuracy compared to observation water levels, but when training rapidly temporal fluctuation flow rates directly in two models, the predicted flow rates deteriorated significantly. Therefore, in this study, in order to accurately predict the rapidly changing flow rate, the water-level data predicted by the two models could be used as input data for the rating curve to significantly improve the prediction accuracy of the flow rates. Finally, the results of this study are expected to be sufficiently used as the data of flood warning system in urban rivers where the observation length of hydrological data is not relatively long and the flow-rate changes rapidly.

• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.74-82
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• 2022

With the introduction of proptech, this study aims to find out the changes and necessity of introducing blockchain technology, one of the most popular technologies, in real estate electronic transactions. In addition, it is intended to develop evaluation indicators that classify newly created values within real estate electronic transactions and calculate the relative importance of each value area through technology application. To this end, the value that can be created when applying blockchain technology to real estate electronic transactions was classified according to the hierarchy, and considering that the evaluation criteria are complex and the importance can be measured differently depending on various factors, an analysis was conducted according to the AHP method for experts in practical and academic fields. As a result of the analysis, general value showed the highest importance in the first dimension, and digitalization of real estate information showed the highest importance in the second dimension.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.555-562
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• 2022

This study measured and analyzed the propagation characteristics at frequencies 6, 10, and 17 GHz to discover the new propagation demands in a semi-basement indoor corridor environment for meeting the 4th industrial revolution requirements. The measured indoor environment is a straight corridor consisting of three lecture rooms and glass windows on the outside. The measurement scenario development and measurement system were constructed to match this environment. The transmitting antenna was fixed, and the frequency domain and time domain propagation characteristics were measured and analyzed in the line-of-sight environment based on the distance of the receiving antenna location. In the frequency domain, reliability was determined by the parameters of the floating intercept (FI) path loss model and an R-squared value of 0.5 or more. In the time domain, the root mean square (RMS) delay spread and the cumulative probability of K-factor were used to determine that 6 GHz had high propagation power and 17 GHz had low propagation power. These research results will be effective in providing ultra-connection and ultra-delay artificial intelligence services for WIFI 6, 5G, and future systems in a semi-basement indoor corridor environment.

• Science of Emotion and Sensibility
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• v.25 no.2
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• pp.71-78
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• 2022

This study investigates the recommendation for tax accounting services used in many companies. In particular, it aims to create guidelines for small businesses with fewer than 100 employees, which are relatively difficult to manage in terms of cost or time. We surveyed 100 corporate officials on basic business information, such as the number of employees, job titles, and business type, as well as the type of tax accounting service, the recommended score for the service, the reason for the score, and other opinions related to tax accounting services. In particular, the recommendation score seeks to obtain more effective results by using the Net Promoter Score method, which is known to be more effective in understanding customer opinions than general customer satisfaction surveys. The survey revealed a Net Promoter Score for a recommendation of -33 points, lower than the general Net Promoter Score. It also indicated that tax accounting services need improvement. Specifically, the opinions of the respondents who gave a non-recommendation score were as follows: "Not inconvenient or comfortable," "It was just okay," "I don't know if it would be helpful," and "There is no differentiation and there are no special alternatives." We concluded that an improved service for raising recommendation scores was necessary. This survey focused on recommendations for companies with fewer than 100 employees; future studies should incorporate larger companies and more variables.

• The Journal of Engineering Geology
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• v.33 no.1
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• pp.39-49
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• 2023

Soil contaminants measured and managed in Korea include those of geologic origin such as arsenic, cadmium, copper, lead, zinc, nickel, mercury, and fluoride. This study identifies the distribution of these contaminants using GIS modeling to analyze the spatial probability of soil contamination originating from geology. The modeling found that cadmium, copper, lead, nickel, and mercury often exceed the regulated standard by <1%. Concentrations of arsenic and zinc greatly exceeded the standard in the vicinity of mines and industrial complexes: mining and industry seemed to have substantial effects on the concentrations of these metals. Although fluoride was sampled at the lowest number of points, its frequency of exceeding the standard was the highest. No obvious source of artificial contamination has been identified, and fluoride's distribution characteristics showed continuity over a wide area, suggesting a strong correlation between geological characteristics and fluoride concentration. The highest frequencies of fluoride exceeding the standard were in Jurassic granite (40.00%) and Precambrian banded gneiss (34.12%). As these rocks contributed to the formation of soil through their weathering, high fluoride concentrations can be expected in soil in areas where these rocks are distributed.