• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.9-16
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• 2022

In this paper, we propose a plan to design and utilize the RDBS(Record Block Data file management System) so that data can be recovered when data files in the Combat Management System are mismatched. The CMS(Combat Management System) manages the same files in multiple IPN(Infomation Processing Node) repositories to support multiplexing. However, mismatches in data files can occur due to equipment maintenance or user immaturity. The existing CMS does not manage the history of changes in data files, and when a mismatch occurs, data file were synchronized based on the latest date. But, It is difficult to say that files with the latest date have the highest reliability, and once the file synchronization has progressed, it cannot be recovered with pre-synchronization data. To solve this problem, data was stored and synchronized in units of record blocks using RDBS proposed in this paper, and the Rsync algorithm was used to reduce the overhead of file synchronization due to units of record blocks. SW applied with RDBS was tested for performance in a simulated environment, and it was confirmed that it could be applied to CMS through normal operation confirmation.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.347-364
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• 2022

Fraudulent companies or sellers strategically manipulate reviews to influence customers' purchase decisions; therefore, the reliability of reviews has become crucial for customer decision-making. Since customers increasingly rely on online reviews to search for more detailed information about products or services before purchasing, many researchers focus on detecting manipulated reviews. However, the main problem in detecting manipulated reviews is the difficulties with obtaining data with manipulated reviews to utilize machine learning techniques with sufficient data. Also, the number of manipulated reviews is insufficient compared with the number of non-manipulated reviews, so the class imbalance problem occurs. The class with fewer examples is under-represented and can hamper a model's accuracy, so machine learning methods suffer from the class imbalance problem and solving the class imbalance problem is important to build an accurate model for detecting manipulated reviews. Thus, we propose an OpenAI-based reviews generation model to solve the manipulated reviews imbalance problem, thereby enhancing the accuracy of manipulated reviews detection. In this research, we applied the novel autoregressive language model - GPT-3 to generate reviews based on manipulated reviews. Moreover, we found that applying GPT-3 model for oversampling manipulated reviews can recover a satisfactory portion of performance losses and shows better performance in classification (logit, decision tree, neural networks) than traditional oversampling models such as random oversampling and SMOTE.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.115-124
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• 2022

Unlike the semi-public system of city buses, Seoul's townbus are operated on a private operating system, which is poor condition to the changes in the environment. Sales decreased due to a decrease in the number of passengers due to COVID-19 and a demand for conversion due to the advent of competitive transportation methods, and the financial support of Seoul Metropolitan Government is continuously increasing. In this study, to analyze the characteristics of townbus operated by a private operating system, the townbus sales and surplus companies were analyzed by what factors were affected. For the analysis data, townbus financial statements of Seoul in 2018 were used, and townbus sales and surplus companies were applied as dependent variables, and townbus operation system, satisfaction survey, humanities and social variables, and subway and public bicycle characteristics were applied as independent variables. As a result of the analysis, the sales is affected by operating hours per vehicle, in-vehicle safety, the number of households, the number of elderly people, and public bicycle variables, and surplus companies are affected by in-vehicle safety, reliability, and public bicycle variables. In particular, public bicycles, a competitive means of transportation, had an impact on industry sales, and the townbus business environment is expected to become more difficult as time goes by. The industry is seeking self-rescue measures, and Seoul is required to strengthen financial support so that townbus can operate stably.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.11-17
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• 2022

In this paper, a model that can increase the efficiency of work in arranging interior furniture by applying augmented reality technology was studied. In the existing system to which augmented reality is currently applied, there is a problem in that information is limitedly provided depending on the size and nature of the company's product when outputting the image of furniture. To solve this problem, this paper presents an AR labeling algorithm. The AR labeling algorithm extracts feature points from the captured images and builds a database including indoor location information. A method of detecting and learning the location data of furniture in an indoor space was adopted using the CNN technique. Through the learned result, it is confirmed that the error between the indoor location and the location shown by learning can be significantly reduced. In addition, a study was conducted to allow users to easily place desired furniture through augmented reality by receiving detailed information about furniture along with accurate image extraction of furniture. As a result of the study, the accuracy and loss rate of the model were found to be 99% and 0.026, indicating the significance of this study by securing reliability. The results of this study are expected to satisfy consumers' satisfaction and purchase desires by accurately arranging desired furniture indoors through the design and implementation of AR labels.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.339-346
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• 2022

Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generation has become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels is increasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to other energy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by the photovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machine learning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms of interpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting results are derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability of the model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increase the forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecasting scheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.325-350
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• 2023

This study focuses on the impact of research data metadata quality evaluation index on the revitalization of K-BDS, a research data platform in the bio field, and examines the mediating effect of motivation factors for utilizing the platform. The investigation employs a structural equation model analysis and bootstrap analysis to explore the interrelationships among the three variables. The findings demonstrate that researchers who prioritize the quality of metadata display higher motivation to use the research data platform, leading to an intention to activate the platform. The study also confirms the mediating effect of motivation factors. Moreover, a comprehensive understanding of the sub-factors within each variable is attained through regression analysis and Sobel test. The results highlight that enhancing searchability is crucial to activate research data sharing in the bio field, while improving discoverability is vital for research data reuse. Interestingly, the study reveals that citationability does not significantly impact platform activation. As a conclusion, to foster platform activation, it is imperative to provide systematic support by enhancing metadata quality. This improvement can not only increase trust in the platform but also institutionally solidify the benefits of citation.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.19-36
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• 2023

As advances in information and communication technology have made it easier for anyone to produce and disseminate information, a new problem has emerged: fake news, which is false information intentionally shared to mislead people. Initially spread mainly through text, fake news has gradually evolved and is now distributed in multimedia formats. Since its founding in 2005, YouTube has become the world's leading video platform and is used by most people worldwide. However, it has also become a primary source of fake news, causing social problems. Various researchers have been working on detecting fake news on YouTube. There are content-based and background information-based approaches to fake news detection. Still, content-based approaches are dominant when looking at conventional fake news research and YouTube fake news detection research. This study proposes a fake news detection method based on background information rather than content-based fake news detection. In detail, we suggest detecting fake news by utilizing related video information from YouTube. Specifically, the method detects fake news through CNN, a deep learning network, from the vectorized information obtained from related videos and the original video using Doc2vec, an embedding technique. The empirical analysis shows that the proposed method has better prediction performance than the existing content-based approach to detecting fake news on YouTube. The proposed method in this study contributes to making our society safer and more reliable by preventing the spread of fake news on YouTube, which is highly contagious.

• Journal of Platform Technology
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• v.11 no.2
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• pp.54-65
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• 2023

Due to the nature of electric vehicles, the batteries used for electric vehicles have a very large rated capacity. If an electric vehicle runs for a long time or an electric vehicle is abandoned due to a traffic accident, the electric vehicle battery becomes a waste battery. Even in vehicles that are being abandoned, the remaining capacity of waste batteries for electric vehicles is sufficient for other purposes. Waste batteries for automobiles are very expensive, so they need to be recycled and reused, but there was a problem that the standards for measuring the performance grade of waste batteries for recycling and reuse were insufficient. As a method for measuring the remaining capacity of waste battery, the most stable and reliable method is to measure the remaining capacity of battery using full charge and discharge. However, the inspection method by the full charging and discharging method varies depending on the capacity of the battery, but it takes more than a day to inspect, and many people are making great efforts to solve this problem. In this paper, an electric vehicle battery residual capacity analysis technique using voltage deviation between cells was studied and analyzed as a method to reduce inspection time for electric vehicle batteries. To this end, a full charging and discharging-based capacity measurement system was constructed, experimental data were collected using a nose or waste battery, and the correlation between the voltage deviation and the remaining capacity of the battery pack was analyzed to verify whether it can be used for battery inspection.

• Journal of the Korean Society of Propulsion Engineers
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• v.26 no.4
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• pp.54-63
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• 2022

A 3-tonf class liquid rocket engine that powers the upper stage of a small launcher and lifts 500 kg payload to 500 km SSO is designed. The small launcher is to utilize the flight-proven technology of the 75-tonf class engine for the first stage. A combination of liquid oxygen and liquid methane has been selected as their cryogenic states can provide an extra boost in specific impulse as well as enable a weight saving via the common dome arrangement. An expander cycle is chosen among others as the low-pressure operation makes it robust and reliable while a specific impulse of over 360 seconds is achievable with the nozzle extension ratio of 120. Key components such as combustion chamber and turbopump are designed for additive manufacturing to a target cost. The engine system provides an evaporated methane for the autogenous pressurization system and the reaction control of the stage. This upper stage propulsion system can be extended to various missions including deep space exploration.