• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.817-839
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• 2021

To manage software safely, the military acquires and manages products in accordance with the RMF A&A. RMF A&A is standard for acquiring IT products used in the military. And it covers the requirements, acquisition through evaluation and maintenance of products. According to the RMF A&A, product development activities should reflect the risks of the military. In other words, developers have mitigated the risks through security by design and supply chain security. And they submit evidence proving that they have properly comply with RMF A&A's security requirements, and the military will evaluate the evidence to determine whether to acquire IT product. Previously, case study of RMF A&A have been already conducted. But it is difficult to apply in real-world, because it only address part of RMF A&A and detailed information is confidential. In this paper, we propose the evidence fulfilling method that can satisfy the requirements of the RMF A&A. Furthermore, we apply the proposed method to real-world drone system for verifying our method meets the RMF A&A.

• Journal of Aerospace System Engineering
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• v.16 no.4
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• pp.95-108
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• 2022

Airworthiness certification is critical, in ensuring the flight safety of military aircraft for development tests and production operations. The MIL-HDBK-516C, latest airworthiness certification document, handles the field of flying qualities in Chapter 6 (flight technology), and refers to specific chapters of MIL-STD-1797B, which is the specification document for developing military aircraft. Since the MIL-STD-1797B released in 2006 by the U.S. Department of Defense is not disclosed to other countries, the Chapter 6 (flight technology) of MIL-HDBK-516B Expanded, the former certification standards pursuant to flying qualities, has to be applied to military aircraft being developed in the Republic of Korea. However the requirements of Chapter 6 of MIL-HDBK-516B Expanded comprise unclear sentences, because of contents from various development specifications. Also, clarification is needed in that the same requirements have to be verified in different criteria. In this paper, the results of this study present an effective verification method, for acquiring the airworthiness certification in field of flying qualities based on MIL-HDBK-516B Expanded.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.77-89
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• 2022

It has been proven through many previous researches that the pretrained language model with a large corpus helps improve performance in various natural language processing tasks. However, there is a limit to building a large-capacity corpus for training in a language environment where resources are scarce. Using the Cross-lingual Post-Training (XPT) method, we analyze the method's efficiency in Korean, which is a low resource language. XPT selectively reuses the English pretrained language model parameters, which is a high resource and uses an adaptation layer to learn the relationship between the two languages. This confirmed that only a small amount of the target language dataset in the relationship extraction shows better performance than the target pretrained language model. In addition, we analyze the characteristics of each model on the Korean language model and the Korean multilingual model disclosed by domestic and foreign researchers and companies.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.387-398
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• 2023

The COVID-19 outbreak has significantly impacted human lifestyles and patterns. It was recommended to avoid face-to-face contact and over-crowded indoor places as much as possible as COVID-19 spreads through air, as well as through droplets or aerosols. Therefore, if a person who has contacted a COVID-19 patient or was at the place where the COVID-19 patient occurred is concerned that he/she may have been infected with COVID-19, it can be fully expected that he/she will search for COVID-19 symptoms on Google. In this study, an exploratory data analysis using deep learning models(DNN & LSTM) was conducted to see if we could predict the number of confirmed COVID-19 cases by summoning Google Trends, which played a major role in surveillance and management of influenza, again and combining it with data on the number of confirmed COVID-19 cases. In particular, search term frequency data used in this study are available publicly and do not invade privacy. When the deep neural network model was applied, Seoul (9.6 million) with the largest population in South Korea and Busan (3.4 million) with the second largest population recorded lower error rates when forecasting including search term frequency data. These analysis results demonstrate that search term frequency data plays an important role in cities with a population above a certain size. We also hope that these predictions can be used as evidentiary materials to decide policies, such as the deregulation or implementation of stronger preventive measures.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.212-215
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• 2022

In the era of so-called digital transformation today, the need for the construction and utilization of big data in various fields has increased. Today, a lot of data is produced and stored in a digital device and media-friendly manner, but the production and storage of data for a long time in the past has been dominated by print books. Therefore, the need for Optical Character Recognition (OCR) technology to utilize the vast amount of print books accumulated for a long time as big data was also required in line with the need for big data. In this study, a system for digitizing the structure and content of a document object inside a scanned book image is proposed. The proposal system largely consists of the following three steps. 1) Recognition of area information by document objects (table, equation, picture, text body) in scanned book image. 2) OCR processing for each area of the text body-table-formula module according to recognized document object areas. 3) The processed document informations gather up and returned to the JSON format. The model proposed in this study uses an open-source project that additional learning and improvement. Intelligent OCR proposed as a system in this study showed commercial OCR software-level performance in processing four types of document objects(table, equation, image, text body).

• Journal of The Korean Association For Science Education
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• v.44 no.3
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• pp.231-248
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• 2024

This study developed a chatbot for first-year high school students, employing open-source software and the Korean Sentence-BERT model for AI-powered document classification. The chatbot utilizes the Sentence-BERT model to find the six most similar Q&A pairs to a student's query and presents them in a carousel format. The initial dataset, built from online resources, was refined and expanded based on student feedback and usability throughout over the operational period. By the end of the 2023 academic year, the chatbot integrated a total of 30,819 datasets and recorded 3,457 student interactions. Analysis revealed students' inclination to use the chatbot when prompted by teachers during classes and primarily during self-study sessions after school, with an average of 2.1 to 2.2 inquiries per session, mostly via mobile phones. Text mining identified student input terms encompassing not only science-related queries but also aspects of school life such as assessment scope. Topic modeling using BERTopic, based on Sentence-BERT, categorized 88% of student questions into 35 topics, shedding light on common student interests. A year-end survey confirmed the efficacy of the carousel format and the chatbot's role in addressing curiosities beyond integrated science learning objectives. This study underscores the importance of developing chatbots tailored for student use in public education and highlights their educational potential through long-term usage analysis.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.5
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• pp.3-16
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• 2008

A Wireless Sensor Network (WSN for short) is a wireless network consisting of distributed small devices which are called sensor nodes or motes. Recently, there has been an extensive research on WSN and also on its security. For secure storage and secure transmission of the sensed information, sensor nodes should be equipped with cryptographic algorithms. Moreover, these algorithms should be efficiently implemented since sensor nodes are highly resource-constrained devices. There are already some existing algorithms applicable to sensor nodes, including public key ciphers such as TinyECC and standard block ciphers such as AES. Stream ciphers, however, are still to be analyzed, since they were only recently standardized in the eSTREAM project. In this paper, we implement over the MicaZ platform nine software-based stream ciphers out of the ten in the second and final phases of the eSTREAM project, and we evaluate their performance. Especially, we apply several optimization techniques to six ciphers including SOSEMANUK, Salsa20 and Rabbit, which have survived after the final phase of the eSTREAM project. We also present the implementation results of hardware-oriented stream ciphers and AES-CFB fur reference. According to our experiment, the encryption speeds of these software-based stream ciphers are in the range of 31-406Kbps, thus most of these ciphers are fairly acceptable fur sensor nodes. In particular, the survivors, SOSEMANUK, Salsa20 and Rabbit, show the throughputs of 406Kbps, 176Kbps and 121Kbps using 70KB, 14KB and 22KB of ROM and 2811B, 799B and 755B of RAM, respectively. From the viewpoint of encryption speed, the performances of these ciphers are much better than that of the software-based AES, which shows the speed of 106Kbps.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.3
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• pp.137-142
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• 2009

Purpose: World Health Organization (WHO) have suggested that an individual's 10-year absolute fracture risk is more reliable than Bone Mineral Density (BMD) measurement as the predictor of osteoporotic fracture. In 2008, Fracture Risk Assessment Tool ($FRAX^{TM}$) was developed by WHO to evaluate fracture risk of patients based on individual's clinical risk factors. The purpose of this study is to offer the comparative analysis of the existing GE prodigy and $FRAX^{TM}$ Tool in Absolute Fracture Risk Assessment Tool. Materials and Methods: 201 women ($55{\pm}3.5$ years) underwent femoral neck BMD measurement using GE Prodigy. The 10-year probability (%) of hip fracture (or a major osteoporosis-related fracture) was estimated using T-scores of GE prodigy and $FRAX^{TM}$. We made a comparative analysis of these data using SPSS (Ver.12). Results: There was a significant difference statistically between T-score ($-0.52{\pm}0.97$) of GE prodigy and T-score ($-1.45{\pm}0.81$) of $FRAX^{TM}$ (r=0.977, p=0.000). Also, there was a significant difference statistically between a major osteoporosis- related fracture ($9.15{\pm}3.71$) of GE prodigy and a major osteoporosis-related fracture ($4.87{\pm}1.51$) of $FRAX^{TM}$ (r=0.909, p=0.000). Moreover, a statistically significant difference was found in the 10-year probability of hip fracture of GE prodigy ($1.56{\pm}1.48$) and of hip fracture ($0.53{\pm}0.61$) of $FRAX^{TM}$ (r=0.905, p=0.000). Conclusions: There was a significant difference statistically between GE prodigy and $FRAX^{TM}$ Tool in Absolute Fracture Risk Assessment Tool. Especially, T-score, a major osteoporosis-related fracture and the 10-year probability of hip fracture that were estimated using GE prodigy tended to show the higher results than one evaluated by $FRAX^{TM}$ Tool. In conclusion, $FRAX^{TM}$ Tool may provide a better tool. The application of $FRAX^{TM}$ Tool as a fracture predictor remains to be clarified.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.4
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• pp.242-255
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• 2006

In the recent years, Popularization of mobile devices such as Smart Phones, PDAs and MP3 Players causes rapid increasing necessity of Power management technology because it is most essential factor of mobile devices. On the other hand, despite low price, hard disk has large capacity and high speed. Even it can be made small enough today, too. So it appropriates mobile devices. but it consumes too much power to embed In mobile devices. Due to these motivations, in this paper we had suggested methods of minimizing Power consumption while playing multimedia data in the disk media for real-time and we evaluated what we had suggested. Strict limitation of power consumption of mobile devices has a big impact on designing both hardware and software. One difference between real-time multimedia streaming data and legacy text based data is requirement about continuity of data supply. This fact is why disk drive must persist in active state for the entire playback duration, from power management point of view; it nay be a great burden. A legacy power management function of mobile disk drive affects quality of multimedia playback negatively because of excessive I/O requests when the disk is in standby state. Therefore, in this paper, we analyze power consumption profile of disk drive in detail, and we develop the algorithm which can play multimedia data effectively using less power. This algorithm calculates number of data block to be read and time duration of active/standby state. From this, the algorithm suggested in this paper does optimal scheduling that is ensuring continual playback of data blocks stored in mobile disk drive. And we implement our algorithms in publicly available MPEG player software. This MPEG player software saves up to 60% of power consumption as compared with full-time active stated disk drive, and 38% of power consumption by comparison with disk drive controlled by native power management method.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.