• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.137-144
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• 2007

In KORDI (Korea Ocean Research and Development Institute), the KOSC (Korea Ocean Satellite Center) construction project is being prepared for acquisition, processing and distribution of sensor data via L-band from GOCI (Geostationary Ocean Color Imager) instrument which is loaded on COMS (Communication, Ocean and Meteorological Satellite); it will be launched in 2008. Ansan (the headquarter of KORDI) has been selected for the location of KOSC between 5 proposed sites, because it has the best condition to receive radio wave. The data acquisition system is classified into antenna and RF. Antenna is designed to be $\phi$ 9m cassegrain antenna which has 19.35 G/T$(dB/^{\circ}K)$ at 1.67GHz. RF module is divided into LNA (low noise amplifier) and down converter, those are designed to send only horizontal polarization to modem. The existing building is re-designed and arranged for the KOSC operation concept; computing room, board of electricity, data processing room, operation room. Hardware and network facilities have been designed to adapt for efficiency of each functions. The distribution system which is one of the most important systems will be constructed mainly on the internet. and it is also being considered constructing outer data distribution system as a web hosting service for offering received data to user less than an hour.

• Convergence Security Journal
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• v.21 no.1
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• pp.191-200
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• 2021

Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.

• Journal of the Korean Institute of Gas
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• v.22 no.6
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• pp.136-143
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• 2018

Cellular automata have been applied to simulations in many fields such as astrophysics, social phenomena, fire spread, and evacuation. Using cellular automata, this study develops a model for consequence analysis of the dispersion of hazardous chemicals, which is required for risk assessments of and emergency responses for frequent chemical accidents. Unlike in cases of detailed plant safety design, real-time accident responses require fast and iterative calculations to reduce the uncertainty of the distribution of damage within the affected area. EPA ALOHA and KORA of National Institute of Chemical Safety have been popular choices for these analyses. However, this study proposes an initiative to supplement the model and code continuously and is different in its development of free software, specialized for small and medium enterprises. Compared to the full-scale computational fluid dynamics (CFD), which requires large amounts of computation time, the relative accuracy loss is compromised, and the convenience of the general user is improved. Using Python open-source libraries as well as meteorological information linkage, it is made possible to expand and update the functions continuously. Users can easily obtain the results by simply inputting the layout of the plant and the materials used. Accuracy is verified against full-scale CFD simulations, and it will be distributed as open source software, supporting GPU-accelerated computing for fast computation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.550-558
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• 2019

As the scale of the Internet of Things (IoT) environment grows and develops day by day, the information collected and shared through IoT devices becomes increasingly diverse and more common. However, because IoT devices have limitations on computing power and a low power capacity due to their miniaturized size, it is difficult to apply security technologies like encryption and authentication that have been directly applied in the previous Internet environment, making the IoT vulnerable to security threats. Because of this weakness, important information that needs to be delivered safely and accurately is exposed to the threat of malicious exploitation, such as data forgery, data leakage, and infringement of personal information. In order to overcome this threat, various security studies are being actively conducted to compensate for the weaknesses in IoT environment devices. In particular, since various devices interact, and share and communicate information collected in the IoT environment, each device should be able to communicate with reliability. With regard to this, various studies have been carried out on techniques for device authentication. This study examines the limitations and problems of the authentication techniques that have been studied thus far, and proposes technologies that can certify IoT devices for safe communication between reliable devices in the Internet environment.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.275-284
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• 2021

The study attempted to systematically and comprehensively analyze individual studies in which proprioceptive neuromuscular facilitation (PNF) was performed with chronic stroke patients. Selection criteria included type of participants (stroke patients), intervention (PNF), comparison (intervention group or non-intervention group), outcomes (effect on dynamic balance), and study design (randomized controlled trial). We searched seven literature databases, and selected 17 papers that met our selection criteria. For meta-analysis, effect size of each individual study was extracted using the R project for Statistical computing version 4.0.3. Rob 2.0 tool, developed by the Cochrane group, was used to evaluate the quality of each individual study. The overall effect size PNF with dynamic balance was 0.59 (95% CI=0.41-1.77), which was significantly different than the median effect size (p<0.05). The sub-group for dynamic balance was analyzed, for effect sizes of BBS (0.50), TUG (0.78), and FRT (0.51). Thus, PNF intervention has a positive impact on improve of dynamic balance by chronic stroke patients.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.41-50
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• 2020

The consequences of wearing high heels can be different according to the heel height, gait speed, shoe design, heel base area, and shoe size. This study aimed to focus on the knee extension and flexion range of motion (ROM) during gait, which were challenged by wearing five different shoe heel types and two different self-selected gait speeds (comfortable and fast) as experimental conditions. Measurement standards of knee extension and flexion ROM were individually calibrated at the time of heel strike, mid-stance, toe-off, and stance phase based on the 2-minute video recordings of each gait condition. Seven healthy young women (20.7 ± 0.8 years) participated and they were asked to walk on a treadmill wearing the five given shoes at a self-selected comfortable speed (average of 2.4 ± 0.3 km/h) and a fast speed (average of 5.1 ± 0.2 km/h) in a random order. All of the shoes were in size 23.5 cm. Three of the given shoes were 9.0 cm in height, the other two were flat shoes and sneakers. A motion capture software (Kinovea 0.8.27) was used to measure the kinematic data; changes in the knee angles during each gait. During fast speed gait, the knee extension angles at heel strike and mid-stance were significantly decreased in all of the 3 high heels (p<0.05). The results revealed that fast gait speed causes knee flexion angle to significantly increase at toe-off in all five types of shoes. However, there was a significant difference in both the knee flexion and extension angles when the gait in stiletto heels and flat shoes were compared in fast gait condition (p<0.05). This showed that walking fast in high heels leads to abnormal knee ROM and thus can cause damages to the knee joints. The findings in this preliminary study can be a basis for future studies on the kinematic changes in the lower extremity during gait and for the analysis of causes and preventive methods for musculoskeletal injuries related to wearing high heels.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.111-123
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• 2022

Artificial intelligence, which is developing as the core of an intelligent information society, is bringing convenience and positive life changes to humans. However, with the development of artificial intelligence, human rights and property are threatened, and ethical problems are increasing, so alternatives are needed accordingly. In this study, the most controversial artificial intelligence ethics problem in the dysfunction of artificial intelligence was aimed at researching and developing artificial intelligence ethical measurement indicators to protect human personality rights and property first under artificial intelligence ethical principles and components. In order to research and develop artificial intelligence ethics measurement indicators, various related literature, focus group interview(FGI), and Delphi surveys were conducted to derive 43 items of ethics measurement indicators. By survey and statistical analysis, 40 items of artificial intelligence ethics measurement indicators were confirmed and proposed through descriptive statistics analysis, reliability analysis, and correlation analysis for ethical measurement indicators. The proposed artificial intelligence ethics measurement indicators can be used for artificial intelligence design, development, education, authentication, operation, and standardization, and can contribute to the development of safe and reliable artificial intelligence.

• Journal of the Korea Society for Simulation
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• v.30 no.4
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• pp.9-19
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• 2021

Electronic voting has been recognized as an alternative to complement the limitations of existing paper voting. At the same time, security concerns are being raised. This paper presents a blockchain-based electronic voting and survey system that can guarantee reliability. Our smart contract was created using Solidity on Ethereum which is a blockchain-based distributed computing platform, and the system was implemented in connection with the Javascript based user interface. In addition, in order to protect the personal information of participants, the system is generating hash of the personal data and storing the hash of users for the contract data. Since we exploited different kinds of languages for the system, we derived items of functionality testing and presented the functionality testing result. Moreover, we made use of the Chrome's performance evaluation functionality to see the response time of the blockchain-based system. In addition, we compared the performance with the system which has the same functionality on database. The contribution of this research is design and implementation of blockchain-based electronic voting system and presentation of the functionality and performance simulation result.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.33-40
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• 2021

Automatic Speech Recognition(ASR) is a technology that analyzes human speech sound into speech signals and then automatically converts them into character strings that can be understandable by human. Speech recognition technology has evolved from the basic level of recognizing a single word to the advanced level of recognizing sentences consisting of multiple words. In real-time voice conversation, the high recognition rate improves the convenience of natural information delivery and expands the scope of voice-based applications. On the other hand, with the active application of speech recognition technology, concerns about related cyber attacks and threats are also increasing. According to the existing studies, researches on the technology development itself, such as the design of the Automatic Speaker Verification(ASV) technique and improvement of accuracy, are being actively conducted. However, there are not many analysis studies of attacks and threats in depth and variety. In this study, we propose a cyber attack model that bypasses voice authentication by simply manipulating voice frequency and voice speed for AI voice recognition service equipped with automated identification technology and analyze cyber threats by conducting extensive experiments on the automated identification system of commercial smartphones. Through this, we intend to inform the seriousness of the related cyber threats and raise interests in research on effective countermeasures.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.323-332
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• 2022

As various services using AI technology are being developed, much attention is being paid to AI service production. Recently, AI technology is acknowledged as one of ICT services, a lot of research is being conducted for general-purpose AI service production. In this paper, I describe the research results in terms of systems for AI service production, focusing on the distribution and production of machine learning models, which are the final steps of general machine learning development procedures. Three different Ubuntu systems were built, and experiments were conducted on the system, using data from 2017 validation COCO dataset in combination of different AI models (RFCN, SSD-Mobilenet) and different communication methods (gRPC, REST) to request and perform AI services through Tensorflow serving. Through various experiments, it was found that the type of AI model has a greater influence on AI service inference time than AI machine communication method, and in the case of object detection AI service, the number and complexity of objects in the image are more affected than the file size of the image to be detected. In addition, it was confirmed that if the AI service is performed remotely rather than locally, even if it is a machine with good performance, it takes more time to infer the AI service than if it is performed locally. Through the results of this study, it is expected that system design suitable for service goals, AI model development, and efficient AI service production will be possible.