• Smart Media Journal
• /
• v.11 no.10
• /
• pp.46-53
• /
• 2022

Today, due to the 4th industrial revolution and extensive R&D funding, domestic companies have begun to possess world-class industrial technologies and have grown into important assets. The national government has designated it as a "national core technology" in order to protect companies' critical industrial technologies. Particularly, technology leaks in the shipbuilding, display, and semiconductor industries can result in a significant loss of competitiveness not only at the company level but also at the national level. Every year, there are more insider leaks, ransomware attacks, and attempts to steal industrial technology through industrial spy. The stolen industrial technology is then traded covertly on the dark web. In this paper, we propose a system for detecting industrial technology leaks in the dark web environment. The proposed model first builds a database through dark web crawling using information collected from the OSINT environment. Afterwards, keywords for industrial technology leakage are extracted using the KeyBERT model, and signs of industrial technology leakage in the dark web environment are proposed as quantitative figures. Finally, based on the identified industrial technology leakage sites in the dark web environment, the possibility of secondary leakage is detected through the PageRank algorithm. The proposed method accepted for the collection of 27,317 unique dark web domains and the extraction of 15,028 nuclear energy-related keywords from 100 nuclear power patents. 12 dark web sites identified as a result of detecting secondary leaks based on the highest nuclear leak dark web sites.

• The Korean Society of Law and Medicine
• /
• v.24 no.4
• /
• pp.67-101
• /
• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.5 no.6
• /
• pp.1094-1103
• /
• 2001

According to standard procedures as defined in the users handbook for sea level data processes, I was compared to Topex/Poseidon sea level data from the first 350days of mission and Tide Gauge sea level data from the Amsterdam- Crozet- Kerguelen region in the South Indian Ocean. The comparison improves significantly when many factors for the corrections were removed, then only the aliased oceanic tidal energy is removed by oceanic tide model(11) in this period. Making the corrections and smoothing the sea level data ()ver 60km along-track segments and the Tide Gauge sea level data for the time series results in the digital correlation and RMS difference between the two data of c=-0.12 and rms= 11.4cm, c=0.55 and rms=5.38cm, c=0.83 and rms=2.83cm, and c=0.24 and rms=6.72 for the Amsterdam, Crozet and Kerguelenplateau, and Kerguelen coast, respectively. It was also found that the Kerguelen plateau has a comparisons due to propagating signals(the baroclinic Rossby wave with velocity of -3.9 ~-4.2cm/sec, period of 167days and amplitude of 10cm) that introduce temporal lags(${\gamma}$: 10~30days) between the altimeter and tide gauge time series. The conclusion is that on timescales longer than about 10days the RMS sea level errors are less than or of the order of several centimeters and are mainly due to the effects of currents rather than the effects of stories(water temperature, density) and winds.

• The KIPS Transactions:PartC
• /
• v.15C no.5
• /
• pp.383-390
• /
• 2008

The mobile sink is most suitable to guarantee the real time processing to events in ubiquitous environment. However it brings many challenges to wireless sensor networks. In particular, the question of how to transfer the collected data to the mobile sink is an important topic in the aspect of effective management of wireless sensor nodes. In this paper, a new data dissemination model is proposed. Since this method uses the home agent and the access node concepts, it provides reliable and efficient data delivery to mobile sink with minimum overhead. In this proposed method, the information of the mobile sink which is constantly moving is informed only to the home agent node and the access node, instead of all sensor nodes. Thus, the collected data from sensor nodes are transferred to the fixed home agent and it sends these data to the mobile sink. Since the confliction phenomenon between data packets in wireless networks could be reduced, the success ratio of data arriving in the mobile sink is highly enhanced. In our experiments, the proposed method reduces the number of broadcast packets so that it saves the amount of energy consumed for transmitting and receiving the data packets. This effect contributes to prolong the lifetime of the wireless sensor networks operated by batteries.

• International Journal of Internet, Broadcasting and Communication
• /
• v.12 no.2
• /
• pp.8-14
• /
• 2020

Recently, there was an increasing demand for an integrated access control system which is capable of user recognition, door control, and facility operations control for smart buildings automation. The market available door lock access control solutions need to be improved from the current level security of door locks operations where security is compromised when a password or digital keys are exposed to the strangers. At present, the access control system solution providers focusing on developing an automatic access control system using (RF) based technologies like bluetooth, WiFi, etc. All the existing automatic door access control technologies required an additional hardware interface and always vulnerable security threads. This paper proposes the user identification and authentication solution for automatic door lock control operations using camera based visible light communication (VLC) technology. This proposed approach use the cameras installed in building facility, user smart devices and IoT open source controller based LED light sensors installed in buildings infrastructure. The building facility installed IoT LED light sensors transmit the authorized user and facility information color grid code and the smart device camera decode the user informations and verify with stored user information then indicate the authentication status to the user and send authentication acknowledgement to facility door lock integrated camera to control the door lock operations. The camera based VLC receiver uses the artificial intelligence (AI) methods to decode VLC data to improve the VLC performance. This paper implements the testbed model using IoT open-source based LED light sensor with CCTV camera and user smartphone devices. The experiment results are verified with custom made convolutional neural network (CNN) based AI techniques for VLC deciding method on smart devices and PC based CCTV monitoring solutions. The archived experiment results confirm that proposed door access control solution is effective and robust for automatic door access control.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.11 no.4
• /
• pp.85-93
• /
• 2008

The MAEULSOOP, Korean traditional village groves have been installed and survived since 7th Century to serve local village dwellers as a community forest. The common sense of their reasons for being is related to the surroundings such as hills, waterways and wind-ways. To understand the roles in a local community, spatial characteristics of distribution, locations and shapes were tested at the two-characterized regions, the Southwestern Flat Region and the Eastern Hilly Region. Approximately more than 500 written evidences related to trees and forests were surveyed, for example village names, folk tales and lists of designated trees for protection. Twenty sites in each region were selected and tested for the spatial analysis. Aerial photographs, DEM and the ArcGIS with a modified AML for slope analysis are applied based on the criteria of the KLCIS(Kwon, 2002; 권진오, 2008). The major factors in the role of the MAEULSOOP based on the spatial character of two regions are; the array and locations of hills for encircling or exposure, locations against corridors and waterway or not, locations of the community to serve, the conservation of energy. Although locations and shapes of the MAEULSOOP are slightly different, it seems that one of the prime roles is what makes their everyday life difficult the most in the community.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.2
• /
• pp.355-365
• /
• 2015

As the global climate has dramatically changed over the past decades, there has been active research on evaluating its effects on food security, which has been recognized as one of the most important issues in the field. In this study, we analyzed the impact of the climate change on the Korean agriculture using meta-analysis methods. Especially, our research focus is on estimating the effect of CO2 concentration and two adaptations (planting-date and cultivar adjustments)on rice that accounts for a larger proportion of the Korean domestic agriculture. Unlike traditional general meta-analysis methods that use summary statistics of effects of interest, meta analysis specific to the agriculture literature was conducted by integrating the data on rice yield that were generated under various CO2 emission scenarios and general circulating models of the 6 collected individual studies. As a modeling approach, the rice yield change ratio was set as the dependent variable and the main and interaction effects of CO2 concentration and adaptation were considered as independent variables in a regression model, As a result, CO2 is estimated to have opposite effects on rice yield depending on whether any of the two adaptations is applied or not; decreasing effect without adaptation and increasing effect with adaptation. In addition, it turns out that the cultivar adjustment has a higher increasing effect on rice yield than the planting-date adjustment. The results of the study are expected to be used as basic quantitative data for establishing responsive polices to the future climate changes.

• Spatial Information Research
• /
• v.17 no.4
• /
• pp.431-443
• /
• 2009

Recently, turbidity problem is one of the hot issues in dam and reservoir management works. Main reason to bring about high density turbid water is sediment yield by rainfall intensity energy. Because existing researches didn't consider diverse types of crops, it was difficult to calculate more accurate soil erosion and sediment yield. This study was evaluated the reduction efficiency of soil erosion and sediment yield using ginseng layer extracted from IKONOS satellite image, and the area and the ratio of ginseng area represented $0.290km^2$ and 0.94%. The reduction efficiency of soil erosion considering ginseng area represented low value in 0.9% using GIS-based RUSLE model, because the area of ginseng was small compared to areas of other agricultural lands. To reflect future land use change, this study was calculated the reduction efficiency of soil erosion and sediment yield by considering many scenarios as kinds of crops of paddy, dry field, orchard, and other agricultural areas convert to the ginseng district. As result of analysis of them according to scenarios, scenario (1) in which dry field was converted to ginseng area and scenario (2) in which fully agricultural lands were converted to ginseng area showed high reduction efficiency as 31.3% and 34.8% respectively, compared to existing research which didn't consider ginseng area. Methodology suggested in this study will be very efficient tools to help reservoir management related to high density turbid water.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.49 no.2
• /
• pp.28-34
• /
• 2012

Various feature extraction algorithms are widely applied to real-time image processing applications for extracting significant features from images. Feature extraction algorithms are mostly combined with image processing algorithms mostly for image tracking and recognition. Feature extraction function is used to supply feature information to the other image processing algorithms and it is mainly implemented in a preprocessing stage. Nowadays, image processing applications are faced with embedded system implementation for a real-time processing. In order to satisfy this requirement, it is necessary to reduce execution time so as to improve the performance. Reducing the time for executing a feature extraction function dose not only extend the execution time for the other image processing algorithms, but it also helps satisfy a real-time requirement. This paper explains FAST (Feature from Accelerated Segment Test algorithm) of E. Rosten and presents FPGA-based embedded hardware accelerator architecture. The proposed acceleration scheme can be implemented by using approximately 2,217 Flip Flops, 5,034 LUTs, 2,833 Slices, and 18 Block RAMs in the Xilinx Vertex IV FPGA. In the Modelsim - based simulation result, the proposed hardware accelerator takes 3.06 ms to extract 954 features from a image with $640{\times}480$ pixels and this result shows the cost effectiveness of the propose scheme.

• Korean Journal of Construction Engineering and Management
• /
• v.18 no.3
• /
• pp.63-73
• /
• 2017

In contemporary society, it is increasingly common to spend more time indoors. As such, there is a continually growing desire to build comfortable and safe indoor environments. Along with this trend, however, there are some serious indoor-environment challenges, such as the quality of indoor air and Sick House Syndrome. To address these concerns the government implements various systems to supervise and manage indoor environments. For example, green building certification is now compulsory for public buildings. There are three categories of green building certification related to indoor air in Korea: Health-Friendly Housing Construction Standards, Green Standard for Energy & Environmental Design(G-SEED), and Indoor Air Certification. The first two types of certification, Health-Friendly Housing Construction Standards and G-SEED, evaluate data in a drawing plan. In comparison, the Indoor Air Certification evaluates measured data. The certification using data from a drawing requires a considerable amount of time compared to other work. A 2D tool needs to be employed to measure the area manually. Thus, this study proposes an automatic assessment process using a Building Information Modeling(BIM) model based on 3D data. This process, using open source Industry Foundation Classes(IFC), exports data for the certification system, and extracts the data to create an Excel sheet for the certification. This is expected to improve the work process and reduce the workload associated with evaluating indoor air conditions.