• Journal of Space Technology and Applications
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• v.4 no.1
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• pp.48-61
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• 2024

In this paper, we introduce the concept of the cube satellite Chungnam National University Laser Unity Bus (CLUB), which can provide an integrated infrastructure for various ground-space laser applications. With the advent of the new space era, the rapid expansion of space utilization has begun to reveal the limitations of conventional radio frequencies. As space missions diversify, lasers are garnering attention as a viable alternative. Between ground and space, lasers are applied in various fields including satellite laser ranging (SLR), laser weapons, and laser communication. However, laser used between the ground and space are significantly influenced by the Earth's atmosphere. Consequently, understanding the atmospheric effects on laser propagation is crucial. In particular, atmospheric turbulence, which refracts and distorts laser beams, intensifies closer to the Earth's surface, exerting a greater impact on the uplink than on the downlink. While downlink verification is facilitated by ground detection, verifying the uplink poses challenges due to the necessity of space-based detection. In response to these challenges, we propose the idea of cube satellite as a means to enhance understanding and verification of laser propagation in the uplink. Additionally, we present the results of conceptual design by analyzing requirements, focusing on mission design of the CLUB cube satellite, following the stages of systems engineering for systematic cube satellite development.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1781-1795
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• 2024

This study investigated the levels of radioactivity in soil surrounding a phosphate fertilizer factory in Egypt, aiming to assess potential risks to the population exposed to radiation. Concentrations of ²³⁸U, ²²⁶Ra, ²³²Th, and ⁴⁰K were measured in soil samples collected from two subsites: one near the factory (subsite 1) and another further away (subsite 2). Two different systems were used for measuring radioactivity, a high-purity gamma ray spectroscopy system with an HPGe detector for gamma-emitting isotopes and a CR-39 solid nuclear track detector for alpha-emitting radon gas. Subsite 1, located close to the factory, displayed significantly elevated levels of ²²⁶Ra compared to global background levels (514 and 456 Bq/kg vs. 35 Bq/kg). Additionally, the concentrations of ²³⁸U (241.06 Bq/kg vs. global average 35 Bq/kg), ²³²Th (16.15 Bq/kg vs. global average 30 Bq/kg), and ⁴⁰K (146.36 Bq/kg vs. global average 400 Bq/kg) were all above global averages. Furthermore, a high concentration of radon gas (337.06 μSv/y) was measured at subsite 1. The strong positive correlation observed between ²²⁶Ra and ²³⁸U (0.96256) provides further evidence of potentially elevated radioactivity levels near the factory. In contrast, subsite 2, situated farther from the factory, exhibited natural radioactive background levels within international limits. Quantitative analysis revealed that gamma ray absorbed doses for ²²⁶Ra and ²³²Th exceeded global averages in some samples. Specifically, ²²⁶Ra doses ranged from 7.8 to 46.26 ppm (exceeding the 20 ppm global average in some cases), and ²³²Th doses ranged from 1.98 to 9.14 ppm (exceeding the 10 ppm global average in some cases). The concentration of ⁴⁰K, however, remained within the global range (0.07%-0.69 %). The observed imbalances in the ratios of Th/U (0.17-0.24 Bq/kg and 0.73-0.24 ppm) and U/Ra (0.81-0.73 Bq/kg and 0.73-0.17 ppm), both of which are significantly lower than their respective global averages of 4 and 2.4, point towards the presence of fertilizer-derived contamination. This conclusion is further supported by the high phosphate concentrations detected in the samples. Overall, this study suggests that radioactive contamination near the phosphate fertilizer factory significantly exceeds global background levels and international limits in some cases. This raises concerns about potential risks posed to surrounding agricultural land and crops.

• Korean Journal of Radiology
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• v.25 no.7
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• pp.613-622
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• 2024

Objective: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). Materials and Methods: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. Results: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. Conclusion: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.3
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• pp.315-328
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• 2024

Precipitation is a crucial meteorological variable widely used as essential input data in most hydrological models. However, due to climate change, there is an escalating precipitation variability. Trend analysis plays an important role in planning and operating water resources systems. As recently developed, Innovative Polygon Trend Analysis (IPTA) is useful in identifying and and analyzing the trends of hydrologic variables. In this study, the IPTA was applied to monthly precipitation data obtained from 13 meteorological observatories in Jeolla province, along with synthesized precipitation data according to Shared Socioeconomic Pathways (SSP) scenarios. The trend results were compared those obtained from the Mann-Kendall test and the Sen's slope estimation which are generally used in practice. The results revealed monthly precipitations from February to July and November had increasing trends, and monthly precipitation in October had a decreasing trend. IPTA, Mann-Kendall test, and Sen's slope estimation detected trends in 75.00 %, 5.13 %, and 5.13 % of 156(13 stations × 12 months) time series of monthly precipitation, respectively, which indicates that the IPTA is more sensitive in trend detection compared to the Mann-Kendall test and Sen's slope estimation.

• Journal of Bio-Environment Control
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• v.33 no.2
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• pp.120-128
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• 2024

Since ChatGPT was released in 2022, the generative artificial intelligence (AI) industry has seen massive growth and is expected to bring significant innovations to cognitive tasks. AI-based image generation, in particular, is leading major changes in the digital world. This study investigates the technical foundations of Midjourney, Stable Diffusion, and Firefly-three notable AI image generation tools-and compares their effectiveness by examining the images they produce. The results show that these AI tools can generate realistic images of tomatoes, strawberries, paprikas, and cucumbers, typical crops grown in greenhouse. Especially, Firefly stood out for its ability to produce very realistic images of greenhouse-grown crops. However, all tools struggled to fully capture the environmental context of greenhouses where these crops grow. The process of refining prompts and using reference images has proven effective in accurately generating images of strawberry fruits and their cultivation systems. In the case of generating cucumber images, the AI tools produced images very close to real ones, with no significant differences found in their evaluation scores. This study demonstrates how AI-based image generation technology can be applied in agriculture, suggesting a bright future for its use in this field.

• Journal of Internet of Things and Convergence
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• v.10 no.3
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• pp.19-24
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• 2024

Recent advances in large-scale data processing technologies such as big data, cloud computing, and artificial intelligence have increased the demand for high-performance storage devices in data centers and enterprise environments. In particular, the fast data response speed of storage devices is a key factor that determines the overall system performance. Solid state drives (SSDs) based on the Non-Volatile Memory Express (NVMe) interface are gaining traction, but new bottlenecks are emerging in the process of handling large data input and output requests from multiple hosts simultaneously. SSDs typically process host requests by sequentially stacking them in an internal queue. When long transfer length requests are processed first, shorter requests wait longer, increasing the average response time. To solve this problem, data transfer timeout and data partitioning methods have been proposed, but they do not provide a fundamental solution. In this paper, we propose a dual queue based scheduling scheme (DQBS), which manages the data transfer order based on the request order in one queue and the transfer length in the other queue. Then, the request time and transmission length are comprehensively considered to determine the efficient data transmission order. This enables the balanced processing of long and short requests, thus reducing the overall average response time. The simulation results show that the proposed method outperforms the existing sequential processing method. This study presents a scheduling technique that maximizes data transfer efficiency in a high-performance SSD environment, which is expected to contribute to the development of next-generation high-performance storage systems

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.39-53
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• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• Journal of Soil and Groundwater Environment
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• v.14 no.2
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• pp.1-9
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• 2009

This study was objected to identify the effect on water quality and contamination by non-agricultural facilities in 'A' reservoir watershed located in OO city, Kyounggi-do, Korea. Ground- and stream water samples showed (Na+K)-Cl, Ca(Cl, SO$_4$) and Ca-Cl type in an illegally discharging area of sewage and a densely industrial area indicating water contamination. Stream water of an illegally discharging area of sewage had high COD, T-N and T-P. In this area, direct incoming of sewage into stream water was induced ground water system by well pumping, and it made a progress of ground water contaminations with those components. Groundwater of a densely industrial area showed high concentrations of T-N, NO$_3$N. From a nitrogen isotope analysis, stream water of an illegally discharging area of sewage has ${\delta}^{15}N-NO_3$values of 0.7%0 was strongly affected by nitrogen originated from agrochemicals, and a densely industrial area of 19.7%0 from septic system. Ground- and stream water of a livestock fanning area were contaminated with NH$_3$-N and Mn, which was affected by intensive livestock facilities. SAR-conductivity plot indicates the water does not pose either alkalinity or salinity hazard for irrigation. COD, T-N, T-P, NO$3$-N, NH$_3$N and Mn concentrations from contaminated areas were diminished by mixing with 'A' reservoir water. There were no water contaminations in silver towns, vacationlands around reservoir and golf links. Consequently, it should be made a plan of systematic managements for past and- present possible contaminants and sewage systems in preventing water contamination by non-agricultural facilities.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.1
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• pp.98-106
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• 2009

This work proposes a 1280-RGB $\times$ 800-Dot 70.78mW 0.l3um CMOS LCD driver IC (LDI) for high-performance 16M-color low temperature poly silicon (LTPS) thin film transistor liquid crystal display (TFT-LCD) systems such as ultra mobile PC (UMPC) and mobile applications simultaneously requiring high resolution, low power, and small size at high speed. The proposed LDI optimizes power consumption and chip area at high resolution based on a resistor-string based architecture. The single column driver employing a 1:12 MUX architecture drives 12 channels simultaneously to minimize chip area. The implemented class-AB amplifier achieves a rail-to-rail operation with high gain and low power while minimizing the effect of offset and output deviations for high definition. The supply- and temperature-insensitive current reference is implemented on chip with a small number of MOS transistors. A slew enhancement technique applicable to next-generation source drivers, not implemented on this prototype chip, is proposed to reduce power consumption further. The prototype LDI implemented in a 0.13um CMOS technology demonstrates a measured settling time of source driver amplifiers within 1.016us and 1.072us during high-to-low and low-to-high transitions, respectively. The output voltage of source drivers shows a maximum deviation of 11mV. The LDI with an active die area of $12,203um{\times}1500um$ consumes 70.78mW at 1.5V/5.5V.