• Journal of the Korean Geotechnical Society
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• v.25 no.5
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• pp.53-64
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• 2009

Many slope failures are induced by rainfall infiltration. A lot of recent researches are therefore focused on rainfall-induced slope instability and the rainfall infiltration is recognized as the important triggering factor. The rainfall infiltrates into the soil slope and makes the matric suction lost in the slope and even the positive pore water pressure develops near the surface of the slope. They decrease the resisting shear strength. In Korea, a few public institutions suggested conservative slope design guidelines that assume a fully saturated soil condition. However, this assumption is irrelevant and sometimes soil properties are misused in the slope design method to fulfill the requirement. In this study, a more relevant slope stability evaluation method is suggested to take into account the real rainfall infiltration phenomenon. Unsaturated soil properties such as shear strength, soil-water characteristic curve and permeability for Korean weathered soils were obtained by laboratory tests and also estimated by artificial neural network models. For real-time assessment of slope instability, failure warning criteria of slope based on deterministic and probabilistic analyses were introduced to complement uncertainties of field measurement data. The slope stability evaluation technique can be combined with field measurement data of important factors, such as matric suction and water content, to develop an early warning system for probably unstable slopes due to the rainfall.

• Journal of Industrial Convergence
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• v.21 no.5
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• pp.1-13
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• 2023

Carbon neutrality is the concept of reducing greenhouse gases emitted by human activities and making actual emissions zero through removal of remaining gases. It is also called "Net-Zero" and "carbon zero". Korea has declared a "2050 Carbon Neutrality policy" to cope with the climate change crisis. Various carbon reduction legislative processes are underway. Since carbon neutrality requires changes in industrial technology, it is important to prepare a system for carbon zero. This paper aims to understand the status and trends of global carbon neutrality technology. Therefore, ROK's web platform "www.naver.com." was selected as the data collection scope. Korean online articles related to carbon neutrality were collected. Carbon neutrality technology trends were analyzed by future signal methodology and Word2Vec algorithm which is a neural network deep learning technology. As a result, technology advancement in the steel and petrochemical sectors, which are carbon over-release industries, was required. Investment feasibility in the electric vehicle sector and technology advancement were on the rise. It seems that the government's support for carbon neutrality and the creation of global technology infrastructure should be supported. In addition, it is urgent to cultivate human resources, and possible to confirm the need to prepare support policies for carbon neutrality.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.9-16
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• 2009

While the broadband network and multimedia technology are being developed, the commercial market of digital contents as well as using IPTV has been widely spreading. In this background, Time-Shift system is developed for requirement of multimedia. This system is independent of Time but is not independent of Place and Occasion. For solving these problems, in this paper, we propose the TPO(Time, Place, Occasion)-Shift system for mobile multimedia devices. The profile that can be applied to the mobile multimedia devices is much different from that of the setter-box. And general mobile multimedia devices could not have such large memories that is for multimedia data. So it is important to continuously store and manage those multimedia data in limited capacity with mobile device's profile. Therefore we compose the basket in a way using defined time unit and manage these baskets for effective buffer management. In addition. since the file name of basket is made up to include a basket's time information, we can make use of this time information as DTS(Decoding Time Stamp). When some multimedia content is converted to be available for portable multimedia devices, we are able to compose new formatted contents using such DTS information. Using basket based buffer systems, we can compose the contents by real time in mobile multimedia devices and save some memory. In order to see the system's real-time operation and performance, we implemented the proposed TPO-Shift system on the basis of mobile device, MS340. And setter-box are desisted by using directshow player under Windows Vista environment. As a result, we can find the usefulness and real-time operation of the proposed systems.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.133-145
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• 2022

Compared to the continuously increasing dog population and industry size in Korea, systematic analysis of related data and research on breed classification methods are very insufficient. In this paper, an automatic breed classification method is proposed using deep learning technology for 14 major dog breeds domestically raised. To do this, dog images are collected for deep learning training and a dataset is built, and a breed classification algorithm is created by performing transfer learning based on VGG-16 and Resnet-34 as backbone networks. In order to check the transfer learning effect of the two models on dog images, we compared the use of pre-trained weights and the experiment of updating the weights. When fine tuning was performed based on VGG-16 backbone network, in the final model, the accuracy of Top 1 was about 89% and that of Top 3 was about 94%, respectively. The domestic dog breed classification method and data construction proposed in this paper have the potential to be used for various application purposes, such as classification of abandoned and lost dog breeds in animal protection centers or utilization in pet-feed industry.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1577-1585
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• 2022

As the interest in the 4th industrial revolution and metaverse increases, metaverse with multi edge structure is proposed and noted. Metaverse is a structure that can create digital doctor-like system through a large amount of image processing and data transmission in a multi edge system. Since metaverse application requires calculating performance, which can reconstruct 3-D space, edge hardware's insufficient calculating performance has been a problem. To provide streamable AI software in runtime, image processing, and data transmission, which is edge's loads, needs to be lightweight. Also lightweight at the edge leads to power consumption reduction of the entire metaverse application system. In this paper, we propose collaborative edge-cloud image processing with remote image processing method and Region of Interest (ROI) to overcome edge's power performance and build streamable and runtime executable AI software. The proposed structure was implemented using a PC and an embedded board, and the reduction of time, power, and network communications were verified.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.7
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• pp.303-314
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• 2023

Voice conversion, a technology that allows an individual's speech data to be regenerated with the acoustic properties(tone, cadence, gender) of another, has countless applications in education, communication, and entertainment. This paper proposes an approach based on the StarGAN-VC model that generates realistic-sounding speech without requiring parallel utterances. To overcome the constraints of the existing StarGAN-VC model that utilizes one-hot vectors of original and target speaker information, this paper extracts feature vectors of target speakers using a pre-trained version of Rawnet3. This results in a latent space where voice conversion can be performed without direct speaker-to-speaker mappings, enabling an any-to-any structure. In addition to the loss terms used in the original StarGAN-VC model, Wasserstein distance is used as a loss term to ensure that generated voice segments match the acoustic properties of the target voice. Two Time-Scale Update Rule (TTUR) is also used to facilitate stable training. Experimental results show that the proposed method outperforms previous methods, including the StarGAN-VC network on which it was based.

• Smart Structures and Systems
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• v.30 no.5
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• pp.521-535
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• 2022

Efficient management of deteriorating civil infrastructure is one of the most important research topics in many developed countries. In particular, the remote displacement measurement of bridges using linear variable differential transformers, global positioning systems, laser Doppler vibrometers, and computer vision technologies has been attempted extensively. This paper proposes a remote displacement measurement system using closed-circuit televisions (CCTVs) and a computer-vision-based method for in-situ bridge bearings having relatively large displacement due to temperature change in long term. The hardware of the system is composed of a reference target for displacement measurement, a CCTV to capture target images, a gateway to transmit images via a mobile network, and a central server to store and process transmitted images. The usage of CCTV capable of night vision capture and wireless data communication enable long-term 24-hour monitoring on wide range of bridge area. The computer vision algorithm to estimate displacement from the images involves image preprocessing for enhancing the circular features of the target, circular Hough transformation for detecting circles on the target in the whole field-of-view (FOV), and homography transformation for converting the movement of the target in the images into an actual expansion displacement. The simple target design and robust circle detection algorithm help to measure displacement using target images where the targets are far apart from each other. The proposed system is installed at the Tancheon Overpass located in Seoul, and field experiments are performed to evaluate the accuracy of circle detection and displacement measurements. The circle detection accuracy is evaluated using 28,542 images captured from 71 CCTVs installed at the testbed, and only 48 images (0.168%) fail to detect the circles on the target because of subpar imaging conditions. The accuracy of displacement measurement is evaluated using images captured for 17 days from three CCTVs; the average and root-mean-square errors are 0.10 and 0.131 mm, respectively, compared with a similar displacement measurement. The long-term operation of the system, as evaluated using 8-month data, shows high accuracy and stability of the proposed system.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.4
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• pp.75-95
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• 2023

This study investigated the overall perceptions of pre-service school librarian teacher on the current education practicum through semi-structured in-depth interviews and suggested improvements to the educational practicum system. For this purpose, interview data were collected from 28 pre-service school librarian teacher (6 teachers' colleges, 14 taking teaching qualification courses, and 8 graduate school of education) who participated in educational practicum in school libraries, and a research method that combines qualitative analysis techniques with text network analysis was applied. The results of the study showed that pre-service school librarian teacher believe that educational practicum can prepare them for various field experiences and cultivate their ability to cope with situations they will encounter in the future. Through qualitative inquiry, we were able to identify their perceptions of school field practicum as a whole, their perceptions of the school field practicum, and their perceptions of educational service activities. Based on this, to improve the current problems of educational practice, we suggested expanding the period of school internship program, distributing the time, establishing a full-time practice system, having continuous discussions with field teachers, and developing a systematic school field practicum.

• Journal of the Korea Computer Graphics Society
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• v.29 no.5
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• pp.21-30
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• 2023

Accurate segmentation of the kidney tumor is necessary to identify shape, location and safety margin of tumor in abdominal CT images for surgical planning before renal partial nephrectomy. However, kidney tumor segmentation is challenging task due to the various sizes and locations of the tumor for each patient and signal intensity similarity to surrounding organs such as intestine and spleen. In this paper, we propose a semi-supervised learning-based mean teacher network that utilizes both labeled and unlabeled data using a kidney local guided map including kidney local information to segment small-sized kidney tumors occurring at various locations in the kidney, and analyze the performance according to the kidney tumor size. As a result of the study, the proposed method showed an F1-score of 75.24% by considering local information of the kidney using a kidney local guide map to locate the tumor existing around the kidney. In particular, under-segmentation of small-sized tumors which are difficult to segment was improved, and showed a 13.9%p higher F1-score even though it used a smaller amount of labeled data than nnU-Net.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.5
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• pp.827-846
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• 2023

Recently, various information technologies such as network communication and sensors have begun to be integrated into weapon systems that were previously operated in stand-alone. This helps the operators of the weapon system to make quick and accurate decisions, thereby allowing for effective operation of the weapon system. However, as the involvement of the cyber domain in weapon systems increases, it is expected that the potential for damage from cyber attacks will also increase. To develop a secure weapon system, it is necessary to implement built-in security, which helps considering security from the requirement stage of the software development process. The U.S. Department of Defense is implementing the Risk Management Framework Assessment and Authorization (RMF A&A) process, along with the introduction of the concept of cybersecurity, for the evaluation and acquisition of weapon systems. Similarly, South Korea is also continuously making efforts to implement the Korea Risk Management Framework (K-RMF). However, so far, there are no cases where K-RMF has been applied from the development stage, and most of the data and documents related to the U.S. RMF A&A are not disclosed for confidentiality reasons. In this study, we propose the method for inferring the composition of the K-RMF based on systematic threat analysis method and the publicly released documents and data related to RMF. Furthermore, we demonstrate the effectiveness of our inferring method by applying it to the naval battleship system.