• Journal of the Korean Society of Radiology
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• v.18 no.5
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• pp.557-566
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• 2024

Ultrasound-guided regional anesthesia is one of the most common techniques used in peripheral nerve blockade by enhancing pain control and recovery time. However, accurate Brachial Plexus (BP) nerve detection and identification remains a challenging task due to the difficulty in data acquisition such as speckle and Doppler artifacts even for experienced anesthesiologists. To mitigate the issue, we introduce a BP nerve small target segmentation network by incorporating BP object detection and U-Net based semantic segmentation into a single deep learning framework based on the multi-scale approach. To this end, the current BP detection and identification was estimated: 1) A RetinaNet model was used to roughly locate the BP nerve region using multi-scale based feature representations, and 2) U-Net was then used by feeding plural BP nerve features for each scale. The experimental results demonstrate that our proposed model produces high quality BP segmentation by increasing the accuracies of the BP nerve identification with the assistance of roughly locating the BP nerve area compared to competing methods such as segmentation-only models.

• Journal of Aerospace System Engineering
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• v.13 no.3
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• pp.78-86
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• 2019

This paper describes the stiffness optimization of the torsion spring hinge of the large SAR antenna considering the deployment performance. A large SAR antenna is folded in a launch environment and then unfolded when performing a mission in orbit. Under these conditions, it is very important to find the proper stiffness of the torsion spring hinge so that the antenna panels can be deployed with minimal impact in a given time. If the torsion spring stiffness is high, a large impact load at the time of full deployment damages the structure. If it is weak, it cannot guarantee full deployment due to the deployment resistance. A multi-body dynamics analysis model was developed to solve this problem using RecurDyn and the development performance were predicted in terms of: development time, latching force, and torque margin through deployment analysis. In order to find the optimum torsion spring stiffness, the deployment performance was approximated by the response surface method (RSM) and the optimal design was performed to derive the appropriate stiffness value of the rotating springs.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.216-227
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• 2022

The MPEG-I (Immersive) group is working on a standardization project for immersive video that provides 6 degrees of freedom (6DoF). The MPEG Immersion Video (MIV) standard technology is intended to provide limited 6DoF based on depth map-based image rendering (DIBR) technique. Many efficient coding methods have been suggested for MIV, but efficient transmission strategies have received little attention in MPEG-I. This paper proposes group-based adaptive rendering method for immersive video streaming. Each group can be transmitted independently using group-based encoding, enabling adaptive transmission depending on the user's viewport. In the rendering process, the proposed method derives weights of group for view synthesis and allocate high quality bitstream according to a given viewport. The proposed method is implemented through the Test Model for Immersive Video (TMIV) test model. The proposed method demonstrates 17.0% Bjontegaard-delta rate (BD-rate) savings on the peak signalto-noise ratio (PSNR) and 14.6% on the Immersive Video PSNR(IV-PSNR) in terms of various end-to-end evaluation metrics in the experiment.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.1
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• pp.63-72
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• 2020

The purpose of this study is to investigate the effects of the application of various numerical models and frequency contents of earthquakes on the performances of the reactor containment building (RCB) in a nuclear power plant (NPP) equipped with an advanced power reactor 1400. Two kinds of numerical models are developed to perform time-history analyses: a lumped-mass stick model (LMSM) and a full three-dimensional finite element model (3D FEM). The LMSM is constructed in SAP2000 using conventional beam elements with concentrated masses, whereas the 3D FEM is built in ANSYS using solid elements. Two groups of ground motions considering low- and high-frequency contents are applied in time-history analyses. The low-frequency motions are created by matching their response spectra with the Nuclear Regulatory Commission 1.60 design spectrum, whereas the high-frequency motions are artificially generated with a high-frequency range from 10Hz to 100Hz. Seismic responses are measured in terms of floor response spectra (FRS) at the various elevations of the RCB. The numerical results show that the FRS of the structure under low-frequency motions for two numerical models are highly matched. However, under high-frequency motions, the FRS obtained by the LMSM at a high natural frequency range are significantly different from those of the 3D FEM, and the largest difference is found at the lower elevation of the RCB. By assuming that the 3D FEM approximates responses of the structure accurately, it can be concluded that the LMSM produces a moderate discrepancy at the high-frequency range of the FRS of the RCB.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.503-512
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• 2014

People would like to be socially engaged not only with humans but also with robots. One of the most common ways in the robotic field to enhance human robot interaction is to use emotion and integrate emotional concepts into robots. Many researchers have been focusing on developing a robot's emotional expressions. However, it is first necessary to establish the psychological background of a robot's emotion generation model in order to implement the whole process of a robot's emotional behavior. Therefore, this article suggests a robot's motivational emotion model with value effectiveness from a Higgins' motivation definition, regulatory focus theory, and Circumplex model. For the test, a game with the best-two-out-of-three rule is introduced. Each step of the game was evaluated by the proposed model. As the results imply, the proposed model generated psychologically appropriate emotions for a robot in the given situation. The empirical survey remains for future work to prove that this research improves social human robot interaction.

• The Journal of Korean Association of Computer Education
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• v.8 no.1
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• pp.55-63
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• 2005

Recently, web-based E-learning systems have entered the spotlight by providing new learning environments that break down spatial and temporal limitations. The key to building the web-based E-learning system is in determining how to effectively use the system and to evaluate the degree of learning achieved by the students that use it. In traditional off-line learning systems, we can evaluate students by counting how many questions, designed to evaluate their learning achievement, he or she answers correctly within a predetermined time limit. But this method would make individualized learning, a strong point of E-learning systems, impossible because these systems provide same learning strategy to all students even though they achieve a different level of learning. Therefore, in this paper, I will find any relationships between given test answers using fuzzy implication theory, I call these fuzzy correlations, and then generate evaluation results that are reflected in those relationships. I will compare the differences between this evaluation method and a traditional evaluation method where a student takes a test to evaluate his or her learning achievement after some learning period. Finally, I will discuss how we can use these results in individualized learning.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2509-2518
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• 2013

Augmented reality (AR) technology visualizes a real type object that cannot simulate in virtual reality technology by overlapping a virtual object and real object in a computer system. This study suggests a methodology and prototype system for applying AR system to rebar distribution work in a civil engineering project. Rebar work in civil engineering project is a representative activity that is progressed by empirical approach of skilled labor rather than formalized manual. AR technology improves the constructability of rebar work because AR tool can identify missing rebars and different rebars comparing with the drawings. AR system developed in this study can enhance the understanding of rebar work using 3D modeling with real image of construction site and save construction cost by reducing reconstruction work.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.11
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• pp.2121-2130
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• 2015

In this paper, we analyze LM-MIMO (load-modulation multiple-input multiple-output) system with single RF chain. And then, we confirm that load modulation technique can support generation of high-order m-PSK modulation and m-QAM modulation in LM-MIMO system. Finally we evaluate performance of LM-MIMO system with load modulation. Conventional MIMO system requires a number of RF chains for expansion of MIMO dimension. A number of RF chains can cause various problems. On the other hand, although LM-MIMO system is expanded, LM-MIMO system requires single RF chain only. Therefore, LM-MIMO system has low-complexity and low power consumption. As results, we can confirm that load modulation of T-model can modulate high-order m-PSK and m-QAM singal. Also, we can confirm that $4{\times}4$ LM-MIMO system using load modulation has a similar performance to conventional $4{\times}4$ MIMO system.

• Journal of The Korean Association of Information Education
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• v.1 no.1
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• pp.92-101
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• 1997

There has been increasing demand of hypermedia as computing and educational environment change. Most authoring tools developed recently have various built-in functions., but they are not enough to create hypermedia program or easy to learn. Even though teachers might try to develop hypermedia program with existing authoring tools, they usually come to face with the difficulties of creating new nodes, adding new data, or keeping user's activity records. Therefore, this study investigated to present a prototype of Educational Hypermedia Editor that can be conveniently used in hypermedia programs. According to the theories of courseware design, instructional design, learning theory, and hypertext and hyperemia, design strategies for the study were selected. Based on the analysis of the characteristics of domestic authoring tools as well as foreign authoring tools, the Educational Hypermedia Editor that has authoring and executing mode was designed and developed. Because the Educational Hypermedia Editor was designed for information retrieval CAI(Computer Assisted Instruction) materials, it is expected that the Educational Hypermedia Editor will be extensively used in the urea of social science classes, investigation studies, and information processing studies.