• Electronics and Telecommunications Trends
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• v.33 no.1
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• pp.11-19
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• 2018

IoT is used not only as a technical terminology but also as a paradigm representation. As the number of IoT devices spread tremendously throughout the world, they are able to be located anywhere,recognize their environment, and achieve adaptable reactions. All market investigation agencies expect the number of IoT devices to reach tens to hundreds of billions in number. They also expect various technical problems owing to the huge number of connected things and data that will emerge during the AI era. The decentralization of centralized computing for AI is the one of the technical solutions to such problems, and the computing roles for AI will be soon distributed into the things, which can be located anywhere. In this article, the traditional distributed intelligence and its current research activities are introduced, and the next distributed intelligence target for the IoT 2.0 era is briefly touched upon using the keyword Socio-Things.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.1
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• pp.21-28
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• 2020

Integrated bidirectional OBC/LDC was developed to reduce the volume for elements, avoid space restriction, and increase efficiency in EV vehicles. In this study, a DC-DC converter in integrated OBC/LDC circuits was composed of an SRC circuit with a stable output voltage relative to an LLC circuit using a theoretical method and simulation. The resonant network of the selected circuit was optimized to minimize the power loss and element volume under constraints for the buck converter and the battery charging range. Moreover, the validity of the optimal model was verified through an analysis using a theoretical method and a numerical analysis based on power loss at the optimized resonant frequency.

• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.54.2-54.2
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• 2018

It is important to understand very fast CMEs which are the main cause of geomagnetic storms and solar particle events (SPEs). During this solar cycle 24, there are 10 very fast CMEs whose speeds are over 2000 km/s. Among these, there were only two fronside events (2012 January 23 and 2012 March 7) and they are associated with two major flares (M8.7 and X5.4) and the most strong SPEs (6310 pfu and 6530 pfu). They have a similar characteristics: there were successive CMEs within 2 hours in the same active region. We analyze their magnetic properties using SDO HMI magnetograms and kinematic ones from STEREO EUVI/COR1/COR2 observations. We can measure their speeds and initial accelerations without projection effects because their source locations are almost the limb. Additionally, we are investigating magnetic and kinematic characteristics of 8 backside events using AI-generated magnetograms constructed by deep learning methods.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2E
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• pp.43-49
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• 1995

In this paper a space search technique is used to detect and locate the faults of the circuit interconnections. The circuit interconnections are represented by the tree structure and the tree space is searched to detect and locate the open faults of the circuit interconnections. The breadth search is used to detect the open faults and reduce the space size. The depth search is used to locate the open faults.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.3
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• pp.300-310
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• 2022

A vast amount of labeled data is required for deep neural network training. A typical strategy to improve the performance of a neural network given a training data set is to use data augmentation technique. The goal of this work is to offer a novel image augmentation method for improving object detection accuracy. An object in an image is removed, and a similar object from the training data set is placed in its area. An in-painting algorithm fills the space that is eliminated but not filled by a similar object. Our technique shows at most 2.32 percent improvements on mAP in our testing on a military vehicle dataset using the YOLOv4 object detector.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.23-25
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• 2021

This paper describes an approach of composing music with multiple AI composers. This approach enriches more the creativity space of artificial intelligence music composition than using only one composer. This paper presents a simple example with 2 different deep learning composers working together for composing one music. For the experiment, the two composers adopt the same deep learning architecture of an LSTM model trained with different data. The output of a composer is a sequence of notes. Each composer alternatively appends its output to the resulting music which is input to both the composers. Experiments compare different music generated by the proposed multiple composer approach with the traditional one composer approach.

• Journal of the Chungcheong Mathematical Society
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• v.8 no.1
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• pp.19-27
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• 1995

In 1974 H.Herrlich invented nearness spaces, a very fruitful concept which enables one to unify topological aspects. In this paper, we introduce the Lindel$\ddot{o}$f nearness structure, countably bounded nearness structure and countably totally bounded nearness structure. And we show that (X, ${\xi}_L$) is concrete and complete if and only if ${\xi}_L={\xi}_t$ in a symmetric topological space (X, t). Also we show that the following are equivalent in a symmetric topological space (X, t): (1) (X, ${\xi}_L$) is countably totally bounded. (2) (X, ${\xi}_t$) is countably totally bounded. (3) (X, t) is countably compact.

• Journal of the Korea Institute of Building Construction
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• v.7 no.3
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• pp.91-98
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• 2007

Recently a construction industry introduces information that brings about many advantages in the early planning phase, design phase and construction phase. Especially it replaces 2D, 3D systems(usually using explanation of drawing information) ai 4D CAD(offering a sort of 4D-having relation of construction schedule and 3D drawing information). Nevertheless a 4D has these benefits, it has limits which are not only usually using 3D modeling but also limit of making full use of practical affairs because of a lack of connecting varietals of progress of work. To solve these uppermost limits, this research is presenting unified systems to use in risk management which are efficient management of space and non-space information, space analysis, making full use of data base, introducing GIS system of easy interaction.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1356-1358
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• 2003

In the paper, a method making use of the characteristics of Dyad Green Function (DGF) and Fluctuation-Dissipation Theorem to get the brightness temperature of layered medium is introduced. Based on the approach and the measured data of multi-channel radiometer and Least Square Method (LSM), the thickness of lunar soil can be retrieved. These methods are significant to study on materials on lunar surface.