• Ceramist
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• v.22 no.3
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• pp.301-315
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• 2019

We prepared hybrids between layered double hydroxide (LDH) and natural plant extract such as Peaonia suffruticosa Andrews (PS) and Peaonia Japonica (PJ) which was confirmed anti-bacterial activity through paper disc diffusion assay. According to X-ray diffractometer, scanning electron microscope, zeta-potential measurement and quantification of extract loading amount in hybrids, we confirmed that similar amount of PS and PJ loaded on inter-particle pore of LDH with partial adsorption on surface of LDH through reconstruction process. We also evaluated the bacterial colony forming inhibition of PS extract, PJ extract, PS-LDH and PJ-LDH hybrids against Escherichia coli as gram negative bacterium and Bacillus subtilis as gram positive bacterium, suggesting that both hybrids have enhanced anti-bacterial activity compared with extract itself.

• The Transactions of the Korea Information Processing Society
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• v.3 no.2
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• pp.390-398
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• 1996

In this paper, a new image compression algorithm using the generality of the multilaryer perceptron is proposed. Proposed algorithm classifies image into some classes, and trains them through the multilayer perceptron. Multilayer perceptron which trained by the above method can do compression and reconstruction of the nontrained image by the generality. Also, it reduces memory size of the side of receiver and quantization error. For the experiment, we divide Lena image into 16 classes and train them through one multilayer perceptron. The experimental results show that we can get excellent reconstruction images by doing compression and reconstruction for Lena image, Dollar image and Statue image.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.09a
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• pp.23-27
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• 2001

Making the safety situation from the various work system is very important in the industrial fields. The proposed neural network technique is the real titre computation method based theory of inter-node diffusion for searching the safety distances from the sudden appearance-objests during the work driving. The main steps of the distance computation using the theory of stereo vision like the eyes of man is following steps. One is the processing for finding the corresponding points of stereo images and the other is the interpolation processing of full image data from nonlinear image data of obejects. All of them request much memory space and titre. Therefore the most reliable neural-network algorithm is drived for real time recognition of obejects, which is composed of a dynamic programming algorithm based on sequence matching techniques. And the real time reconstruction of nonlinear image information is processed through several simulations. I-D LIPN hardware has been composed, and the real time reconstruction is verified through the various experiments.

• Journal of English Language & Literature
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• v.56 no.2
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• pp.301-328
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• 2010

Many recent British novels engage with the construction and deconstruction of history and identity; and in dealing with these historical, or historicised novels it seems to be an untouchable ground that truth is beyond grasp. Even when approached, its authenticity should be examined under the post-modern "incredulity toward metanarrative" discourses. Julian Barnes's 1998 novel England, England may be one of these. Yet, unlike others it achieves a complicated and controversial status as a new kind of historiographic metafiction by providing selfconscious reflections on the invention of innocence and the questionable notion of historical authenticity against the background of current postmodern historical, cultural, and literary explorations. The book, set in a near-future, namely post-post-modern England, starts with a story of a young girl, Martha Cochrane, whose first memory goes back to her early infantile years. Yet, the narrator comments that it is a lie, "her first artfully, innocently arranged lie," since memory, or history, is a product of identity, and vice versa. Her memory of the jigsaw puzzle is both a reminiscent and a significant component of who she is now, both a simulacrum and the original of herself. The correlation between her individual memory and identity parallels that of a region, England, in formation of its history and nationality. "England, England" is the replicated miniature of the former glorious Kingdom as well as a becoming der Ding an sich (the thing itself). In search of the English history and identity, the author satirizes the modern mind's perception of the unreliability and arbitrariness of memory and history, and further explores the alternative to the postmodern discourses by suggesting the probability of inventing innocence glimpsed in children's face "believing while disbelieving." In doing so, the author reconstructs not only the history of Englishness on the ground where nothing seems to be solid, but more importantly also the postmodern theme of relativity in relation to memory, history and identity.

• Journal of radiological science and technology
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• v.22 no.1
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• pp.5-11
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• 1999

This paper reviews recent major activities on instrumentation and methodology of PET. The performance of the PET instrumentation can be expressed by four physical characteristics, 1) spatial resolution, 2) coincidence resolving time, 3) energy resolution, and 4) detection efficiency. The physical and technical aspects of PET systems are briefly discussed along with these characteristics. Toward high resolution PET the recent trend has been to design multiple rings of densely packed detector arrays with scintillators. In order to satisfy the sampling requirement in reconstruction, continuous detector units has been developed. Iterative image reconstruction algorithms have received considerable attention for improvement of both the sampling requirement and image quality toward the stationary PET. Better resolving time improves the maximum true coincidence rate, which is also increased with more detectors placed in coincidence with each other. It suggests that volume PET is promising for enhancement of detection efficiency. The scattered coincidence event rate may be reduced by using detectors with better energy resolution. The use of interplane septa, however, takes over improvement of energy resolution in 2D PET. Energy resolution becomes an important factor for image quality under the condition of septa removal such as volume PET. Toward full utilization of emitting photons, 3D reconstruction incorporating oblique rays has been studied, and volume reconstruction algorithms have been developed. Practical volume PET systems impose heavy burden not only to detector sets and coincidence circuits, but also to computers in the memory requirements and the data processing. In conclusion, there have been many ingenious methods in development of PET instrumentation, which are based on unique capability of PET. They will be expected to overcome technical limitations, and to approach the fundamental limits.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.98-101
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• 2019

At present, deep convolutional neural networks have made a very important contribution in single-image super-resolution. Through the learning of the neural networks, the features of input images are transformed and combined to establish a nonlinear mapping of low-resolution images to high-resolution images. Some previous methods are difficult to train and take up a lot of memory. In this paper, we proposed a simple and compact deep recursive residual network learning the features for single image super resolution. Global residual learning and local residual learning are used to reduce the problems of training deep neural networks. And the recursive structure controls the number of parameters to save memory. Experimental results show that the proposed method improved image qualities that occur in previous methods.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.59-70
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• 2006

Since communication systems software is very large and complex, it requires component based architecture for software reusability, hardware transparency, high performance, and easy software reconstruction in different applications. In order to meet these requirements, we analyze performance and inter-process communication techniques of existing CORBA IIOP, and designed a shared memory-based CORBA inter-ORB protocol that would best fit for communication systems software. The designed protocol supports the same interface and can minimize the message transfer overhead in the same host environment. The test results of our protocol compared with other protocols show that the performance is increased by about 15%-200%. We are thus assumed that our protocol can be used in developing CORBA-based component software architecture for communication systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.193-198
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• 2020

The various uses of the Convolutional Neural Network technology are accelerating the evolution of the computing area, but the opposite is causing serious hardware performance shortages. Neural network accelerators, next-generation memory device technologies, and high-bandwidth memory architectures were proposed as countermeasures, but they are difficult to actively introduce due to the problems of versatility, technological maturity, and high cost, respectively. This study proposes DRAM-based main memory technology that enables read operations to be completed without waiting until the end of the refresh operation using pre-stored XOR bit values, even when the refresh operation is performed in the main memory. The results showed that the proposed technique improved performance by 5.8%, saved energy by 1.2%, and improved EDP by 10.6%.

• Journal of the Korea Computer Graphics Society
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• v.23 no.3
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• pp.19-29
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• 2017

When a fixed-sized memory allocation method is used for sparse volume data, a considerable memory space is in general wasted, which becomes more serious for a large volume of high resolution. In this paper, in order to reduce such unnecessary memory consumption, we propose a volume representation method to store mostly voxels that represent valid information rather than all voxels in a fixed volume space. Then our method is compared with the conventional static memory allocation method, an octree-based representation, and a voxel hashing method in terms of memory usage and computation speed. In particular, we compare the proposed method and the voxel hashing method with respect to implementation of the GPU-based Marching Cubes algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.13-20
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• 2009

Recently there is growing interest in LBS requiring real-time services and the spatial main memory DBMS for efficient Telematics services. In order to optimize existing disk-based spatial indexes of the spatial main memory DBMS in the main memory, spatial index structures have been proposed, which minimize failures in cache access by reducing the entry size. However, because the reduction of entry size requires compression based on the MBR of the parent node or the removal of redundant MBR, the cost of MBR reconstruction increases in index update and the efficiency of search is lowered in index search. Thus, to reduce the cost of MBR reconstruction, this paper proposed the RSMB (relative-sized MBR)compression technique, which applies the base point of compression differently in case of broad distribution and narrow distribution. In case of broad distribution, compression is made based on the left-bottom point of the extended MBR of the parent node, and in case of narrow distribution, the whole MBR is divided into cells of the same size and compression is made based on the left-bottom point of each cell. In addition, MBR was compressed using a relative coordinate and size to reduce the cost of search in index search. Lastly, we evaluated the performance of the proposed RSMBR compression technique using real data, and proved its superiority.