• Journal of the Society of Naval Architects of Korea
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• v.42 no.4 s.142
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• pp.299-306
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• 2005

To determine a guidance law which is suitable for Target Tracking System(TTS) of an underwater vehicle, the performance (hitting probability) of TTS were calculated with four different guidance schemes, considering underwater vehicle's manoeuvrability and characteristics of seeking equipment such as sonar To evaluate the performance of TTS with each guidance law, numerous target-tracking simulations of underwater vehicle were performed under the condition of target's various motion scenario. Furthermore, the effect of sonar characteristics to the performance of guidance law in TTS was studied by changing parameters of sonar such as frequency of ping and detecting error of target. The pursuit-tail guidance law showed the best performance among four different guidance laws. Complex motion of target from straight line to turning circle and zigzag movement, low frequency of sonar ping and large detecting error of target decreased the hitting probability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.136-151
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• 2016

Flexible large-scale WLANs are now widely deployed in crowded and highly mobile places such as campus, airport, shopping mall and company etc. But network management is hard for large-scale WLANs due to highly uneven interference and throughput among links. So the traffic is difficult to predict accurately. In the paper, through analysis of traffic in two real large-scale WLANs, Granger Causality is found in both scenarios. In combination with information entropy, it shows that the traffic prediction of target AP considering Granger Causality can be more predictable than that utilizing target AP alone, or that of considering irrelevant APs. So We develops new method -Granger Causality and Vector Auto-Regression (GCVAR), which takes APs series sharing Granger Causality based on Vector Auto-regression (VAR) into account, to predict the traffic flow in two real scenarios, thus redundant and noise introduced by multivariate time series could be removed. Experiments show that GCVAR is much more effective compared to that of traditional univariate time series (e.g. ARIMA, WARIMA). In particular, GCVAR consumes two orders of magnitude less than that caused by ARIMA/WARIMA.

• Molecules and Cells
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• v.39 no.5
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• pp.375-381
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• 2016

MicroRNAs (miRNAs) are small non-coding RNAs (~22 nucleotides) regulating gene expression at the post-transcriptional level. By directing the RNA-induced silencing complex (RISC) to bind specific target mRNAs, miRNA can repress target genes and affect various biological phenotypes. Functional miRNA target recognition is known to majorly attribute specificity to consecutive pairing with seed region (position 2-8) of miRNA. Recent advances in a transcriptome-wide method of mapping miRNA binding sites (Ago HITS-CLIP) elucidated that a large portion of miRNA-target interactions in vivo are mediated not only through the canonical "seed sites" but also via non-canonical sites (~15-80%), setting the stage to expand and determine their properties. Here we focus on recent findings from transcriptome-wide non-canonical miRNA-target interactions, specifically regarding "nucleation bulges" and "seed-like motifs". We also discuss insights from Ago HITS-CLIP data alongside structural and biochemical studies, which highlight putative mechanisms of miRNA target recognition, and the biological significance of these non-canonical sites mediating marginal repression.

• Journal of the Semiconductor & Display Technology
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• v.13 no.3
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• pp.35-38
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• 2014

Flat panel displays fabricated on glass substrate use amorphous Si for data processing circuit. Recent progress in display technology requires a new material to replace the amorphous Si, and ZnO is a good candidate. ZnO is a wide bandgap (3.3 eV) semiconductor with high mobility and good optical transparency. ZnO is usually grown by sputtering using ZnO ceramic target. However, ceramic target is more expensive than metal target, and making large area target is very difficult. In this work we studied characteristics of ZnO thin-film transistor grown by rf sputtering using Zn metal target and $CO_2$. ZnO film was grown at $450^{\circ}C$ substrate temperature, with -70 V substrate bias voltage applied. By using these methods, our ZnO TFT showed $5.2cm^2/Vsec$ mobility, $3{\times}10^6$ on-off ratio, and -7 V threshold voltage.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.4
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• pp.422-429
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• 2014

Proximity sensor networks are aimed at estimation kinematic state of target using estimated position of the target by each sensor node or target parameter. To analyze the kinematic state of target, traditional approaches require detections on multiple sensors, very large number of sensors to achieve acceptable performance. In this paper, we propose a novel method which can estimate predicted position of the underwater target using minimum proximity sensor with bearing information to this problem. The proposed algorithm was verified performance through simulation.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.8-13
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• 2023

We propose a method for removing interference and noise to estimate target information. In wireless channels, information signals are subject to interference and noise, making it is difficult to accurately estimate the desired signal. To estimate the desired information signal, it is essential to remove the noise and interference from the received signal, extracting only the desired signal. If the received signal noise and interference are not removed, the estimated information signal will have a large error in distance and direction, and the exact location of the target cannot be estimated. This study aims to accurately estimate the desired target in space. The objective is to achieve more presice target estimation than existing methods and enhance target resolution.An estimation method is proposed to improve the accuracy of target estimation. The proposed target estimation method obtains optimal weights using linear constraints and the minimum variance method. Through simulation, the performance of the proposed method and the existing method is analyzed. The proposed method successfully estimated all four targets, while the existing method only estimated two targets. The results show that the proposed method has better resolutiopn and superior estimation capability than the existing method.

• Journal of Mechanical Science and Technology
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• v.15 no.12
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• pp.1750-1756
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• 2001

Three-dimensional and unsteady flow analysis is a practical target of high performance computation. As recently advances of computers, a numerical prediction by the large eddy simulation (LES) are introduced and evaluated for various engineering problems. Its advanced methods for the complex turbulent flows are discussed by several examples applied for aerodynamic designs, analysis of fluid flow mechanisms and their interaction to complex phenomena. These results of time-dependent and three-dimensional phenomena are visualized by interactive graphics and animations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1121-1141
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• 2020

We propose a robust visual object tracking algorithm fusing a convolutional neural network tracker trained offline from a large number of video repositories and a color histogram based tracker to track objects for mixing immersive audio. Our algorithm addresses the problem of occlusion and large movements of the CNN based GOTURN generic object tracker. The key idea is the offline training of a binary classifier with the color histogram similarity values estimated via both trackers used in this method to opt appropriate tracker for target tracking and update both trackers with the predicted bounding box position of the target to continue tracking. Furthermore, a histogram similarity constraint is applied before updating the trackers to maximize the tracking accuracy. Finally, we compute the depth(z) of the target object by one of the prominent unsupervised monocular depth estimation algorithms to ensure the necessary 3D position of the tracked object to mix the immersive audio into that object. Our proposed algorithm demonstrates about 2% improved accuracy over the outperforming GOTURN algorithm in the existing VOT2014 tracking benchmark. Additionally, our tracker also works well to track multiple objects utilizing the concept of single object tracker but no demonstrations on any MOT benchmark.

• Current Optics and Photonics
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• v.7 no.3
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• pp.263-272
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• 2023

A collimator refers to an optical system that images a collimated beam at a desired point. A resolution target located at a near distance can be converted into a virtual image located at a long distance. To test the resolution for mobile cameras, a large target is placed at a long distance. If a collimator system is used, the target can be placed at a near distance. The space required for a resolution inspection can thus be drastically reduced. However, to inspect a mobile camera, the exit pupil of the collimator system and the entrance pupil of the mobile camera must match, and the stop of the collimator system must be located on the last surface. Because a collimator system cannot be symmetrical with respect to the stop, the distortion becomes extremely large, which can be corrected by combining the collimator symmetrically with respect to the object plane. A novel system was designed to inspect an optical lens on a mobile phone. After arranging the refractive power, lenses were added using the equivalent lens design method. The distortion was reduced to less than 1%. This optical system satisfies a half-field angle of 45° and an optical performance sufficient for inspection.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1261-1267
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• 2015

The problem of underwater target detection and classification has been attracted a substantial amount of attention and studied from many researchers for both military and non-military purposes. The difficulty is complicate due to various environmental conditions. In this paper, we study classifier ensemble methods for active sonar target classification to improve the classification performance. In general, classifier ensemble method is useful for classifiers whose variances relatively large such as decision trees and neural networks. Bagging, Random selection samples, Random subspace and Rotation forest are selected as classifier ensemble methods. Using the four ensemble methods based on 31 neural network classifiers, the classification tests were carried out and performances were compared.