• Current Optics and Photonics
• /
• v.2 no.3
• /
• pp.269-279
• /
• 2018

We are currently investigating the feasibility of a 1.6 m telescope with a laser-guide star adaptive optics (AO) system. The telescope, if successfully commissioned, would be the first dedicated adaptive optics observatory in South Korea. The 1.6 m telescope is an f/13.6 Cassegrain telescope with a focal length of 21.7 m. This paper first reviews atmospheric seeing conditions measured over a year in 2014~2015 at the Bohyun Observatory, South Korea, which corresponds to an area from 11.6 to 21.6 cm within 95% probability with regard to the Fried parameter of 880 nm at a telescope pupil plane. We then derive principal seeing conditions such as the Fried parameter and Greenwood frequency for eight astronomical spectral bands (V/R/I/J/H/K/L/M centered at 0.55, 0.64, 0.79, 1.22, 1.65, 2.20, 3.55, and $4.77{\mu}m$). Then we propose an AO system with a laser guide star for the 1.6 m telescope based on the seeing conditions. The proposed AO system consists of a fast tip/tilt secondary mirror, a $17{\times}17$ deformable mirror, a $16{\times}16$ Shack-Hartmann sensor, and a sodium laser guide star (589.2 nm). The high order AO system is close-looped with 2 KHz sampling frequency while the tip/tilt mirror is independently close-looped with 63 Hz sampling frequency. The AO system has three operational concepts: 1) bright target observation with its own wavefront sensing, 2) less bright star observation with wavefront sensing from another bright natural guide star (NGS), and 3) faint target observation with tip/tilt sensing from a bright natural guide star and wavefront sensing from a laser guide star. We name these three concepts 'None', 'NGS only', and 'LGS + NGS', respectively. Following a thorough investigation into the error sources of the AO system, we predict the root mean square (RMS) wavefront error of the system and its corresponding Strehl ratio over nine analysis cases over the worst ($2{\sigma}$) seeing conditions. From the analysis, we expect Strehl ratio >0.3 in most seeing conditions with guide stars.

• The Journal of the Acoustical Society of Korea
• /
• v.23 no.1
• /
• pp.54-60
• /
• 2004

In order to mitigate the problems in storage space and network bandwidth for the full CD quality audio, current digital audio is always restricted by sampling rate and bandwidth. This restriction normally results in low sample rate audio or calls for the data compression scheme such as MP3. However, they can only reproduce a lower frequency range than a regular CD quality because of the Nyquist sampling theory. Consequently they lose rich spatial information embedded in high frequency. The propose of this paper is to propose efficient high frequency enhancement of low sample rate audio using n adaptive filtering and DWT analysis and synthesis. The proposed algorithm uses the LMS adaptive algorithm to estimate the missing high frequency contents in DWT domain and it then reconstructs the spectrally enhanced audio by using the DWT synthesis procedure. Several experiments with real speech and audio are performed and compared with other algorithm. From the experimental results of spectrogram and sonic test, we confirm that the proposed algorithm outperforms the other algorithm and reasonably works well for the most of audio cases.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.9
• /
• pp.401-410
• /
• 2016

3D registration is a computer vision technique of aligning multi-view range images with respect to a reference coordinate system. Various 3D registration algorithms have been introduced in the past few decades. Iterative Closest Point (ICP) is one of the widely used 3D registration algorithms, where various modifications are available nowadays. In the ICP-based algorithms, the closest points are considered as the corresponding points. However, this assumption fails to find matching points accurately when the initial pose between point clouds is not sufficiently close. In this paper, we propose a new method to solve this problem using the 6D distance (3D color space and 3D Euclidean distances). Moreover, a color segmentation-based adaptive sampling technique is used to reduce the computational time and improve the registration accuracy. Several experiments are performed to evaluate the proposed method. Experimental results show that the proposed method yields better performance compared to the conventional methods.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.10
• /
• pp.6296-6302
• /
• 2014

A new technique, which can increase the channel bandwidth in an optical wireless orthogonal frequency division multiplexing (OFDM) link based on a light emitting diode (LED), is proposed. The technique uses adaptive sampling to convert an OFDM signal to a sparse waveform. In compressive sensing (CS), a sparse signal that is sampled below the Nyquist/Shannon limit can be reconstructed successively with sufficient measurements. The data rate of the proposed CS-based visible light communication (VLC)-OFDM link increases from 30.72 Mb/s to 51.2 Mb/s showing an error vector magnitude (EVM) of 31 % at the quadrature phase shift keying (QPSK) symbol.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.13 no.2
• /
• pp.49-61
• /
• 2017

Transactional Memory (TM) has greatly changed the parallel programming paradigm for transaction processing and is classified into STM, HTM, HyTM according to hardware or software frameworks. However, the existing studies have a problem that they provide static retry policy for all workloads. To solve the problems, we propose an hybrid transactional memory scheme using sampling-based adaptive retry policy in multi-core environment. First, the proposed scheme determines whether to use STM or HTM according to the characteristic of a transaction. Otherwise, it executes HTM and STM concurrently by using a bloom filter. Second, the proposed scheme provides adaptive retry policy for HTM according to the characteristic of transactions in each workload. Finally, through the experimental performance evaluation using STAMP, the proposed scheme shows 10~20% better performance than the existing schemes.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.46-46
• /
• 2022

유기물의 복잡한 혼합물인 CDOM(Colored or Chromophoric Dissolved Organic Matter)은 하천 내 BOD(Biological Oxygen Demand), COD(Chemical Oxygen Demand) 및 유기 오염물질과 상당한 관련이 있다. CDOM은 가시광선 영역에서 빛을 흡수하는 성질을 가지고 있으며, 최근 원격감지 기술로 CDOM을 모니터링하기 위한 연구가 진행되고 있다. 본 연구에서는 백제보 상류 23km 구간에서 3년(2016~2018) 중 13일의 초분광영상을 활용하여 머신러닝 기반 CDOM을 추정 알고리즘을 개발하고자 한다. 초분광영상은 400~970 nm의 범위의 4 nm 간격 127개 대역의 분광해상도와 2 m의 공간해상도를 가진 항공기 탑재 AsiaFENIX 초분광 센서를 통해 수집하였으며 CDOM은 Millipore polycarbonate filter (𝚽47, 0.2 ㎛)에서 여과된 CDOM 샘플 자료를 200~800 nm의 흡수계수 스펙트럼으로 추출하여 사용하였다. CDOM 값은 전체기간 동안 2.0~11.0 m^-1의 값 분포를 보였으며 5 m^-1이상의 고농도 구간 자료개수가 전체 153개 샘플자료 중 21개로 불균형하다. 따라서 ADASYN(Adaptive Synthesis Sampling Approach)의 oversampling 방법으로 생성된 합성 데이터를 사용하여 원본 데이터의 소수계층 데이터 불균형을 해결하고 모델 예측 성능을 개선하고자 하였다. 생성된 합성 데이터를 입력변수로 하여 ANN(Artificial Neural Netowk)을 활용한 CDOM 예측 알고리즘을 구축하였다. ADASYN 기법을 통한 합성 데이터는 관측된 데이터의 불균형을 해결하여 기계학습 모델의 CDOM 탐지 성능을 향상시킬 수 있으며, 저수지 내 유기 오염물질 관리를 위한 설계를 지원하는데 사용할 수 있을 것으로 판단된다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.8
• /
• pp.3498-3511
• /
• 2016

We investigate the channel state information (CSI) in multi-input multi-output (MIMO) cooperative networks that employ the amplify-and-forward transmission scheme. Least squares and expectation conditional maximization have been proposed in the system. However, neither of these two approaches takes advantage of channel sparsity, and they cause estimation performance loss. Unlike linear channel estimation methods, several compressed channel estimation methods are proposed in this study to exploit the sparsity of the MIMO cooperative channels based on the theory of compressed sensing. First, the channel estimation problem is formulated as a compressed sensing problem by using sparse decomposition theory. Second, the lower bound is derived for the estimation, and the MIMO relay channel is reconstructed via compressive sampling matching pursuit algorithms. Finally, based on this model, we propose a novel algorithm so called sparsity adaptive expectation maximization (SAEM) by using Kalman filter and expectation maximization algorithm so that it can exploit channel sparsity alternatively and also track the true support set of time-varying channel. Kalman filter is used to provide soft information of transmitted signals to the EM-based algorithm. Various numerical simulation results indicate that the proposed sparse channel estimation technique outperforms the previous estimation schemes.

• International Journal of Reliability and Applications
• /
• v.2 no.3
• /
• pp.209-240
• /
• 2001

Computer-based simulation and analysis is used extensively in engineering for a variety of tasks. Despite the steady and continuing growth of computing power and speed, the computational cost of complex high-fidelity engineering analyses and simulations limit their use in important areas like design optimization and reliability analysis. Statistical approximation techniques such as design of experiments and response surface methodology are becoming widely used in engineering to minimize the computational expense of running such computer analyses and circumvent many of these limitations. In this paper, we compare and contrast five experimental design types and four approximation model types in terms of their capability to generate accurate approximations for two engineering applications with typical engineering behaviors and a wide range of nonlinearity. The first example involves the analysis of a two-member frame that has three input variables and three responses of interest. The second example simulates the roll-over potential of a semi-tractor-trailer for different combinations of input variables and braking and steering levels. Detailed error analysis reveals that uniform designs provide good sampling for generating accurate approximations using different sample sizes while kriging models provide accurate approximations that are robust for use with a variety of experimental designs and sample sizes.

• The KIPS Transactions:PartB
• /
• v.10B no.6
• /
• pp.711-718
• /
• 2003

In this paper, we propose a robust face tracking algorithm. It is based on Condensation algorithm [7] and uses skin color and facial shape as the observation measure. It is hard to integrate color weight and shape weight. So we propose the method that has two separate trackers which uses skin color and facial shape as the observation measure respectively. One tracker tracks skin colored region and the other tracks facial shape. We used importance sampling technique to limit sampling region of two trackers. For skin-colored region tracker, we propose an adaptive color model to avoid the effect of illumination change. The proposed face tracker performs robustly in clutter background and in the illumination changes.

• Advances in Computational Design
• /
• v.1 no.3
• /
• pp.235-251
• /
• 2016

We have performed a design optimization of a stiffened panel with curvilinear stiffeners using an artificial neural network (ANN) residual kriging based surrogate modeling approach. The ANN residual kriging based surrogate modeling involves two steps. In the first step, we approximate the objective function using ANN. In the next step we use kriging to model the residue. We optimize the panel in an iterative way. Each iteration involves two steps-shape optimization and size optimization. For both shape and size optimization, we use ANN residual kriging based surrogate model. At each optimization step, we do an initial sampling and fit an ANN residual kriging model for the objective function. Then we keep updating this surrogate model using an adaptive sampling algorithm until the minimum value of the objective function converges. The comparison of the design obtained using our optimization scheme with that obtained using a traditional genetic algorithm (GA) based optimization scheme shows satisfactory agreement. However, with this surrogate model based approach we reach optimum design with less computation effort as compared to the GA based approach which does not use any surrogate model.