• 전자공학회논문지SC
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• 제38권3호
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• pp.31-39
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• 2001

본 논문에서는 CMOS 이미지 센서의 동작 범위(Dynamic Range; DR)를 높이기 위해서， multiple sampling 방법과 조건적 reset 기능을 갖는 새로운 픽셀 회로를 제안한다. 제안된 구조는 한 번의 integration 시간 내에서 픽셀의 출력이 일정한 간격으로 여러 번 sampling되고 sampling된 각 신호는 기준 전압과 비교되며 이 결과에 따라 해당 픽셀을 rest 할지의 여부가 결정된다. 제안된 방법을 사용하면 이미지 센서의 최대 DR은 축적 기간 동안의 총 sampling 회수인 N 배로 증가될 수 있다. 테스트 칩은 0.65-${\mu}m$ CMOS 공정(2-P, 2-M)으로 제작되었으며 이에 대한 측정결과로 본 논문의 알고리듬이 DR의 증가에 효과적임을 확인하였다.

• Smart Structures and Systems
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• 제17권4호
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• pp.647-667
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• 2016

In this paper, dynamic displacement is estimated with high accuracy by blending high-sampling rate acceleration data with low-sampling rate displacement measurement using a two-stage Kalman estimator. In Stage 1, the two-stage Kalman estimator first approximates dynamic displacement. Then, the estimator in Stage 2 estimates a bias with high accuracy and refines the displacement estimate from Stage 1. In the previous Kalman filter based displacement techniques, the estimation accuracy can deteriorate due to (1) the discontinuities produced when the estimate is adjusted by displacement measurement and (2) slow convergence at the beginning of estimation. To resolve these drawbacks, the previous techniques adopt smoothing techniques, which involve additional future measurements in the estimation. However, the smoothing techniques require more computational time and resources and hamper real-time estimation. The proposed technique addresses the drawbacks of the previous techniques without smoothing. The performance of the proposed technique is verified under various dynamic loading, sampling rate and noise level conditions via a series of numerical simulations and experiments. Its performance is also compared with those of the existing Kalman filter based techniques.

• 대한전자공학회논문지SP
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• 제46권5호
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• pp.25-31
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• 2009

Compressed sensing은 기존의 Nyquist sampling 이론에 기반을 두었던 dynamic MRI에서의 시 공간 해상도의 제한을 획기적으로 향상시킴으로써, 최근 몇 년 사이, MR reconstruction 분야에서 가장 큰 이슈가 되고 있는 연구주제이다. Dynamic MRI 는 대부분 시간방향의 redundancy 가 매우 크므로, 쉽게 sparse 변환이 가능하다. 따라서 sparsity를 기본 조건으로 하는 compressed sensing은 거의 모든 dynamic MRI 에 대해 효과적으로 적용될 수 있다. 본 review 페이퍼에서는 최근 compressed sensing 에 기반을 두거나 영상의 sparsity를 이용하여 개발된 dynamic MR imaging algorithm 들을 간략히 소개하고, 비교 분석함으로써, compressed sensing과 같은 새로운 접근 방식의 dynamic MRI가 실제 임상에서 가져다 줄 발전 가능성을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.4940-4957
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• 2019

Compared with the localization methods in the static sensor networks, node localization in dynamic sensor networks is more complicated due to the mobility of the nodes. Dynamic Sampling Localization Algorithm Based on Virtual Anchor (DSLA) is proposed in this paper to localize the unknown nodes in dynamic sensor networks. Firstly, DSLA algorithm predicts the speed and movement direction of nodes to determine a sector sampling area. Secondly, a method of calculating the sampling quantity with the size of the sampling area dynamically changing is proposed in this paper. Lastly, the virtual anchor node, i.e., the unknown node that got the preliminary possible area (PLA), assists the other unknown nodes to reduce their PLAs. The last PLA is regarded as a filtering condition to filter out the conflicting sample points quickly. In this way, the filtered sample is close to its real coordinates. The simulation results show that the DSLA algorithm can greatly improve the positioning performance when ensuring the execution time is shorter and the localization coverage rate is higher. The localization error of the DSLA algorithm can be dropped to about 20%.

• 반도체디스플레이기술학회지
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• 제14권3호
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• pp.37-43
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• 2015

In this paper, a new background subtraction algorithm for video based fence monitoring surveillance systems is proposed. We adopt the sampling based background subtraction technique and focus on the two main issues: handling highly dynamic environment and handling the flickering nature of pulse based IR (infrared) lamp. Natural scenes from fence monitoring system are usually composed of several dynamic entities such as swaying trees, moving water, waves and rain. To deal with such dynamic backgrounds, we utilize the confidence factor for each background value of the input image. For the flickering IR lamp, the original sampling based technique is extended to handle double background models. Experimental results revealed that our method works well in real fence monitoring surveillance systems.

• 센서학회지
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• 제21권1호
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• pp.7-12
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• 2012

In this paper, we propose a 4-transistor active pixel sensor(APS) with a novel correlated double sampling(CDS) circuit for the purpose of extending dynamic range. Dual sampling techniques can overcome low-sensitivity and temporal disparity problems at low illumination. To accomplish this, two images are obtained at the same time using different sensitivities. The novel CDS circuit proposed in this paper contains MOS switches that make it possible for the capacitance of a conventional CDS circuit to function as a charge pump, so that the proposed APS exhibits an extended dynamic range as well as reduced noise. The designed circuit was fabricated by using $0.35{\mu}m$ 2-poly 4-metal standard CMOS technology and its characteristics have been evaluated.

• Bulletin of the Korean Chemical Society
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• 제33권3호
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• pp.980-986
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• 2012

In this paper we develop a minimalist model of single molecule spectroscopy in a dynamic environment. Our model is based upon a lattice system consisting of a probe molecule embedded in an Ising-model like environment. We assume that the probe molecule interacts with the Ising spins via a dipole-dipole potential, and calculate free energy curves and lineshapes of the system. To investigate fluctuation behavior of the system we exploit the metadynamics sampling method. In particular, using the method, we calculate the free energy curve of magnetization of the lattice and that of the transition energy of the probe molecule. Furthermore, we compare efficiencies of three different sampling methods used; unbiased, umbrella, and metadynamics sampling methods. Finally, we explore the lineshape behavior of the probe molecule as the system undergoes a phase transition from a sub-critical and to a super-critical temperature. We show that the transition energy of a probe molecule is broadly distributed due to the heterogeneous, local environments.

• International Journal of Reliability and Applications
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• 제2권1호
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• pp.27-36
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• 2001

This Paper proposes a new methodology for assessing the reliability of an accident management, which Is based on the reliability physics and the scheme to generate dynamic event tree. The methodology consists of 3 main steps: screening; uncertainty propagation; and probability estimation. Sensitivity analysis is used for screening the variables of significance. Latin Hypercube sampling technique and MAAP code are used for uncertainty propagation, and the dynamic event tree generation method is used for the estimation of non-success probability of implementing an accident management strategy. This approach is applied in assessing the non-success probability of implementing a cavity flooding strategy, which is to supply water into the reactor cavity using emergency fire systems during the sequence of station blackout at the reference plant.

• 대한의용생체공학회:의공학회지
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• 제9권1호
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• pp.87-92
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• 1988

In this paper, a new focusing method, to be called the pipelined sampled delay focusing (PSDF), is implemented. This method improves the lateral resolution in ultrasound imaging system. In PSDF, the analog belay lines are no longer necessary because sampling sum process can replace the conventional delay sum process. Also, the method offers continuous dynamic focusing on the resolution pixel basis, and eliminates the constraint that the maximum delay time is less than the sampling interval. Second order sampling is adopted in order to extend the sampling interval.

• 대한임베디드공학회논문지
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• 제3권2호
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• pp.109-118
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• 2008

We present a robust localization algorithm using particle filter for mobile robots in a dynamic environment. It is difficult to describe moving obstacles like people or other robots on the map and the environment is changed after mapping. A mobile robot cannot estimate its pose robustly with this incomplete map because sensor observations are corrupted by un-modeled obstacles. The proposed algorithms provide robustness in such a dynamic environment by suppressing the effect of corrupted sensor observations with a selective update or a sampling from non-corrupted window. A selective update method makes some particles keep track of the robot, not affected by the corrupted observation. In a sampling from non-corrupted window method, particles are always sampled from several particle sets which use only non-corrupted observation. The robustness of proposed algorithm is validated with experiments and simulations.