• 한국산학기술학회논문지
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• 제18권6호
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• pp.681-691
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• 2017

클러터 환경에서는 레이다 시스템을 통해 얻어지는 많은 측정치 정보 중 표적 신호를 찾아내어 표적추적 필터를 쇄신해야 트랙이 발산하지 않고 추정성능이 유지된다. 다수의 측정치 중 표적의 트랙과 가장 연관성이 높은 측정치를 대응시키는 방법을 자료결합(Data Association)이라 한다. 자료결합 방법 중 신호세기기반 표적 추적방법에는 PSNF, PSNF-m이 있다. 본 논문에서는 PSNF-m 알고리듬에 표적의 존재 유/무에 대한 트랙존재확률 기반의 Track Management 기법을 적용한 IPSNF-m(Integrated Probabilistic Strongest Neighbor Filter-m) 알고리듬을 제안한다. 이 알고리듬은 표적 존재의 유/무 뿐만아니라 표적이 존재하지만 탐지가 되지 않을 사건 등을 고려하여 각각의 사건에 대한 확률을 구함으로써 트랙에 대하여 효율적인 관리를 가능하게 해준다. 제안하는 IPSNF-m의 성능 확인을 위해 PSNF-m과 유사한 성능을 지니는 것으로 알려진 PSNF에 Track Management 기법을 적용한 IPSNF 알고리듬의 트랙존재확률을 유도하였다. 그리고 동일한 환경에서의 시뮬레이션을 통해 제안하는 알고리듬이 기존의 PSNF-m과 IPSNF 알고리듬보다 트랙 유지 및 추정 측면에서 우수한 성능을 나타내는 것을 RMSE, Confiremd True Track, 트랙존재확률을 통해 비교 및 분석하였다.

• International Journal of Control, Automation, and Systems
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• 제6권3호
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• pp.413-423
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• 2008

The template matching method is used as a simple method to track objects or patterns that we want to search for in the input image data from image sensors. It recognizes a segment with the highest correlation as a target. The concept of this method is similar to that of SNF (Strongest Neighbor Filter) that regards the measurement with the highest signal intensity as target-originated among other measurements. The SNF assumes that the strongest neighbor (SN) measurement in the validation gate originates from the target of interest and the SNF utilizes the SN in the update step of a standard Kalman filter (SKF). The SNF is widely used along with the nearest neighbor filter (NNF), due to computational simplicity in spite of its inconsistency of handling the SN as if it is the true target. Probabilistic Strongest Neighbor Filter for m validated measurements (PSNF-m) accounts for the probability that the SN in the validation gate originates from the target while the SNF assumes at any time that the SN measurement is target-originated. It is known that the PSNF-m is superior to the SNF in performance at a cost of increased computational load. In this paper, we suggest an image tracking algorithm that combines the template matching and the PSNF-m to estimate the states of a tracked target. Computer simulation results are included to demonstrate the performance of the proposed algorithm in comparison with other algorithms.

• Journal of Microbiology and Biotechnology
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• 제25권8호
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• pp.1291-1298
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• 2015

Five different polymer nanofibers, namely, polyaniline nanofiber (PANI), magnetically separable polyaniline nanofiber (PAMP), magnetically separable DEAE cellulose fiber (DEAE), magnetically separable CM cellulose fiber (CM), and polystyrene nanofiber (PSNF), have been used for the immobilization of lactase (E.C. 3.2.1.23). Except for CM and PSNF, three polymers showed great properties. The catalytic activities (k_cat) of the free, PANI, PAMP, and magnetic DEAE-cellulose were determined to be 4.0, 2.05, 0.59, and 0.042 mM/min·mg protein, respectively. The lactase immobilized on DEAE, PANI, and PAMP showed improved stability and recyclability. PANI- and PAMP-lactase showed only a 0-3% decrease in activity after 3 months of vigorous shaking conditions (200 rpm) and at room temperature (25℃). PANI-, PAMP-, and DEAE-lactase showed a high percentage of conversion (100%, 47%, and 12%) after a 1 h lactose hydrolysis reaction. The residual activities of PANI-, PAMP-, and DEAE-lactase after 10 times of recycling were 98%, 96%, and 97%, respectively.