• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.681-691
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• 2017

In the clutter environment, it is necessary to update the target tracking filter by detecting the target signal among many measured value data obtained via the radar system, the track does not diverge, and tracking performance is maintained. The method of associating the measurement most relevant to the target track among numerous measurement values is referred to as data association. PSNF and PSNF-m are data association methods of SN-series. In this paper, we provide an IPSNF-m(Integrated Probabilistic Strongest Neighbor Filter-m) algorithm with a track management method based on the track existence probability in PSNF-m algorithm. This algorithm considers not only the presence of the target but also the case where the target is present but not detected. Calculating the probability of each caseenables efficient management. In order to verify the performance of the proposed IPSNF-m, the track existence probability of the IPSNF algorithm applying the track management technique to PSNF, which is known to have similar performance to PSNF-m, is derived. Through simulation in the same environment, we compare and analyze the proposed algorithm with RMSE, Confirmed True Track, and Track Existence Probability that show better performance in terms of track retention and estimation than the existing PSNF-m and IPSNF algorithms.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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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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• v.25 no.8
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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.