• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1362-1364
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• 2003

As non-isothermal mixed pixel is widely existed, the pixel-mean temperature cannot adequately represent the actual thermal state of land surface. The row crop was chosen as target to discuss the problem of component temperature retrieval. At first, the matrix model was found to express the thermal radiant directionality of the target. Then correlation of multi-angle infrared radiance was analyzed. In order to increase the retrieving accuracy, we chose the retrievable parameters and established the iterative method combining with inverse matrix to retrieve component temperature. It was proved by field experiment that the method could improve the retrieving accuracy and stability remarkably.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1464-1480
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• 2019

Robust tracking of infrared (IR) pedestrian targets with various backgrounds, e.g. appearance changes, illumination variations, and background disturbances, is a great challenge in the infrared image processing field. In the paper, we address a new tracking method for IR pedestrian targets via multi-feature local sparse representation (SR), which consists of three important modules. In the first module, a multi-feature local SR model is constructed. Considering the characterization of infrared pedestrian targets, the gray and edge features are first extracted from all target templates, and then fused into the model learning process. In the second module, an effective tracker is proposed via the learned model. To improve the computational efficiency, a sliding window mechanism with multiple scales is first used to scan the current frame to sample the target candidates. Then, the candidates are recognized via sparse reconstruction residual analysis. In the third module, an adaptive dictionary update approach is designed to further improve the tracking performance. The results demonstrate that our method outperforms several classical methods for infrared pedestrian tracking.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.819-832
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• 2022

Cross-domain recommender system transfers knowledge across different domains to improve the recommendation performance in a target domain that has a relatively sparse model. However, they suffer from the "negative transfer" in which transferred knowledge operates as noise. This paper proposes a novel Multiple Fusion-based Deep Cross-Domain Recommendation named MFDCR. We exploit Doc2Vec, one of the famous word embedding techniques, to fuse data user-wise and transfer knowledge across multi-domains. It alleviates the "negative transfer" problem. Additionally, we introduce a simple multi-layer perception to learn the user-item interactions and predict the possibility of preferring items by users. Extensive experiments with three domain datasets from one of the most famous services Amazon demonstrate that MFDCR outperforms recent single and cross-domain recommendation algorithms. Furthermore, experimental results show that MFDCR can address the problem of "negative transfer" and improve recommendation performance for multiple domains simultaneously. In addition, we show that our approach is efficient in extending toward more domains.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.5
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• pp.94-104
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• 2000

The accuracy and reliability of the target tracking is very critical issue in the design of automotive collision warning radar A significant problem in multi-target tracking (MTT) is the target-to-measurement data association If an incorrect measurement is associated with a target, the target could diverge the track and be prematurely terminated or cause other targets to also diverge the track. Most methods for target-to-measurement data association tend to coalesce neighboring targets Therefore, many algorithms have been developed to solve this data association problem. In this paper, a new multi-target data association method based on order statistics is described The new approaches. called the order statistics probabilistic data association (OSPDA) and the order statistics joint probabilistic data association (OSJPDA), are formulated using the association probabilities of the probabilistic data association (PDA) and the joint probabilistic data association (JPDA) filters, respectively Using the decision logic. an optimal or near optimal target-to-measurement data association is made A computer simulation of the proposed method in a heavy cluttered condition is given, including a comparison With the nearest-neighbor CNN). the PDA, and the JPDA filters, Simulation results show that the performances of the OSPDA filter and the OSJPDA filter are superior to those of the PDA filter and the JPDA filter in terms of tracking accuracy about 18% and 19%, respectively In addition, the proposed method is implemented using a developed digital signal processing (DSP) board which can be interfaced with the engine control unit (ECU) of car engine and with the d?xer through the controller area network (CAN)

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.255-259
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• 2004

본 논문에서는 다목적 위성 2호의 주 탑재체인 MSC (Multi-Spectral Camera)의 영상자료 검보정을 위한 검보정 target 준비 작업에 대해 설명한다. MSC 영상 자료에 대한 검보정 작업은 다목적 위성 2호의 발사 후 초기 운영 기간 (LEOP: Launch and Early Operation Phase)인 3개월 동안 수행될 예정이다. 위성 발사 전까지 MSC 영상 자료에 대한 검보정을 수행하기 위해 필요한 준비 작업들이 현재 한국항공우주연구원에서 진행중이다. LEOP 기간 동안 MSC 영상 자료를 검보정하기 위해서, MSC의 센서 특성에 따라 7가지 정도의 검보정 target에 대한 설계 초안이 완성되었으며, 향후 target에 대한 설계를 완성한 후에 2004년 중에 한 두 부지에 몇 가지 target들을 건설하고, 다목적 위성 2호의 궤도 특성을 고려하여 일부 target은 운반이 가능하도록 제작할 예정이다. 검보정 target이 촬영된 MSC 영상 자료의 분석을 통해, GSD (Ground Sample Distance), Aliasing, Linearity, Edge Slope & Response, MTF (Modulation Transfer Function), FOV & IFOV, Absolute radiometric validation, Position Accuracy 등의 MSC 검보정 요소 값들을 측정할 계획이다.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.11
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• pp.1125-1135
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• 2010

A fundamental characteristic of PMHT (Probabilistic Multi-Hypothesis Tracker) is that the number of targets and initial states of targets in the surveillance area must be a priori known. This requirement is impossible to fulfil in almost every realistic scenario. In the paper, we present two track initiation methods to solve the problem. The proposed track initiation methods are 2-point track initiation and Hough transform track initiation, and they are used to evaluate track initial states and weights for FTD (False Track Discrimination) of the PMHT algorithm. Also suggested as automatic target detection for tracking systems that combines track initiation for target detection with the PMHT algorithm for target tracking in a cluttered environment. A series of Monte-Carlo simulation runs is employed to evaluate the overall system performance with the two track initiation methods and the results are compared and analyzed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2339-2355
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• 2021

In this paper, a joint probabilistic data association algorithm based on loss function (LJPDA) is proposed so that the computation load and accuracy of the multi-target tracking algorithm can be guaranteed simultaneously. Firstly, data association is divided in to three cases based on the relationship among validation gates and the number of measurements in the overlapping area for validation gates. Also the contribution coefficient is employed for evaluating the contribution of a measurement to a target, and the loss function, which reflects the cost of the new proposed data association algorithm, is defined. Moreover, the equation set of optimal contribution coefficient is given by minimizing the loss function, and the optimal contribution coefficient can be attained by using the Newton-Raphson method. In this way, the weighted value of each target can be achieved, and the data association among measurements and tracks can be realized. Finally, we compare performances of LJPDA proposed and joint probabilistic data association (JPDA) algorithm via numerical simulations, and much attention is paid on real-time performance and estimation error. Theoretical analysis and experimental results reveal that the LJPDA algorithm proposed exhibits small estimation error and low computation complexity.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.437-441
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• 2022

In this study, we developed a neutron time-of-flight (nTOF) measurement system for a 1.7-MV tandem proton accelerator with a target covered with 300-nm-thick lithium (Li) layer. With implementation of beam chopping module after its ion source, the accelerator is configured to operate in pulsed-beam mode with a pulse width <50 ns at 20-kHz repetition rate. This enables the gamma flash-type nTOF measurement system to identify the neutron generated with 3-MeV proton beam energy. The nTOF system consists of a 30" cylindrical NaI(Tl) and four stilbene scintillation detectors. The NaI(Tl) scintillator is placed 50 cm from the Li target to measure the time of beam irradiation on the target, and the stilbene detectors are placed 2 and 2.4 m away to measure nTOF at each location. The nTOF system successfully measured the generated neutron energy at irradiated proton energies of 2.6 and 3.0 MeV with an average energy resolution of 15%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.908-928
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• 2022

Multiple target tracking mainly focuses on tracking unknown number of targets in the complex environment of clutter and missed detection. The generalized labeled multi-Bernoulli (GLMB) filter has been shown to be an effective approach and attracted extensive attention. However, in the scenarios where the clutter rate is high or measurement-outliers often occur, the performance of the GLMB filter will significantly decline due to the Gaussian-based likelihood function is sensitive to clutter. To solve this problem, this paper presents a robust GLMB filter and smoother to improve the tracking performance in the scenarios with high clutter rate, low detection probability, and measurement-outliers. Firstly, a Student-T distribution variational Bayesian (TDVB) filtering technology is employed to update targets' states. Then, The likelihood weight in the tracking process is deduced again. Finally, a trajectory smoothing method is proposed to improve the integrative tracking performance. The proposed method are compared with recent multiple target tracking filters, and the simulation results show that the proposed method can effectively improve tracking accuracy in the scenarios with high clutter rate, low detection rate and measurement-outliers. Code is published on GitHub.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.837-841
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• 1990

Track While Scan(DS) system which can track the multitargets in dense target environment is designed. There are three tasks to be performed: I) Target Detection and 'plot' formation, ii) Plot to track association and, iii) Track updatement. The conventional approach has been to tackle each of these tasks separately. This paper outlines a method for jointly optimizing all the three tasks and presents implementation aspects.