• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.151-153
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• 2006

we present a method of sequential speech enhancement, where we infer clean speech signal using a Rao-Blackwellized particle filter (RBPF), given a noise-contaminated observed signal. In contrast to Kalman filtering-based methods, we consider a non-Gaussian speech generative model that is based on the generalized auto-regressive (GAR) model. Model parameters are learned by a sequential Newton-Raphson expectation maximization (SNEM), incorporating the RBPF. Empirical comparison to Kalman filter, confirms the high performance of the proposed method.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.633-636
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• 2011

This paper describes a implementation of virtual interactive interview system. A hand motion recognition algorithm based on the particle filters is applied for this system. The particle filter is well operated for human hand motion recognition than any other recognition algorithm. Through the experiments, we show that the proposed scheme is stable and works well in virtual interview system's environments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.16-22
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• 2011

This paper presents the multi-object tracking approach using the background difference and particle filtering by monte carlo sampling. We apply particle filters based on probabilistic importance sampling to multi-object independently. We formulate the object observation model by the histogram distribution using color information and the object dynaminc model for the object motion information. Our approach does not increase computational complexity and derive stable performance. We implement the whole Bayesian maximum likelihood framework and describes robust methods coping with the real-world object tracking situation by the observation and transition model.

• Proceedings of the SAREK Conference
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• 2007.11a
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• pp.387-392
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• 2007

In this study performance evaluation of filtering system were made on the clean air supply system to show it's ability to eliminate the air contaminants. The evaluation was conducted inside the 3,200CMH scale wind tunnel and under the same environment that is effected by yellow dust and similar particle and gas phase contaminants in semi-conductor and FPDs industries. (1) The result of experimental for particle contaminants, the particle removing efficiency was 40% on condition that the air velocity is 2.5m/s, L/G ratio : 0.05, electrified voltage : (+)5.8kV with electric charger and (-)3.5kV with eliminator. (2) The gas phase removing efficiency for $NH_3$ : 80%, $SO_X$ : 70% and $NO_X$ : 40% on condition that the air velocity is 2.5m/s, L/G ratio : 0.05.

• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.308-314
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• 2008

Recently, simultaneous localization and mapping (SLAM) approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, no research is conducted to analyze the result representation of SLAM using RBPF (RBPF-SLAM) when particle diversity is preserved. After finishing the particle filtering, the results such as a map and a path are stored in the separate particles. Thus, we propose several result representations and provide the analysis of the representations. For the analysis, estimation errors and their variances, and consistency of RBPF-SLAM are dealt in this study. According to the simulation results, combining data of each particle provides the better result with high probability than using just data of a particle such as the highest weighted particle representation.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.232-238
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• 2013

This paper study the position estimation of MBK system according to the non-linear filter for non-Gaussian noise in underwater sensor networks. In the filter to estimate location, recently, the extended Kalman filter (EKF) and particle filter are getting attention. EKF is widely used due to the best algorithm in the Gaussian noise environment, but has many restrictions on the usage in non-Gaussian noise environment such as in underwater. In this paper, we propose the improved One-Dimension Particle Filter (ODPF) using the distribution re-interpretation techniques based on the maximum likelihood. Through the simulation, we compared and analyzed the proposed particle filter with the EKF in non-Gaussian underwater sensor networks. In the case of both the sufficient statistical sample and the sufficient calculation capacity, we confirm that the ODPF's result shows more accurate localization than EKF's result.

• Journal of Power System Engineering
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• v.5 no.1
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• pp.64-72
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• 2001

According to the regulation of IMO, oil discharge from ships is allowed under 15ppm only and an oil filtering equipment is essential. However, for large ships using heavy fuel oil of over S.G 0.98 and viscosity 380cSt and system oil, it has been in difficulty to process with existing filtering type of oily water separator. A parallel plate type oily water separator which is one of gravity type separators can be used as an assistant equipment for the oil filtering system to meet the present IMO standard of 15 ppm, because it is an efficient method in dealing with a large amount of rich oil with high specific gravity. This work is focused on the fundamental investigation of the performance of the plate type oily water separator by visualization method and PIV(Particle Image Velocimetry) measurement to acquire multi-point velocity data simultaneously. The experimental results showed that the space of the plates acts a significant role in separating process and it was found that an important point to minimize a vortex flow is to flow a large amount of fluid in space of the plates in order to promote the efficiency of separation.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.736-747
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• 2006

Simultaneous Localization and Map Building(SLAM) is one of the fundamental problems in robot navigation. The Extended Kalman Filter(EKF), which is widely adopted in SLAM approaches, requires extensive computation. The conventional particle filter also needs intense computation to cover a high dimensional state space with particles. This paper proposes an efficient SLAM method based on the recursive unscented Kalman filtering in an environment including a large number of landmarks. The posterior probability distributions of the robot pose and the landmark locations are represented by their marginal Gaussian probability distributions. In particular, the posterior probability distribution of the robot pose is calculated recursively. Each landmark location is updated with the recursively updated robot pose. The proposed method reduces filtering dimensions and computational complexity significantly, and has produced very encouraging results for navigation experiments with noisy multiple simultaneous observations.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2088-2091
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• 2003

We presents a new algorithm for tracking person in video sequence that integrates the meanshift iteration procedure into the particle filtering. Utilizing the nice property of convergence to the modes in the meanshift iteration we show that only a few sample points are sufficient, while in general the particle filtering requires a large number of sample points. Multi-parts of a person is tracked independently of each other based on the color Then, the similarity against the reference model color and the geometric constraints between multi-parts are reflected as the sample weights. Also presented is the computer simulation results, which show successful tracking even for complex background clutter.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.4 no.3
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• pp.39-44
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• 2005

With the development of high speed and accuracy machining, the micro-chips are formed in the machining process and broken particles are circulated with the cutting fluid. The surface roughness and accuracy of part are deteriorated because the metal particles included in the cutting fluid are embedded on machined surface. In this study, the influences of particles for the machined surface according to filtering degrees are evaluated and the embedding mechanism is suggested.