• Journal of Communications and Networks
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• v.18 no.1
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• pp.8-18
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• 2016

In this paper, we propose a number of blind equalization approaches for time-varying andmulti-path channels. The approaches employ cost reference particle filter (CRPF) as the symbol estimator, and additionally employ either least mean squares algorithm, recursive least squares algorithm, or $H{\infty}$ filter (HF) as a channel estimator such that they are jointly employed for the strategy of "Rao-Blackwellization," or equally called "mixture filtering." The novel feature of the proposed approaches is that the blind equalization is performed based on direct channel estimation with unknown noise statistics of the received signals and channel state system while the channel is not directly estimated in the conventional method, and the noise information if known in similar Kalman mixture filtering approach. Simulation results show that the proposed approaches estimate the transmitted symbols and time-varying channel very effectively, and outperform the previously proposed approach which requires the noise information in its application.

• The Journal of the Acoustical Society of Korea
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• v.39 no.1
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• pp.64-68
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• 2020

In a conventional speech recognition system using Gaussian Mixture Model-Hidden Markov Model (GMM-HMM), the cepstral feature normalization method based on pole filtering was effective in improving the performance of recognition of short utterances in noisy environments. In this paper, the usefulness of this method for the state-of-the-art speech recognition system using Deep Neural Network (DNN) is examined. Experimental results on AURORA 2 DB show that the cepstral mean and variance normalization based on pole filtering improves the recognition performance of very short utterances compared to that without pole filtering, especially when there is a large mismatch between the training and test conditions.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.184-189
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• 2013

In this paper, we propose the injurious moving picture filtering system using certain sounds contained in the injurious moving picture to filter injurious moving picture which is distributed without limitation in internet and internet storage space. For this purpose, the Gaussian Mixture Model which can well represent the characteristics of the sound, is used and frame level likelihood estimation is used to calculate the likelihood between filtering target data and the sound models. Also, the pruning method which can real-time proceed by reducing the comparing number of data, is applied for real-time processing, and MWMR method which showed good performance from existing speaker identification, is applied for the distinguish performance of high precision. In the identification experiment result, in case of the frame rate which is the proportion of total frame to high likelihood frame, is set to 50%, identification error rate is 6.06%, and in case of frame rate is set to 60%, error rate is 3.03%. As the result, the proposed system can distinguish between general and injurious moving picture effectively.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.539-551
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• 2018

Images are unavoidably contaminated with different types of noise during the processes of image acquisition and transmission. The main forms of noise are impulse noise (is also called salt and pepper noise) and Gaussian noise. In this paper, an effective method of removing mixed noise from images is proposed. In general, different types of denoising methods are designed for different types of noise; for example, the median filter displays good performance in removing impulse noise, and the wavelet denoising algorithm displays good performance in removing Gaussian noise. However, images are affected by more than one type of noise in many cases. To reduce both impulse noise and Gaussian noise, this paper proposes a denoising method that combines adaptive median filtering (AMF) based on impulse noise detection with the wavelet threshold denoising method based on a Gaussian mixture model (GMM). The simulation results show that the proposed method achieves much better denoising performance than the median filter or the wavelet denoising method for images contaminated with mixed noise.

• Journal of the Korean Society of Safety
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• v.13 no.2
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• pp.39-44
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• 1998

An experimental study on oil absorbent was conducted to investigate the feasibility of utilizing absorbents in oil separation from water-oil mixture for spilt oil recovery. Experiments included investigations of absorptivity and filtering performance of a commercial oil absorbent for different diesel oil concentrations. The measured average absorptivity of the absorbent was above 92% for oil concentrations, 5, 10, 15vo1%, that shows good absorbing performance. Filtering the oil-water mixture, however, was too slow to be used for oil separation. An absorbent baffle system was suggested for oil separation which collects oil panicles by increasing contact between the absorbent and oil particles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1635-1656
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• 2023

In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality.

• Journal of Wetlands Research
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• v.3 no.1
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• pp.75-89
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• 2001

Constructed wetlands were typically cost less to build and operate, and require less energy than standard mechanical treatment technology but they have similar performance to centralized wastewater treatment plants. Therefore, they were constructed especially many in rural areas, where are small villages but not industries. Accordingly, plantless column tests were performed to investigate the possibility on using zeolite as a filter medium of constructed wetland for the wastewater treatment. $COD_{cr}$ removal efficiency was 94.63% at hydraulic load $314L/m^2{\cdot}d$ and filtering hight 100cm filled with a zeolite mixture. This zeolite mixture consisted of 1 : 1 by volume of a zeolite in the diameter range of 0.5 to 1mm to a zeolite in the diameter range of 1 to 3mm. According, hydraulic load $314L/m^2{\cdot}d$ was considered as optimal. Three zeolite mixture were used to determine the optimal mixing ratio by volume of a zeolite(A) in the diameter range of 0.5 to 1mm to a zeolite(B) in the diameter range of 1 to 3mm diameter. 1 : 3, 1 : 1 and only B in A to B by volume were tested at hydraulic load $314L/m^2{\cdot}d$ and filtering hight 100cm. $COD_{cr}$ removal efficiency was more than 89% at mixing ratios of 1 : 3 and 1 : 1 in A to B. Removal efficiency was lower at the column filled with only B. Removal efficiency was better at filter medium filled with mixing ratio 1 : 1 in A to B than with the other mixing ratios. Thus, it was found that the mixture of mixing ratio 1 : 1 in A to B was appropriate for filter medium of constructed wetland. Removal efficiency was higher in down-flow than in up-flow, and $COD_{cr}$ and BOD were removed best in 20cm filter height near feeding area.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.42-47
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• 1999

In this paper, a mixture interacting multiple model (MIMM) algorithm is proposed to enhance speech contaminated by additive nonstationary noise. In this approach, a mixture hidden filter model (HFM) is used to model the clean speech and the noise process is modeled by a single hidden filter. The MIMM algorithm, however. needs large computation time because it is a recursive method based on multiple Kalman filters with mixture HFM. Thereby, a computationally efficient implementation of the algorithm is developed by exploiting the structure of the Kalman filtering equation. The simulation results show that the proposed method offers performance gain compared to the previous results in [4,5] with slightly increased complexity.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.281-288
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• 1994

In this paper, a new decomposition method of the interference EMG signal using MAMDF filtering and digital signal processor. The efficient software and hardware signal processing techniques are employed. The MAMDF filter is employed in order to estimate the presence and likely location of the respective templates which may include in the observed mixture, and high-resolution waveform alignment is employed in order to provide the optimal combination set and time delays of the selected templates. The TMS320C25 digital signal processor chip is employed in order to execute the intensive calculation part of the software. The method is verified through a simulation with real templates which are obtain ed from needle EMG. As a result, the proposed method provides an overall speed improvement of 32-40 times.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.592-601
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• 2013

Object segmentation is a challenging task in image processing and computer vision. In this paper, we present a visual attention based segmentation method to segment small sized interesting objects in natural images. Different from the traditional methods, we first search the region of interest by using our novel saliency-based method, which is mainly based on band-pass filtering, to obtain the appropriate frequency. Secondly, we applied the Gaussian Mixture Model (GMM) to locate the object region. By incorporating the visual attention analysis into object segmentation, our proposed approach is able to narrow the search region for object segmentation, so that the accuracy is increased and the computational complexity is reduced. The experimental results indicate that our proposed approach is efficient for object segmentation in natural images, especially for small objects. Our proposed method significantly outperforms traditional GMM based segmentation.