• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.840-847
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• 2022

In a deep neural network (DNN)-based speech enhancement, using global and local input speech information is closely related to model performance. Recently, a nested U-Net structure that utilizes global and local input data information using multi-scale has bee n proposed. This nested U-Net was also applied to speech enhancement and showed outstanding performance. However, a single skip connection used in nested U-Nets must be modified for the nested structure. In this paper, we propose a multi-level skip connection (MLS) to optimize the performance of the nested U-Net-based speech enhancement algorithm. As a result, the proposed MLS showed excellent performance improvement in various objective evaluation metrics compared to the standard skip connection, which means th at the MLS can optimize the performance of the nested U-Net-based speech enhancement algorithm. In addition, the final proposed m odel showed superior performance compared to other DNN-based speech enhancement models.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.2
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• pp.95-102
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• 2008

This paper proposes an adaptive speech enhancement system based on an auditory system to enhance speech that is degraded by various background noises. As such, the proposed system detects voiced and unvoiced sections, adaptively adjusts the coefficients for both the lateral inhibition and the amplitude component according to the detected sections for each input fame, then reduces the noise signal using a time-delay neural network. Based on measuring the signal-to-noise ratio, experiments confirm that the proposed system is effective for speech degraded by various noises.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.165-172
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• 2005

In speech recognition under a noisy environment, it is necessary to construct a system which reduces the noise and enhances the speech. Then it is effective to imitate the human auditory system which has an excellent analytical spectrum mechanism for speech enhancement. Accordingly, this paper proposes an adaptive method using the auditory mechanism which is called lateral inhibition. This method first estimates the noise intensity by neural network, then adaptively adjusts both the coefficients of the lateral inhibition and the adjusting coefficient of amplitude component according to the noise intensity for each input frame. It is confirmed that the proposed method is effective for speech degraded by white noise, colored noise, and road noise based on the spectral distortion measurement.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1177-1185
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• 2000

In this paper, We report on the performance issues faced by TCP based applications on satellite link having long propagation delay and high probability of bit erros and propose ENA(Error Notification Ack) algorithm for TCP Performance Enhancement. TCP Protocol cannot distinguish errored segments(in noisy medium) from losses of genuine network congestion and react as if there is network congestion. Therefore, Slow Start and Congestion avoidance mechanism are initiated. It happen this case in satellite link. Therefore it reduce the transmission rate and drop the performance. So, in this paper We propose ENA algorithm which is distinguished errored segments from losses of network congestion. And We propose the method of algorithm's implementation. And We evaluate the Performance of Tahoe, Reno, Sack TCP with ENA. As results, TCP Performance is better.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1078-1084
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• 2010

This paper presents a speech enhancement system which restores the amplitude components and phase components by discrete Fourier transform (DFT), using neural network training by back-propagation algorithm. First, a neural network is trained using DFT amplitude components and phase components of noisy speech signal, then the proposed system enhances speech signals that are degraded by white noise using a neural network. Experimental results demonstrate that speech signals degraded by white noise are enhanced by the proposed system using the neural network, whose inputs are DFT amplitude components and phase components. Based on measuring spectral distortion measurement, experiments confirm that the proposed system is effective for white noise.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.638-648
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• 2013

Together with the GPS-based approach, geo-location through mobile communication networks is a key technology for location-based service. To save the cost, most geo-location system is implemented on the existed network service, which has a cellular structure. Still, multilateration is limited in cellular structure because it is difficult for the mobile terminal to acquire distance measurements from multiple base stations. This low hearability in the receiver is caused by co-channel interference and multipath environment. Therefore, hearability enhancement is necessary for multilateration under multipath and interference environment. Former time domain based hearability methods were designed for real signals. However, orthogonal frequency division multiplexing (OFDM) signal, which its usage has been increased in digital wireless communication, is a complex signal. Thus, different hearability enhancement method is needed for OFDM signals. This paper proposes a hearability enhancement method for forward-link multilateration using OFDM signals, which employ interference cancellation and multipath mitigation. A novel interference cancellation and multipath mitigation strategy for complex-valued OFDM signals is presented that has an iterative structure. Simulation results show that the proposed multilateration method provides the user's position with an accuracy of less than 80m through the mobile WiMAX cellular network in multipath environment.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.1-6
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• 2022

Exploration has always been an instinct of humans, and underwater life is as fascinating as it seems. So, for studying flora and fauna below water, there is a need for high-quality images. However, the underwater images tend to be of impaired quality due to various factors, which calls for improved and enhanced underwater images. There are various Histogram Equalization (HE) based techniques which could aid in solving these issues. Classifying the HE methods broadly, there is Global Histogram Equalization (GHE), Mean Brightness Preserving HE (MBPHE), Bin Modified HE (BMHE), and Local HE (LHE). Each of these HE extensions have their own pros and cons and thus, by considering them we have considered BBHE, CLAHE, BPDHE, BPDFHE, and DSIHE enhancement algorithms, which are based on Mean Brightness Preserving HE and Local HE, for this study. The performance is evaluated with non-reference performance measures like Entropy, UCIQE, UICM, and UIQM. In this study, we apply the enhancement algorithms on 300 images from the UIEB benchmark dataset and then apply the techniques of cascading fusion on the best-performing algorithms.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1041-1044
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• 2000

The enhancement to the back-propagation algorithm presented in this paper has resulted from the need to extract sparsely connected networks from networks employing product terms. The enhancement works in conjunction with the back-propagation weight update process, so that the actions of weight zeroing and weight stimulation enhance each other. It is shown that the error measure, can also be interpreted as rate of weight change (as opposed to ${\Delta}W_{ij}$), and consequently used to determine when weights have reached a stable state. Weights judged to be stable are then compared to a zero weight threshold. Should they fall below this threshold, then the weight in question is zeroed. Simulation of such a system is shown to return improved learning rates and reduce network connection requirements, with respect to the optimal network solution, trained using the normal back-propagation algorithm for Multi-Layer Perceptron (MLP), Higher Order Neural Network (HONN) and Sigma-Pi networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1452-1455
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• 2021

Recently, point clouds are generated by capturing real space in 3D, and it is actively applied and serviced for performances, exhibitions, education, and training. These point cloud data require post-correction work to be used in virtual environments due to errors caused by the capture environment with sensors and cameras. In this paper, we propose an enhancement technique for 3D point cloud data by applying generative adversarial network(GAN). Thus, we performed an approach to regenerate point clouds as an input of GAN. Through our method presented in this paper, point clouds with a lot of noise is configured in the same shape as the real object and environment, enabling precise interaction with the reconstructed content.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.175-184
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• 2008

Uncertainties are unavoidable in engineering applications. In this paper we propose an alpha reliable multi-variable network design problem under demand uncertainty. In order to decide the optimal capacity enhancement, three performance measures based on 3E(Efficiency, Equity, and Environmental) are considered. The objective is to minimize the total budget required to satisfy alpha reliability constraint of total travel time, equity ratio, and total emission, while considering the route choice behavior of network users. The problem is formulated as the chance-constrained model for application of alpha confidence level and solved as a lexicographic optimization problem to consider the multi-variable. A simulation-based genetic algorithm procedure is developed to solve this complex network design problem(NDP). A simple numerical example ispresented to illustrate the features of the proposed NDP model.