• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1368-1375
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• 2013

In this paper, we presents a spatially adaptive noise detection and removal algorithm. Under the assumption that an observed image and the additive noise have Gaussian distribution, the noise parameters are estimated with local statistics, and the parameters are used to define the constraints on the noise detection process, where the first order Markov Random Field (MRF) is used. In addition, an adaptive low-pass filter having a variable window sizes defined by the constraints on noise detection is used to control the degree of smoothness of the reconstructed image. Experimental results demonstrate the capability of the proposed algorithm.

• The Journal of the Korea Contents Association
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• v.14 no.3
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• pp.11-21
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• 2014

Multi-scale image segmentation is important in many applications such as image stylization and medical diagnosis. This paper proposes a novel segmentation algorithm based on MSER(maximally stable extremal region) which captures multi-scale structure and is stable and efficient. The algorithm collects MSERs and then partitions the image plane by redrawing MSERs in specific order. To denoise and smooth the region boundaries, hierarchical morphological operations are developed. To illustrate effectiveness of the algorithm's multi-scale structure, effects of various types of LOD control are shown for image stylization. The proposed technique achieves this without time-consuming multi-level Gaussian smoothing. The comparisons of segmentation quality and timing efficiency with mean shift-based Edison system are presented.

• The Journal of the Acoustical Society of Korea
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• v.30 no.7
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• pp.396-401
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• 2011

In this paper, we propose a novel voice activity detection (VAD) algorithm to effectively distinguish speech from nonspeech in various noisy environments. The presented VAD utilizes the power spectral deviation (PSD) based on Teager energy (TE) instead of the conventional PSD scheme to improve the performance of decision for speech segments. In addition, the speech absence probability (SAP) is derived in each frequency subband to modify the PSD for further VAD. Performances of the proposed VAD algorithm are evaluated by objective test under various environments and better results compared with the conventional methods are obtained.

• Journal of Information Processing Systems
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• v.9 no.3
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• pp.365-378
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• 2013

In Wireless Mesh Networks (WMNs), we usually deploy multiple Internet Gateways (IGWs) to improve the capacity of WMNs. As most of the traffic is oriented towards the Internet and may not be distributed evenly among different IGWs, some IGWs may suffer from bottleneck problem. To solve the IGW bottleneck problem, we propose an efficient scheme to balance the load among different IGWs within a WMN. Our proposed load-balancing scheme consists of two parts: a traffic load calculation module and a traffic load migration algorithm. The IGW can judge whether the congestion has occurred or will occur by using a linear smoothing forecasting method. When the IGW detects that the congestion has occurred or will occur, it will firstly select another available IGW that has the lightest traffic load as the secondary IGW and then inform some mesh routers (MPs) which have been selected by using the Knapsack Algorithm to change to the secondary IGW. The MPs can return to their primary IGW by using a regression algorithm. Our Qualnet 5.0 experiment results show that our proposed scheme gives up to 18% end-to-end delay improvement compared with the existing schemes.

• Journal of Biosystems Engineering
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• v.39 no.3
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• pp.174-183
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• 2014

Purpose: A robust, efficient auto-grading computer vision system for meat carcasses is in high demand by researchers all over the world. In this paper, we discuss our study, in which we developed a system to speed up line processing and provide reliable results for pork grading, comparing the results of our algorithms with visual human subjectivity measurements. Methods: We differentiated fat and lean using an entropic correlation algorithm. We also developed a self-designed robust segmentation algorithm that successfully segmented several porkcut samples; this algorithm can help to eliminate the current issues associated with autothresholding. Results: In this study, we carefully considered the key step of autoextracting lean tissue. We introduced a self-proposed scheme and implemented it in over 200 pork-cut samples. The accuracy and computation time were acceptable, showing excellent potential for use in online commercial systems. Conclusions: This paper summarizes the main results reported in recent application studies, which include modifying and smoothing the lean area of pork-cut sections of commercial fresh pork by human experts for an auto-grading process. The developed algorithms were implemented in a prototype mobile processing unit, which can be implemented at the pork processing site.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2C
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• pp.272-282
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• 2004

This paper introduces a regularized mixed norm multi-channel image restoration algorithm using both within-and between- channel deterministic information. For each channel a functional which combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional is proposed. We introduce a mixed norm parameter that controls the relative contribution between the LMS and the LMF, and a regularization parameter defining the degree of smoothness of the solution, where both parameters are updated at each iteration according to the noise characteristics of each channel. The novelty of the proposed algorithm is that no knowledge of the noise distribution for each channel is required and that the parameters mentioned above are adjusted based on the partially restored image.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.3
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• pp.71-82
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• 2009

As a solution to spectrum under-utilization problem, Cognitive radio (CR) introduces a dynamic spectrum access technology. In the area, one of the most important problems is how secondary users (SUs) should choose between the available channels, which means how to achieve load balancing between channels. We consider spectrum load balancing problem for CR system in frequency domain and especially in time domain. Our objective is to balance the load among the channels and balance the occupied time length of slots for a fixed channel dynamically in order to obtain a user-optimal solution. In frequency domain, we refer to Dynamic Noncooperative Scheme with Communication (DNCOOPC) used in distributed system and a distributed Dynamic Spectrum Load Balancing algorithm (DSLB) is formed based on DNCOOPC. In time domain, Spectrum Load Balancing method with QoS support is proposed based on Dynamic Feed Back theory and Hash Table (SLBDH). The performance of DSLB and SLBDH are evaluated. In frequency domain, DSLB is more efficient compared with existing Compare_And_Balance (CAB) algorithm and gets more throughput compared with Spectrum Load Balancing (SLB) algorithm. Also, DSLB is a fair scheme for all devices. In time domain, SLBDH is an efficient and precise solution compared with Spectrum Load Smoothing (SLS) method.

• Journal of the Korea Concrete Institute
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• v.17 no.6 s.90
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• pp.1075-1084
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• 2005

The compressive strength of concrete is commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time-consuming. More importantly, it is too late to make improvement even if the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is being highly required. In this study, the estimation of the compressive strength of concrete was performed by probabilistic neural network(PNN) on the basis of concrete mix proportions. The estimation performance of PNN was improved by considering the correlation between input data and targeted output value. Improved probabilistic neural network was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment (DDA) algorithm. The conventional PNN and the PNN with DDA algorithm(IPNN) were applied to predict the compressive strength of concrete using actual test data of two concrete companies. IPNN showed better results than the conventional PNN in predicting the compressive strength of concrete.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.533-535
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• 2018

As the use frequency of electronic communication equipment increases due to the influence of the 4th industrial revolution, the importance of image and signal processing is increasing. However, due to noise caused by various causes, the reliability of the equipment is degraded and malfunctions are caused. In this paper, we propose an algorithm to remove AWGN in most environments. The existing methods show relatively poor performance due to the smoothing phenomenon at the boundary of the image. To overcome this problem, we proposed a filter algorithm that adapts to the boundary region using the Sobel edge detection to remove the noise. And using the PSNR compared with traditional methods, such as to demonstrate the performance of the proposed algorithm.

• Journal of Navigation and Port Research
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• v.26 no.2
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• pp.183-188
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• 2002

The forecast of a container traffic has been very important for port and development. Generally, Statistic methods, such as moving average method, exponential smoothing, and regression analysis have been much used for traffic forecasting. But, considering various factors related to the port affect the forecasting of container volume, neural network of parallel processing system can be effective to forecast container volume based on various factors. This study discusses the forecasting of volume by using the neural, network with back propagation learning algorithm. Affected factors are selected based on impact vector on neural network, and these selected factors are used to forecast container volume. The proposed the forecasting algorithm using neural network was compared to the statistic methods.