• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.104-111
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• 2012

In this paper, we propose a novel method to improve the performance of the improved minima controlled recursive averaging (IMCRA). From an examination for various noise environment, it is shown that the IMCRA has a fundamental drawback for the noise power estimate at the offset region of continuity speech signals. Espectially, it is difficult to obtain the robust estimates of the noise power in non-stationary noisy environments that is rapidly changed the spectral characteristics such as babble noise. To overcome the drawback, we apply the global speech absence probability (GSAP) conditioned on both a priori SNR and a posteriori SNR to the speech detection algorithm of IMCRA. With the performance criteria of the ITU-T P.862 perceptual evaluation of speech quality (PESQ) and a composite measure test, we show that the proposed algorithm yields better results compared to the conventional IMCRA-based scheme under various noise environments. In particular, in the case of babble 5 dB, the proposed method produced a remarkable improvement compared to the IMCRA ( PESQ = 0.026, composite measure = 0.029 ).

• Geophysics and Geophysical Exploration
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• v.15 no.3
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• pp.136-143
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• 2012

In this case study, we present the various and consistent processing techniques for the reasonable interpretation of aeromagnetic data. In the processing stage, we especially focused on the three major respects. First, in the low latitude area, severe artifacts are occurred as a result of reduction to the pole technique. To overcome this problem, variable alternative methods were investigated. From the comparison of each technique, we concluded that energy balancing method gives more fruitful result. Second, because of limited a priori information, it is nearly impossible to employ detailed geological survey due to wide and thick spreading of soils in the survey area. So we especially investigated the new techniques such as extracting slope, curvature and aspect information mainly used in GIS field as well as conventional methods. Finally, by using the Euler deconvolution, we extracted the depth information on the magnetic anomalous body. From the synthetic analysis between depth information and previous discussed results, the detailed future survey area was proposed. We think that a series of processing techniques discussed in this study may perform an important role in the domestic and abroad resource development project as a useful guideline.

• Journal of the Korea institute for structural maintenance and inspection
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• v.8 no.2
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• pp.137-145
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• 2004

The paper provides a methodology of identifying a substructure model when sectional and material properties of the structure are not the a priori information. In defining a substructure model, it is required that structural responses be consistent with the actual behavior of the part of the structure. Substructure model is identified by estimating boundary spring constants and stiffness properties of the substructure. Static and modal system identification methods have been applied using responses measured at limited locations within the substructure. Simulation studies for static and dynamic responses have been carried. The results and associated problems are discussed in the paper. The procedure has been also applied to an actual multi-span plate-girder Gerber-type bridge with dynamic responses obtained from a moving truck test and construction blasting vibrations.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.215-220
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• 2010

In this paper, we propose a new texture-based page segmentation and classification method in which table region, background region, image region and text region in a given document image are automatically identified. The proposed method for document images consists of two stages, document segmentation and contents classification. In the first stage, we segment the document image, and then, we classify contents of document in the second stage. The proposed classification method is based on a texture analysis. Each contents in the document are considered as regions with different textures. Thus the problem of classification contents of document can be posed as a texture segmentation and analysis problem. Two-dimensional Gabor filters are used to extract texture features for each of these regions. Our method does not assume any a priori knowledge about content or language of the document. As we can see experiment results, our method gives good performance in document segmentation and contents classification. The proposed system is expected to apply such as multimedia data searching, real-time image processing.

• Journal of KIISE:Software and Applications
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• v.33 no.2
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• pp.252-266
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• 2006

Many problems in computer vision are mainly based on mathematical models. Their optimal solutions can be found by estimating the parameters of each model. However, provided an input data set is involved outliers which are relative]V larger than normal noises, they lead to incorrect results. RANSAC is a representative robust algorithm which is used to resolve the problem. One major problem with RANSAC is that it needs priori knowledge(i.e. a percentage of outliers) of the distribution of data. To solve this problem, we propose a FRANSAC algorithm which improves the rejection rate of outliers and the accuracy of solutions. This is peformed by categorizing all data into good sample set, bad sample set and vague sample set using a fuzzy classification at each iteration and sampling in only good sample set. In the experimental results, we show that the performance of the proposed algorithm when it is applied to the linear regression and the calculation of a homography.

• The KIPS Transactions:PartA
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• v.14A no.6
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• pp.357-362
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• 2007

In distributed systems like cluster systems, in order to get more performance improvement, the initial task placement system precisely estimates and correctly assigns the resource requirement by the process. The resource-based initial job placement scheme needs the prediction of resource usage of a task in order to fit it to the most suitable hosts. However, the wrong prediction of resource usage causes serious performance degradation in dynamic load balancing systems. Therefore, in this paper, to resolve the problem due to the wrong prediction, we propose a new load metric. By the new load metric, the resource-based initial job placement scheme can work without priori knowledge about the type of process. Simulation results show that the dynamic load balancing system using the proposed approach achieves shorter execution times than the conventional approaches.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.102-108
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• 2011

Support vector machines are well known for their outstanding performance in pattern recognition fields. One example of their applications is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel scheme that improves the speech/music classification of support vector machines based on the second-order conditional maximum a priori. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. According to experimental results, the proposed algorithm shows its compatibility and potential for improving the performance of support vector machines.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.1
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• pp.129-139
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• 1989

During a signal is being transmitted, an interference signal can be introduced through an unknown channel. In these cases, an adaptive system, so called adaptive noise canceller, can restore the original signal from the corrupted signal by first identifying the unknown interference channel on the minimum mean square error criteron, and then by cancelling the interference signal using the identified interference channel. Whereas this method is quite effective when the a priori knowledges about the characteristics of the interference signal and of the intrference channel are unknown or time-varyng, but has a drawback that the presence of the original signal has a severe effect on the optimum value of the interference channel to be identified on the miniumum mean square eror criterion In this paper an adaptive noise canceller and its algorithms are introduced that can restore the original signal more accurately especially when the correlatedness between the original signal and the interference signal is small.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.117-122
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• 2007

For multiple antenna systems, we consider the hybrid iterative detection of the maximum a posteriori probability(MAP) detection and the linear detection such as the minimum-mean-square-error(MMSE) filtering with soft cancelation. We devise methods to obtain both the lower complexity of the linear detection and the superior performance of the MAP detection. Using the a prior probability of the coded bit which is extrinsic of the outer decoder, we compute the threshold of grouping and determine the detection scheme symbol by symbol. Through the simulation results, it is shown that the proposed receiver obtains the superior performance to the MMSE detector and the lower complexity than the MAP detector.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.179-186
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• 2008

In this paper we analyse the effects of Bayesian algorithm in teaming class imbalance problems and compare the performance evaluation methods. The teaming performance of the Bayesian algorithm is evaluated over the class imbalance problems generated by priori data distribution, imbalance data rate and discrimination complexity. The experimental results are calculated by the AUC(Area Under the Curve) values of both ROC(Receiver Operator Characteristic) and PR(Precision-Recall) evaluation measures and compared according to imbalance data rate and discrimination complexity. In comparison and analysis, the Bayesian algorithm suffers from the imbalance rate, as the same result in the reported researches, and the data overlapping caused by discrimination complexity is the another factor that hampers the learning performance. As the discrimination complexity and class imbalance rate of the problems increase, the learning performance of the AUC of a PR measure is much more variant than that of the AUC of a ROC measure. But the performances of both measures are similar with the low discrimination complexity and class imbalance rate of the problems. The experimental results show 4hat the AUC of a PR measure is more proper in evaluating the learning of class imbalance problem and furthermore gets the benefit in designing the optimal learning model considering a misclassification cost.