• The Journal of the Acoustical Society of Korea
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• v.21 no.7
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• pp.641-647
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• 2002

In this paper, we propose the reduction method of the number of parameters in the segmental-feature HMM using trend quantization method. The proposed method shares the trend information of the polynomial trajectories by quantization. The trajectory is obtained by the sequence of feature vectors of speech signals and can be divided by trend and location information. The trend indicates the variation of consequent frame features, while the location points to the positional difference of the trajectories. Since the trend occupies the large portion of SFHMM, if the trend is shared, the number of parameters maybe decreases. To exploit the proposed system the experiments are performed on TIMIT corpus. The experimental results show that the performance of the proposed system is roughly similar to that of previous system. Therefore, the proposed system can be considered one of parameter reduction method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1418-1433
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• 2019

In computer graphics, 3D mesh segmentation is a challenging research field. This paper presents a 3D mesh model segmentation algorithm that focuses on removing exterior salient parts from the original 3D mesh model based on prominent feature points and marching plane. To begin with, the proposed approach uses multi-dimensional scaling to extract prominent feature points that reside on the tips of each exterior salient part of a given mesh. Subsequently, a set of planes intersect the 3D mesh; one is the marching plane, which start marching from prominent feature points. Through the marching process, local cross sections between marching plane and 3D mesh are extracted, subsequently, its corresponding area are calculated to represent local volumes of the 3D mesh model. As the boundary region of an exterior salient part generally lies on the location at which the local volume suddenly changes greatly, we can simply cut this location with the marching plane to separate this part from the mesh. We evaluated our algorithm on the Princeton Segmentation Benchmark, and the evaluation results show that our algorithm works well for some categories.

• Smart Structures and Systems
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• v.32 no.4
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• pp.207-218
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• 2023

Real-time monitoring of the behavior of reinforced soil retaining wall (RSW) is required for safety checks. In this study, a targetless displacement measurement technology (TDMT) consisting of an image registration module and a displacement calculation module was proposed to monitor the behavior of RSW, in which facing displacement and settlement typically occur. Laboratory and field experiments were conducted to compare the measuring performance of natural target (NT) with the performance of artificial target (AT). Feature count- and location-based performance metrics and displacement calculation performance were analyzed to determine their correlations. The results of laboratory and field experiments showed that the feature location-based performance metric was more relevant to the displacement calculation performance than the feature count-based performance metric. The mean relative errors of the TDMT were less than 1.69 % and 5.50 % for the laboratory and field experiments, respectively. The proposed TDMT can accurately monitor the behavior of RSW for real-time safety checks.

• Journal of Biomedical Engineering Research
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• v.8 no.1
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• pp.81-86
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• 1987

An image feature extraction method for the low contrast fluoresceln angiogram in dlabetes was studied. To obtain effective image segmentation, an adaptive local difference image is generated and relaxation process are applied to this difference Image. By the use of distance transformed data with segmented image, shape and location of feature regions were obtained. It was shown that the location and shape descriptions of Impaired blood vessel networks and retinal regions are can he utilized for the diagnosis of diabetes and other disease.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.1
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• pp.101-120
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• 2017

Healthcare professionals have edited many health questions (HQs) and their answers for healthcare consumers on the Internet. The HQs provide both readable and reliable health information, and hence retrieval of those HQs that are relevant to a given question is essential for health education and promotion through the Internet. However, retrieval of relevant HQs needs to be based on the recognition of the intention of each HQ, which is difficult to be done by predefining syntactic and semantic rules. We thus model the intention recognition problem as a text classification problem, and develop two techniques to improve a learning-based text classifier for the problem. The two techniques improve the classifier by location-based and area-based feature weightings, respectively. Experimental results show that, the two techniques can work together to significantly improve a Support Vector Machine classifier in both the recognition of HQ intentions and the retrieval of relevant HQs.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.317-319
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• 2022

Rich morphology language such as Arabic needs more investigation and method to improve the sentiment analysis task. Using all document parts in the process of the sentiment analysis may add some unnecessary information to the classifier. Therefore, this paper shows the ongoing work to use sentence location as a feature with Arabic sentiment analysis. Our proposed method employs a supervised sentiment classification method by enriching the feature space model with some information from the document. The experiments and evaluations that were conducted in this work show that our proposed feature in the sentiment analysis for Arabic improves the performance of the classifier compared to the baseline model.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.359-367
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• 2020

WSNs as the main technology of IoT often deliver information or authenticate based on location. Thus, verifying location information is essential. This paper aims to present the comprehensive analysis and classification of location verification techniques in WSN. For this, classification criteria are suggested based on the result of feature analysis of existing techniques. In addition, the existing techniques are classified according to the suggested criteria, and each characteristic and development direction are described. The result of this paper is expected to be a useful reference material when designing a new technique.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.177-182
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• 2009

Location Based Service(LBS) is various and widely spreading service based on the Location information. Those services are under the control of Privacy Act for Location Information to protect from misusing or overusing of personal location information. In this paper, we analyze a concept and a feature of personal location information and how to use and control the flow of location information by the Location Information Act. As the result of this paper, we expect to use the location information in various fields to develop more efficient service.

• Smart Structures and Systems
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• v.16 no.1
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• pp.213-222
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• 2015

Early detection and precise location of leakage is of great importance for life-cycle maintenance and management of municipal pipeline system. In the past few years, acoustic emission (AE) techniques have demonstrated to be an excellent tool for on-line leakage detection. Regarding the multi-mode and frequency dispersion characteristics of AE signals propagating along a pipeline, the direct cross-correlation technique that assumes the constant AE propagation velocity does not perform well in practice for acoustic leak location. This paper presents an improved cross-correlation method based on wavelet transform, with due consideration of the frequency dispersion characteristics of AE wave and the contribution of different mode. Laboratory experiments conducted to simulate pipeline gas leakage and investigate the frequency spectrum signatures of AE leak signals. By comparing with the other methods for leak location identification, the feasibility and superiority of the proposed method are verified.

• Journal of Korea Multimedia Society
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• v.24 no.6
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• pp.796-804
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• 2021

In computer vision, visual tracking method addresses the problem of localizing an specific object in video sequence according to the bounding box. In this paper, we propose a tracking method by introducing the feature correlation comparison into the siamese network to increase its matching identification. We propose a way to compute location of object to improve matching performance by a correlation operation, which locates parts for solving the searching problem. The higher layer in the network can extract a lot of object information. The lower layer has many location information. To reduce error rate of the object center point, we built a siamese network that extracts the distribution and location information of target objects. As a result of the experiment, the average center error rate was less than 25%.