• MALSORI
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• no.67
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• pp.167-180
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• 2008

This paper describes a novel spectral envelope conversion method based on Gaussian mixture model (GMM). The core of this paper is rearranging source feature vectors in input space to the transformed feature vectors in feature space for the better modeling of GMM of source and target features. The quality of statistical modeling is dependent on the distribution and the dimension of data. The proposed method transforms both of the distribution and dimension of data and gives us the chance to model the same data with different configuration. Because the converted feature vectors should be on the input space, only source feature vectors are rearranged in the feature space and target feature vectors remain unchanged for the joint pdf of source and target features using KPCA. The experimental result shows that the proposed method outperforms the conventional GMM-based conversion method in various training environment.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1747-1751
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• 2000

It is well known that acoustic signals, even measured in an anechoic chamber, can be contaminated due to the wall interference. Therefore, it is necessary to reconstruct the original signal from the measured data, which is very critical for the case of measurement of source signal in a water tunnel. In this thesis, new methods for the reconstruction of sound sources are proposed and validated by using Boundary Element Method from measured data in a closed space. The inverse Helmholtz integral equation and its normal derivative are used for the reconstruction of sound sources in a closed space. An arbitrary Kirchhoff surface over the sources is proposed to solve the surface information instead of direct solution for the source. Although sound sources are not directly known by the inverse Helmholtz equation, the original sound source of pressure-field outside of the wall can be indirectly obtained by using this new method.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.622-625
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• 2004

This paper presents multi-source spatial data integration models based on probability theory for landslide hazard assessment. Four probabilistic models such as empirical likelihood ratio estimation, logistic regression, generalized additive and predictive discriminant models are proposed and applied. The models proposed here are theoretically based on statistical relationships between landslide occurrences and input spatial data sets. Those models especially have the advantage of direct use of continuous data without any information loss. A case study from the Gangneung area, Korea was carried out to quantitatively assess those four models and to discuss operational issues.

• ETRI Journal
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• v.42 no.3
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• pp.341-350
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• 2020

In a wireless sensor network (WSN), the data transmission technique based on the cooperative multiple-input multiple-output (CMIMO) scheme reduces the energy consumption of sensor nodes quite effectively by utilizing the space-time block coding scheme. However, in networks with high node density, the scheme is ineffective due to the high degree of correlated data. Therefore, to enhance the energy efficiency in high node density WSNs, we implemented the distributed source coding (DSC) with the virtual multiple-input multiple-output (MIMO) data transmission technique in the WSNs. The DSC-MIMO first compresses redundant source data using the DSC and then sends it to a virtual MIMO link. The results reveal that, in the DSC-MIMO scheme, energy consumption is lower than that in the CMIMO technique; it is also lower in the DSC single-input single-output (SISO) scheme, compared to that in the SISO technique at various code rates, compression rates, and training overhead factors. The results also indicate that the energy consumption per bit is directly proportional to the velocity and training overhead factor in all the energy saving schemes.

• ETRI Journal
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• v.29 no.5
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• pp.655-666
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• 2007

There are nodes and edges in a topological map. Node data has been used as a main source of information for the localization of mobile robots. In contrast, edge data is regarded as a minor source of information, and it has been used in an intuitive and heuristic way. However, edge data also can be used as a good source of information and provide a way to use edge data efficiently. For that purpose, we define a data format which describes the shape of an edge. This format is called local generalized Voronoi graph's angle (LGA). However, the LGA is constituted of too many samples; therefore, real time localization cannot be performed. To reduce the number of samples, we propose a compression method which utilizes wavelet transformation. This method abstracts the LGA by key factors using far fewer samples than the LGA. Experiments show that the LGA accurately describes the shape of the edges and that the key factors preserve most information of the LGA while reducing the number of samples.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.209-214
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• 2005

In the present age, the quality of MT(magnetotellurics) data highly depends on the level of industrial interference in data. We analyzed the correlation between the spatial distributions of man-made EM noise source and the characteristics of MT data. The noise source analysis shows the correlation between the noise source density and the power spectral density of measured magnetic field in the frequency band of 60 Hz harmonics. In the MT 'dead band', the strong polarization observed on the magnetic field reveals that the severe artificial noises are caused by the adjacent metropolis.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3617-3619
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• 2012

Background: The completeness of cancer registration is a major validity index of any reported cancer incidence. The present study aimed to evaluate the esophageal cancer incidence registered in the Tehran Metropolitan Area Cancer Registry. Materials and methods: The data on esophageal cancer abstracted from three sources of 1) pathology departments, 2) medical records, and 3) death certificates during 2003 till 2007 were utilized. The completeness of the data sources were evaluated using coverage (defined as the proportion of a community population with esophageal cancer identified by the source) and density (defined as the proportion of non-empty fields of the data by source). Results: A total 1,404 cases of esophageal cancer were reported for the duration of the study. Pathology provided 771, medical records 432, and death certificates 609. The coverage was 0.55 for pathology, 0.31 for medical records, and 0.43 for death certificates. The respective density values were 0.82, 0.96 and 0.98, respectively. Pathology (0.45) was the most complete source followed by medical records (0.42), and death certificates (0.29). Discussion: A low degree of completeness dictates putting more effort into case finding plus abstracting data more thoroughly.

• The Journal of the Acoustical Society of Korea
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• v.20 no.1E
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• pp.31-37
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• 2001

Matched Field Processing (MFP) is a successive process of correcting mismatches between true and assumed parameters by matching the measured acoustic field data with numerically simulated data which we call replica. The MFP is widely used both in geo-acoustic parameter inversions and in source localizations. Whether a certain parameter can be inverted effectively or whether a source can be localized correctly depends on the amount of the influence that a parameter has on the acoustic field during the matching process. Sensitive parameters can be better estimated than the less sensitive ones in MFP. On the contrary, the sensitive parameters affect adversely on the source localization results when they have uncertainties. In this paper, a sensitivity index is defined based upon the field variation resulting from the perturbed parameters. Numerical test results show that the index behaves in accordance with the results of source localization under a mismatched environment and also with the inversion solutions.

• Journal of KSNVE
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• v.7 no.4
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• pp.621-629
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• 1997

For computer aided design and costruction of low noisy power plants, indoor and outdoor noise prediction program has been developed. The program utilizes the predefined data of noise sources and building materials and has the faculty to estimate the source level using the empirical formula in case of the measured data not being available. In the noise prediction, the mutual noise propagation between indoor and outdoor sites are considered. The outdoor noise source in the calculation of geometric divergence effects is modelled as the omni-directional finite line or planar source according to the source geometry and the receiving points. Outdoor noise prediction is carried out to consider the diffraction effect due to plant structures as well as the attenuation effect due to atmospheric absorption and soft ground. The results of indoor and outdoor noise prediction for a recently constructed diesel engine power plant show good agreement with the measured.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.767-773
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• 2008

This paper presents a localization system based on the use of electric compass and tracking IR light source. Digital RGB(Red, Green, Blue)signal of digital CMOS Camera is sent to CPLD which converts the color image to binary image at 30 frames per second. CMOS camera has IR filter and UV filter in front of CMOS cell. The filters cut off above 720nm light source. Binary output data of CPLD is sent to DSP that rapidly tracks the IR light source by moving Camera tilt DC motor. At a robot toward north, electric compass signals and IR light source angles which are used for calculating the data of the location system. Because geomagnetic field is linear in local position, this location system is possible. Finally, it is shown that position error is within ${\pm}1.3cm$ in this system.