• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2003.05a
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• pp.87-91
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• 2003

It has well known that MUSIC and ESPRIT algorithms estimate angle of arrival(AOA) with high resolution by eigenvalue decomposition of the covariance matrix which were obtained from the array antennas. However, the disadvantage of MUSIC and ESPRIT is that they are computationally ineffective, and then they are difficult to implement in real time. The other problem of MUSIC and ESRPIT is to require calibrated antennas with uniform features, and are sensitive to the manufacturing facult and other physical uncertainties. To overcome these disadvantages, several method using neural model have been study. For multiple signals, those require huge training data prior to AOA estimation. This paper proposes the algorithm for AOA estimation by interconnected hopfield neural model. Computer simulations show the validity of the proposed algorithm. The proposed method does not require huge training procedure and only assigns interconnected coefficients to the neural network prior to AOA estimation.

• Journal of Navigation and Port Research
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• v.27 no.4
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• pp.465-469
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• 2003

It has well known that MUSIC and ESPRIT algorithms estimate angle of arrival(AOA) with high resolution by eigenvalue decomposition of the covariance matrix which were obtained from the array antennas, However, the disadvantage of MUSIC and ESPRIT is that they are computationally ineffective, and then they are difficult to implement in real time. the other problem of MUSIC and ESPRIT is to require calibrated antennas with uniform features, and are sensitive ti the manufacturing fault and other physical uncertainties. To overcome these disadvantages, several method using neural model have been study. For multiple signals, those methods require huge training data prior to AOA estimation. This paper proposes the algorithm for AOA estimation by interconnected Hopfield neural model. Computer simulations show the validity of the proposed algorithm. It follows that the proposed method yields better AOA estimates than MUSIC. Moreover, out method does not require huge training procedure and only assigns interconnected coefficients to the neural network prior to AOA estimation.

• Smart Structures and Systems
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• v.18 no.3
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• pp.501-523
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• 2016

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.3
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• pp.291-298
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• 2019

In this paper, we propose a Fresnel transform method using various wavelet functions to efficiently decompose digital holograms. After implementing the proposed wavelet function-based Fresnelet transforms, we apply it to the digital hologram and analyze the energy characteristics of the coefficients. The implemented wavelet transform-based Fresnelet transform is well suited for reconstructing and processing holograms which are optically obtained or generated by computer-generated hologram technique. After analyzing the characteristics of the spline function, we discuss wavelet multiresolution analysis method based on it. Through this process, we proposed a transform tool that can effectively decompose fringe patterns generated by optical interference phenomena. We implement Fresnelet transform based on wavelet function with various decomposition properties and show the results of decomposing fringe pattern using it. The results show that the energy distribution of the coefficients is significantly different depending on whether the random phase is included or not.

• Journal of Mushroom
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• v.19 no.1
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• pp.33-40
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• 2021

The genome sequence of various Flammulina species has recently been reported, thereby revealing a diverse genetic repertoire. In this study, we identified laccase genes and analyzed their structural characteristics in Flammulina elastica (F. elastica) genome. Through genome analysis and bioinformatics approaches, three laccase genes (Fe-lac1, -lac2, and -lac3) were identified, ranging from 1,548 to 1,602 bp in length. These genes contained a copper ion-binding region with ten histidine residues and one cysteine residue and a disulfide bond-forming region with four cysteine residues. Full-length cDNA sequencing analysis revealed that laccase genes contain 12 to 16 introns and signal peptides between 17 and 22 bp in the N-terminus. Structural characterization of the laccase genes identified in this study should help in better understanding the biomass decomposition of F. elastica.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1926-1933
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• 2022

Parameterized multichannel Wiener filter (PMWF) is a linear filter that can control the trade-off between residual noise and signal distortion using the embedded parameter. To apply the PMWF to noisy inputs, accurate noise estimation is important and multichannel minima-controlled recursive averaging (MMCRA) is widely used. However, in the case of the MMCRA, the accuracy of noise estimation decreases when a directional interference is involved into the array inputs. Consequently, the performance of the PMWF is degraded. Therefore, we propose a noise power spectral density (PSD) estimation method for the PMWF in this paper. The proposed method is based on a consecutive process of eigenvalue decomposition on noisy input PSD, estimation of the target component contribution using directional information, and exponential weighting for improved estimation of the target contribution. For evaluation, four objective measures were compared with the MMCRA and we verify that the PMWF with the proposed noise estimation method can improve performance in environments where directional interfereces exist.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.68-74
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• 2017

This paper presents a joint demosaicking and arbitrary-ratio down sampling algorithm for color filter array (CFA) images. Color demosaiking is a necessary part of image signal processing pipeline for many types of digital image recording system using single sensor. Also, such as smart phone, obtained high resolution image from image sensor has to be down-sampled to be displayed on the screen. The conventional solution is "Demosaicking first and down sampling later". However, this scheme requires a significant amount of memory and computational cost. Also, artifacts can be introduced or details get damaged during demosaicking and down sampling process. In this paper, we propose a method in which demosaicking and down sampling are working simultaneously. We use inverse mapping of Bayer CFA and then joint demosaicking and down sampling with arbitrary-ratio scheme based on signal decomposition of high and low frequency component in input data. Experimental results show that our proposed algorithm has better image quality performance and much less computational cost than those of conventional solution.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.4 no.3
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• pp.170-179
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• 1999

The SUS (Signal, Underwater Sound)-OAT experiment was carried out in the Ulleung Basin of the East Sea on 3 June 1997. The SUS-OAT system consisted of aircraft deployed shots as sources and a vertical line array (VLA) tethered by a receiver ship was used to survey a large area where a mesoscale warm eddy appears frequently. The experiment was carried out such that explosive charges set to detonate at 800 ft depth were dropped in a rectangular ($120{\times}120$ km). Sources were a rapidly deployable SUS charge (MK 61 MOD 0), and receiver is a fixed VLA, 90 m in length (150-240 m in receiver depth), composed of 10 elements equally spaced. The reference ray paths are computed by range-dependent acoustic model in canonical ocean based on the historical data. The singular value decomposition (SVD) method is used to obtain the horizontal perturbation of the temperature fields. Horizontal distributions of temperature fields at 150 m and 200 m depth show a weak warm eddy observed by AXBT and the inversely estimated temperature shows similar patterns in terms of the location of the warm eddy. In conclusion, the SUS-OAT experiment has been successful to estimate the position of warm eddy and its temperature field in the East Sea of Korea.

• Journal of Mushroom
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• v.19 no.4
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• pp.285-293
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• 2021

The purpose of this study was to identify and characterize the laccase genes of Flammulina velutipes var. lupinicola. Five laccase genes (g1934, g1937, g2415, g2539, g5858) were selected based on the copper binding site and signal peptide analysis results using the laccase gene selected from the F. velutipes var. lupinicola genome. The size of the laccase genes of F. velutipes var. lupinicola were 1,488 bp~1,662 bp. As a result of cDNA sequence analysis, 14 to 17 introns were identified in the laccase genes. The cleavage site predicted as the signal peptide of the laccase gene was found to be located between 20 bp and 34 bp from the N-terminus. In addition, separation and purification were performed to characterize the F. velutipes var. lupinicola laccases, and the optimal activity of the separated and purified proteins were analyzed by pH, temperature and time. Five bands with laccase activity were found from zymogram analysis. The optimal pH of the reaction was 5.5, the optimal temperature was found to be 40℃. Therefore, characterization of the laccase genes identified in this study should help in better understanding the biomass decomposition of F. velutipes var. lupinicola.

• Investigative Magnetic Resonance Imaging
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• v.20 no.1
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• pp.44-52
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• 2016

Purpose: To quantitatively and qualitatively compare fat-suppressed MRI quality using iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) with that using frequency selective fat-suppression (FSFS) T2- and postcontrast T1-weighted fast spin-echo images of the head and neck at 3T. Materials and Methods: The study was approved by our Institutional Review Board. Prospective MR image analysis was performed in 36 individuals at a single-center. Axial fat suppressed T2- and postcontrast T1-weighted images with IDEAL and FSFS were compared. Visual assessment was performed by two independent readers with respect to; 1) metallic artifacts around oral cavity, 2) susceptibility artifacts around upper airway, paranasal sinus, and head-neck junction, 3) homogeneity of fat suppression, 4) image sharpness, 5) tissue contrast of pathologies and lymph nodes. The signal-to-noise ratios (SNR) for each image sequence were assessed. Results: Both IDEAL fat suppressed T2- and T1-weighted images significantly reduced artifacts around airway, paranasal sinus, and head-neck junction, and significantly improved homogeneous fat suppression in compared to those using FSFS (P < 0.05 for all). IDEAL significantly decreased artifacts around oral cavity on T2-weighted images (P < 0.05, respectively) and improved sharpness, lesion-to-tissue, and lymph node-to-tissue contrast on T1-weighted images (P < 0.05 for all). The mean SNRs were significantly improved on both T1- and T2-weighted IDEAL images (P < 0.05 for all). Conclusion: IDEAL technique improves image quality in the head and neck by reducing artifacts with homogeneous fat suppression, while maintaining a high SNR.