• Mycobiology
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• v.37 no.2
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• pp.121-127
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• 2009

Purification and characterization of intracellular cellulase produced by A. oryzae ITCC-4857.01 are reported. The enzyme was purified by ion-exchange chromatography using DEAE-cellulose followed by Gel filtration. The purification achieved was 41 fold from the crude extract with yield of 27%. The purified enzyme showed single band on poly acrylamide gel. The molecular weight as determined by SDS-PAGE and gel filtration was 38 KDa and 38.6 KDa respectively and contained only one subunit. The enzyme is glycoprotien as nature and contained 0.67% neutral sugar. The apparent Km value of the enzyme against cellulose was 0.83%. The enzyme showed the highest relative ativities on CMC followed by avicel, salicin and filter paper. The optimum pH of activity was 5.5 and very slight activity was observed at or above pH 7.5 as well as bellow pH 3.5. The optimum tempreture of the activity was $45^{\circ}C$ and the highest activity was exhibited in 35 to $45^{\circ}C$. The enzyme lost their activities almost completely (95${\sim}$100%) at $80^{\circ}C$ or above and as well as bellow $25^{\circ}C$.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.247-252
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• 2015

Adaptive algorithms based on the criterion of zero-error probability (ZEP) have robustness to impulsive noise and their decision feedback (DF) versions are known to compensate effectively for severe multipath channel distortions. However the ZEP-DF algorithm computes several summation operations at each iteration time for each filter section and this plays an obstacle role in practical implementation. In this paper, the ZEP-DF with recursive gradient estimation (RGE) method is proposed and shown to reduce the computational burden of O(N) to a constant which is independent of the sample size N. Also the weight update of the initial state and the steady state is a continuous process without bringing about any propagation of gradient estimation error in DF structure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.995-1000
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• 2016

The maximum cross-correntropy (MCC) algorithm with a set of random symbols keeps its optimum weights undisturbed from impulsive noise unlike MSE-based algorithms and its main factor has been known to be the input magnitude controller (IMC) that adjusts the input intensity according to error power. In this paper, a normalization of the step size of the MCC algorithm by the power of IMC output is proposed. The IMC output power is tracked recursively through a single-pole low-pass filter. In the simulation under impulsive noise with two different multipath channels, the steady state MSE and convergence speed of the proposed algorithm is found to be enhanced by about 1 dB and 500 samples, respectively, compared to the conventional MCC algorithm.

• Journal of Power Electronics
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• v.15 no.4
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• pp.939-950
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• 2015

A single-stage AC-AC converter has been designed for a wind energy conversion system (WECS) that eliminates multistage operation and DC-link filter elements, thus resolving size, weight, and reliability issues. A simple switching strategy is used to control the switches that changes the variable-frequency AC output of an electrical generator to a constant-frequency supply to feed into a distributed electrical load/grid. In addition, a modified random sinusoidal pulse width modulation (RSPWM) technique has been developed for the designed converter to make the overall system more efficient by increasing generating power capacity and reducing the effects of inter-harmonics and sub-harmonics generated in the WECS. The technique uses carrier and reference waves of variable switching frequency to calculate the firing angles of the switches of the converter so that the three-phase output voltage of the converter is very close to a sine wave with reduced THD. A comparison of the performance of the proposed RSPWM technique with the conventional SPWM demonstrated that the power generated by a turbine in the proposed approximately increased by 5% to 10% and THD reduces by 40% both in voltage and current with respect to conventional SPWM.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.173-179
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• 2015

In this paper, object-based classification of urban areas based on a combination of information from lidar and aerial images is introduced. High resolution images are frequently used in automatic classification, making use of the spectral characteristics of the features under study. However, in urban areas, pixel-based classification can be difficult since building colors differ and the shadows of buildings can obscure building segmentation. Therefore, if the boundaries of buildings can be extracted from lidar, this information could improve the accuracy of urban area classifications. In the data processing stage, lidar data and the aerial image are co-registered into the same coordinate system, and a local maxima filter is used for the building segmentation of lidar data, which are then converted into an image containing only building information. Then, multiresolution segmentation is achieved using a scale parameter, and a color and shape factor; a compactness factor and a layer weight are implemented for the classification using a class hierarchy. Results indicate that lidar can provide useful additional data when combined with high resolution images in the object-oriented hierarchical classification of urban areas.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.167-177
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• 2019

Even though so much progresses have been achieved in Multiple Object Tracking (MOT), most of reported MOT methods are not still satisfactory for commercial embedded products like Pan-Tilt-Zoom (PTZ) camera. In this paper, we propose a real-time multiple pedestrians tracking method for embedded environments. First, we design a new light weight convolutional neural network(CNN)-based pedestrian detector, which is constructed to detect even small size pedestrians, as well. For further saving of processing time, the designed detector is applied for every other frame, and Kalman filter is employed to predict pedestrians' positions in frames where the designed CNN-based detector is not applied. The pose orientation information is incorporated to enhance object association for tracking pedestrians without further computational cost. Through experiments on Nvidia's embedded computing board, Jetson TX2, it is verified that the designed pedestrian detector detects even small size pedestrians fast and well, compared to many state-of-the-art detectors, and that the proposed tracking method can track pedestrians in real-time and show accuracy performance comparably to performances of many state-of-the-art tracking methods, which do not target for operation in embedded systems.

• Journal of Sensor Science and Technology
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• v.28 no.4
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• pp.240-245
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• 2019

Phase interpolation is widely adopted in frequency synthesizers and clock-and-data recovery systems to produce an intermediate phase from two existing phases. The intermediate phase is typically generated by combining two input phases with different weights. Unfortunately, this results in non-uniform phase steps. Alternatively, the intermediate phase can be generated by successive approximation, where the interpolated phase at each approximation stage is obtained using the same weight for the two intermediate phases. As a proof of concept, this study presents a 2-GHz 8-bit successive approximation digital-to-phase converter that is designed using 65-nm CMOS technology. The converter receives an 8-phase clock signal as input, and the most significant bit (MSB) section selects four phases to create two sinusoidal waveforms using a harmonic rejection filter. The remaining least significant bit (LSB) section applies the successive approximation to generate the required intermediate phase. Monte-Carlo simulations show that the proposed converter exhibits 0.46-LSB integral nonlinearity and 0.31-LSB differential nonlinearity with a power consumption of 3.12 mW from a 1.2-V supply voltage.

• Smart Media Journal
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• v.11 no.8
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• pp.84-92
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• 2022

Precision livestock monitoring promises greater management efficiency for farmers and higher welfare standards for animals. Recent studies on video-based animal activity recognition and tracking have shown promising solutions for understanding animal behavior. To achieve that, surveillance cameras are installed diagonally above the barn in a typical cattle farm setup to monitor animals constantly. Under these circumstances, tracking individuals requires addressing challenges such as occlusion and visual appearance, which are the main reasons for track breakage and increased misidentification of animals. This paper presents a framework for multi-cattle tracking in closed barns with appearance and motion models. To overcome the above challenges, we modify the DeepSORT algorithm to achieve higher tracking accuracy by three contributions. First, we reduce the weight of appearance information. Second, we use an Ensemble Kalman Filter to predict the random motion information of cattle. Third, we propose a supplementary matching algorithm that compares the absolute cattle position in the barn to reassign lost tracks. The main idea of the matching algorithm assumes that the number of cattle is fixed in the barn, so the edge of the barn is where new trajectories are most likely to emerge. Experimental results are performed on our dataset collected on two cattle farms. Our algorithm achieves 70.37%, 77.39%, and 81.74% performance on HOTA, AssA, and IDF1, representing an improvement of 1.53%, 4.17%, and 0.96%, respectively, compared to the original method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.133-135
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• 2022

With the development of IoT and AI technologies and media, various digital devices are being used, and unmanned and automation is progressing rapidly. In particular, high-level image processing technology is required in fields such as smart factories, autonomous driving technology, and intelligent CCTV. However, noise present in the image affects processes such as edge detection and object recognition, and causes deterioration of system accuracy and reliability. In this paper, we propose a filtering algorithm using fuzzy weights to reconstruct images damaged by complex noise. The proposed algorithm obtains a reference value using noise judgment and calculates the final output by applying a fuzzy weight. Simulation was conducted to verify the performance of the proposed algorithm, and the result image was compared with the existing filter algorithm and evaluated.

• Structural Engineering and Mechanics
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• v.90 no.5
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• pp.493-504
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• 2024

This paper presents the methodology and results for the investigation of the structural safety of 40 aged underground water tanks to support the weight of photovoltaic (PV) systems that were supposed to be placed on their roof reinforced concrete (RC) slabs. The investigation procedure included (1) review of available documents; (2) visual inspection of the roof RC slabs; (3) carrying out a series of nondestructive (ND) tests; and (4) analysis of results. Out of the 40 tanks, eleven failed the visual inspection phase and were discarded from further investigation. The roof RC slabs of the tanks that passed the visual inspection were subjected to a series of ND tests that included infrared thermography, impact echo, ultrasonic pulse velocity (UPV), Schmidt hammer, concrete core compressive strength, and water-soluble chloride content. The NDT results proved that eight more tanks were not suitable to support the PV systems. Based on the results of the visual inspection and testing, a probabilistic decision-making criterion was established to reach a decision regarding the structural integrity of the roof slabs. The study concluded that the condition of the drainage filter was essential in protecting the tanks and its intact presence can be used as a strong indication of the structural integrity of the roof RC slabs.