• KSCI Review
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• v.2 no.1
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• pp.55-67
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• 1995

Recently, it is possible to high speed transmission of the non-voiced data, including voice, data, moving image instead of voice only in the past by changing the communication method to digital form from analog owing to the development of semiconductor and computer technology which for information transmission of the remote point. By doing so, we can get the improvement of the noise effect and low cost but the loss of transmission bandwidth. It is necessary to take some method in oreder to reducing the fading which is propotional to transmission bandwidth during the transmission of radio communication channel, especially. When we design the digital communication system, we must considered to the fading effect in order to determination of the transmitting power, modulation /demodulation method, transmission speed, bit error rate. This paper mainly concerns to the method to the channel simulator which descrives the fading effect during the transmission by computer model and digital filter modeling of the radio fading channel by unsing the transmitting and received signal. By taking the inverse of the characteristic of the modeled filter, it is possible to improvement of the communication system by reducing the distortion and inter-symbol interference which occurs in the channel.

• Smart Structures and Systems
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• v.23 no.2
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• pp.107-122
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• 2019

As one of the most important parameters in structural health monitoring, structural frequency has many advantages, such as convenient to be measured, high precision, and insensitive to noise. In addition, frequency-change-ratio based method had been validated to have the ability to identify the damage occurrence and location. However, building a precise enough finite elemental model (FEM) for the test structure is still a huge challenge for this frequency-change-ratio based damage detection technique. In order to overcome this disadvantage and extend the application for frequencies in structural health monitoring area, a novel method was developed in this paper by combining the cross-model cross-mode (CMCM) model updating algorithm with the frequency-change-ratio based method. At first, assuming the physical parameters, including the element mass and stiffness, of the test structure had been known with a certain value, then an initial to-be-updated model with these assumed parameters was constructed according to the typical mass and stiffness distribution characteristic of shear buildings. After that, this to-be-updated model was updated using CMCM algorithm by combining with the measured frequencies of the actual structure when no damage was introduced. Thus, this updated model was regarded as a representation of the FEM model of actual structure, because their modal information were almost the same. Finally, based on this updated model, the frequency-change-ratio based method can be further proceed to realize the damage detection and localization. In order to verify the effectiveness of the developed method, a four-level shear building was numerically simulated and two actual shear structures, including a three-level shear model and an eight-story frame, were experimentally test in laboratory, and all the test results demonstrate that the developed method can identify the structural damage occurrence and location effectively, even only very limited modal frequencies of the test structure were provided.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.335-340
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• 2018

Commercialized speech recognition systems that have an accuracy recognition rates are used a learning model from a type of speaker dependent isolated data. However, it has a problem that shows a decrease in the speech recognition performance according to the quantity of data in noise environments. In this paper, we proposed the vector quantization based speech recognition performance improvement using maximum log likelihood in Gaussian distribution. The proposed method is the best learning model configuration method for increasing the accuracy of speech recognition for similar speech using the vector quantization and Maximum Log Likelihood with speech characteristic extraction method. It is used a method of extracting a speech feature based on the hidden markov model. It can improve the accuracy of inaccurate speech model for speech models been produced at the existing system with the use of the proposed system may constitute a robust model for speech recognition. The proposed method shows the improved recognition accuracy in a speech recognition system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.4
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• pp.349-358
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• 2019

This paper aimed at collecting sensor signals to extract characteristic parameter of the rotor. A vibration test rig has been developed to perform model tests. Signal characteristics were analyzed when driving normally. Envelope FFT Analysis is used to extract vibration components caused by periodic impacts from other vibration factors. Signal analysis was performed when load changes were given to speed sensors and vibration test rigs that show low frequency characteristics of the rotor and signal analysis according to rotational speed. The acceleration signal measured in the bearing housing has a small amplitude and produces only the rotational frequency component and harmonic component of the motor. As the number of rotations increases, the amplitude of acceleration can be seen. As the rotational speed increases, it can be seen that there is a difference in the shape of the original data and compared with the acceleration FFT graph, it can be seen that the noise is strong at low frequencies and the corresponding rotational frequency components are clearly represented. It can be seen that changing the load does not increase the main rotational frequency component.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3729-3749
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• 2021

At present, deep convolution network-based salient object detection (SOD) has achieved impressive performance. However, it is still a challenging problem to make full use of the multi-scale information of the extracted features and which appropriate feature fusion method is adopted to process feature mapping. In this paper, we propose a new adjacency auxiliary network (AANet) based on multi-scale feature fusion for SOD. Firstly, we design the parallel connection feature enhancement module (PFEM) for each layer of feature extraction, which improves the feature density by connecting different dilated convolution branches in parallel, and add channel attention flow to fully extract the context information of features. Then the adjacent layer features with close degree of abstraction but different characteristic properties are fused through the adjacent auxiliary module (AAM) to eliminate the ambiguity and noise of the features. Besides, in order to refine the features effectively to get more accurate object boundaries, we design adjacency decoder (AAM_D) based on adjacency auxiliary module (AAM), which concatenates the features of adjacent layers, extracts their spatial attention, and then combines them with the output of AAM. The outputs of AAM_D features with semantic information and spatial detail obtained from each feature are used as salient prediction maps for multi-level feature joint supervising. Experiment results on six benchmark SOD datasets demonstrate that the proposed method outperforms similar previous methods.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.917-926
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• 2020

Friction mechanisms is a very important role in the industrial machinery. However, many experiments have been conducted to reduce the loss of energy resources and parts used due to friction because the friction force adversely affects parts, efficiency, noise, and the like of the power unit. Therefore, in this study, the friction coefficient according to the characteristics of the lubricant was measured to find out which Grease Lubricant maintains the low friction coefficient without being most affected by external conditions. A total of five grease lubricants were tested in this study: GHP CAL 301, GHP EP 2, GHP KG 10, GHP HPG 2, and GHP HTG 2. And the friction coefficient was conducted by changing the load conditions (2, 4, 6, 8, 10N) and rotational speed (24, 48, 67, 86, 105, 124, 143, 162vrpm) using a pin-on-disk wear test system. Also, duty number were calculated. As a result, it was confirmed that in all grease lubricants, the speed did not significantly affect the friction coefficient, and it was confirmed that in all lubricants, the size of the friction coefficient decreased as the load increased from a small load to a large load. In addition, it was determined from the experimental results that GHP EP 2 is the most suitable as a grease lubricant and GHP CAL 301 is not the most suitable.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.34 no.2
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• pp.136-141
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• 2021

Heating cables, widely used in office buildings, factories, streets and railways, deteriorate in electrical insulation during operation. The insulation deterioration of heating cables leads to electric discharges that can cause electrical fires. With this background, this paper dealt with a condition monitoring technique for heating cables by the analysis of discharge signals to prevent electrical fires. Insulation deterioration was simulated using an arc generator specified in UL1699 under AC operation, and the characteristic and propagation of discharge signals were analyzed on a 100 meter-long heating cable. Discharge signals produced by insulation deterioration were detected as a voltage pulse because they are as small as a few mV and they are attenuated through propagation path. The frequency spectrum of discharge signals mainly existed in the range from 70 kHz to 110 kHz, and the maximum attenuation of the signal was 84.8% at 100 meters away from the discharge point. Based on the experimental results, a monitoring device, which is composed of a high pass filter with the cut-off frequency of 70 kHz, a comparator, a wave shaper and a microprocessor, was designed and fabricated. Also, an algorithm was designed to discriminate the discharge signal in the presence of noise, compared with the pulse repetition period and the number of pulse counts per 100ms. In the experiment, the result showed that the prototype monitoring device could detect and discriminate the discharge signals produced at every discharge point on a heating cable.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.123-128
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• 2022

The torque of the SRM((Switched Reluctance Motor)) is proportional to the inductance slope, so it has a non-linear torque characteristic, and has a disadvantage in that the torque pulsation is large and noise is severe. In particular, the biggest obstacle to the commercialization of SRM is the pulsating torque generated from the rotating shaft, which has various adverse effects not only on the device itself but also on the peripheral devices. Therefore, various methods for reducing the pulsating torque have been published by domestic and foreign researchers, and there is a study result that the hysteresis controller has an advantage in that it can flow a smooth current compared to the chopping control. However, in determining the hysteresis band, if the band is too small, it has a disadvantage in that it may cause a switching loss due to many switching and an unstable initial start when the encoder is used. Therefore, in this paper, a variable hysteresis controller that can reduce torque ripple in a steady state while having a more stable and fast speed response through the change of the hysteresis band according to the speed error.

• Korean Journal of Materials Research
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• v.32 no.8
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• Progress in Superconductivity
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• v.4 no.1
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• pp.1-9
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• 2002

Transferring single flux quanta across a Josephson junction at an exactly determined rate has made highly precise voltage measurements possible. Making use of self-shunted Nb-based SINIS junctions, programmable fast-switching DC voltage standards with output voltages of up to 10 V were produced. This development is now extended from fundamental DC measurements to the precise determination of AC voltages with arbitrary waveforms. Integrated RSFQ circuits will help to replace expensive semiconductor devices for frequency control and signal coding. Easy-to-handle AC and inexpensive quantum voltmeters of fundamental accuracy would be of interest to industry. In analogy to the development in the flux regime, metallic nanocircuits comprising small-area tunnel junctions and providing the coherent transport of single electrons might play an important role in quantum current metrology. By precise counting of single charges these circuits allow prototypes of quantum standards for electric current and capacitance to be realised. Replacing single electron devices by single Cooper pair circuits, the charge transfer rates and thus the quantum currents could be significantly increased. Recently, the principles of the gate-controlled transfer of individual Cooper pairs in superconducting A1 devices in different electromagnetic environments were demonstrated. The characteristics of these quantum coherent circuits can be improved by replacing the small aluminum tunnel Junctions by niobium junctions. Due to the higher value of the superconducting energy gap ($\Delta_{Nb}$ ＝ $7\Delta_{Al}$), the characteristic energy and the frequency scales for Nb devices are substantially extended as compared to A1 devices. Although the fabrication of small Nb junctions presents a real challenge, the Nb-based metrological devices will be faster and more accurate in operation. Moreover, the Nb-based Cooper pair electrometer could be coupled to an Nb single Cooper pair qubit which can be beneficial for both, the stability of the qubit and its readout with a large signal-to-noise ratio..