• Transactions on Electrical and Electronic Materials
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• v.12 no.6
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• pp.262-266
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• 2011

A simulation study of a current-mode direct current (DC)-DC boost converter is presented in this paper. This converter, with a fully-integrated power module, is implemented by using bipolar complementary metal-oxide semiconductor (BiCMOS) technology. The current-sensing circuit has an op-amp to achieve high accuracy. With the sense metal-oxide semiconductor field-effect transistor (MOSFET) in the current sensor, the sensed inductor current with the internal ramp signal can be used for feedback control. In addition, BiCMOS technology is applied to the converter, for accurate current sensing and low power consumption. The DC-DC converter is designed with a standard 0.35 ${\mu}m$ BiCMOS process. The off-chip inductor-capacitor (LC) filter is operated with an inductance of 1 mH and a capacitance of 12.5 nF. Simulation results show the high performance of the current-sensing circuit and the validity of the BiCMOS converter. The output voltage is found to be 4.1 V with a ripple ratio of 1.5% at the duty ratio of 0.3. The sensing current is measured to be within 1 mA and follows to fit the order of the aspect ratio, between sensing and power FET.

• Journal of the Korean Society of Propulsion Engineers
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• v.15 no.3
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• pp.86-93
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• 2011

Recently, development and application of condition monitoring and diagnostic system for improvement of durability and reliability and reduction of operating cost is generalized in the aircraft propulsion system. Expecially, for reliable operation of the high altitude and a long time and condition monitoring system to identify faults and degradations of its propulsion system should be needed. This work proposed a GUI-based On-line condition monitoring program using LabVIEW by PT6A-67 turboprop engine. The proposed on-line condition program can monitor the real engine performance as well as the trend through precise comparison between performance results calculated by the base performance simulation program and measuring engine performance signals. In the development phase of this monitoring system, a signal generation module is proposed to evaluate the proposed on-line monitoring system.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.50-56
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• 2018

The Internet of Things (IoT) and Visible Light Communication (VLC) is opening up new services in lighting industry by integrating sensory network features in addition to standard illumination functionality. In this progressive developments, the next generation lighting devices for smart homes are capable to sense the environmental conditions and transfer the captured data through lights to gateway controller to access remotely. The smart home environmental sensor information's are few kbps only so VLC systems need to built-in with low rate light connectivity to transfer data to the gateway. To provide error free communication, the quality of a received light signal is important to be considered when designing an VLC receiver. Therefore, this paper proposes the design of robust low rate IoL receiver design using NCP302 voltage detector for micro controller to adapt the IoT/IoL front end module for system integration. To evaluate the proposed system performance, the Arduino UNO based IoT/IoL controller designed with lighting, sensors and lights connectivity interfaces. The experimental result shows that the robust interference rejection is feasible on proposed VOL receiver and possible to have an error-free communication up to 10 kbps at a low SNR using OOK modulation.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.87-96
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• 2019

Flexible organic light-emitting diode (OLED) displays with organic thin-film transistors (OTFTs) backplanes have been studied. A gate driver is required to drive the OLED display. The gate driver is integrated into the panel to reduce the manufacturing cost of the display panel and to simplify the module structure using fabrication methods based on low-temperature, low-cost, and large-area printing processes. In this paper, pseudo complementary metal oxide semiconductor (CMOS) logic gates are implemented using OTFTs for the gate driver integrated in the flexible OLED display. The pseudo CMOS inverter and NAND gates are designed and fabricated on a flexible plastic substrate using inkjet-printed OTFTs and the same process as the display. Moreover, the operation of the logic gates is confirmed by measurement. The measurement results show that the pseudo CMOS inverter can operate at input signal frequencies up to 1 kHz, indicating the possibility of the gate driver being integrated in the flexible OLED display.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.66-69
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• 2016

In this paper, we suggests a Ka-band LNB considering next-generation UHD satellite TVRO. Since Ka-band has grater attenuation than Ku-band in atmosphere, we designed the low-noise down-converter to improve receiving sensitivity and to extend a dynamic range of receiver. It aims to compensate a quality of ultra high definition transmission signal for rainfall. The low-noise block diagram consists of a three-staged amplifier (LNA), band-pass filter for deleting image (BPF), mixer and IF when considering nonlinear characteristics in the receiver RF front end module. Also, we showed a LNB through optimization processes affecting dynamic range directly in receiver FEM. Asa resuly of experiment, the gain of low-noise down-converter show between 58.5dB and 60.7dB, the noise figure has a high characteristic as 1.38dB. Finally, the phase noise of local oscillator is -63.10dBc at 100MHz offset frequency.

• Plant Journal
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• v.17 no.4
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• pp.53-60
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• 2021

The purpose of this study was to develop a portable vibration analyzer that is effective for acquiring and analyzing vibration data of rotating equipment of a power plant and a domestic vibration monitoring system manufacturer Nada Co., Ltd. The hardware of the developed portable vibration analyzer minimizes measurement errors by calibrating the measured values obtained through measurement uncertainty for calibration of the measuring devices in the system, and is composed of a signal processing device with high resolution through high speed data processing. The software structure implements a variety of vibration plots to execute a detailed analysis program, and applies algorithms to measure and remove noise caused by disturbances while operating a rotating machine. The developed product contributed greatly to increase the user's mobility and performance, as well as to reduce the purchase cost due to localization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4065-4083
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• 2021

Super-resolution can improve the clarity of low-resolution (LR) images, which can increase the accuracy of high-level compute vision tasks. Portable devices have low computing power and storage performance. Large-scale neural network super-resolution methods are not suitable for portable devices. In order to save the computational cost and the number of parameters, Lightweight image processing method can improve the processing speed of portable devices. Therefore, we propose the Enhanced Information Multiple Distillation Network (EIMDN) to adapt lower delay and cost. The EIMDN takes feedback mechanism as the framework and obtains low level features through high level features. Further, we replace the feature extraction convolution operation in Information Multiple Distillation Block (IMDB), with Ghost module, and propose the Enhanced Information Multiple Distillation Block (EIMDB) to reduce the amount of calculation and the number of parameters. Finally, coordinate attention (CA) is used at the end of IMDB and EIMDB to enhance the important information extraction from Spaces and channels. Experimental results show that our proposed can achieve convergence faster with fewer parameters and computation, compared with other lightweight super-resolution methods. Under the condition of higher peak signal-to-noise ratio (PSNR) and higher structural similarity (SSIM), the performance of network reconstruction image texture and target contour is significantly improved.

• 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.

• Journal of IKEEE
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• v.26 no.1
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• pp.27-35
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• 2022

This paper proposes an ultrasound vessel-pattern imaging algorithm with low computational complexity. The proposed imaging algorithm reconstructs blood-vessel patterns by only detecting blood flow, and can be applied to a real-time signal processing hardware that extracts an ultrasonic finger-vessel pattern. Unlike a blood-flow imaging mode of typical ultrasound medical imaging device, the proposed imaging algorithm only reconstructs a presence of blood flow as an image. That is, since the proposed algorithm does not use an I/Q demodulation and detects a presence of blood flow by accumulating an absolute value of the clutter-filter output, a structure of the algorithm is relatively simple. To verify a complexity of the proposed algorithm, a simulation model for finger vessel was implemented using Field-II program. Through the behavioral simulation, it was confirmed that the processing time of the proposed algorithm is around 54 times less than that of the typical color-flow mode. Considering the required main building blocks and the amount of computation, the proposed algorithm is simple to implement in hardware such as an FPGA and an ASIC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.