• Coupled systems mechanics
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• v.5 no.2
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• pp.191-201
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• 2016

Coupled structures are widely seen in civil and mechanical engineering. In coupled structures, monitoring the translational motion of its key components is of great importance. For instance, some coupled arms are equipped with a hydraulic piston to provide the stiffness along the piston axial direction. The piston moves back and forth and a distance sensing system is necessary to make sure that the piston is within its stroke limit. The measured motion data also give us insight into how the coupled structure works and provides information for the design optimization. This paper develops two distance sensing systems for coupled structures. The first system measures distance with ultrasonic sensor. It consists of an ultrasonic sensing module, an Arduino interface board and a control computer. The system is then further upgraded to a three-sensor version, which can measure three different sets of distance data at the same time. The three modules are synchronized by the Arduino interface board as well as the self-developed software. Each ultrasonic sensor transmits high frequency ultrasonic waves from its transmitting unit and evaluates the echo received back by the receiving unit. From the measured time interval between sending the signal and receiving the echo, the distance to an object is determined. The second distance sensing system consists of a wire draw encoder, a data collection board and the control computer. Wire draw encoder is an electromechanical device to monitor linear motion by converting a central shaft rotation into electronic pulses of the encoder. Encoder can measure displacement, velocity and acceleration simultaneously and send the measured data to the control computer via the data acquisition board. From experimental results, it is concluded that both the ultrasonic and the wire draw encoder systems can obtain the linear motion of structures in real-time.

• Journal of Broadcast Engineering
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• v.2 no.1
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• pp.8-15
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• 1997

In this paper, a real-time operating Motion Picture Experts Group-1 (MPEG-1) audio encoder system is implemented using a TMS320C31 Digital Signal Processor (DSP) chip. The basic operation of the MPEG-1 audio encoder algorithm based on audio layer-2 and psychoacoustic model-1 is first verified by C-language. It is then realized using the Texas Instruments (Tl) assembly in order to reduce the overall execution time. Finally, the actual BSP circuit board for the encoder system is designed and implemented. In the system, the side-modules such as the analog-to-digital converter (ADC) control, the input/output (I/O) control, the bit-stream transmission from the DSP board to the PC and so on, are utilized with a field programmable gate array (FPGA) using very high speed hardware description language (VHDL) codes. The complete encoder system is able to process the stereo audio signal in real-time at the sampling frequency 48 kHz, and produces the encoded bit-stream with the bit-rate 192 kbps. The real-time operation capability of the encoder system and the good quality of the decoded sound are also confirmed using various types of actual stereo audio signals.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11C
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• pp.901-906
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• 2010

Low density parity check (LDPC) code, first introduced by Gallager and re-discovered by MacKay et al, has attracted researcher's interest mainly due to their performance and low decoding complexity. It was remarkable that the performance is very close to Shannon capacity limit under the assumption of having long codeword length and iterative decoder. However, comparing to turbo codes widely used in the current mobile communication, the encoding complexity of LDPC codes has been regarded as the drawback. This paper proposes a solution for DVB-S2 LDPC encoder to reduce the encoder latency. We use the fast IRA encoder that use the transformation of the parity check matrix into block-wise form and the partial parallel process to reduce the number of system clocks for the IRA code encoding. We compare the proposed encoder with the current DVB-S2 encoder to show that the performance of proposal is better than that of the current DVB-S2 encoder.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.452-459
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• 2019

In this paper, we describe a PCM encoder structure that can update the telemetry system parameters in real time. In the PCM encoder, an analog signal control unit for FPGA, flash memory, and sensor data acquisition was constructed. UART communication, analog signal control, flash memory control, and frame generation are possible through logic inside FPGA of PCM encoder. UART communication allows the PC to transmit parameter data to the PCM encoder, and flash memory is controlled to update the parameter of the telemetry system in real time and finally the frame is formed. Simulation and verification were performed to confirm whether the parameter data is updated in real time, and the proposed structure was used to construct a telemetry system with enhanced flexibility and convenience.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.401-404
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• 2016

This paper proposes entropy coding method of HEVC CABAC Encoder for efficient hardware architecture. The Binary Arithmetic Encoder requires data dependency at each step, which is difficult to be operated in a fast. Proposed Binary Arithmetic Encoder is designed 4 stage pipeline to quickly process the input value bin. According to bin approach, either MPS or LPS is selected and the binary arithmetic encoding is performed. Critical path caused by repeated operation is reduced by using the LUT and designed as a shift operation which decreases hardware size and not using memory. The proposed Binary Arithmetic Encoder of CABAC is designed using Verilog-HDL and it was implemented in 65nm technology. Its gate count is 3.17k and operating speed is 1.53GHz.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.31-38
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• 2022

This paper applies an expert independent unsupervised neural network learning-based multivariate time series data analysis model, MSCRED(Multi-Scale Convolutional Recurrent Encoder-Decoder), and to overcome the limitation, because the MCRED is based on Auto-encoder model, that train data must not to be contaminated, by using learning data sampling technique, called Subset Sampling Validation. By using the vibration data of power plant equipment that has been labeled, the classification performance of MSCRED is evaluated with the Anomaly Score in many cases, 1) the abnormal data is mixed with the training data 2) when the abnormal data is removed from the training data in case 1. Through this, this paper presents an expert-independent anomaly diagnosis framework that is strong against error data, and presents a concise and accurate solution in various fields of multivariate time series data.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.9-17
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• 2023

In this paper, we propose a one-class vibration anomaly detection system for bearing defect diagnosis. In order to reduce the economic and time loss caused by bearing failure, an accurate defect diagnosis system is essential, and deep learning-based defect diagnosis systems are widely studied to solve the problem. However, it is difficult to obtain abnormal data in the actual data collection environment for deep learning learning, which causes data bias. Therefore, a one-class classification method using only normal data is used. As a general method, the characteristics of vibration data are extracted by learning the compression and restoration process through AutoEncoder. Anomaly detection is performed by learning a one-class classifier with the extracted features. However, this method cannot efficiently extract the characteristics of the vibration data because it does not consider the frequency characteristics of the vibration data. To solve this problem, we propose an AutoEncoder model that considers the frequency characteristics of vibration data. As for classification performance, accuracy 0.910, precision 1.0, recall 0.820, and f1-score 0.901 were obtained. The network design considering the vibration characteristics confirmed better performance than existing methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.2
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• pp.89-103
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• 2020

As we face an aging society, the demand for personal mobility for disabled and aged people is increasing. In fact, as of 2017, the number of electric wheelchair in the country continues to increase to 90,000. However, people with disabilities and seniors are more likely to have accidents while driving, because their judgment and coordination are inferior to normal people. One of the causes of the accident is the interference of personal vehicle steering control due to unbalanced road surface conditions. In this paper, we introduce a encoder type semantic segmentation algorithm that can recognize road conditions at high speed to prevent such accidents. To this end, more than 1,500 training data and 150 test data including road surface damage were newly secured. With the data, we proposed a deep neural network composed of encoder stages, unlike the Auto-encoding type consisting of encoder and decoder stages. Compared to the conventional method, this deep neural network has a 4.45% increase in mean accuracy, a 59.2% decrease in parameters, and an 11.9% increase in computation speed. It is expected that safe personal transportation will be come soon by utilizing such high speed algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.221-222
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• 2012