• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.9
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• pp.2053-2061
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• 1997

The motion comensated video coding technology is very improtant to compress video signal since it reduces the temporal redundancies in successive frames. But the computational complexity of the motion estimation(ME) is too enormous to use in the area of real-time and/or resolution video processing applications. To reduce the complexity of ME, fast search algoritjms and hardware design methods are developed. Especially, the three step search(TSS) is well known method which shows stable performance in various video sequences. And other variations of TSS are developed to get better performance andto reduce the complexity. In this paepr, we present the modified TSS using neighboring block's motion vectors to determine first step motion vector in TSS. The presented method uses the correlation of the adjacent blocks with same motion field. The simualtion resutls show that it has a good MAE performance and low complexity comparing with original TSS.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2488-2491
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• 2000

A Neural network is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping. When a fault occur in system, a state of system is changed with transient state. Because of a previous state signal is considered as a information. DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights, so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.9
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• pp.512-519
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• 2000

A Neural networks has been used for a expert system and fault diagnosis system. It is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping.쪼두 a fault occur in system a state of system is changed with transient state. Because of a previous state signal is considered as a information DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1780-1787
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• 2014

Arterial oxygen saturation ($SpO_2$) monitoring for newborns requires special attention in neonatal intensive care units (NICUs). Newborns have very low photo-plethysmogram (PPG) amplitudes and their body movements are difficult to contain. Hardware design and its associated signal processing algorithms should be robust enough so that faulty measurements can be avoided. In this study, improved designs were implemented to deal with low perfusion, motion artifact, and the influence of ambient light. Dynamic range was increased by using different LED intensities and a feedback system. To minimize the effects of motion artifact and to discard other unqualified data, four additional algorithms were used, which were based on dual-trace detection, continuity of DC level, morphology of PPG, and simultaneity check of $SpO_2$. Our $SpO_2$ system was tested with newborns with normal respiration in the NICU. Our system provided fast, real-time responses and 100% artifact detection was accomplished under 84% of $SpO_2$.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.465-470
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• 2002

A novel phased array antenna system for space-borne polarimetric SAR is proposed and completed in this paper.The antenna system assures polarimetric and multi-mode capability of SAR. It has broadband, high polarization isolation and high port to port isolation. The antenna system is composed of broadband polarimetric microstrip antenna, T/R modules and multifunction beam controller nit. The polarimetric microstrip antenna has more than 100MHz bandwidth at L-band with -30dB polarization isolation and high port to port isolation. The microstrip element and T/R module's structure and characteristics, the subarray's performances measuring results are presented in detail in this paper. A design scheme on beam controller of the phased array antenna is also proposed and completed, which is based on Digital Signal Processing (DSP) chip -TMS320F206. This beam controller unit has small size and high reliability compared with general beam controller. In addition, the multifunction beam controller unit can acquire and then send the T/R module's working states to detection system in real time.

• Journal of Advanced Navigation Technology
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• v.17 no.3
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• pp.271-278
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• 2013

In this paper, ECEF to ECI coordinate transformation algorithm which uses EOP parameters in GPS civil navigation message is introduced, and ECEF to ECI coordinate transformation simulation results were analyzed. The ECEF to ECI coordinate transformation includes GPS to UTC, and UTC to other types of time conversions and EOP data processing algorithms. The ECEF to ECI coordinate conversion algorithm was certified using real LEO satellite position, velocity GPS data, and EOP data which offered by the Earth Orientation Center.

• Nuclear Engineering and Technology
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• v.47 no.7
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• pp.804-813
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• 2015

The electrical-impedance method has been widely used for void-fraction measurement in two-phase flow due to its many favorable features. In the impedance method, the response characteristics of the electrical signal heavily depend upon flow pattern, as well as phasic volume. Thus, information on the flow pattern should be given for reliable void-fraction measurement. This study proposes an improved electrical-conductance sensor composed of a three-electrode set of adjacent and opposite electrodes. In the proposed sensor, conductance readings are directly converted into the flow pattern through a specified criterion and are consecutively used to estimate the corresponding void fraction. Since the flow pattern and the void fraction are evaluated by reading conductance measurements, complexity of data processing can be significantly reduced and real-time information provided. Before actual applications, several numerical calculations are performed to optimize electrode and insulator sizes, and optimal design is verified by static experiments. Finally, the proposed sensor is applied for air-water two-phase flow in a horizontal loop with a 40-mm inner diameter and a 5-m length, and its measurement results are compared with those of a wire-mesh sensor.

• Journal of the Korean Society of Visualization
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• v.19 no.1
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• pp.62-67
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• 2021

Selective catalytic reduction(SCR) is an exhaust gas reduction device to remove nitro oxides (NOx). SCR operation of ship can be controlled through valves for minimizing economic loss from SCR. Valve in SCR-high pressure (HP) system is directly connected to engine exhaust and operates in high temperature and high pressure. Long-term thermal deformation induced by engine heat weakens the sealing of the valve, which can lead to unexpected failures during ship sailing. In order to prevent the unexpected failures due to long-term valve thermal deformation, a failure prediction system using autoregressive integrated moving average (ARIMA) was proposed. Based on the heating experiment, virtual data mimicking temperature range around the SCR-HP valve were produced. By detecting abnormal temperature rise and fall based on the short-term ARIMA prediction, an algorithm determines whether present temperature data is required for failure prediction. The signal processed by the data collection algorithm was interpolated for the failure prediction. By comparing mean average error (MAE) and root mean square error (RMSE), ARIMA model and suitable prediction instant were determined.

• Korean Journal of Optics and Photonics
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• v.32 no.3
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• pp.108-113
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• 2021

We propose and experimentally demonstrate a method of remote sound extraction using laser Doppler interferometry. The output frequency of a laser Doppler interferometer changes to be the same as the frequency of the acoustic wave from than object vibrated by the sound due to the Doppler effect. Based on this phenomenon, we measure the vibrational frequency of a remote target affected by a sound wave in real time, via laser Doppler interferometry. We track the peak frequency of the interferometer's output via appropriate signal processing, which confirms that the characteristics of the so detected wave are the same as that of the original sound source. We also confirm that the same method can retrieve the sound waves not only from remote sources of single tones, but from those of any sound.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.145-152
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• 2024

Closed quotient (CQ) represents the time ratio for which the vocal folds remain in contact during voice production. Because analyzing CQ values serves as an important reference point in vocal training for professional singers, these values have been measured mechanically or electrically by either inverse filtering of airflows captured by a circumferentially vented mask or post-processing of electroglottography waveforms. In this study, we introduced a novel algorithm to predict the CQ values only from audio signals. This has eliminated the need for mechanical or electrical measurement techniques. Our algorithm is based on a gated recurrent unit (GRU)-type neural network. To enhance the efficiency, we pre-processed an audio signal using the pitch feature extraction algorithm. Then, GRU-type neural networks were employed to extract the features. This was followed by a dense layer for the final prediction. The Results section reports the mean square error between the predicted and real CQ. It shows the capability of the proposed algorithm to predict CQ values.