• Molecules and Cells
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• v.39 no.8
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• pp.587-593
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• 2016

As sessile organisms, plants must be able to adapt to the environment. Plants respond to the environment by adjusting their growth and development, which is mediated by sophisticated signaling networks that integrate multiple environmental and endogenous signals. Recently, increasing evidence has shown that a bHLH transcription factor PIF4 plays a major role in the multiple signal integration for plant growth regulation. PIF4 is a positive regulator in cell elongation and its activity is regulated by various environmental signals, including light and temperature, and hormonal signals, including auxin, gibberellic acid and brassinosteroid, both transcriptionally and post-translationally. Moreover, recent studies have shown that the circadian clock and metabolic status regulate endogenous PIF4 level. The PIF4 transcription factor cooperatively regulates the target genes involved in cell elongation with hormone-regulated transcription factors. Therefore, PIF4 is a key integrator of multiple signaling pathways, which optimizes growth in the environment. This review will discuss our current understanding of the PIF4-mediated signaling networks that control plant growth.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.1
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• pp.95-102
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• 2007

The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis and prognosis are of increasing importance. This study introduces a technique to detect and identify faults in induction motors. Stator currents were measured and stored by time domain. The time domain is not suitable for representing current signals, so wavelet transform is used to convert the signals onto frequency domain. The raw signals can not show the significant feature, therefore difference values between the signal of the health conditions and that of the fault conditions are applied. The difference values were transformed by wavelet transform and the features are extracted from the transformed signals. The dynamic time warping method was used to identify the fault type. This study describes the results of detecting fault using wavelet analysis.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.27-35
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• 2008

A navigation system is one of the important components of an unmanned ground vehicle (UGV). A GPS receiver collects data signals transmitted by (Earth orbiting) satellites. However, these data signals may contain many errors resulting misinformation and depending on one's position (environment), reception may be impossible. The proposed self-driven algorithm uses three low-cost GPS in order to minimize errors of existing inexpensive single GPS's driving algorithm. By using reliable final data, which is analyzed and combined from each of three GPS's received data signals, gathering a vehicle's steering performance information and its current pin-point position is improved even with error containing signals or from a place where signal gathering is impossible. The purpose of this thesis is to explain navigation system algorithm using multiple GPS and compass sensor and prove the algorithm through experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.1076-1094
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• 2022

Technology for emotion recognition is an essential part of human personality analysis. To define human personality characteristics, the existing method used the survey method. However, there are many cases where communication cannot make without considering emotions. Hence, emotional recognition technology is an essential element for communication but has also been adopted in many other fields. A person's emotions are revealed in various ways, typically including facial, speech, and biometric responses. Therefore, various methods can recognize emotions, e.g., images, voice signals, and physiological signals. Physiological signals are measured with biological sensors and analyzed to identify emotions. This study employed two sensor types. First, the existing method, the binary arousal-valence method, was subdivided into four levels to classify emotions in more detail. Then, based on the current techniques classified as High/Low, the model was further subdivided into multi-levels. Finally, signal characteristics were extracted using a 1-D Convolution Neural Network (CNN) and classified sixteen feelings. Although CNN was used to learn images in 2D, sensor data in 1D was used as the input in this paper. Finally, the proposed emotional recognition system was evaluated by measuring actual sensors.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.4
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• pp.315-324
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• 2004

In eddy current testing(ECT), differential probes have been frequently used since they .an reduce the number of parameters that influence ECT signals. However, differential signal is actually the difference of the two coils' impedance so that signal prediction and interpretation are not easy, On the other hand, absolute coil signal is rather straightforward to predict and analyze. Therefore, combined use of the two types of signals would increase the test reliability. In this paper, absolute coil signals from Inconel plate and tubes are predicted by the finite element analysis and efforts of lift-off, fill-factor, conductivity, operating frequency, test specimen thickness, inner diameter defects, and outer diameter defects are investigated. As a result, various impedance plane diagrams are drawn and analyzed. Significant practical knowldege about absolute signals is accumulated and similar characteristics of the two types of signal could be understood. Finally, slope angle versus defect depth calibration corves are prepared for three different frequencies.

• International Journal of Safety
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• v.5 no.1
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• pp.1-4
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• 2006

The safety performance of the current collection system is evaluated by conducting a test run in which accelerometer and load cell signals are analyzed. It has been found that the current collection performance is strongly influenced by the train speed, with the major frequency components arising from the train traversing the span spacing and the 8.5 Hz component originating from the panhead resonance. The train acceleration is found to have significant influence on the span passing frequency but negligible effect on the resonant response.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.616-624
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• 2015

Flashover of power transmission line insulators due to contamination is a major threat to the reliable operation of power system. This paper deals with the analysis of leakage current characteristics of polymeric insulator using S-Transform technique in order to develop a better diagnostic tool to identify the surface condition of outdoor polymeric insulators. In this work, experiments were carried out on 11 kV silicone rubber insulator under AC voltage at different pollution levels. Moving average technique was adopted to find the trend followed by LC peak at different relative humidity conditions. S-Transform was used to find the relationship between energy and frequency content of the leakage current signal with respect to increase in pollution level over a period of time. From the S-Transform time-frequency contour analysis, point of transition to severe arcing due to increase in pollution and its thershold limit were evaluated. Reported results show that the surface condition of insulators could be easily identified from the S-Transform time-frequency analysis of leakage current signals.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.99-104
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• 2009

The turn-to-turn short is one major fault of the motor faults of BLDC motors and can appear frequently. When the fault happens, the motor can be operated without breakdown, but it is necessary to maintain the motor for continuous working. In past research, several methods have been applied to detect winding faults. The representative approaches have been focusing on current signals, which can give important information to extract features and to detect faults. In this study, current sensors were installed to measure signals for fault detection of BLDC motors. In this study, the Park's vector method was used to extract the features and to isolate the faults from the current measured by sensors. Because this method can consider the three-phase current values, it is useful to detect features from one-phase and three-phase faults. After extracting two-dimensional features, the final feature was generated by using the two-dimensional values using the distance equation. The values were used in fuzzy similarity to isolate the faults. Fuzzy similarity is an available tool to diagnose the fault without model generation and the fault was converted to the percentage value that can be considered as possibility of the fault.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.108-113
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• 2005

In this study, rotary type diamond dressing system for ultra-precision centerless grinding for ferrule was developed at the first time and experiments were conducted with AE sensor and hall sensor system to verify the optimum dressing condition for ultra-precision centerless grinding for ferrule. The correlations with the condition of dressing are evaluated by AE signal analysis with root mean square (RMS) and frequency analysis. And current signals from hall sensor are also studied as a factor of dressing optimum condition selection. Dressing process was conducted to investigate the effects of depth of cut, rotating speed, and the number of overlap to select the optimum condition of rotary dressing system of ultra-precision centerless grinding machine for ferrule fabrication. In order to verify the optimum condition of dressing, AE and current signals were compared with the surface quality of dressing wheel and grinding wheel for ultra-precision ferrule grinding. All of these experiments were completed by Taguchi Methodology to reduce experimental time. Hence, the optimum condition of rotary dressing system for ultra-precision centerless grinding for ferrule fabrication can be selected following to the experiment result from signals of AE and hall sensor.

• Journal of the Korean Society for Nondestructive Testing
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• v.7 no.2
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• pp.16-26
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• 1988

In the in-service inspections for the steam generator tubing of the nuclear power plants by the Eddy Current Testing, the ECT signals are evaluated by their phase. If oxidized copper sludge is piled up in the secondary side, however, big sludge signals occur in large quantities which originate from copper layers forming in the sludge piles due to the pitting mechanism of the steam generator tubing by $Cu^{2+}$, and modulate the defect signals, causing the difficulty in the defect detection. In this research, sludge specimens were prepared considering the formations of the sludge signal sources and multi-frequency ECT mixing experiments by different choices of the mixing standards were performed. The results were found to be 5 to 30% of the tube wall thickness over-estimated. Experiments using the ring-type mixing standards showed the least errors of all, while those with the mixing standards nearing the sludge conditions brought larger errors as a result of the influence of the interference between the defect and the copper layers.