Effect of input signal type and time delay in sensors on wave velocity in rock specimens

2019 ◽  
Vol 260 ◽  
pp. 105225 ◽  
Author(s):  
Jin-Yeon Kim ◽  
Jaewon Jang ◽  
Tae Sup Yun
Keyword(s):  
Time Delay ◽  
Wave Velocity ◽  
Input Signal ◽  
Signal Type ◽  
Rock Specimens

scholarly journals Expansion-Induced Crack Propagation in Rocks Monitored by Using Piezoelectric Transducers

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6054 ◽  
Author(s):  
Chi-Hyung Ahn ◽  
Dong-Ju Kim ◽  
Yong-Hoon Byun
Keyword(s):  
Crack Propagation ◽  
Wave Velocity ◽  
Rock Specimen ◽  
Maximum Amplitude ◽  
Heating System ◽  
Piezoelectric Transducers ◽  
Rapid Expansion ◽  
Crack Control ◽  
Expansion Pressure ◽  
Rock Specimens

The objective of this study is to develop a new vibration-free excavation method based on vermiculite expansion for rock cracking and to evaluate the performance of the heating system via elastic wave monitoring. Natural vermiculites expand rapidly in volume when heated above 800 °C. MgO powder is used to evenly transmit the surface temperature of a heater rod, which can attain high temperatures rapidly, to the vermiculites. The insertion direction of the heater rod greatly affects the expansion pressure. Three cuboid rock specimens are prepared and equipped with the heating system at different hole-to-face distances. Crack propagation is monitored by a pair of disk-shaped piezoelectric transducers. For short hole-to-face distances, the wave velocity and maximum amplitude rapidly decrease after certain time. For the greatest hole-to-face distance, the shear wave velocity remains constant during the test, while the maximum amplitude decreases after a certain time. The time taken for the velocity and amplitude of the shear waves to decrease reasonably corresponded to that taken for detectable crack propagation to occur on the surface of the rock specimen. The proposed method and materials may be useful from the viewpoints of rapid expansion, economy, and crack control.

scholarly journals Prediction-Based Control for Nonlinear Systems with Input Delay

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
I. Estrada-Sánchez ◽  
M. Velasco-Villa ◽  
H. Rodríguez-Cortés
Keyword(s):  
Time Delay ◽  
Nonlinear Systems ◽  
Input Signal ◽  
Lyapunov Functional ◽  
The State ◽  
Constant Time Delay ◽  
Change Of Variable ◽  
Lipschitz Systems ◽  
Suitable Change ◽  
Prediction Strategy

This work has two primary objectives. First, it presents a state prediction strategy for a class of nonlinear Lipschitz systems subject to constant time delay in the input signal. As a result of a suitable change of variable, the state predictor asymptotically provides the value of the state τ units of time ahead. Second, it proposes a solution to the stabilization and trajectory tracking problems for the considered class of systems using predicted states. The predictor-controller convergence is proved by considering a complete Lyapunov functional. The proposed predictor-based controller strategy is evaluated using numerical simulations.

Time Delay of Index Calculation

2006 ◽  
Vol 104 (3) ◽  
pp. 488-494 ◽  
Author(s):  
Stefanie Pilge ◽  
Robert Zanner ◽  
Gerhard Schneider ◽  
Jasmin Blum ◽  
Matthias Kreuzer ◽  
...  
Keyword(s):  
Time Delay ◽  
Input Signal ◽  
Time Delays ◽  
Time Lags ◽  
Medical Systems ◽  
State Index ◽  
Electroencephalographic Signals ◽  
Index Value ◽  
Narcotrend Index ◽  
Index Calculation

Background On the basis of electroencephalographic analysis, several parameters have been proposed as a measure of the hypnotic component of anesthesia. All currently available indices have different time lags to react to a change in the level of anesthesia. The aim of this study was to determine the latency of three frequently used indices: the Cerebral State Index (Danmeter, Odense, Denmark), the Bispectral Index (Aspect Medical Systems Inc., Newton, MA), and the Narcotrend Index (MonitorTechnik, Bad Bramstedt, Germany). Methods Artificially generated signals were used to produce up to 14 constant index values per monitor that indicate "awake state," "general anesthesia," and "deep anesthesia" and smaller steps in between. The authors simulated loss of and return to consciousness by changing between the artificial electroencephalographic signals in a full-step and two stepwise approaches and measured the time necessary to adapt the indices to the particular input signal. Results Time delays between 14 and 155 s were found for all indices. These delays were not constant. Results were different for decreasing and increasing values and between the full-step and the stepwise approaches. Calculation time depended on the particular starting and target index value. Conclusions The time delays of the tested indices may limit their value in prevention of recall of intraoperative events. Furthermore, different latencies for decreasing and increasing values may indicate a limitation of these monitors for pharmacodynamic studies.

Vibrational resonance in a discrete neuronal model with time delay

2014 ◽  
Vol 28 (16) ◽  
pp. 1450103 ◽  
Author(s):  
Canjun Wang ◽  
Keli Yang ◽  
Shixian Qu
Keyword(s):  
Time Delay ◽  
Input Signal ◽  
High Frequency ◽  
Phase Synchronization ◽  
Low Frequency ◽  
Neuronal Model ◽  
Vibrational Resonance ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Neuron System

The effects of time delay on the vibrational resonance (VR) in a discrete neuron system with a low-frequency signal and a high-frequency signal are investigated by numerical simulations. The results show that there exists a delay time that optimizes the phase synchronization between the low-frequency input signal and the output signal. VR is induced by the time delay. Furthermore, the time delay can improve the response to a low-frequency input signal. Therefore, the time delay plays a constructive role in the transmission of a low-frequency signal by inducing and enhancing VR.

WATER PROPULSION SPEEDS AND POWER OUTPUT BY COMB PLATES OF THE CTENOPHORE PLEUROBRACHIA PILEUS UNDER DIFFERENT CONDITIONS

1993 ◽  
Vol 183 (1) ◽  
pp. 149-164
Author(s):  
D. Barlow ◽  
M. A. Sleigh
Keyword(s):  
Time Delay ◽  
Angular Velocity ◽  
Wave Velocity ◽  
Phase Difference ◽  
Power Output ◽  
Beat Frequency ◽  
Flow Speed ◽  
Metachronal Wave ◽  
Pleurobrachia Pileus ◽  
Recovery Stroke

Parameters of ciliary beating and water propulsion can be studied in a unique fashion in ctenophores because the beat frequency can be controlled. Pleurobrachia pileus comb plates were driven at frequencies between 2 and 25 Hz and at temperatures between 10 and 25°C. As frequency is increased from 5 to 25 Hz, the rest period between beats is first shortened and then disappears: the duration of the effective stroke is reduced because the angular velocity (which is directly proportional to the sliding velocity of the microtubules) and the tip speed of each plate increase whilst the amplitude of the beat remains unchanged. The recovery stroke is shortened because the recovery bend is propagated more quickly to the tip of the plate. The phase difference between adjacent plates in the metachronal wave (expressed as a percentage of the cycle) is increased in spite of a sharp decrease in the time delay between adjacent plates, a reduction in the number of plates per wave and an increase in the metachronal wave velocity. The water flow speed becomes more continuous and increases in approximate proportion to the tip speed whilst the estimated power output of a metachronal wave increases exponentially, from 10–10 W at a tip speed of about 20 mm s-1 to 10-8 W at a tip speed of about 75 mm s-1. When comb plates are driven to beat at 10 Hz and the temperature is raised from 10°C towards 20°C, the duration of the effective stroke is reduced and the comb plates have a somewhat higher angular velocity and tip speed; the duration of the recovery stroke is reduced with a faster propagation of the recovery bend; a rest phase first appears between successive beats and then becomes longer. The phase difference between adjacent plates in a metachronal wave (expressed as a percentage of the cycle time) is decreased, as is the time delay between successive plates in a metachronal wave, so that the number of plates per wave and the wave velocity are increased. The water flow speed and power output are increased by a modest amount (a rise in temperature from 10 to 20°C produces changes equivalent to those produced by a 5 Hz increase in frequency at 20°C). The cooperation between adjacent plates in the antiplectic metachronal wave makes a major contribution to the dramatic increase in power output of each metachronal wave that is seen as the beat frequency is increased.

ACTIVITY COMMAND ENCODING OF CEREBRAL CORTEX M1-EVOKED POTENTIALS OF THE SPRAGUE DAWLEY RAT USING TIME DELAY NEURAL NETWORKS

2020 ◽  
Vol 32 (04) ◽  
pp. 2050034
Author(s):  
Tung-Tai Kuo ◽  
Rong-Chin Lo ◽  
Ren-Guey Lee ◽  
Yuan-Hao Chen ◽  
Shang-Hsien Cai
Keyword(s):  
Time Delay ◽  
Primary Motor Cortex ◽  
Treatment Strategies ◽  
Movement Control ◽  
Energy Operator ◽  
Suitable Model ◽  
Sprague Dawley ◽  
Comprehensive Information ◽  
Signal Type ◽  
The Brain

Understanding the neurons that transmit messages in the brain while we thinking, feeling, or acting is critical for research on the causes of neurological disease and treatment strategies. This research focuses on the primary motor cortex M1 region, which is involved in human motor function as an activity command center. Understanding this region can help us to determine the mechanism of movement control by the brain, with applicability to other activity mechanisms. A time delay neural network (TDNN) is a suitable model for studying brain signals. TDNN can analyze comprehensive information for a period of successive signals, which is similar to the transmission mechanism of the M1 region. Therefore, this study used a TDNN to build a three-stage encoding system corresponding to the signal type, type arrangement, and time sequence of the brainwave signal from the M1 region and the encoded results were defined as codes, symbols, and commands, respectively. This study aimed to understand the relationship between movement and the M1 region by decoding the signal when the rat undertakes an action. First, we recorded the M1 signal from three rat action types (walk, stand up, and shift head) and performed signal processing. This included using a nonlinear energy operator to find the response points of each action signal. The signals were separated into several sections according to the response time points and independent component analysis was then used to extract the features of the signal (the signal of interest). Finally, we found 16 representative sample signals through a dynamic dimension increasing algorithm to train a three-stage TDNN. We then input the remaining feature signals of interest into the three-stage TDNN for encoding and classification. The results showed an accuracy rate for the three actions of 51.4%, 80.0%, and 54.3%, which means that it is feasible to explain the brain signal of M1 from the free-moving animal using a three-stage TDNN encoding model.

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