Arrival Time Detection Research Articles

Travelling Wave Fault Location of HVAC Transmission Line Based on Frequency-Dependent Characteristic

Travelling wave (TW) fault location has been widely utilized for high-voltage alternating current (HVAC) transmission lines, which is proven to have a higher accuracy than conventionalpower frequency data based fault location. However, as known, the performance of travelling wave-based fault location (TWFL) principles is often challenged by several factors including fault location, fault initial angle, fault resistance as well as frequency dependency of HVAC transmission lines. In this context, this paper proposes a novel off-line TW fault location principle for transposed HVAC transmission lines to address the robustness against those uncertain factors. Firstly, the frequency-dependent characteristic of HVAC transmission lines is analyzed. Then upon that, a novel TW fault location principle is proposed, which consists of two important components: i) arrival time detection of TW and ii) accurate frequency calculation of TW. Eventually, simulations are carried out on a 500 kV AC transmission system using PSCAD/EMTDC to validate the effectiveness of the proposed method and itsrobustness against different fault types, resistances, locations, initial angles and noises.

Relay-Aided Random Access in Space-Air-Ground Integrated Networks

The Internet of Things (IoT) has been extensively investigated as an enabling technology for improving our daily life. With increasing demands for seamless connectivity, the space-air-ground integrated network (SAGIN) is expected to provide reliable communications for IoT devices anywhere and anytime. Since IoT devices have sparse activity and low signaling overhead, random access can be an efficient means to relieve the burden of machine-type communication, which provides connections among massive IoT devices. However, there are a few drawbacks. For example, random access suffers from signal collisions, and low-energy IoT devices may have limited transmission coverage. To overcome these drawbacks, in this article, relay-aided random access (RRA) is proposed as a promising strategy to support massive IoT devices in the SAGIN. We first introduce the RRA structure and scheme as well as several advantages over conventional random access. Then we present some of the key technologies of RRA, including energy harvesting, arrival time detection, and signal number detection. Finally, different scenarios or use cases for practical applications of RRA schemes in the SAGIN are discussed.

Effective Audio Signal Arrival Time Detection Algorithm for Realization of Robust Acoustic Indoor Positioning

To achieve high precision and robust positioning on mobile devices, we propose a novel audio signal arrival time detection algorithm consisting of both coarse and fine searches. A coarse search uses the cosine theorem to extract the audio data segment (ADS) from the received signal of a mobile device, and a fine search then uses the waveform characteristics of the autocorrelation function of the source signal to estimate the audio arrival time (AAT) from the ADS. An indoor threshold determination experiment, a static positioning experiment, and a moving target positioning experiment were carried out on the developed acoustic indoor positioning system (AIPS) in a hall. The performance of the proposed audio detection algorithm was compared to two other typical detection algorithms. The results of the static positioning experiment show that, in a nonline of sight (NLOS) environment, 90% of the positioning results estimated by the proposed algorithm have an error of less than 0.50 m, whereas only 56% and 6% of the positioning results estimated by the other two classical algorithms have errors of less than 0.50 m. In the target-moving positioning experiment, the positions estimated by the proposed algorithm are closer to the true trajectories and have fewer (less than 0.5%) abnormal positioning errors than the other two classical algorithms. The experimental results also show that the reliability judgment of the positioning results can further improve the robustness of the system. The proposed method has an important application value for the implementation of a highly precise and reliable AIPS.

Automatic debris flow detection using geophones

Ground vibration recorded using geophones can provide information on debris flows. This information can be used for debris flow detection and warning. However, real debris flow signals change not only from site to site but also during different times at the same location. Thus, a constant threshold calibrated with historic data at one location may not accurately detect debris flow at the same location, let alone at other locations. Therefore, a debris flow detection method requires a criterion that can transform current environmental conditions into a detection criterion. The characteristic of a debris flow tremor was discussed and transformed into two mathematical indices. We used accumulation energy and the rate of energy change in a specific spectrum domain as the representative indicators. To distinguish debris flows from noise, we assumed debris flow energy to be at least an order of magnitude larger than noise before debris flow arrival. To avoid a short-term spike event confusing the detection of debris flow, accumulation energy at an interval longer than 2 s was used. This method was tested on three debris flow events with complete records at the Ai-Yu-Zi Creek, Taiwan. The detection of arrival time for debris flows using geophone data had a delay of less than 10 s compared with the detection of arrival time through video recording.

UHF Measurement in Power Transformers: An Algorithm to Optimize Accuracy of Arrival Time Detection and PD Localization

Partial discharges (PDs) are among the various indicators that are utilized for monitoring of power transformers. PDs emit electromagnetic waves that can be received by inserting ultrahigh frequency probes inside the transformer tank, thus an advantage of this method is its higher robustness against external noises. In this contribution, an algorithm is proposed to localize PDs in power transformers by capturing and analyzing multiple signal sets. Detecting exact arrival times (ATs) of a signal set is a challenging task in the PD localization. Different AT detection methods are applied to several signal sets to compare their effectiveness. Furthermore, modifications are introduced to enhance the precision of the AT detection. A fraction of the analyzed signal sets provide acceptable coordinates for the PD location. The localization algorithm consists of two approaches including wavelet denoising and AT sequence determination to eliminate PD coordinates that lie significantly far from the real location of the PD. The rest of the PD coordinates are then divided into two sections using a clustering method, and the center of the favorable cluster yields the definite PD coordinates. Precision of the PD localization algorithm is validated by three measurements inside a specially designed transformer tank.

A new power-based method to determine the first arrival information of an acoustic emission wave

Acoustic emission is a powerful experimental structural health monitoring technique for determining the location of cracks formed in a member. Pinpointing wave arrival time is essential for accurate source location. Conventional arrival detection technique’s accuracy deteriorates rapidly in low signal to noise ratio (5–40 dB) region, thus unsuitable for source location due to this inaccuracy. A new technique to pinpoint the arrival time based on the power of the wave is proposed. We have designed an adaptive filter based on the power characteristics of acoustic emission wave. After filtration of the acoustic emission wave, sliding window is employed to accurately identify the region of wave arrival based on the change in transmitted power. The results from various experimental and numerical arrival time detection experiments consistently show that the proposed methodology is stable and accurate for a wide range of signal to noise ratio values (5–100 dB). Particularly, in signal to noise ratio region (5–40 dB), the method is significantly more accurate as compared to the other methods described in the literature. The method was then employed to study the localized damage progression in a steel fiber–reinforced beam under four-point bending. The results suggest that calculated source location using the new method is consistent with that from visual inspection of the member at failure and more accurate than the localization results from existing method.

Automatic Arrival Time Detection for Earthquakes Based on Stacked Denoising Autoencoder

The accurate detection of P-wave arrival time is imperative for determining the hypocenter location of an earthquake. However, precise detection of onset time becomes more difficult when the signal-to-noise ratio (SNR) of the seismic data is low, such as during microearthquakes. In this letter, a stacked denoising autoencoder (SDAE) is proposed to smooth the background noise. The SDAE acts as a denoising filter for the seismic data. In the proposed algorithm, the SDAE is utilized to reduce background noise such that the onset time becomes more clear and sharp. Afterward, a hard decision with one threshold is used to detect the onset time of the event. The proposed algorithm is evaluated on both synthetic and field seismic data. As a result, the proposed algorithm outperforms the short-time average/long-time average and the Akaike information criterion algorithms. The proposed algorithm accurately picks the onset time of 94.1% for 407 field seismic waveforms with a standard deviation error of 0.10 s. In addition, the results indicate that the proposed algorithm can pick arrival times accurately for weak SNR seismic data with SNR higher than −14 dB.

Automatic arrival time detection for earthquakes based on Modified Laplacian of Gaussian filter

Precise identification of onset time for an earthquake is imperative in the right figuring of earthquake's location and different parameters that are utilized for building seismic catalogues. P-wave arrival detection of weak events or micro-earthquakes cannot be precisely determined due to background noise. In this paper, we propose a novel approach based on Modified Laplacian of Gaussian (MLoG) filter to detect the onset time even in the presence of very weak signal-to-noise ratios (SNRs). The proposed algorithm utilizes a denoising-filter algorithm to smooth the background noise. In the proposed algorithm, we employ the MLoG mask to filter the seismic data. Afterward, we apply a Dual-threshold comparator to detect the onset time of the event. The results show that the proposed algorithm can detect the onset time for micro-earthquakes accurately, with SNR of −12 dB. The proposed algorithm achieves an onset time picking accuracy of 93% with a standard deviation error of 0.10 s for 407 field seismic waveforms. Also, we compare the results with short and long time average algorithm (STA/LTA) and the Akaike Information Criterion (AIC), and the proposed algorithm outperforms them.

Traveling-Wave Detection Technique using Short-Time Matrix Pencil Method

This paper proposes a new technique for detection of traveling-wave arrival times in power systems. Precise estimation of wave arrival times improves most of the traveling-wave-based fault location methods. The proposed technique is based on the short-time matrix pencil method that decomposes the measurement samples in a sliding window into time-indexed complex frequencies. Traveling-wave arrival times are estimated based on the fact that the extracted damping factors (i.e., the real part of the complex frequencies) are zero when the arriving wave is located in the middle of the sliding window. The proposed technique is compared to the existing transient-detection techniques such as discrete wavelet transform with commonly used mother wavelets and Hilbert transform (HT), using MATLAB Wavelet Toolbox and MATLAB HT. The sensitivity analysis based on the simulation results demonstrates that the proposed technique is less affected by the measurement noise, sampling frequency, and fault parameters.

Tsunami arrival time detection system applicable to discontinuous time series data with outliers

Abstract. Timely detection of tsunamis with water level records is a critical but logistically challenging task because of outliers and gaps. Since tsunami detection algorithms require several hours of past data, outliers could cause false alarms, and gaps can stop the tsunami detection algorithm even after the recording is restarted. In order to avoid such false alarms and time delays, we propose the Tsunami Arrival time Detection System (TADS), which can be applied to discontinuous time series data with outliers. TADS consists of three algorithms, outlier removal, gap filling, and tsunami detection, which are designed to update whenever new data are acquired. After calibrating the thresholds and parameters for the Ulleung-do surge gauge located in the East Sea (Sea of Japan), Korea, the performance of TADS was discussed based on a 1-year dataset with historical tsunamis and synthetic tsunamis. The results show that the overall performance of TADS is effective in detecting a tsunami signal superimposed on both outliers and gaps.

WE-FG-206-05: New Arterial Spin Labeling Method for Simultaneous Estimation of Arterial Cerebral Blood Volume, Cerebral Blood Flow and Arterial Transit Time

Purpose: To demonstrate the feasibility of a novel Arterial Spin Labeling (ASL) method for simultaneously measuring cerebral blood flow (CBF), arterial transit time (ATT), and arterial cerebral blood volume (aCBV) without the use of a contrast agent. Methods: A series of multi-TI ASL images were acquired from one healthy subject on a 3T Siemens Skyra, with the following parameters: PCASL labeling with variable TI [300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500, 3000, 3500, 4000] ms, labeling bolus 1400 ms when TI allows, otherwise 100 ms less than TI, TR was minimized for each TI, two sinc shaped pre-saturation pulses were applied in the imaging plane immediately before 2D EPI acquisition. 64×64×24 voxels, 5 mm slice thickness, 1 mm gap, full brain coverage, 6 averages per TI, no crusher gradients, 11 ms TE, scan time of 4:56. The perfusion weighted time-series was created for each voxel and fit to a novel model. The model has two components: 1) the traditional model developed by Buxton et al., accounting for CBF and ATT, and 2) a box car function characterizing the width of the labeling bolus, with variable timing and height in proportion to the aCBV. All three parameters were fit using a nonlinear fitting routine that constrained all parameters to be positive. The main purpose of the high-temporal resolution TI sampling for the first second of data acquisition was to precisely estimate the blood volume component for better detection of arrival time and magnitude of signal. Results: Whole brain maps of CBF, ATT, and aCBV were produced, and all three parameters maps are consistent with similar maps described in the literature. Conclusion: Simultaneous mapping of CBF, ATT, and aCBV is feasible with a clinically tractable scan time (under 5 minutes).

An Automated System to Quantify Aircraft Encounters with Convectively Induced Turbulence over Europe and the Northeast Atlantic

AbstractIt is well known that encounters with moderate or severe turbulence can lead to passenger and crew injuries and incur high insurance costs for airlines. Atmospheric convection is thought to induce a significant proportion of turbulence experienced by commercial aircraft, but its relative importance over Europe and the northeastern Atlantic Ocean area has not yet been quantified in a systematic way. In this study, a new approach is developed to automatically detect turbulent events associated with convective sources. Observations of convection over Europe and the northeastern Atlantic were obtained from the Met Office Arrival Time Detection system (ATDnet) and from Meteosat Second Generation satellite imagery. The system is run for all in situ reports of turbulence received from a commercial airline for two 6-month periods (summer 2013 and summer 2014). It is found that, as a monthly average, 14% of all aircraft encounters with turbulence occur in the proximity of a convective storm. These findings are interpreted and discussed together with the limitations of the system and observations that were used in this study.

RETRACTED ARTICLE: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Determining P and S wave arrival times while minimizing noise is a major problem in seismic signal analysis. Precise determination of earthquake onset arrival timing, determination of earthquake magnitude, and calculation of other parameters that can be used to make more accurate seismic maps are possible with the detection of these waves. Experts try to determine these waves by manual analysis. But this process is time-consuming and painful. In this study, a new method that enables the determination of P and S wave arrival times in noisy recordings is recommended. This method is based on the hybrid usage of empirical mode decomposition and Teager---Kaiser energy operator algorithms. The results show that the proposed system gives effective results in the automatic detection of P and S wave arrival times. Promisingly, the recommended system might serve as a novel and powerful candidate for the effective detection of P and S wave arrival time.

Automatic Shear Wave Velocity Estimation in Bender Element Testing

Dynamic properties of soils, such as shear wave velocity and damping, are some of critical parameters in the design of civil infrastructures subjected to vibrations from earthquakes and machine foundations. Bender element (BE) testing is used in the laboratory to determine shear wave velocity; however, there is no standard procedure for the interpretation of the results. The main limitation in BE testing is the correct estimation of the shear wave arrival time leading to uncertainty in the values of shear wave velocity. In this paper, three different automatic methods for the shear wave arrival time detection were tested. From these methods, a new procedure was proposed for the automatic determination of the shear wave arrival time using the Akaike information criterion. The method is tested on a series of bender element measurements giving less than 5 % error when the results were compared with the standard method for a signal-to-noise ratio higher than four.

Asymmetric chirplet transform for sparse representation of seismic data

For a fast and accurate extraction of important information in seismic signals, a sparse representation based on the physical parameters of the given data is crucial. We have used the asymmetric Gaussian chirplet model (AGCM) and established a dictionary-free variant of the orthogonal matching pursuit, a greedy algorithm for sparse approximation. The atoms of AGCM, so-called chirplets, display asymmetric oscillation attenuation properties, which make the AGCM very suitable for sparse representation of absorption decay seismic signals. Unlike the Fourier transform, which assumes that the seismic signals consist of plane waves, the AGCM assumes that the seismic signal consists of nonstationary compressed plane waves, i.e., symmetric or asymmetric chirplets. Thus, AGCM is a general model for seismic wave simulation, and its model parameters include an envelope part and a phase part. We have mainly concentrated on the parameters of the envelope part, such as the envelope amplitude and arrival time. We provide numerical examples using the algorithm for seismic signal approximation and arrival-time detection. The results indicated promising performance but also may be improved considering the spatial correlations of seismic data.

An integrated circuit of a universal comparator

An ABMK-1.3 analog array chip for the high-precision detection of arrival times of signals in the wide frequency range in nuclear electronics is created. The chip contains a high-speed comparator with a built-in hysteresis and low-pass filter (LPF). The propagation delay of the comparator signal does not exceed 1.25 ns, and the hysteresis of the transfer characteristic can be regulated from 40 to 200 mV. The bandwidth of the LPF is below 130 kHz. The circuit design of separate stages of the chip and experimental characteristics are given.

Wavelet based approach to signal activity detection and phase picking: Application to acoustic emission

Locating the sources of elastic waves during rapid local stress relaxation in solids under load is a central element in acoustic emission non-destructive testing, seismology, etc. The location problem relies heavily on the accuracy of arrival time detection. To increase the reliability of real time signal detection and to ensure precise phase picking of transient waveforms of a low amplitude, we propose a novel Wavelet transform-based algorithm. Benefiting strongly from the neighboring concepts in the wavelet theory, the shortcomings of conventional amplitude threshold-based and Short Term Average/Long Term Average methods are addressed. The proposed method was validated in a variety of acoustic emission tests, demonstrating the excellent temporal localization of the picked phases even for the signals with very low signal-to-noise ratio.

GPR data noise attenuation on the curvelet transform

Signal extraction is critical in GRP data processing and noise attenuation. When the target depth is shallow, its reflection echo signal will overlap with the background noise, affecting the detection of arrival time and localization of the target. Thus, we propose a noise attenuation method based on the curvelet transform. First, the original signal is transformed into the curvelet domain, and then the curvelet coefficients of the background noise are extracted according to the distribution features that differ from the effective signal. In the curvelet domain, the coarse-scale curvelet atom is isotropic. Hence, a two-dimensional directional filter is designed to estimate the high-energy background noise in the coarsescale domain, and then, attenuate the background noise and highlight the effective signal. In this process, we also use a subscale threshold value of the curvelet domain to filter out random noise. Finally, we compare the proposed method with the average elimination and 2D continuous wavelet transform methods. The results show that the proposed method not only removes the background noise but also eliminates the coherent interference and random noise. The numerical simulation and the real data application suggest and verify the feasibility and effectiveness of the proposed method.

Wave separation: Application for arrival time detection in ultrasonic signals

A method to detect and accurately measure the arrival time of wave packets in ultrasonic signals using a nonlinear decomposition technique is presented. We specifically address the problem of extracting events that are not well separated in the time, space and frequency domains. Analysis of complex ultrasonic signals, even those composed of poorly separated echoes, provided exceptional estimates of the desired time of arrival, from the media under investigation.

Predictive Strategy for Transit Signal Priority at Fixed-Time Signalized Intersections: Case Study in Nanjing, China

It is essential to give priority to transit vehicles at signalized intersections in the megacities of China such as Nanjing. This study developed a transit signal priority (TSP) strategy for fixed-time signals with the use of techniques for advanced and continuous vehicle detection and prediction of arrival time. In the proposed model, transit vehicles were traced once they were positioned at one intersection and were advancing to the subject intersection. For instance, the location and the speed of a transit vehicle were measured every few seconds, and the prediction was made simultaneously by a model integrating historic and real-time data. With the predicted arrival time, the length of the transit signal phase would be adjusted to give the green light to the transit vehicle. The proposed strategy was evaluated with VISSIM simulation and compared with conventional TSP strategies (green extension and early green) in a typical roadway and traffic setting of Nanjing. Results indicated that the conventional TSP strategy could hardly result in significant improvement in transit delay and that the proposed strategy significantly reduced the transit travel time. Despite these findings, the adverse impacts on the delay of other road traffic by TSP could not be ignored. Results of the simulation model indicated that the proposed predictive TSP strategy was less disruptive to other traffic and that its performance in nonpeak periods was superior to that in peak periods. The results also suggest that a well-designed algorithm for the prediction of arrival time is the key to success in implementing predictive TSP control.