Real-time arrival picking of rock microfracture signals based on convolutional-recurrent neural network and its engineering application

Accurately picking P-and S-wave arrivals of microseismic(MS)signals in real-time directly influences the early warning of rock mass failure.A common contradiction between accuracy and computation exists in the current arrival picking methods.Thus,a real-time arrival picking method of MS signals is constructed based on a convolutional-recurrent neural network(CRNN).This method fully utilizes the advantages of convolutional layers and gated recurrent units(GRU)in extracting short-and long-term features,in order to create a precise and lightweight arrival picking structure.Then,the synthetic signals with field noises are used to evaluate the hyperparameters of the CRNN model and obtain an optimal CRNN model.The actual operation on various devices indicates that compared with the U-Net method,the CRNN method achieves faster arrival picking with less performance consumption.An application of large underground caverns in the Yebatan hydropower station(YBT)project shows that compared with the short-term average/long-term average(STA/LTA),Akaike information criterion(AIC)and U-Net methods,the CRNN method has the highest accuracy within four sampling points,which is 87.44%for P-wave and 91.29%for S-wave,respectively.The sum of mean absolute errors(MAESUM)of the CRNN method is 4.22 sampling points,which is lower than that of the other methods.Among the four methods,the MS sources location calculated based on the CRNN method shows the best consistency with the actual failure,which occurs at the junction of the shaft and the second gallery.Thus,the proposed method can pick up P-and S-arrival accurately and rapidly,providing a reference for rock failure analysis and evaluation in engineering applications.

Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

Due to fast-growing urbanization,the traffic management system becomes a crucial problem owing to the rapid growth in the number of vehicles The research proposes an Intelligent public transportation system where informa-tion regarding all the buses connecting in a city will be gathered,processed and accurate bus arrival time prediction will be presented to the user.Various linear and time-varying parameters such as distance,waiting time at stops,red signal duration at a traffic signal,traffic density,turning density,rush hours,weather conditions,number of passengers on the bus,type of day,road type,average vehi-cle speed limit,current vehicle speed affecting traffic are used for the analysis.The proposed model exploits the feasibility and applicability of ELM in the travel time forecasting area.Multiple ELMs(MELM)for explicitly training dynamic,road and trajectory information are used in the proposed approach.A large-scale dataset(historical data)obtained from Kerala State Road Transport Corporation is used for training.Simulations are carried out by using MATLAB R2021a.The experiments revealed that the efficiency of MELM is independent of the time of day and day of the week.It can manage huge volumes of data with less human intervention at greater learning speeds.It is found MELM yields prediction with accuracy in the range of 96.7%to 99.08%.The MAE value is between 0.28 to 1.74 minutes with the proposed approach.The study revealed that there could be regularity in bus usage and daily bus rides are predictable with a better degree of accuracy.The research has proved that MELM is superior for arrival time pre-dictions in terms of accuracy and error,compared with other approaches.

P-wave velocity structure in the crust and the uppermost mantle of Chao Lake region of the Tan-Lu Fault inferred from teleseismic arrival time tomography

Chao Lake is a Geoheritage site on the active Tan-Lu Fault between the Yangtze craton,the North China craton,and the Dabie orogenic belt in the southeast.This segment of the fault is not well constrained at depth partly due to the overprinting of the fault zone by intrusive materials and its relatively low seismic activity and sparse seismic station coverage.This study took advantage of a dense seismic array deployed around Chao Lake to delineate the P-wave velocity variations in the crust and uppermost mantle using teleseismic earthquake arrival time tomography.The station-pair double-difference with waveform crosscorrelation technique was employed.We used a multiscale resolution 3-D initial model derived from the combination of highresolution 3-D v S models within the region of interest to account for the lateral heterogeneity in the upper crust.The results revealed that the velocity of the upper crust is segmented with structures trending in the direction of the strike of the fault.Sedimentary basins are delineated on both sides of the fault with slow velocities,while the fault zone is characterized by high velocity in the crust and uppermost mantle.The high-velocity structure in the fault zone shows characteristics of magma intrusion that may be connected to the Mesozoic magmatism in and around the Middle and Lower Yangtze River Metallogenic Belt(MLYMB),implying that the Tan-Lu fault might have formed a channel for magma intrusion.Magmatic material in Chao Lake is likely connected to the partial melting,assimilation,storage,and homogenization of the uppermost mantle and the lower crustal rocks.The intrusions,however,seem to have suffered severe regional extension along the Tan-Lu fault driven by the eastward Paleo-Pacific plate subduction,thereby losing its deep trail due to extensional erosion.

Shifted first arrival point travel time NMO inversion

Serious stretch appears in shallow long offsset signals after NMO correction. In this article we study the generation mechanism of NMO stretch, demonstrate that the conventional travel time equation cannot accurately describe the travel time of the samples within the same reflection wavelet. As a result, conventional NMO inversion based on the travel time of the wavelet＇s central point occurs with errors. In this article, a travel time equation for the samples within the same wavelet is reconstructed through our theoretical derivation （the shifted first arrival point travel time equation）, a new NMO inversion method based on the wavelet＇s first arrival point is proposed. While dealing with synthetic data, the semblance coefficient algorithm equation is modified so that wavelet first arrival points can be extracted. After that, NMO inversion based on the new velocity analysis is adopted on shot offset records. The precision of the results is significantly improved compared with the traditional method. Finally, the block move NMO correction based on the first arrival points travel times is adopted on long offset records and non-stretched results are achieved, which verify the proposed new equation.

Deep learning for P-wave arrival picking in earthquake early warning

Fast and accurate P-wave arrival picking significantly affects the performance of earthquake early warning(EEW)systems.Automated P-wave picking algorithms used in EEW have encountered problems of falsely picking up noise,missing P-waves and inaccurate P-wave arrival estimation.To address these issues,an automatic algorithm based on the convolution neural network(DPick)was developed,and trained with a moderate number of data sets of 17,717 accelerograms.Compared to the widely used approach of the short-term average/long-term average of signal characteristic function(STA/LTA),DPick is 1.6 times less likely to detect noise as a P-wave,and 76 times less likely to miss P-waves.In terms of estimating P-wave arrival time,when the detection task is completed within 1 s,DPick′s detection occurrence is 7.4 times that of STA/LTA in the 0.05 s error band,and 1.6 times when the error band is 0.10 s.This verified that the proposed method has the potential for wide applications in EEW.

Dual-cavity beam arrival time monitor design for the Shanghai soft X-ray FEL facility

Free electron lasers provide high-power and ultrashort pulses with extreme brightness. In order to improve a facility's capabilities and explore the possibility of performing high-resolution time-resolved experiments, a beam arrival time resolution under 100 fs is required. In this article, a novel beam arrival time monitor(BAM)equipped with two cavities has been designed and a beam flight time measurement scheme based on the BAM prototype has been proposed to estimate phase jitter in the signal measurement system. The two BAM cavities work at different frequencies and the frequency difference is designed to be 35 MHz. Therefore, a self-mixing intermediate frequency signal can be generated using the two cavities. The measured beam flight time shows a temporal deviation of 37 fs(rms).

Bus Arrival Time Prediction Based on Mixed Model

How to predict the bus arrival time accurately is a crucial problem to be solved in Internet of Vehicle. Existed methods cannot solve the problem effectively for ignoring the traffic delay jitter. In this paper,a three-stage mixed model is proposed for bus arrival time prediction. The first stage is pattern training. In this stage,the traffic delay jitter patterns(TDJP)are mined by K nearest neighbor and K-means in the historical traffic time data. The second stage is the single-step prediction,which is based on real-time adjusted Kalman filter with a modification of historical TDJP. In the third stage,as the influence of historical law is increasing in long distance prediction,we combine the single-step prediction dynamically with Markov historical transfer model to conduct the multi-step prediction. The experimental results show that the proposed single-step prediction model performs better in accuracy and efficiency than short-term traffic flow prediction and dynamic Kalman filter. The multi-step prediction provides a higher level veracity and reliability in travel time forecasting than short-term traffic flow and historical traffic pattern prediction models.

Automatic pickup of arrival time of channel wave based on multi-channel constraints

Accurately detecting the arrival time of a channel wave in a coal seam is very important for in-seam seismic data processing. The arrival time greatly affects the accuracy of the channel wave inversion and the computed tomography （CT） result. However, because the signal-to-noise ratio of in-seam seismic data is reduced by the long wavelength and strong frequency dispersion, accurately timing the arrival of channel waves is extremely difficult. For this purpose, we propose a method that automatically picks up the arrival time of channel waves based on multi-channel constraints. We first estimate the Jaccard similarity coefficient of two ray paths, then apply it as a weight coefficient for stacking the multi- channel dispersion spectra. The reasonableness and effectiveness of the proposed method is verified in an actual data application. Most importantly, the method increases the degree of automation and the pickup precision of the channel-wave arrival time.

Collaborative prediction for bus arrival time based on CPS

To improve the accuracy of real-time public transport information release system, a collaborative prediction model was proposed based on cyber-physical systems architecture. In the model, the total bus travel time was divided into three parts: running time, dwell time and intersection delay time, and the data were divided into three categories of historical data, static data and real-time data. The bus arrival time was obtained by fusion computing the real-time data in perception layer together with historical data and static data in collaborative layer. The validity of the collaborative model was verified by the data of a typical urban bus line in Shanghai, and 1538 sets of data were collected and analyzed from three different perspectives. By comparing the experimental results with the actual results, it is shown that the experimental results are with higher prediction accuracy, and the collaborative prediction model adopted is able to meet the demand for bus arrival prediction.

Passive Localization Using Time Difference of Arrival and Frequency Difference of ArrivalWC

In order to improve the accuracy and engineering feasibility of four-Satellite localization system, the frequency difference measurement is introduced to the four-Satellite TDOA (Time Difference of Arrival) localization algorithm. The TDOA/FDOA (Frequency Difference of Arrival) localization algorithm is used to optimize the GDOP (geometric dilution of precision) of four-Satellite localization. The simulation results show that the absolute position measurement accuracy has little influence on TDOA/FDOA localization accuracy as compared with TDOA localization. Under the same conditions, TDOA/FDOA localization has better accuracy and its GDOP shows more uniform distribution in diamond configuration case. The localization accuracy of four-Satellite TDOA/FDOA is better than the localization accuracy of four-Satellite TDOA.

Route Temporal⁃Spatial Information Based Residual Neural Networks for Bus Arrival Time Prediction

Bus arrival time prediction contributes to the quality improvement of public transport services.Passengers can arrange departure time effectively if they know the accurate bus arrival time in advance.We proposed a machine⁃learning approach,RTSI⁃ResNet,to forecast the bus arrival time at target stations.The residual neural network framework was employed to model the bus route temporal⁃spatial information.It was found that the bus travel time on a segment between two stations not only had correlation with the preceding buses,but also had common change trends with nearby downstream/upstream segments.Two features about bus travel time and headway were extracted from bus route including target section in both forward and reverse directions to constitute the route temporal⁃spatial information,which reflects the road traffic conditions comprehensively.Experiments on the bus trajectory data of route No.10 in Shenzhen public transport system demonstrated that the proposed RTSI⁃ResNet outperformed other well⁃known methods(e.g.,RNN/LSTM,SVM).Specifically,the advantage was more significant when the distance between bus and the target station was farther.

TECHNOLOGY OF QUANTITATIVE ANALYSIS ON VIBRATORY STRESS RELIEF BASED ON ULTRASONIC TIME-OF-ARRIVAL METHOD

The effect of vibratory stress relief （VSR） is usually evaluated with the indirect method of observing the change of amplitude frequency response characteristics of structures. A new kind of evaluating method of VSR based on the ultrasonic time-of-arrival method （UTM）, which can obtain the residual stress directly through measuring the propagation time of ultrasonic wave in the material, is presented. At first, the principle of the measuring method of residual stress based on UTM is analyzed. Then the measuring system of the method is described, which is in virtue of ultrasonic flaw detector and high-sampling-rate digital oscillograph. And a set of calibration system that contains a piece of standard specimen is also introduced. Experimental results prove the relation between the residual stress and the propagation time of ultrasonic in workpieces. Finally, the measuring and calibration systems are applied in evaluating the effect of VSR. The final test results show that the method is effective.

Arrival time measurements of first arrival phases P and PKIKP using the method of fixed scale wavelet transformation ratio

The arrival times of first teleseismic phases are difficult to be measured precisely because of slowly and gradually changed onsets and weak amplitudes. The arrival times measured manually are usually behind the real ones. In this paper, using the ratio method of fixed scale wavelet transformations improved by us, the arrival times for the first arrival phases (such as P and PKIKP) at the teleseismic and far-teleseismic distances were measured. The results are reasonable and reliable based on the analysis and discussion of the reliabilities and errors.

Average Arrival Time:an Alternative Approach for Studying Fluorescent Behavior of Single Quantum Dotst

Due to photoluminescence intermittency of single tional exponential fluorescence lifetime analysis is colloidal quantum dots （QDs）, the tradinot perfect to characterize QDs＇ fluores- cent emission behavior. In this work we used the time-tagged time-resolved （TTTR） mode to record the fluorescent photons from single QDs. We showed that this method is compatible with the traditional lifetime analysis. In addition, by constructing the trajectory over time and the distribution of average arrival time （AAT） of the fluorescent photons, inore details about the emission behavior of QDs were revealed.

Development of readout electronics for bunch arrival-time monitor system at SXFEL

A bunch arrival-time monitor(BAM) system,based on electro-optical intensity modulation scheme, is under study at Shanghai Soft X-ray Free Electron Laser.The aim of the study is to achieve high-precision time measurement for minimizing bunch fluctuations. A readout electronics is developed to fulfill the requirements of the BAM system. The readout electronics is mainly composed of a signal conditioning circuit, field-programmable gate array(FPGA), mezzanine card(FMC150), and powerful FPGA carrier board. The signal conditioning circuit converts the laser pulses into electrical pulse signals using a photodiode. Thereafter, it performs splitting and low-noise amplification to achieve the best voltage sampling performance of the dual-channel analog-to-digital converter(ADC) in FMC150. The FMC150 ADC daughter card includes a 14-bit 250 Msps dual-channel high-speed ADC,a clock configuration, and a management module. The powerful FPGA carrier board is a commercial high-performance Xilinx Kintex-7 FPGA evaluation board. To achieve clock and data alignment for ADC data capture at a high sampling rate, we used ISERDES, IDELAY, and dedicated carry-in resources in the Kintex-7 FPGA. This paper presents a detailed development of the readout electronics in the BAM system and its performance.

Backward Dijkstra Algorithms for Finding the Departure Time Based on the Specified Arrival Time for Real-Life Time-Dependent Networks

A practical transportation problem for finding the “departure” time at “all source nodes” in order to arrive at “some destination nodes” at specified time for both FIFO (i.e., First In First Out) and Non-FIFO “Dynamic ” Networks is considered in this study. Although shortest path (SP) for dynamic networks have been studied/documented by various researchers, contributions from this present work consists of a sparse matrix storage scheme for efficiently storing large scale sparse network’s connectivity, a concept of Time Delay Factor (TDF) combining with a “general piece- wise linear function” to describe the link cost as a function of time for Non-FIFO links’ costs, and Backward Dijkstra SP Algorithm with simple heuristic rules for rejecting unwanted solutions during the backward search algorithm. Both small-scale (academic) networks as well as large- scale (real-life) networks are investigated in this work to explain and validate the proposed dynamic algorithms. Numerical results obtained from this research work have indicated that the newly proposed dynamic algorithm is reliable, and efficient. Based on the numerical results, the calculated departure time at the source node(s), for a given/specified arrival time at the destination node(s), can be non-unique, for some Non-FIFO networks’ connectivity.

On Time-of-Arrival Statistic of Gaussian Channel Model

A model of an angle-spread source, termed the “Gaussian Channel Model” is considered. The cumulative distribution function of the Time-of-Arrival of the multipath components is derived for an arbitrary angle spread. The simple approximate expressions for the Time-of-Arrival cumulative distribution function and probability density function are proposed. Numerical results obtained with the help of the derived expressions show the good coincidence with the experimental data and other known results.

RHMX:Bus Arrival Time Prediction via Mixed Model

With the widespread use of information technologies such as IoT and big data in the transportation business,traditional passenger transportation has begun to transition and upgrade into intelligent transportation,providing passengers with a better riding experience.Giving precise bus arrival times is a critical link in achieving urban intelligent transportation.As a result,a mixed model-based bus arrival time prediction model(RHMX)was suggested in this work,which could dynamically forecast bus arrival time based on the input data.First,two sub-models were created:bus station stopping time prediction and interstation running time prediction.The former predicted the stopping time of a running bus at each downstream station in an iterative manner,while the latter projected its running time on each downstream road segment(stations as the break points).Using the two models,a group of time series data on interstation running time and bus station stopping time may be predicted.Following that,the time series data from the two sub-models was fused using long short-term memory(LSTM)to generate an approximate bus arrival time.Finally,using Kalman filtering,the LSTM prediction results were dynamically updated in order to eliminate the influence of aberrant data on the anticipated value and obtain a more precise bus arrival time.The experimental findings showed that the suggested model's accuracy and stability were both improved by 35%and 17%,respectively,over AutoNavi and Baidu.

Analysis of electro-optical intensity modulator for bunch arrival-time monitor at SXFEL

A bunch arrival-time monitor(BAM) based on an electro-optical intensity modulation scheme is currently under development at Shanghai Soft X-ray Free-Electron Laser to meet the high-resolution requirements for bunch stability. The BAM uses a radio frequency signal generated by a pickup cavity to modulate the reference laser pulses in an electro-optical intensity modulator(EOM), and the bunch arrival-time information is derived from the amplitude change of the laser pulse after laser pulse modulation.EOM is a key optical component in the BAM system.Through the basic principle analysis of BAM, many parameters of the EOM are observed to affect the measurement resolution of the BAM system. Therefore, a systematic analysis of the EOM is crucial. In this paper, we present two schemes to compare and analyze an EOM and provide a reference for selecting a new version of the EOM.

Bilevel Programming Model for Joint Scheduling of Arrival and Departure Flights Based on Traffic Scenario

In order to meet the needs of collaborative decision making,considering the different demands of air traffic control units,airlines,airports and passengers in various traffic scenarios,the joint scheduling problem of arrival and departure flights is studied systematically.According to the matching degree of capacity and flow,it is determined that the traffic state of arrival/departure operation in a certain period is peak or off-peak.The demands of all parties in each traffic state are analyzed,and the mathematical models of arrival/departure flight scheduling in each traffic state are established.Aiming at the four kinds of joint operation traffic scenarios of arrival and departure,the corresponding bi-level programming models for joint scheduling of arrival and departure flights are established,respectively,and the elitism genetic algorithm is designed to solve the models.The results show that:Compared with the first-come-firstserved method,in the scenarios of arrival peak&departure off-peak and arrival peak&departure peak,the departure flight equilibrium satisfaction is improved,and the runway occupation time of departure flight flow is reduced by 38.8%.In the scenarios of arrival off-peak&departure off-peak and departure peak&arrival off-peak,the arrival flight equilibrium delay time is significantly reduced,the departure flight equilibrium satisfaction is improved by 77.6%,and the runway occupation time of departure flight flow is reduced by 46.6%.Compared with other four kinds of strategies,the optimal scheduling method can better balance fairness and efficiency,so the scheduling results are more reasonable.