Intelligent Displacement Measuring System for Rock Stratum

2011 ◽  
Vol 103 ◽  
pp. 583-586
Author(s):  
Feng Ling Li ◽  
Jian Hua Rong ◽  
Yu Ping Zhang
Keyword(s):  
Civil Engineer ◽  
Real Time ◽  
Sampling Rate ◽  
Measuring System ◽  
Displacement Sensor ◽  
Rock Stratum ◽  
Baud Rate ◽  
Real Time Clock ◽  
High Sampling Rate ◽  
Displacement System

Measuring rock stratum displacement in dam grouting process is very important. A new displacement system is designed, comprising a programmable microcontroller Atmega16, a new grating capacitive displacement sensor(GCDS), DS1302 real time clock chip and announciator etc. The system has high sampling rate of 9600 baud rate and can trap the displacement equal to 0.001 millimeter in one second. Equipped with mechanical conveyance system, the system can be applied to the civil engineer. The experiment results show the instrument can measure accurately the displacement value and alarm geologic disaster in time, which can conduct continuous and accurate monitoring and provide operation decisions for dam engineers.

A Multi-Rate Implementation of Auditory Filter Bank

2013 ◽  
Vol 373-375 ◽  
pp. 579-582
Author(s):  
Jin Lun Chen
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Filter Bank ◽  
Sampling Rate ◽  
Audio Signal ◽  
Audio Signal Processing ◽  
Auditory Model ◽  
Auditory Filter ◽  
Centre Frequency ◽  
High Sampling Rate

The auditory filter-bank is the key component of auditory model, and its implementation involves a lot of computations. The time spent by an auditory filter-bank to finish its work has a significant effect on the real-time implementation of auditory model-based audio signal processing systems. In this paper, a multi-rate implementation of auditory filter bank is presented. Through using low sampling rate for the filters with low centre frequency, and using high sampling rate for the filters with high centre frequency, we can greatly reduce the computation requirement.

Development of a System for Collecting Loop-Detector Event Data for Individual Vehicles

2003 ◽  
Vol 1855 (1) ◽  
pp. 168-175 ◽  
Author(s):  
Xiaoping Zhang ◽  
Yinhai Wang ◽  
Nancy L. Nihan ◽  
Mark E. Hallenbeck
Keyword(s):  
Real Time ◽  
Sampling Rate ◽  
Field Application ◽  
Normal Operation ◽  
Vehicle Speed ◽  
Event Data ◽  
Loop Detectors ◽  
Loop Detector ◽  
High Sampling Rate ◽  
Loop Detection

Typical freeway inductive loop detection systems, under normal operation, aggregate individual loop-detector actuations sampled at 60 Hz into 20-s or 30-s averages of velocity, flow, and lane-occupancy measurements. While such aggregations are appropriate for serving as inputs to control system algorithms, and they save disk space for archiving loop data, a large amount of useful data regarding individual vehicles is lost. For single-loop detectors, the lost information includes individual vehicle arrival, departure, and presence times. For speed traps, the lost information also includes the calculated individual vehicle speed and length. Yet this information about individual vehicles is desirable to transportation researchers and planners. The unavailability of this information makes in-depth investigation of detector errors difficult or even impossible. A system for collecting detector event data is proposed. This system can sample loop actuations with sampling rates of 60 Hz or higher and then save, process, and present the collected event data in real time without interfering with the detector controller’s normal operation. A stand-alone Windows program was developed for performing real-time high-frequency loop event data collection. A system reliability test and field application indicate that the system can collect realtime detector event data at a high sampling rate (60 Hz or higher). Additionally, this system makes real-time loop data quality evaluation, loop malfunction identification, and loop error correction feasible.

Methodology for Full Waveform Near Real-Time Automatic Detection and Localization of Microseismic Events Using High (8 kHz) Sampling Rate Records in Mines: Application to the Garpenberg Mine (Sweden)

2019 ◽  
Vol 91 (1) ◽  
pp. 399-414 ◽  
Author(s):  
Kadek Hendrawan Palgunadi ◽  
Natalia Poiata ◽  
Jannes Kinscher ◽  
Pascal Bernard ◽  
Francesca De Santis ◽  
...  
Keyword(s):  
Real Time ◽  
Sampling Rate ◽  
Seismic Network ◽  
Event Extraction ◽  
Noise Sources ◽  
Full Waveform ◽  
High Sampling ◽  
High Sampling Rate ◽  
Wide Range ◽  
Microseismic Events

Abstract Recent studies have demonstrated the success of automatic full-waveform detection and location methods in analyzing and monitoring natural and induced seismicity. These approaches have been shown to provide a significant improvement in events detectability, increasing the significance of statistical analysis that permits to identify small changes of seismicity rates in space and time. Although currently nontrivial and by far nonstandard, application of such methods to seismic monitoring of active mines could significantly improve forecasting of potential destructive rockburst events. The main challenges of such applications are related to the presence of a wide range of seismic noise sources that have to do with mining activity and a high sampling rate of recorded data (several kHz), posing problems for real-time data transfer and processing. In this study, we propose an adapted full-waveform-based automatic method for the detection and location of microseismic events that makes use of continuous seismic records from an in-mine seismic network and can be adjusted to a near-real-time monitoring scheme. The method consists of two steps: (1) event extraction and amplitude ratio-based preliminary location and (2) event relocation using a coherency-based backprojection approach. The event extraction, based on multiband signal characterization implemented in the first step, allows us to overcome the challenge of high sampling rate data (8 kHz), reducing the overall volume of transferred data and providing an energy-based signal classification scheme. This allows us to remove a significant number of machinery noise sources. The technique is developed and tested on the case study of the Garpenberg mine (Sweden) monitored by a local seismic network that is maintained by Ineris. We demonstrate the improvement in event detection capacity by a factor of 50, compared with the standard triggered-based monitoring schemes. This increased number of detected microseismic events permits us to investigate the migration pattern of induced microseismicity that is generated in response to production blast.

scholarly journals A machine learning approach for accurate and real-time DNA sequence identification

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yiren Wang ◽  
Mashari Alangari ◽  
Joshua Hihath ◽  
Arindam K. Das ◽  
M. P. Anantram
Keyword(s):  
Real Time ◽  
Dna Sequence ◽  
Single Molecule ◽  
Identification Accuracy ◽  
Measuring System ◽  
Identification System ◽  
Genetic Sequencing ◽  
Sequence Identification ◽  
Tree Classifier ◽  
Single Mismatch

Abstract Background The all-electronic Single Molecule Break Junction (SMBJ) method is an emerging alternative to traditional polymerase chain reaction (PCR) techniques for genetic sequencing and identification. Existing work indicates that the current spectra recorded from SMBJ experimentations contain unique signatures to identify known sequences from a dataset. However, the spectra are typically extremely noisy due to the stochastic and complex interactions between the substrate, sample, environment, and the measuring system, necessitating hundreds or thousands of experimentations to obtain reliable and accurate results. Results This article presents a DNA sequence identification system based on the current spectra of ten short strand sequences, including a pair that differs by a single mismatch. By employing a gradient boosted tree classifier model trained on conductance histograms, we demonstrate that extremely high accuracy, ranging from approximately 96 % for molecules differing by a single mismatch to 99.5 % otherwise, is possible. Further, such accuracy metrics are achievable in near real-time with just twenty or thirty SMBJ measurements instead of hundreds or thousands. We also demonstrate that a tandem classifier architecture, where the first stage is a multiclass classifier and the second stage is a binary classifier, can be employed to boost the single mismatched pair’s identification accuracy to 99.5 %. Conclusions A monolithic classifier, or more generally, a multistage classifier with model specific parameters that depend on experimental current spectra can be used to successfully identify DNA strands.

scholarly journals An Improved Multipath Mitigation Method and Its Application in Real-Time Bridge Deformation Monitoring

2021 ◽  
Vol 13 (12) ◽  
pp. 2259
Author(s):  
Ruicheng Zhang ◽  
Chengfa Gao ◽  
Qing Zhao ◽  
Zihan Peng ◽  
Rui Shang
Keyword(s):  
Real Time ◽  
Sampling Rate ◽  
Threshold Value ◽  
Deformation Monitoring ◽  
Success Rates ◽  
Multipath Mitigation ◽  
Mode Decomposition ◽  
Observation Noise ◽  
Original Observation ◽  
Better Than

A multipath is a major error source in bridge deformation monitoring and the key to achieving millimeter-level monitoring. Although the traditional MHM (multipath hemispherical map) algorithm can be applied to multipath mitigation in real-time scenarios, accuracy needs to be further improved due to the influence of observation noise and the multipath differences between different satellites. Aiming at the insufficiency of MHM in dealing with the adverse impact of observation noise, we proposed the MHM_V model, based on Variational Mode Decomposition (VMD) and the MHM algorithm. Utilizing the VMD algorithm to extract the multipath from single-difference (SD) residuals, and according to the principle of the closest elevation and azimuth, the original observation of carrier phase in the few days following the implementation are corrected to mitigate the influence of the multipath. The MHM_V model proposed in this paper is verified and compared with the traditional MHM algorithm by using the observed data of the Forth Road Bridge with a seven day and 10 s sampling rate. The results show that the correlation coefficient of the multipath on two adjacent days was increased by about 10% after residual denoising with the VMD algorithm; the standard deviations of residual error in the L1/L2 frequencies were improved by 37.8% and 40.7%, respectively, which were better than the scores of 26.1% and 31.0% for the MHM algorithm. Taking a ratio equal to three as the threshold value, the fixed success rates of ambiguity were 88.0% without multipath mitigation and 99.4% after mitigating the multipath with MHM_V. The MHM_V algorithm can effectively improve the success rate, reliability, and convergence rate of ambiguity resolution in a bridge multipath environment and perform better than the MHM algorithm.

scholarly journals RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3955
Author(s):  
Jung-Cheng Yang ◽  
Chun-Jung Lin ◽  
Bing-Yuan You ◽  
Yin-Long Yan ◽  
Teng-Hu Cheng
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
High Frequency ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Sampling Rate ◽  
Outdoor Environment ◽  
Position Accuracy ◽  
Graph Optimization ◽  
Pose Graph Optimization

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

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