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Journal articles on the topic 'Train signals'
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1
Buhaiov, M. V., and S. P. Samoilyk. "RANGE AND RADIAL VELOCITY MEASUREMENT AMBIGUITY ELIMINATION WITH TRAINS OF MULTICOMPONENT SIGNALS." Проблеми створення, випробування, застосування та експлуатації складних інформаційних систем, no. 18 (December 30, 2020): 91–100. http://dx.doi.org/10.46972/2076-1546.2020.18.09.
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When designing pulse-Doppler radar, one of the key points is the choice of the pulse repetition period, which determines the boundaries of unambiguous measurement of range and radial velocity and creates contradictions in the measurement of these values. This contradiction is especially acute in the analysis of signals reflected from the propellers and turbines of aircraft. The main approaches to solving the problem of expanding the boundaries of unambiguous measurement of range and radial velocity is the use of variable pulse repetition period and the creation of signal ensembles to separate them by shape. Generation of an ensemble of sounding signals for a pulsed radar must be carried out taking into account both cross-correlation and auto-correlation properties. An approach to the generation of multicomponent signal trains with the possibility of pulse separation inside the train is proposed. Each of the pulses in the train is formed by adding a number of chirp signals, which differ in the values of amplitude and frequency deviation. As the frequency deviation increases, the amplitude of the component decreases. Reducing the cross-correlation coefficient of multicomponent signals from the formed ensemble can be achieved by increasing the number of components of each signal. The size of the signal ensemble, which can be formed on the basis of multicomponent chirp signals, depends on the requirements for the cross-correlation coefficient and auto-correlation function of the signals. It is shown that in order to expand the limits of coordinate measurement at a fixed wavelength, it is necessary to increase the number of pulses in the train. The results of the research demonstrate the potential possibility of using the proposed multicomponent chirp signal to form train of pulses with its subsequent separation.
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Lavoué, François, Olivier Coutant, Pierre Boué, Laura Pinzon-Rincon, Florent Brenguier, Romain Brossier, Philippe Dales, Meysam Rezaeifar, and Christopher J. Bean. "Understanding Seismic Waves Generated by Train Traffic via Modeling: Implications for Seismic Imaging and Monitoring." Seismological Research Letters 92, no. 1 (October 21, 2020): 287–300. http://dx.doi.org/10.1785/0220200133.
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Abstract Trains are now recognized as powerful sources for seismic interferometry based on noise correlation, but the optimal use of these signals still requires a better understanding of their source mechanisms. Here, we present a simple approach for modeling train-generated signals inspired by early work in the engineering community, assuming that seismic waves are emitted by sleepers regularly spaced along the railway and excited by passing train wheels. Our modeling reproduces well seismological observations of tremor-like emergent signals and of their harmonic spectra. We illustrate how these spectra are modulated by wheel spacing, and how their high-frequency content is controlled by the distribution of axle loads over the rail, which mainly depends on ground stiffness beneath the railway. This is summarized as a simple rule of thumb that predicts the frequency bands in which most of train-radiated energy is expected, as a function of train speed and of axle distance within bogies. Furthermore, we identify two end-member mechanisms—single stationary source versus single moving load—that explain two types of documented observations, characterized by different spectral signatures related to train speed and either wagon length or sleeper spacing. In view of using train-generated signals for seismic applications, an important conclusion is that the frequency content of the signals is dominated by high-frequency harmonics and not by fundamental modes of vibrations. Consequently, most train traffic worldwide is expected to generate signals with a significant high-frequency content, in particular in the case of trains traveling at variable speeds that produce truly broadband signals. Proposing a framework for predicting train-generated seismic wavefields over meters to kilometers distance from railways, this work paves the way for high-resolution passive seismic imaging and monitoring at different scales with applications to near-surface surveys (aquifers, civil engineering), natural resources exploration, and natural hazard studies (landslides, earthquakes, and volcanoes).
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Maler, Len. "Train signals for electric fish." Nature 384, no. 6609 (December 1996): 517–18. http://dx.doi.org/10.1038/384517a0.
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Huang, Qiongdan, Yong Li, Yaoping Zeng, and Yinjuan Fu. "Design and Characteristic Analysis of Multicarrier Chaotic Phase Coded Radar Pulse Train Signal." International Journal of Antennas and Propagation 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/724294.
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By introducing phase code into multicarrier orthogonal frequency division multiplex signal, the multicarrier phase coded (MCPC) radar signal possesses a good spectrum utilization rate and can achieve a good combination of narrowband and wideband processing. Radar pulse train signal not only reserves the high range resolution of monopulse signal, but also has the same velocity resolution performance as continuous wave signal does. In this study, we use the chaotic biphase code generated by Chebyshev mapping to conduct a phase modulation on MCPC pulse train so as to design two different types of multicarrier chaotic phase coded pulse train signal. The ambiguity functions of the two pulse train signals are compared with that of P4 code MCPC pulse train. In addition, we analyze the influences of subcarrier number, phase-modulated bit number, and period number on the pulse train’s autocorrelation performance. The low probability of intercept (LPI) performance of the two signals is also discussed. Simulation results show that the designed pulse train signals have a thumbtack ambiguity function, a periodic autocorrelation side lobe lower than P4 code MCPC pulse train, and excellent LPI performance, as well as the feature of waveform diversity.
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Li, Wang, Shang Guan Wei, Bai Gen Cai, and Jian Wang. "Signal Simulation of the Train Integrated Positioning System." Advanced Materials Research 740 (August 2013): 86–91. http://dx.doi.org/10.4028/www.scientific.net/amr.740.86.
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In order to simulate the multi-sensor signals of the train integrated positioning system, and make signal fusion and verification, the train integrated positioning signal simulation system is introduced in this paper, which is based on GNSS and multi-sensor integrated positioning thoughts, the mathematical models and failure characteristics of the sensors are analyzed, the pulse speed sensor and gyro error models are established, the data simulation approach and data acquisition modules are adopted to simulate pulse speed sensor and gyroscope signals, the GNSS simulator is used to simulate GNSS signal under different scenes and conditions, signal fusion algorithms are used to conduct fusion and filtering of the integrated positioning signal. The results show the availability of the simulated signals. The train integrated positioning signal simulation system provides test environments with the parameters controllable, and lays a good foundation for the researches of the Beidou based integrated positioning system and the multi-mode satellite positioning technology.
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Sushkova, Olga Sergeevna, Alexei Alexandrovich Morozov, Alexandra Vasilievna Gabova, Alexei Vyacheslavovich Karabanov, and Sergey Nikolaevich Illarioshkin. "A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease Investigation." Sensors 21, no. 14 (July 9, 2021): 4700. http://dx.doi.org/10.3390/s21144700.
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A statistical method for exploratory data analysis based on 2D and 3D area under curve (AUC) diagrams was developed. The method was designed to analyze electroencephalogram (EEG), electromyogram (EMG), and tremorogram data collected from patients with Parkinson’s disease. The idea of the method of wave train electrical activity analysis is that we consider the biomedical signal as a combination of the wave trains. The wave train is the increase in the power spectral density of the signal localized in time, frequency, and space. We detect the wave trains as the local maxima in the wavelet spectrograms. We do not consider wave trains as a special kind of signal. The wave train analysis method is different from standard signal analysis methods such as Fourier analysis and wavelet analysis in the following way. Existing methods for analyzing EEG, EMG, and tremor signals, such as wavelet analysis, focus on local time–frequency changes in the signal and therefore do not reveal the generalized properties of the signal. Other methods such as standard Fourier analysis ignore the local time–frequency changes in the characteristics of the signal and, consequently, lose a large amount of information that existed in the signal. The method of wave train electrical activity analysis resolves the contradiction between these two approaches because it addresses the generalized characteristics of the biomedical signal based on local time–frequency changes in the signal. We investigate the following wave train parameters: wave train central frequency, wave train maximal power spectral density, wave train duration in periods, and wave train bandwidth. We have developed special graphical diagrams, named AUC diagrams, to determine what wave trains are characteristic of neurodegenerative diseases. In this paper, we consider the following types of AUC diagrams: 2D and 3D diagrams. The technique of working with AUC diagrams is illustrated by examples of analysis of EMG in patients with Parkinson’s disease and healthy volunteers. It is demonstrated that new regularities useful for the high-accuracy diagnosis of Parkinson’s disease can be revealed using the method of analyzing the wave train electrical activity and AUC diagrams.
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Grzechca, Damian, and Adam Szczeponik. "Comparison of Filtering Methods for Enhanced Reliability of a Train Axle Counter System." Sensors 20, no. 10 (May 12, 2020): 2754. http://dx.doi.org/10.3390/s20102754.
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This paper presents signal filtering methods that can be effectively applied to train detection systems based on the axle counter systems that are currently in operation for train detection and provide information on the unoccupied status of railway tracks and turnouts. Signals from the wheel detectors contain noise, may be impulsive and time-varying, which means that even for the same train, the signals from the following wheels may be different. A problem appears when already homologated hardware (axle counter system) is working in a harsh environment, exposed to disturbances whose parameters significantly exceed standard thresholds. Despite this, the system must continue to provide reliable information. The authors present research on the application of such filters as median, Savitzkey-Golay, and moving average which can be implemented in the equipment currently in use under specific constraints (e.g., limited computational resources). The research results show that appropriately adjusted filters, for example, in terms of type and window size, increase the signal quality and thereby provide reliable information about passing trains, as well as enhance the availability and safety of the axle counter system performance.
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Malekjafarian, Abdollah, Eugene OBrien, Paraic Quirke, and Cathal Bowe. "Railway Track Monitoring Using Train Measurements: An Experimental Case Study." Applied Sciences 9, no. 22 (November 13, 2019): 4859. http://dx.doi.org/10.3390/app9224859.
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This paper investigates the use of drive-by train measurements for railway track monitoring. An in-service Irish Rail train was instrumented while using accelerometers and a global positioning system. The measurements were taken over two months and the train bogie accelerations from 60 passes on the Dublin-Belfast line were used for this study. A 6 km section of the line is the particular focus, where the maintenance measurements from a Track Recording Vehicle (TRV) were available. The Hilbert transform is used to obtain the instantaneous amplitudes of the acceleration signals. A new representation of the signal is proposed to show the signal energy level as a function of train location. It is shown that the forward speed of the train has a significant influence on the energy level of the signals. Therefore, a two-step speed correction is applied to the data. First, data from passes with forward speed below a certain limit are removed from the data set. Subsequently, a scaling factor is defined for the remaining signals and the energy levels of those signals are scaled while using online speed measurements. The scaled amplitudes are compared with the TRV data. It is shown that the energy levels of the signals match the TRV measurements very well.
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HOWLETT, PHIL. "THE TWO-TRAIN SEPARATION PROBLEM ON LEVEL TRACK WITH DISCRETE CONTROL." ANZIAM Journal 60, no. 2 (October 2018): 137–74. http://dx.doi.org/10.1017/s1446181118000214.
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When two trains travel along the same track in the same direction, it is a common safety requirement that the trains must be separated by at least two signals. This means that there will always be at least one clear section of track between the two trains. If the safe-separation condition is violated, then the driver of the following train must adopt a revised strategy that will enable the train to stop at the next signal if necessary. One simple way to ensure safe separation is to define a prescribed set of latest allowed section exit times for the leading train and a corresponding prescribed set of earliest allowed section entry times for the following train. We will find strategies that minimize the total tractive energy required for both trains to complete their respective journeys within the overall allowed journey times and subject to the additional prescribed section clearance times. We assume that the drivers use a discrete control mechanism and show that the optimal driving strategy for each train is defined by a sequence of approximate speedholding phases at a uniquely defined optimal driving speed on each section and that the sequence of optimal driving speeds is a decreasing sequence for the leading train and an increasing sequence for the following train. We illustrate our results by finding optimal strategies and associated speed profiles for both trains in some elementary but realistic examples.
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Tandon, Shantam, Karthik Subramanian, Tanmai Moundekar, and Vansh Khanna. "Improvement in Speed of Trains in Poor Visibility & Advancement of Train Transport Signaling Mechanism." International Journal of Engineering & Technology 7, no. 2.23 (April 20, 2018): 221. http://dx.doi.org/10.14419/ijet.v7i2.23.11923.
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The Railway system is heavily hampered by poor weather conditions like fog, smog and heavy rainfall especially in Northern India. These factors lead to poor visibility and reduces the average speed of trains which is the main reason for their delay, particularly in winter season. The average speed of the train is kept low so that the locomotive pilot can easily watch out signals and control the movement of the train. This paper focuses on techniques to boost the average speed of trains during low visibility by improving the transport signaling mechanism using wireless communication mechanisms using pulse code modulated signals.
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Kaewunruen, S., J. Sresakoolchai, and A. Thamba. "Machine learning-aided identification of train weights from railway sleeper vibration." Insight - Non-Destructive Testing and Condition Monitoring 63, no. 3 (March 1, 2021): 151–59. http://dx.doi.org/10.1784/insi.2021.63.3.151.
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Within the UK and many other countries, the weight of a train is a critical factor in determining how much the train operator must pay to use the railway network. This study explores the use of different machine learning algorithms to predict the weight of a train based on the vibration signals recorded in a railway sleeper. The vibration signals are split into two groups: time-domain and frequency-domain signals. Then, different algorithms are developed for each group, to see which domain offers the best representation of a vibration signal for use within models to determine target values. From the study, it can be seen that machine learning has the potential to predict the weight of a train effectively. This insight can lead to the use of mobile sensors in practice, such as the application of wireless accelerometers connected with a smartphone, to help engineers audit and provide assurance of the train access integrity.
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Wiesmeyr, Christoph, Martin Litzenberger, Markus Waser, Adam Papp, Heinrich Garn, Günther Neunteufel, and Herbert Döller. "Real-Time Train Tracking from Distributed Acoustic Sensing Data." Applied Sciences 10, no. 2 (January 8, 2020): 448. http://dx.doi.org/10.3390/app10020448.
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In the context of railway safety, it is crucial to know the positions of all trains moving along the infrastructure. In this contribution, we present an algorithm that extracts the positions of moving trains for a given point in time from Distributed Acoustic Sensing (DAS) signals. These signals are obtained by injecting light pulses into an optical fiber close to the railway tracks and measuring the Rayleigh backscatter. We show that the vibrations of moving objects can be identified and tracked in real-time yielding train positions every second. To speed up the algorithm, we describe how the calculations can partly be based on graphical processing units. The tracking quality is assessed by counting the inaccurate and lost train tracks for two different types of cable installations.
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Sanger, Terence D. "Decoding Neural Spike Trains: Calculating the Probability That a Spike Train and an External Signal Are Related." Journal of Neurophysiology 87, no. 3 (March 1, 2002): 1659–63. http://dx.doi.org/10.1152/jn.00121.2001.
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Experimental and clinical applications of extracellular recordings of spiking cell activity frequently are used to relate the activity of a cell to externally measurable signals such as surface potentials, sensory stimuli, or movement measurements. When the external signal is time-varying, correlation methods have traditionally been used to quantify the degree of relation with the neural firing. However, in some circumstances correlation methods can give misleading results. A new algorithm is described that estimates the extent to which a spike train is related to a continuous time-varying signal. The technique calculates the probability of generating a spike train with Poisson statistics if the time-varying signal determines the Poisson rate. This is accomplished by successive division of the signal and the spike train into halves and recursive calculation of the probability of each half-signal. The performance of the new algorithm is compared with the performance of correlation methods on simulated data.
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Liu, Pengyu, Xiaojuan Zhou, and Zhangdui Zhong. "Outage Analysis of Train-to-Train Communication Model over Nakagami-mChannel in High-Speed Railway." International Journal of Antennas and Propagation 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/617895.
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This paper analyzes the end-to-end outage performance of high-speed-railway train-to-train communication model in high-speed railway over independent identical and nonidentical Nakagami-mchannels. The train-to-train communication is inter-train communication without an aid of infrastructure (for base station). Source train uses trains on other rail tracks as relays to transmit signals to destination train on the same track. The mechanism of such communication among trains can be divided into three cases based on occurrence of possible-occurrence relay trains. We first present a new closed form for the sum of squared independent Nakagami-mvariates and then derive an expression for the outage probability of the identical and non-identical Nakagami-mchannels in three cases. In particular, the problem is improved by the proposed formulation that statistic for sum of squared Nakagami-mvariates with identicalmtends to be infinite. Numerical analysis indicates that the derived analytic results are reasonable and the outage performance is better over Nakagami-mchannel in high-speed railway scenarios.
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Long, Gary. "Easy-to-Apply Solution to a Persistent Safety Problem: Clearance Time for Railroad-Preempted Traffic Signals." Transportation Research Record: Journal of the Transportation Research Board 1856, no. 1 (January 2003): 239–47. http://dx.doi.org/10.3141/1856-26.
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Traffic signals near railroad grade crossings having active warning systems are normally interconnected and receive a signal from the railroad track circuitry when trains are approaching. This train approach signal is used by the traffic signal to interrupt and preempt its normal phasing and enter into higher priority special phasings to clear the track of any vehicles that might be stopped in the pathway of a train. The amount of preemption time needed to clear a vehicle from the pathway of a train is necessary information for appropriate preemption signal settings but there are no definitive guidelines on how to determine this amount of time. It is usually left to the judgment of the signal engineer, and frequently unfounded assumptions are used. This study investigated the time required to clear a vehicle in a queue backed up onto a track at railroad-preempted traffic signals. The two key time components are start-up delay and repositioning time. Queues where all preceding vehicles are short passenger cars cause the longest start-up delays, and heavy trucks at the track involve the longest repositioning times. The model developed is convenient because once it is decided that preemption is needed, only easily determined values are required—the minimum track clearance distance, the clear storage distance, and the types of vehicles that are permitted to use the roadway. The model adopts a high level of confidence to minimize the risk of crashes but avoids the "worst-case" concept to avert an invitation for litigation when an improbable or unforeseen worst case results in a crash.
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Pai, Namrata, Joseph W. Hall, and John Grose. "Acoustic reflex decay for pulse train signals." Journal of the Acoustical Society of America 101, no. 5 (May 1997): 3124. http://dx.doi.org/10.1121/1.418980.
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Saito, S., D. Kaida, K. Hattori, F. Febriani, and C. Yoshino. "Signal discrimination of ULF electromagnetic data with using singular spectrum analysis – an attempt to detect train noise." Natural Hazards and Earth System Sciences 11, no. 7 (July 7, 2011): 1863–74. http://dx.doi.org/10.5194/nhess-11-1863-2011.
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Abstract. Electromagnetic phenomena associated with crustal activities have been reported in a wide frequency range (DC-HF). In particular, ULF electromagnetic phenomena are the most promising among them because of the deeper skin depth. However, ULF geoelctromagnetic data are a superposition of signals of different origins. They originated from interactions between the geomagnetic field and the solar wind, leak current by a DC-driven train (train noise), precipitation, and so on. In general, the intensity of electromagnetic signals associated with crustal activity is smaller than the above variations. Therefore, in order to detect a smaller signal, signal discrimination such as noise reduction or identification of noises is very important. In this paper, the singular spectrum analysis (SSA) has been performed to detect the DC-driven train noise in geoelectric potential difference data. The aim of this paper is to develop an effective algorithm for the DC-driven train noise detection.
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Huang, Bin, and Ying Huang. "Multimode Intelligent Control Based on Multidata Fusion Filtering in High-Speed Train Traffic Signal and Control." Journal of Sensors 2021 (May 31, 2021): 1–10. http://dx.doi.org/10.1155/2021/6081999.
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As the speed of high-speed trains continues to increase, the intelligent monitoring of high-speed trains has become a concern of people. This research mainly discusses the application of multimode intelligent control of multidata fusion filtering in high-speed train traffic signal and control. In multimodal intelligent control, BangBang, PI control, adaptive fuzzy PID control, and expert monitoring control under special circumstances can be used, respectively, according to the error and the rate of change of the error, which can achieve the best control effect under safe conditions. Take the allowable speed of ATP as the target speed of the control system, and combine the operation process, operation requirements, traction characteristics, braking characteristics of high-speed trains, and meet the two conditions for improving the operating efficiency of high-speed trains. According to the dynamic expected speed value of high-speed trains, dynamically adjust the switching threshold. This study uses a pulse signal generator to simulate the speed data of the vehicle speed sensor (all pulse data), and then read the speed (pulse) signal data through the pulse signal acquisition card, and display the simulated speed data under the Kingview software. The monitoring computer is used to collect train speed information, display speed information, manage speed information, and output speed information. Then, through OPC technology, the simulation speed data is transmitted to MATLAB software for multidata fusion filtering processing and multimodal control simulation. In the simulation process, the train adopts a multimodal intelligent control response scheme, with a total time of 2183.7 s, which is shortened by 214.5 s and improved by nearly 10%. The multimode intelligent control scheme of multidata fusion filtering proposed in this study can better meet the control of high-speed train traffic signals.
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Zhang, Shangbin, Qingbo He, Haibin Zhang, Kesai Ouyang, and Fanrang Kong. "Signal separation and correction with multiple Doppler acoustic sources for wayside fault diagnosis of train bearings." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 14 (March 22, 2016): 2664–80. http://dx.doi.org/10.1177/0954406216639342.
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The extraction of single train signal is necessary in wayside fault diagnosis because the acoustic signal acquired by a microphone is composed of multiple train bearing signals and noises. However, the Doppler distortion in the signal acquired by a microphone effectively hinders the signal separation and fault diagnosis. To address this issue, we propose a novel method based on the generalized S-transform, morphological filtering, and time–frequency amplitude matching-based resampling time series for multiple-Doppler-acoustic-source signal separation and correction. First, the original time–frequency distribution is constructed by applying generalized S-transform to the raw signal acquired by a microphone. Based on a morphological filter, several time–frequency distributions corresponding to different single source Doppler fault signals are extracted from the original time–frequency distribution. Subsequently, the time–frequency distributions are reverted to time signals by inverse generalized S-transform. Then, a resampling time series is built by time–frequency amplitude matching to obtain the correct signals without Doppler distortion. Finally, the bearing fault is diagnosed by the envelope spectrum of the correction signal. The effectiveness of this method is verified by simulated and practical signals.
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Dick, C. Tyler, Darkhan Mussanov, Leonel E. Evans, Geordie S. Roscoe, and Tzu-Yu Chang. "Relative Capacity and Performance of Fixed- and Moving-Block Control Systems on North American Freight Railway Lines and Shared Passenger Corridors." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (April 4, 2019): 250–61. http://dx.doi.org/10.1177/0361198119841852.
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North American railroads are facing increasing demand for safe, efficient, and reliable freight and passenger transportation. The high cost of constructing additional track infrastructure to increase capacity and improve reliability provides railroads with a strong financial motivation to increase the productivity of their existing mainlines by reducing the headway between trains. The objective of this research is to assess potential for advanced Positive Train Control (PTC) systems with virtual and moving blocks to improve the capacity and performance of Class 1 railroad mainline corridors. Rail Traffic Controller software is used to simulate and compare the delay performance and capacity of train operations on a representative rail corridor under fixed wayside block signals and moving blocks. The experiment also investigates possible interactions between the capacity benefits of moving blocks and traffic volume, traffic composition, and amount of second main track. Moving blocks can increase the capacity of single-track corridors by several trains per day, serving as an effective substitute to construction of additional second main track infrastructure in the short term. Moving blocks are shown to have the greatest capacity benefit when the corridor has more second main track and traffic volumes are high. Compared with three-aspect signal systems, much of the benefits of moving blocks can be obtained from adding signals and implementing a four-aspect signal system. Knowledge of train delay performance and line capacity under moving blocks will aid railway practitioners in determining if the benefits of these systems justify the required incremental investment over current PTC overlay implementations.
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Olaszek, Piotr, Ireneusz Wyczałek, Damian Sala, Marek Kokot, and Andrzej Świercz. "Monitoring of the Static and Dynamic Displacements of Railway Bridges with the Use of Inertial Sensors." Sensors 20, no. 10 (May 12, 2020): 2767. http://dx.doi.org/10.3390/s20102767.
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In the case of the monitoring of bridges, the determination of vertical displacements is one of the most important issues. A new measuring system has been developed and implemented for assessment of railway bridges based on measurements of the structural response to passing trains. The system uses inertial sensors: Inclinometers and accelerometers that do not need any referential points. The system records signals related to the passage of a train over a monitored bridge. The signals from inclinometers before the train’s entry are used to determine the static movement. Integrated signals from inclinometers and accelerometers are used to determine dynamic displacements when the train goes through the bridge. Signals from inclinometers are used to determine the so-called “quasi-static” component of the displacement and signal from the accelerometer to determine the dynamic component. Field tests have been carried out on a viaduct along a high-speed railway line. Periodic comparative measurements are carried out using a Total Station to verify static measurements and using inductive sensors to verify dynamic measurements. Tests of the system carried out so far have proven its usefulness for monitoring bridges in a high-speed railway (up to 200 km/h) with high accuracy while determining dynamic displacements.
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Noorbasha, Fazal, K. Hari Kishore, P. Phani Sarad, A. Renuka, SK Meera Mohiddin, K. Jagadeesh Babu, B. V S. Phanindra, and M. Manasa. "A VLSI implementation of train collision avoidance system using Verilog HDL." International Journal of Engineering & Technology 7, no. 2.8 (March 19, 2018): 386. http://dx.doi.org/10.14419/ijet.v7i2.8.10468.
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Now a days we see many train accidents that occur in railways. These accidents occur mainly due to cracks in the track, human errors and not identifying the opposite train at the right time. When the train meets with the accident lot of people lose their lives and huge amount of railway property is destroyed and it also takes lot of time to hold back to the normal situations. Most of the accidents happen due to human error and due to lack of communication between the trains and irregularity of Train Traffic Control System. Normally to prevent these accidents we place sensors on either side of the platform to identify the train at right time and to receive traffic signals at the platform properly. Here we came with some different approach which is easy to manage and implement and cost effective. Normally collision occurs when two trains approaching in opposite directions on same track. So, if we manage to prevent two trains travel on the same track then collision can be avoided. Here in this project we have implemented Verilog code to solve this problem. The purpose of this project is to write a Verilog code to detect the opposite train and deviate the train based on priority of the trains thus avoiding collision. In this project we have chosen four different types of trains namely Goods, Passenger, Superfast, Express and we have implemented train collision avoidance using Verilog code by giving priority to each type of train and preference is given to one train to avoid collision.
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Liu, Pengyu, Bo Ai, Zhangdui Zhong, and Xiaojuan Zhou. "A Novel Train-to-Train Communication Model Design Based on Multihop in High-Speed Railway." International Journal of Antennas and Propagation 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/475492.
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Railway telematics applications are currently attracting attention and are under intense research. Reliable railway telematics applications increasingly tend to require a subsidiary means to help existent control system make train operation safer and more efficient. Since 2006, train-to-train communication has been studied to respond to such requirements. A key characteristic of train-to-train communication is that operation control to avoid possible accidents is conducted among trains without help of a base station. This paper proposes a novel train-to-train communication model in a physical layer based on multihop and cooperation, taking a high-speed railway propagation channel into account. The mechanism of this model lies in the idea that a source train uses trains on other tracks as relays to transmit signals to destination train on the same track. Based on occurrence of these potential relays, such mechanism can be divided into three cases. In each case, BER is applied to evaluate properties of the proposed communication model. Simulation results show that BER of the train-to-train communication model decreases to10−6when SNR is 10 dB and that the minimum receiving voltage of this model is −84 dBm, which is 8 dBm lower than the standards established by the International Union of Railways (UIC) in a high-speed railway scenario.
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Mortimer, Rudolf G. "Oh! Say, Can You Hear that Train Coming to the Crossing?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 38, no. 14 (October 1994): 898–902. http://dx.doi.org/10.1177/154193129403801422.
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How effective is the horn on trains in alerting motorists, cyclists and pedestrians of the approach of a train to a highway-railroad crossing? Road users become aware of trains approaching crossings either by seeing them, hearing the warning horn or by warning devices at the crossing. Auditory warnings have some advantages over others, but practical problems of sound transmission and the noise environment of road users limit their effectiveness. Factors that affect the audibility of train horns and crossing bells are discussed. Train and crossing bells are generally effective for persons in their immediate environment, but are relatively ineffective for occupants of closed vehicles. Because of the lack of reliability of the detectability of auditory warning signals provided to motorists and others approaching rail-highway crossings, they should visually scan the tracks to ascertain the presence of a train.
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Yu, Yang, and Ming Yu Zhang. "Identify on Acoustic Emission Signals of Tank Bottom Corrosion Using Hidden Markov Model." Applied Mechanics and Materials 239-240 (December 2012): 42–46. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.42.
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Aiming at the acoustic emission signals of oil storage tank bottom injured, hidden Markov algorithm is proposed to identify the tank bottom corrosion signal. Typical corrosion acoustic emission signal is divided into transient acoustic signal, continuous acoustic emission signal and mixed acoustic emission.Baum-Welch algorithm is used to train these typical corrosion acoustic emission signals model, then establish HMM model library. The forward-backward algorithm is used to compute each acoustic emission model’s output probability. The simulation experiments shows that the hidden Markov algorithm can correctly identified the acoustic emission signals.
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He, Xuhui, Kehui Yu, Chenzhi Cai, Yunfeng Zou, and Xiaojie Zhu. "Dynamic Responses of a Metro Train-Bridge System under Train-Braking: Field Measurements and Data Analysis." Sensors 20, no. 3 (January 29, 2020): 735. http://dx.doi.org/10.3390/s20030735.
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This paper focuses on the dynamic responses of a metro train–bridge system under train-braking. Experiments were performed on the elevated Metro Line 21 of Guangzhou (China). A continuous, three-span, rigid-frame bridge (42 m + 65 m + 42 m) and a standard B-type metro train were selected. The acceleration signals were measured at the center-points of the main span and one side-span, and the acceleration signals of the car body and the bogie frame were measured simultaneously. The train–bridge system’s vibration characteristics and any correlations with time and frequency were investigated. The Choi–Williams distribution method and wavelet coherence were introduced to analyze the obtained acceleration signals of the metro train–bridge system. The results showed that the Choi–Williams distribution provided a more explicit understanding of the time–frequency domain. The correlations between different parts of the bridge and the train–bridge system under braking conditions were revealed. The present study provides a series of measured dynamic responses of the metro train–bridge system under train-braking, which could be used as a reference in further investigations.
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Verhaegen, Paul K., and Rudi W. Ryckaert. "Vigilance of Train Engineers." Proceedings of the Human Factors Society Annual Meeting 30, no. 4 (September 1986): 403–7. http://dx.doi.org/10.1177/154193128603000422.
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Thirty-seven train engineers, a stratified random sample of a population of 200 passenger train engineers, were studied by interviews and personality questionnaires. For one month they registered in detail their daily activities (work, sleep, meals, etc.). For the same month records were automatically registered for speeding, false alarms, and delayed reactions to yellow signals. The results show that the errors are much more related to personality aspects than to aspects of work organization.
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Zhao, Jie, Jianhui Lin, Jinbao Yao, and Jianming Ding. "Two-Dimensional Impact Reconstruction Method for Rail Defect Inspection." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/236574.
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The safety of train operating is seriously menaced by the rail defects, so it is of great significance to inspect rail defects dynamically while the train is operating. This paper presents a two-dimensional impact reconstruction method to realize the on-line inspection of rail defects. The proposed method utilizes preprocessing technology to convert time domain vertical vibration signals acquired by wireless sensor network to space signals. The modern time-frequency analysis method is improved to reconstruct the obtained multisensor information. Then, the image fusion processing technology based on spectrum threshold processing and node color labeling is proposed to reduce the noise, and blank the periodic impact signal caused by rail joints and locomotive running gear. This method can convert the aperiodic impact signals caused by rail defects to partial periodic impact signals, and locate the rail defects. An application indicates that the two-dimensional impact reconstruction method could display the impact caused by rail defects obviously, and is an effective on-line rail defects inspection method.
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Watters, Nicholas, and George N. Reeke. "Neuronal Spike Train Entropy Estimation by History Clustering." Neural Computation 26, no. 9 (September 2014): 1840–72. http://dx.doi.org/10.1162/neco_a_00627.
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Neurons send signals to each other by means of sequences of action potentials (spikes). Ignoring variations in spike amplitude and shape that are probably not meaningful to a receiving cell, the information content, or entropy of the signal depends on only the timing of action potentials, and because there is no external clock, only the interspike intervals, and not the absolute spike times, are significant. Estimating spike train entropy is a difficult task, particularly with small data sets, and many methods of entropy estimation have been proposed. Here we present two related model-based methods for estimating the entropy of neural signals and compare them to existing methods. One of the methods is fast and reasonably accurate, and it converges well with short spike time records; the other is impractically time-consuming but apparently very accurate, relying on generating artificial data that are a statistical match to the experimental data. Using the slow, accurate method to generate a best-estimate entropy value, we find that the faster estimator converges to this value more closely and with smaller data sets than many existing entropy estimators.
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Maust-Mohl, Maria, Joseph Soltis, and Diana Reiss. "Underwater click train production by the hippopotamus (Hippopotamus amphibius) suggests an echo-ranging function." Behaviour 155, no. 2-3 (2018): 231–51. http://dx.doi.org/10.1163/1568539x-00003484.
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Abstract Common hippos (Hippopotamus amphibius) live in murky waters and produce a variety of acoustic signals including underwater click trains considered to be social in function. We tested the hypothesis that click trains may function for underwater detection. We used observational and experimental methods involving 16 captive hippos to document the occurrence of click trains in different contexts and describe the acoustic parameters of the clicks. Male and female hippos produced click trains correlated with searching underwater for food items placed in their pools. Males produced click trains when alone supporting the hypothesis that these signals function for detection and are not only social in function. The frequency bandwidth of individual clicks varied and most were below 10 000 Hz. Click train production by hippos during underwater searches suggests a rudimentary form of echo-ranging that may function when other sensory systems are limited in their aquatic environment.
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Bartczak, Marek. "Adaptation of the PKP PLK S.A. Train Light Signaling to Higher Number of Signal Aspects." Problemy Kolejnictwa - Railway Reports 64, no. 186 (March 2020): 75–82. http://dx.doi.org/10.36137/1861e.
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The article discusses the train signaling system developed in OSŻD. The traffic light signaling currently used on the network managed by PKP Polskie Linie Kolejowe S.A. (Polish Railway Lines) is also described. A proposal of traffic light signals is presented, enabling the transmission of information on travel speeds on the turning tracks of turnouts operated on the PKP PLK S.A. network. The proposed signaling has been developed based on OSŻD (in Rus. Организация со-трудничества железных дорог – ОСЖД) signaling. The number of signals needed was obtained by introducing a second yellow light strip and optionally two different flashing frequencies. Keywords: railway signaling, signaling for trains, lineside signals, railway traffic control devices
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Brandman, Relly, and Mark E. Nelson. "A Simple Model of Long-Term Spike Train Regularization." Neural Computation 14, no. 7 (July 1, 2002): 1575–97. http://dx.doi.org/10.1162/08997660260028629.
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A simple model of spike generation is described that gives rise to negative correlations in the interspike interval (ISI) sequence and leads to long-term spike train regularization. This regularization can be seen by examining the variance of thekth-order interval distribution for large k (the times between spike i and spike i Ck). The variance is much smaller than would be expected if successive ISIs were uncorrelated. Such regularizing effects have been observed in the spike trains of electrosensory afferent nerve fibers and can lead to dramatic improvement in the detectability of weak signals encoded in the spike train data (Ratnam & Nelson, 2000). Here, we present a simple neural model in which negative ISI correlations and long-term spike train regularization arise from refractory effects associated with a dynamic spike threshold. Our model is derived from a more detailed model of electrosensory afferent dynamics developed recently by other investigators (Chacron, Longtin, St.-Hilaire, & Maler, 2000; Chacron, Longtin, & Maler, 2001). The core of this model is a dynamic spike threshold that is transiently elevated following a spike and subsequently decays until the next spike is generated. Here, we present a simplified version—the linear adaptive threshold model—that contains a single state variable and three free parameters that control the mean and coefficient of variation of the spontaneous ISI distribution and the frequency characteristics of the driven response. We show that refractory effects associated with the dynamic threshold lead to regularization of the spike train on long timescales. Furthermore, we show that this regularization enhances the detectability of weak signals encoded by the linear adaptive threshold model. Although inspired by properties of electrosensory afferent nerve fibers, such regularizing effects may play an important role in other neural systems where weak signals must be reliably detected in noisy spike trains. When modeling a neuronal system that exhibits this type of ISI correlation structure, the linear adaptive threshold model may
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Manwani, Amit, Peter N. Steinmetz, and Christof Koch. "The Impact of Spike Timing Variability on the Signal-Encoding Performance of Neural Spiking Models." Neural Computation 14, no. 2 (February 1, 2002): 347–67. http://dx.doi.org/10.1162/08997660252741158.
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It remains unclear whether the variability of neuronal spike trains in vivo arises due to biological noise sources or represents highly precise encoding of temporally varying synaptic input signals. Determining the variability of spike timing can provide fundamental insights into the nature of strategies used in the brain to represent and transmit information in the form of discrete spike trains. In this study, we employ a signal estimation paradigm to determine how variability in spike timing affects encoding of random time-varying signals. We assess this for two types of spiking models: an integrate-and-fire model with random threshold and a more biophysically realistic stochastic ion channel model. Using the coding fraction and mutual information as information-theoretic measures, we quantify the efficacy of optimal linear decoding of random inputs from the model outputs and study the relationship between efficacy and variability in the output spike train. Our findings suggest that variability does not necessarily hinder signal decoding for the biophysically plausible encoders examined and that the functional role of spiking variability depends intimately on the nature of the encoder and the signal processing task; variability can either enhance or impede decoding performance.
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Li, Xiaofeng, Limin Jia, and Xin Yang. "Fault Diagnosis of Train Axle Box Bearing Based on Multifeature Parameters." Discrete Dynamics in Nature and Society 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/846918.
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Failure of the train axle box bearing will cause great loss. Now, condition-based maintenance of train axle box bearing has been a research hotspot around the world. Vibration signals generated by train axle box bearing have nonlinear and nonstationary characteristics. The methods used in traditional bearing fault diagnosis do not work well with the train axle box. To solve this problem, an effective method of axle box bearing fault diagnosis based on multifeature parameters is presented in this paper. This method can be divided into three parts, namely, weak fault signal extraction, feature extraction, and fault recognition. In the first part, a db4 wavelet is employed for denoising the original signals from the vibration sensors. In the second part, five time-domain parameters, five IMF energy-torque features, and two amplitude-ratio features are extracted. The latter seven frequency domain features are calculated based on the empirical mode decomposition and envelope spectrum analysis. In the third part, a fault classifier based on BP neural network is designed for automatic fault pattern recognition. A series of tests are carried out to verify the proposed method, which show that the accuracy is above 90%.
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Tiede, Regina, Karla Krautwald, Anja Fincke, and Frank Angenstein. "NMDA-Dependent Mechanisms Only Affect the BOLD Response in the Rat Dentate Gyrus by Modifying Local Signal Processing." Journal of Cerebral Blood Flow & Metabolism 32, no. 3 (December 14, 2011): 570–84. http://dx.doi.org/10.1038/jcbfm.2011.182.
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The role of N-methyl-d-aspartate (NMDA) receptor-mediated mechanisms in the formation of a blood oxygen level-dependent (BOLD) response was studied using electrical stimulation of the right perforant pathway. Stimulation of this fiber bundle triggered BOLD responses in the right hippocampal formation and in the left entorhinal cortex. The perforant pathway projects to and activates the dentate gyrus monosynaptically, activation in the contralateral entorhinal cortex is multisynaptic and requires forwarding and processing of signals. Application of the NMDA receptor antagonist MK801 during stimulation had no effect on BOLD responses in the right dentate gyrus, but reduced the BOLD responses in the left entorhinal cortex. In contrast, application of MK801 before the first stimulation train reduced the BOLD response in both regions. Electrophysiological recordings revealed that the initial stimulation trains changed the local processing of the incoming signals in the dentate gyrus. This altered electrophysiological response was not further changed by a subsequent application of MK801, which is in agreement with an unchanged BOLD response. When MK801 was present during the first stimulation train, a dissimilar electrophysiological response pattern was observed and corresponds to an altered BOLD response, indicating that NMDA-dependent mechanisms indirectly affect the BOLD response, mainly via modifying local signal processing and subsequent propagation.
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He, Xuhui, Kehui Yu, Chenzhi Cai, and Yunfeng Zou. "Dynamic Responses of the Metro Train’s Bogie Frames: Field Tests and Data Analysis." Shock and Vibration 2020 (January 23, 2020): 1–10. http://dx.doi.org/10.1155/2020/1484285.
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This paper focuses on the dynamic characteristics of the metro train’s bogie frames based on the field test data. The acceleration signals of both motor bogie frame and trailer bogie frame of a standard B-type metro train were measured. Running tests on the Metro line 21 of Guangzhou (China) were carried out. The acquired acceleration signals of bogie frames were analyzed through several methods to identify the dynamic characteristics of the motor and trailer bogies in the time-frequency domain. The spectral analysis and time-frequency representations show that noise components exist in the high-frequency domain of the original signal, especially for the acceleration signal of the motor bogie frame. Then, the soft thresholding process and discrete wavelet transform decomposition process are conducted to obtain a denoised version of the original signals in the time-frequency domain. The vibration frequency domain and energy distribution of bogie frames under different train speeds are analyzed. The track irregularity wavelength of the metro line is calculated and analyzed based on the measured bogie frames’ acceleration signals. The dynamic characteristics of the metro train’s bogie frames in this paper can be adopted as a reference in the track diagnosis of the elevated metro line.
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Mehaffey, W. Hamish, Fernando R. Fernandez, Leonard Maler, and Ray W. Turner. "Regulation of Burst Dynamics Improves Differential Encoding of Stimulus Frequency by Spike Train Segregation." Journal of Neurophysiology 98, no. 2 (August 2007): 939–51. http://dx.doi.org/10.1152/jn.00423.2007.
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Distinguishing between different signals conveyed in a single sensory modality presents a significant problem for sensory processing. The weakly electric fish Apteronotus leptorhynchus use electrosensory information to encode both low-frequency signals associated with environmental and prey signals and high-frequency communication signals between conspecifics. We identify a mechanism whereby the GABAB component of a feedback pathway to the electrosensory lobe is recruited to regulate the intrinsic burst dynamics and coding properties of pyramidal cells for these behaviorally relevant input signals. Through recordings in an in vitro slice preparation and a reduced model of pyramidal cells, we show that recruitment of dendritic GABAB currents can shift the timing of a backpropagating spike and its influence on an intrinsic burst mechanism. This regulation of burst firing alters the coding properties of pyramidal cells by improving the correlation of burst and tonic spikes with respect to low- or high-frequency components of complex stimuli. GABAB modulation of spike backpropagation thus improves the segregation of burst and tonic spikes evoked by simulated sensory input, allowing pyramidal cells to parcel the spike train into coding streams for the low- and high-frequency components. As the feedback pathway is predicted to be activated in circumstances where environmental and communication stimuli coexist, these data reveal a novel means by which inhibitory input can regulate spike backpropagation to improve signal segregation.
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Prell, Julian, Stefan Rampp, Johann Romstöck, Rudolf Fahlbusch, and Christian Strauss. "Train time as a quantitative electromyographic parameter for facial nerve function in patients undergoing surgery for vestibular schwannoma." Journal of Neurosurgery 106, no. 5 (May 2007): 826–32. http://dx.doi.org/10.3171/jns.2007.106.5.826.
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Object The authors describe a quantitative electromyographic (EMG) parameter for intraoperative monitoring of facial nerve function during vestibular schwannoma removal. This parameter is based on the automated detection of A trains, an EMG pattern that is known to be associated with postoperative facial nerve paresis. Methods For this study, 40 patients were examined. During the entire operative procedure, free-running EMG signals were recorded in muscles targeted by the facial nerve. A software program specifically designed for this purpose was used to analyze these continuous recordings offline. By automatically adding up time intervals during which A trains occurred, a quantitative parameter was calculated, which was named “train time.” A strong correlation between the length of train time (measured in seconds) and deterioration of postoperative facial nerve function was demonstrated. Certain consecutive safety thresholds at 0.5 and 10 seconds were defined. Their transgression reliably indicated postoperative facial nerve paresis. At less than a 10-second train time, discrete worsening, and at more than 10 seconds, profound deterioration of facial nerve function can be anticipated. Conclusions Train time as a quantitative parameter was shown to be a reliable indicator of facial nerve paresis after surgery for vestibular schwannoma.
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Zhou, Wen, and Hanneke Paulssen. "Compaction of the Groningen gas reservoir investigated with train noise." Geophysical Journal International 223, no. 2 (August 10, 2020): 1327–37. http://dx.doi.org/10.1093/gji/ggaa364.
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Summary Induced seismicity in the Groningen gas field in the Netherlands has been related to reservoir compaction caused by gas pressure depletion. In situ measurement of compaction is therefore relevant for seismic hazard assessment. In this study, we investigated the potential of passively recorded deep borehole noise data to detect temporal variations in the Groningen reservoir. Train signals recorded by an array of 10 geophones at reservoir depth were selected from the continuous noise data for two 5-month deployments in 2015. Interferometry by deconvolution was applied to the high-frequency train signals that acted as stable, repetitive noise sources. Direct intergeophone P and S wave traveltimes were then used to construct the P- and S-wave velocity structure along the geophone array. The resulting models agree with independently obtained velocity profiles and have very small errors. Most intergeophone P wave traveltimes showed decreasing traveltimes per deployment period, suggestive of compaction. However, the retrieved traveltime changes are very small, up to tens of microseconds per deployment period, with uncertainties that are of similar size, about 10 microseconds. An unambiguous interpretation in terms of compaction is therefore not warranted, although the 10 μs error per 5-month period is probably smaller than can be achieved from active time-lapse seismic surveys that are commonly used to measure reservoir compaction. The direct P-wave amplitudes of the train-signal deconvolutions were investigated for additional imprints of compaction. Whereas the P-wave amplitudes consistently increased during the second deployment, suggestive of compaction, no such trend was observed for the first deployment, rendering the interpretation of compaction inconclusive. Our results therefore present hints, but no obvious effects of compaction in the Groningen reservoir. Yet, this study demonstrates that the approach of deconvolution interferometry applied to deep borehole data allows monitoring of small temporal changes in the subsurface for stable repetitive noise sources such as trains.
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Lin, Chen-Yu, Mohd Rapik Saat, and Christopher PL Barkan. "Quantitative causal analysis of mainline passenger train accidents in the United States." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 8 (September 19, 2019): 869–84. http://dx.doi.org/10.1177/0954409719876128.
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The need for shared freight and passenger rail corridors in the United States is increasing due to the growing demand for regional and intercity passenger transport. Several researches have been conducted on reducing the risk of freight train accidents, but little research has been done on the risk of passenger train accidents. The accident rates of passenger trains have declined in the past two decades; however, faster and more frequent passenger train services require even higher safety standards, and therefore further reduction to the risk of passenger train accidents is needed. The research presented in this paper analyzed the passenger train accidents in the United States using the Federal Railroad Administration train accident database to understand the trend of passenger train accident rates, the frequency and severity of different types of accidents, and to explore the major factors that cause them. Derailments and collisions were identified as the most significant types of passenger train accidents, and track failures and human factors, respectively, were the primary causes of those accidents. Accidents caused due to human factors and train operations such as train speed violations and failure to obey signals are often high-consequence accidents and therefore pose the greatest risk. Higher risk infrastructure-related factors include track geometry defects and broken rails or welds. This study on passenger train accidents provides a solid foundation for further research on improving the safety of passenger rail and shared-use rail corridors.
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Sysyn, Mykola, Dimitri Gruen, Ulf Gerber, Olga Nabochenko, and Vitalii Kovalchuk. "Turnout Monitoring with Vehicle Based Inertial Measurements of Operational Trains: A Machine Learning Approach." Communications - Scientific letters of the University of Zilina 21, no. 1 (February 20, 2019): 42–48. http://dx.doi.org/10.26552/com.c.2019.1.42-48.
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A machine learning approach for the recent detection of crossing faults is presented in the paper. The basis for the research are the data of the axle box inertial measurements on operational trains with the system ESAH-F. Within the machine learning approach the signal processing methods, as well as data reduction classification methods, are used. The wavelet analysis is applied to detect the spectral features at measured signals. The simple filter approach and sequential feature selection is used to find the most significant features and train the classification model. The validation and error estimates are presented and its relation to the number of selected features is analysed, as well.
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He, Jing, Xiaoyu Wang, and Mei Lin. "Coherent Structure of Flow Based on Denoised Signals in T-junction Ducts with Vertical Blades." Entropy 21, no. 2 (February 21, 2019): 206. http://dx.doi.org/10.3390/e21020206.
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The skin friction consumes some of the energy when a train is running, and the coherent structure plays an important role in the skin friction. In this paper, we focus on the coherent structure generated near the vent of a train. The intention is to investigate the effect of the vent on the generation of coherent structures. The ventilation system of a high-speed train is reasonably simplified as a T-junction duct with vertical blades. The velocity signal of the cross duct was measured in three different sections (upstream, mid-center and downstream), and then the coherent structure of the denoised signals was analyzed by continuous wavelet transform (CWT). The analysis indicates that the coherent structure frequencies become abundant and the energy peak decreases with the increase of the velocity ratio. As a result, we conclude that a higher velocity ratio is preferable to reduce the skin friction of the train. Besides, with the increase of velocity ratio, the dimensionless frequency St of the high-energy coherent structure does not change obviously and St = 3.09 × 10−4–4.51 × 10−4.
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Zhang, Yu, Zhuoyou Fan, Xiaorong Gao, and Lin Luo. "A Fault Diagnosis Method of Train Wheelset Rolling Bearing Combined with Improved LMD and FK." Journal of Sensors 2019 (December 4, 2019): 1–11. http://dx.doi.org/10.1155/2019/6207847.
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Trackside acoustic signals contain intense noise and nonstationary features even after Doppler distortion correction. Information on bearing defects in these signals is either weak or heavily attenuated. Thus, an improved compound interpolation envelope local mean decomposition (ICIE LMD) method combined with a fast kurtogram (FK) is proposed for wheelset bearings. In this methodology, cubic Hermite interpolation and cubic spline interpolation are employed to find the envelope of the extremal points in the ICIE LMD algorithm to improve accuracy and decrease the computing time of the decomposed signal component. An FK is sensitive to the impact signal and extracts the fault impact features efficiently. In the application, the proposed method uses ICIE LMD to decompose the multicomponent signal into several specific single product function (PF) components. The kurtosis index of the PF is calculated to select the component which contains the most fault information. Then, the selected component of PF is filtered by FK. Finally, the squared envelope spectrum is used to obtain the fault frequency and identify the fault location. The advantages of the ICIE LMD method are verified by simulation analysis. In the application, the results show that the proposed method efficiently extracts the fault features and enhances the target characteristics of the sound signals from a trackside microphone array. Furthermore, the influence of rotating frequency on locating the fault is reduced.
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Li, Ming, Hua Shen Wang, and Fan Yang. "Test and Analysis on the Interference from Wi-Fi Signals to the CBTC Systems." Applied Mechanics and Materials 543-547 (March 2014): 2526–30. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2526.
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Influenced by Wi-Fi signal interference, Shenzhen subway is forced to stop frequently, which has caused enormous safety hazard. This paper proposes a method to test the interference transferred from Wi-Fi signals to the Communication Based Train Control (CBTC) systems, then the testing data are collected and analysed. The test is aim to two signal transmission modes: free wave and leaky cable. The test result shows that the signal transmission mode of leaky cable is more reliable, while the transmission mode of free wave has the worse anti-interference capability to Wi-Fi signals. Particularly, it is an effective way to apply for specific frequency bands, which will make a contribution to reducing the interference effect on the CBTC systems caused by Wi-Fi signals.
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Chen, Zhe. "An Overview of Bayesian Methods for Neural Spike Train Analysis." Computational Intelligence and Neuroscience 2013 (2013): 1–17. http://dx.doi.org/10.1155/2013/251905.
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Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.
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Wang, Ci, Li Min Jia, and Xiao Feng Li. "Fault Diagnosis Method for the Train Axle Box Bearing Based on KPCA and GA-SVM." Applied Mechanics and Materials 441 (December 2013): 376–79. http://dx.doi.org/10.4028/www.scientific.net/amm.441.376.
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Online fault diagnosis for the train axle box bearings is a wide and important study topic since it plays a critical role in train safety. Due to the vibration signals nonlinear and non-stationary characteristics, accuracies of the methods such as neural network and hierarchical clustering are less than 90% which are not satisfying. In this paper, kernel principal component analysis (KPCA), a nonlinear process technique, was to tackle each signals 18 feature parameters for extracting the main features to reflect the signal characteristics. Then, in fault pattern recognition, support vector machine (SVM) based on genetic algorithm (GA) was used to identify the current fault type of the bearings, including normal, outer ring fault, inner ring fault and rolling element fault. The results show that the prediction accuracy of GA-SVM method reaches to 96.33%, which is quite effective.
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Lee, Hyunsoo, Seok-Youn Han, Keejun Park, Hoyoung Lee, and Taesoo Kwon. "Real-Time Hybrid Deep Learning-Based Train Running Safety Prediction Framework of Railway Vehicle." Machines 9, no. 7 (June 29, 2021): 130. http://dx.doi.org/10.3390/machines9070130.
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Train running safety is considered one of the key criteria for advanced highway trains and bogies. While a number of existing research studies have focused on its measurement and monitoring, this study proposes a new and effective train running a safety prediction framework. The wheel derail coefficient, wheel rate of load reduction, and wheel lateral pressure are considered the decision variables for the safety framework. Data for actual measured rail conditions and vibration-based signals are used as the input data. However, advanced trains and bogies are influenced more by their inertial structures and mechanisms than by railway conditions and external environments. In order to reflect their inertial influences, past data of output variables are used as recurrent data. The proposed framework shares advantages of a general deep neural network and a recurrent neural network. To prove the effectiveness of the proposed hybrid deep-learning framework, numerical analyses using an actual measured train-railway model and transit simulation are conducted and compared with the existing deep learning architectures.
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Prisukhina, I. V., and D. V. Borisenko. "Machine classification of code signals in electric train warning systems." Omsk Scientific Bulletin, no. 166 (2019): 39–47. http://dx.doi.org/10.25206/1813-8225-2019-166-39-47.
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Imai, Satoshi, and Chieko Furuichi. "Impulse train equivalent excitation signals for high-quality speech synthesis." Electronics and Communications in Japan (Part I: Communications) 70, no. 3 (1987): 41–53. http://dx.doi.org/10.1002/ecja.4410700305.
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Levanon, N. "CW alternatives to the coherent pulse train-signals and processors." IEEE Transactions on Aerospace and Electronic Systems 29, no. 1 (1993): 250–54. http://dx.doi.org/10.1109/7.249132.
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