Annoyance of construction site noise: development of relevant indices

Third order time and frequency analysis has exhibited great potential for correlation analysis of multi-sensor datasets, but is usually presented as separate time domain and frequency domain approaches. A combined framework of both frequency domain and time domain has rarely been used. This paper proposes a non-parametric third order time and frequency domain framework which used two dimensional Fourier transforms to bridge the gap between time domain and frequency domain. A unified framework offers flexibility and efficiency to apply to data. In this paper we study neural spike train data treated as stochastic point processes. In time domain direct analysis, third order cumulant densities of spike trains are applied, which need all first-, secondand third order product densities to be calculated before constructing the third order cumulant density, which brings additional challenges. The novelty in this study is that a new framework is proposed which can offer an alternative approach without calculating lower order quantities and can reveal nonlinear relationship between neural recordings. The results show that the present framework provides a novel non-parametric method to estimate both time and frequency domain measurements which is applicable to neural spike trains.

Conventional time-frequency Transformers primarily focus on the global features of signals in the time-frequency domain while neglecting the local features in both the time domain and frequency domain. This limitation hinders the ability of the model to effectively capture the shared features among time, frequency, and time-frequency representations. To address this issue, a Multi-Branch Joint Time-Frequency Transformer (MBJTF-Transformer) is proposed for domain generalization (DG) fault diagnosis of rotating machinery. Specifically, a time-branch Transformer is designed to extract temporal features, while a frequency-branch Transformer captures frequency-domain information. In addition, a time-frequency Transformer is employed to learn the shared representations across time, frequency, and time-frequency domains. Finally, a multi-decision fusion strategy of MBJTF-Transformer is adopted to enhance the generalization capability of the model. Experimental results on both the SCARA (Selective Compliance Assembly Robot Arm, SCARA) dataset and the PU (Paderborn University) bearing dataset demonstrate that the proposed MBJTF-Transformer achieves superior DG performance compared to multiple state-of-the-art sequential models.

We describe the main properties of the EQUATOR airborne geophysical system and how the system can be employed to solve some engineering and environmental problems. To date, the EQUATOR system has been used for groundwater search in 2014 and 2015 in Siberia. The name “EQUATOR” originates from the intention to EQUATE, or to balance time and frequency domain approaches in airborne electromagnetics, which is why the system was originally designed. To the best of our knowledge, EQUATOR is the only system to collect electromagnetic data both in time and in frequency domains. We further explain the difference between the two methods on some numerical and field examples. Also we describe some advantages of their coupling. In particular, by virtue of our approach, a numerically more robust solution of the one-dimensional electromagnetic inverse problem may be achieved. The recorded full-time measurements are further converted to in-phase, quadrature and off-time responses, as opposed to standard frequency or time domain solutions, which have been widely adapted within the industry. When inverting the data, our main focus is within the top 100 m of the Earth, which are the most relevant for engineering and environmental studies. We emphasize that a combined use of frequency and time domain data gives more detailed information about geological structures in the near-surface. As an example, we present some case studies obtained over a platform (Siberia) and a crystalline shield (Western Africa) setting. We further suggest using these results in such near-surface studies as sediment thickness estimation, soil state analysis, groundwater search, and geological structural analysis.

In this article, theoretical modeling and simulations are introduced to explore the contact failure characteristics for connectors both in time and frequency domains. The equivalent models are developed to describe the impedance performances of un‐degraded and degraded connectors with the time domain and frequency domain, respectively. The experiments using a Network Analyzer for time and frequency domains analyses are conducted to verify the proposed models. The measured results agree well with the simulation results in the time and frequency domains.

PurposePenetration state is one of the most important factors for judging the quality of a gas tungsten arc welding (GTAW) joint. The purpose of this paper is to identify and classify the penetration state and welding quality through the features of arc sound signal during robotic GTAW process.Design/methodology/approachThis paper tried to make a foundation work to achieve on‐line monitoring of penetration state to weld pool through arc sound signal. The statistic features of arc sound under different penetration states like partial penetration, full penetration and excessive penetration were extracted and analysed, and wavelet packet analysis was used to extract frequency energy at different frequency bands. The prediction models were established by artificial neural networks based on different features combination.FindingsThe experiment results demonstrated that each feature in time and frequency domain could react the penetration behaviour, arc sound in different frequency band had different performance at different penetration states and the prediction model established by 23 features in time domain and frequency domain got the best prediction effect to recognize different penetration states and welding quality through arc sound signal.Originality/valueThis paper tried to make a foundation work to achieve identifying penetration state and welding quality through the features of arc sound signal during robotic GTAW process. A total of 23 features in time domain and frequency domain were extracted at different penetration states. And energy at different frequency bands was proved to be an effective factor for identifying different penetration states. Finally, a prediction model built by 23 features was proved to have the best prediction effect of welding quality.

Heart period variability has been considered for clinical assessment of autonomic function, determining the presence of haemorrhage or disease states, and for predicting mortality from traumatic injury. However, for heart period variability to be clinically useful, a number of important methodological issues should be addressed, including the minimum number of R-R intervals (RRI) required for accurate derivation, and the reproducibility of these metrics. ECGs were recorded for > or =10 min in 18 resting, supine subjects (12 M/6 F; 19-55 years). Heart period variability analyses included 21 time, frequency and complexity domain metrics. For assessment of minimum RRIs required, measurements were made from ECG recordings of 5 min down to 30 s for time and frequency domain metrics, and from 800 RRIs down to 100 RRIs for complexity metrics, by methodical truncation of the data set. Inter-subject variability was assessed by calculating the range and co-efficient of variation (%CV) across all subjects. Two independent 30 s or 100 RRI ECG segments were used to assess intra-subject variability via calculation of %CV in each subject. Six time and frequency domain metrics were robust down to 30 s of data, while five complexity metrics were robust down to 100 RRIs. All time and frequency domain metrics (except for RRI) exhibited high inter-subject variability (CVs > or = 30.0%), while five of eleven complexity metrics displayed low inter-subject variability (CVs < or = 8.5%). In the assessment of intra-subject variability in metrics valid with 30 s or 100 RRIs of ECG, only one time domain and four complexity metrics had CVs < 10%. Metrics that are highly reproducible and require few RRIs are advantageous for patient monitoring as less time is required to assess physiological status and initiate early interventions. Based on our analyses from healthy, resting humans, we have identified a select cohort of heart period variability metrics that performed well in regards to these two criteria.

An improved mathematical model describing acoustic emission (AE) signals generated by different types of partial discharges (PD) that occur in electric power transformer insulation system is presented in the paper. AE signals are analyzed within the AE method as applied for power transformer failure detection due to occurrence of PD. There are several types of basic defects, which are characterized by different types of PD. The mathematical model presented here is crucial for numerical analyses and simulations, where it acts as the function describing the acoustic source in an acoustic model of power transformer insulation system. The regression procedure was performed based on empirical AE signals, registered in a laboratory experiment. The AE signals are described by a mathematical model being a multi-parameter function, which involve both the time domain and the frequency domain. Goodness of the model was evaluated based on analysis of 480 data samples in the time, frequency and time-frequency domains. Also coherence between the registered and modeled signals was calculated. It was stated that the improved model fits very well to the real data, although, due to high level of noise embodied in signals registered in experiments, the coherence values remain low. Moreover, analyses of the estimated data were performed and some example results are presented in this paper. Based on the achieved outcomes a collection of parameter values was prepared for each of the eight considered PD basic types. One can simple use it now in a numerical model for simulation of AE signal source generated by specified type of PD, what corresponds to a particular power transformer insulation system failure. Furthermore, the regression procedure presented in this paper can be easily transferred to any other types of AE sources including processes of compression, tension and cracking.

In this paper, we propose a novel method for the detection of road accidents by analyzing audio streams for road surveillance application. In the last decade, due to the increase in a number of people and transportation vehicles, traffic accidents have become one of a major public issue worldwide. The vast number of injuries and death due to road traffic accident reveals the story of a global crisis of road safety. A total of 52,160 road traffic accidents (RTA), 1130 injuries and 85 fatalities were registered during the year 2000, in the state of Qatar. An increase in the number of transportation vehicles around cities has risen the need for more security and safety in public environments. The most obvious reason for a person's death during accidents is the absence or prolong response time of the first aid facility, which is due to the delay in the information of the accident being reached to the police, hospital or ambulance team. In the last couple of years, several surveillance systems have been proposed based on image and video processing for automatically detecting road accidents and car crashes to ensure a quick response by emergency teams. However, in some situations such as adverse weather conditions or cluttered environments, the visual information is not sufficient enough, whereas analyzing the audio tracks can significantly improve the overall reliability of surveillance systems. In this paper we propose a novel method that automatically identifies hazardous situations such as tire skidding and car crashes in presence of background noises, by analyzing the audio streams. Previous studies show that methods for the detection, estimation, and classification of nonstationary signals can be enhanced by utilizing the time-frequency (TF) characteristics of such signals. The TF-based techniques have been proved to outperform classical techniques based on either time or frequency domains, in analyzing real-life nonstationary signals. Time-frequency distributions (TFDs) give additional information about signals that cannot be extracted from either the time domain or frequency domain representations, i.e. the instantaneous frequency of the components of a signal. In order to utilize this extra information provided by TF domain representation, the proposed approach extracts TF image features from quadratic time-frequency distributions (QTFDs), for the detection of audio anomalies. The extended modified-B distribution (EMBD) is utilized to transform a 1-dimensional audio signal into 2-dimensional TF representation which is interpreted as an image. The image descriptors based features are then extracted from the TF representation to classify visually the audio signals into background or abnormal activity patterns in the TF domain. The proposed features are based on Haralick's texture features extracted from the TF representation of audio signals considered and processed as a textured image. These features are used to characterize and hence classify audio signals into M classes. This research study demonstrates that a TF image pattern recognition approach offers significant advantages over standard signal classification methods that utilize either t-domain only or f-domain only features. The proposed method has been experimentally validated on a large open source database of sounds, including several kinds of background noise. The events to be recognized are superimposed on different background sounds of roads and traffic jam. The obtained results are compared with a recent study, utilizing the same large and complex data set of audio signals, and the same experimental setup. The overall classification results confirm the superior performance of the proposed approach with accuracy improvement up to 6%.

Time domain reflectometry (TDR) is finding increased applications for civil engineering field instrumentation. New applications are being identified based on innovative design of TDR sensor system. Numerical simulation plays an important role for guiding and evaluation of these new designs. This paper discusses the implementation of numerical simulation of frequency domain and time domain models of nonuniform TDR system. The frequency domain model is based on the concept of impedance transform, which can easily incorporate the dispersive characteristics of construction materials. The time domain simulation is carried out by FDTD method. Results of simulation based on both frequency domain model and time domain FDTD method are compared to identify the relative merits of these numerical methods. The simulation of TDR system responses establish the basis for aiding TDR sensor system design and is incorporated into the inversion procedures to be investigated in the future.

During the long-term operation of wind turbines, due to environmental factors and equipment aging, the health and reliability of each component will gradually decline, leading to failure. To assess the health status of wind turbines, timely grasp the subsequent changes and development trends, it is necessary to extract degradation characteristics, including time domain, frequency domain, and time-frequency domain characteristics. These degradation characteristics can reflect the operating status of the equipment, help build health indicator curves, and evaluate the health status of high-speed shaft bearings of wind turbines. Selecting reasonable degradation characteristics is an important prerequisite for constructing a health index curve, and using evaluation indicators to construct a comprehensive evaluation function to screen degradation characteristics. The feature fusion method based on a self-organizing feature mapping network is used to fuse multiple selected degradation features and fuse the selected multiple degradation features into a curve that can reflect the bearing degradation process. Finally, a quantitative analysis is performed on the health index curve to scientifically assess the health status of bearings. Bearings are one of the key components of wind turbines. Based on the health index curve constructed in this article, an appropriate prediction model is selected to predict the health index trend of bearings. A timely and effective grasp of the health trends of wind turbine bearings is of great practical significance for formulating scientific and reasonable maintenance measures for wind farms. The work of this article will be divided into the following four parts: (1) Extracting the time domain, frequency domain, and time-frequency domain degradation characteristics of high-speed shaft bearing vibration signals of wind turbines; (2) Comprehensive evaluation using monotonicity, correlation, and robustness constructs function to screen degenerate features; (3) Use self-organizing feature mapping network. The network integrates the selected degradation features and constructs a health index curve; (4) Based on the constructed health index curve, optimize the BiLSTM network hyperparameters through Bayesian and establish a BO-BiLSTM network model to achieve a more accurate and scientific prediction of the health index trend.

Abstract: The principal objectives of this study were to examine in-cylinder combustion pressure oscillation characteristics of soybean biodiesel in time domain and time-frequency domain, and their influences on the control and operational parameters, such as injection timing, exhaust gas recirculation (EGR) ratio, engine load and engine speed. In this study, the combustion pressure oscillation characteristics of biodiesel engine for various injection timing, EGR ratio and engine speed were investigated. The corresponding relation of pressure characteristics in the time domain and frequency domain were obtained. The results showed that the pressure oscillation and peak pressure rise acceleration occurred mainly in the diffusion combustion, and the peak pressure rise rate located in the premixed combustion. The in-cylinder pressure level curve can be divided into three stages. The pressure levels of stage 1, stage 2 and stage 3 represent the peak in-cylinder pressure, the maximum amplitude of pressure rise rate and pressure rise acceleration, respectively. As the injection timing retards, the pressure levels of stage 1 and stage 3 decrease gradually. The pressure level curve of stage 3 with 25o before top dead center (BTDC) is the highest and the oscillation is the most significant. It is worth noting that the location of each stage with various operate conditions is not fixed. At 0.41 MPa indicated mean effective pressure (IMEP), with the increase of EGR rate, the pressure levels of stage 1 and stage 2 decrease gradually. The pressure level curve of stage 3 and the maximum amplitude of pressure rise acceleration with 0% EGR rate are the highest. The oscillation with 0% EGR rate is the most significant at 0.41 MPa IMEP. Compared to 0.41 MPa IMEP, the frequency bands of stage 1 and stage 2 at 1.1 MPa IMEP are relatively low due to the soft combustion in the cylinder. As EGR rate increases, the pressure level of stage 1 decreases, and those of stage 2 and stage 3 increase gradually. The oscillation with 30% EGR rate is the most significant. With the increase of engine speed, the pressure levels of stage 1 and stage 2 decrease, and move to the low frequency. The pressure level in the high frequency domain at 1600 r/min is less than that at 1100 r/min, and the combustion process is smooth. Keywords: combustion pressure oscillation, soybean biodiesel, injection timing, exhaust gas recirculation, engine speed DOI: 10.3965/j.ijabe.20160906.1680 Citation: Ma P, Liu H L, Liu S Y. Experimental study on combustion pressure oscillation of soybean bio diesel oil. Int J Agric & Biol Eng, 2016; 9(6): 156－166.

We investigate the statistical similarity of partially polarized, partially coherent electromagnetic fields in time and frequency domains, and the relationship between statistical similarity and complete coherence. We find that, both in time domain and frequency domain, the complete coherence of two fields is equivalent to the fields being both fully polarized and statistically similar. Unlike in scalar coherence theory, statistical similarity alone is found not to constitute a sufficient condition for complete coherence. We derive the conditions under which spectrally completely coherent fields are also temporally fully coherent, and we point out that temporally completely coherent fields are necessarily fully spectrally coherent at all frequencies. Complete temporal and spectral coherence of electromagnetic fields are found to be related to the recently introduced concept of strict cross-spectral purity, but in contrast to the scalar case, strict cross-spectral purity is not a necessary condition for complete temporal coherence if the fields have different spectral polarization states.

Echo cancellation technique enables full duplex transmission using the same transmission band and thereby increases the aggregate data throughput of a channel. This tutorial paper introduces high speed echo cancellation for Full Rate (ITU-T Recommendation G.992.1) and Splitterless (ITU-T Recommendation G.992.2, aliased G.Lite) ADSL Transceivers. Symmetric and multirate Asymmetric echo cancellation Transceiver structures based completely on time domain or frequency domain as well as both time and frequency domain echo cancellation have been analyzed using different algorithms for performance and computational complexity. Combined Time and Frequency Domain Echo Cancellation (TAFDEC) structure with the so called DMT algorithm appears to be most suitable to achieve lower complexity with better performance as compared to echo cancellation in either time or frequency domain using LMS algorithm.

BackgroundIn investigations of the human motor system, two approaches are generally employed toward the identification of common modulating drives from motor unit recordings. One is a frequency domain method and uses the coherence function to determine the degree of linear correlation between each frequency component of the signals. The other is a time domain method that has been developed to determine the strength of low frequency common modulations between motor unit spike trains, often referred to in the literature as 'common drive'.MethodsThe relationships between these methods are systematically explored using both mathematical and experimental procedures. A mathematical derivation is presented that shows the theoretical relationship between both time and frequency domain techniques. Multiple recordings from concurrent activities of pairs of motor units are studied and linear regressions are performed between time and frequency domain estimates (for different time domain window sizes) to assess their equivalence.ResultsAnalytically, it may be demonstrated that under the theoretical condition of a narrowband point frequency, the two relations are equivalent. However practical situations deviate from this ideal condition. The correlation between the two techniques varies with time domain moving average window length and for window lengths of 200 ms, 400 ms and 800 ms, the r2 regression statistics (p < 0.05) are 0.56, 0.81 and 0.80 respectively.ConclusionsAlthough theoretically equivalent and experimentally well correlated there are a number of minor discrepancies between the two techniques that are explored. The time domain technique is preferred for short data segments and is better able to quantify the strength of a broad band drive into a single index. The frequency domain measures are more encompassing, providing a complete description of all oscillatory inputs and are better suited to quantifying narrow ranges of descending input into a single index. In general the physiological question at hand should dictate which technique is best suited.

Summary form only given. Currently High-Power Microwave (HPM) systems make use of antennas to radiate the generated microwave pulse. Typically, HPM sources generate a single or dominant frequency with a pulse duration of several hundred microwave cycles. Typically, these antenna systems are calibrated or verified with a frequency domain technique using a Continuous Wave (CW) signal. The radiating system vacuum window or radome may be designed for the dominant frequency of the HPM source. However, the antenna/radome system may not properly accommodate the finite frequency bandwidth due to the finite pulse length or frequency variation. These finite microwave packet size issues can now be accommodated both computationally and experimentally. We will be presenting data comparing frequency domain and time domain simulations, along with experiments. The frequency domain simulations are done with the High Frequency Structure Simulator (HFSS), while the time domain simulations will be done with a Finite-Difference-Time-Domain (FDTD) code such as ICEPIC or VOLMAX. The experiments will compare the standard frequency domain measurements done with a Vector Network Analyzer, while the time domain measurements will be done with packets generated with a CW oscillator and an Arbitrary Waveform Generator (AWG). An example antenna/radome system is our existing Vlasov Antenna and fiberglass belljars. We will also be making use of a TEM to TM/sub 01/ launcher, which has a measured return loss of -20 dB over the frequency range of 1.1 to 1.6 GHz.