Fault diagnosis using noise modeling and a new artificial immune system based algorithm

A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms (GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.

HBT's High Frequency Noise Modeling and Analysis

A T equivalent high frequency heterojunction bipolar transistor (HBT) noise model is reported.This model is derived from Hawkins noise model commonly used in Si BJT.The main modifications include the influence of the ideality factor,emitter resistance,intrinsic base collector capacitance,extrinsic base collector capacitance and other parasitic elements of HBT represented in equivalent circuit topology.In order to calculate accurate noise parameters from the equivalent circuit,the noise correlation matrix method is used to avoid any simplifications generated in circuit transformations and complex noise measurements.The analysis of the influence of the equivalent circuit elements on the minimum noise figure is reported,the results of analysis agree well with the physics explanations.By means of the formulae derived from device physics of HBT,the influence of device parameters on the minimum noise figure is also represented.

Orbital pacing and secular evolution of lake-level changes reconstructed by sedimentary noise modeling during the Early Jurassic icehouses-(super)greenhouses

Lake-level changes can significantly affect paleoenvironmental evolution,resource occurrence,terrestrial carbon budget,and biodiversity in continental basins.Climate is one of the most critical factors controlling lake-level changes.Paleoclimate of the Early Jurassic has been evidenced by oscillating icehouses to(super)greenhouses with interrupted intermittent extreme climatic events(hyperthermal and cooling),e.g.,the Toarcian oceanic anoxic event(~183 Ma)and the late Pliensbachian cooling event(~185 Ma).Lake-level evolution and hydrologic cycling on Earth’s surface during the Early Jurassic icehouses-to-(super)greenhouses are thus far poorly understood due to a lack of continuous high-resolution nonmarine evidence.Here we present a super-long nonmarine lake level record for this pivotal interval from the early Pliensbachian to Toarcian by sedimentary noise modeling,and construct a 16.7-Myr-long astronomical time scale(174.2 Ma to 190.9 Ma)based on cyclostratigraphy analysis of rock color datasets(CIE b*)of the Qaidam Basin.Our results document lake-level oscillations on a 5-to 10-million-year(Myr)scale which shows a pronounced correlation with long-term climate variation and extreme climatic events,and 1-to 2.5-Myr-scale lake-level changes that are prominently paced by the 2.4-Myr long-eccentricity forcing and the 1.2-Myr obliquity forcing.At the Pliensbachian Stage,the 1.2-Myr-scale lake-level changes are in phase with the coeval sealevel variations.Orbitally forced growth and decay of the ephemeral or permanent ice sheets in polar regions are interpreted to control the synchronous ups-and-downs of continental lake level and global sea level.However,during the Toarcian ice-free greenhouses to(super)greenhouses,the 1.2-Myr-scale lake-level variations show an anti-phase relationship with global sea level,indicating a‘seesaw’interaction between continental reservoirs(lakes and groundwater)and global oceans.The 2.4-Myr longeccentricity cycles mainly regulate variations of lake level and sea level by controlling the growth and decay of small-scale continental ice sheets,which is especially notable during the Pliensbachian Stage.These findings indicate a remarkable transition of hydrological cycling pattern during the Pliensbachian-Toarcian icehouses to(super)greenhouses,which provides new perspectives and evidence for investigating the hypothesis of global sea-level changes(e.g.,glacio-eustasy and aquifer-eustasy)and long-period astronomical forcing in nonmarine stratigraphy.

A novel approach to conductive EMI noise source modeling

A new approach to conductive electromagnetic interference （EMI） noise source modeling, i. e. the source internal impedance extraction, is presented. First, the impedance magnitude is achieved through an exciting probe and a detecting probe, or through calculations based on insertion loss measurement results when inserting a series nigh-value known impedance or a shunt low-value known impedance in the circuit. Then the impedance phase is extracted by the Hilbert transform （HT） of the logarithm of the obtained impedance magnitude. Performance studies show that the estimated phase error can increase greatly at a zero frequency in the Hilbert transform because of the existence of a singular point, and this effect can be eliminated by introducing a zero-point when the noise source does not include a series-connected capacitive component. It is also found that when the frequency is nigher than 150 kHz, the estimated phase error is not sensitive to the inductive source but sensitive to the capacitive source. Finally, under the conditions of the same measurement accuracies for impedance magnitude, the accuracy of complex impedance based on the HT can be improved about 10 times when compared with the accuracy of estimated parameters based on the impedance magnitude fitting method （IMFM）.

Modeling random telegraph signal noise in CMOS image sensor under low light based on binomial distribution

The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.

Spatial Distribution Pattern and Influencing Factors of Bed-and-breakfasts(B&Bs)from the Perspective of Urban-rural Differences:A Case Study of Jiaodong Peninsula,China

There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteristics and influencing factors of each type,is essential for creating urban and rural B&B agglomeration areas.This study used density-based spatial clustering of applications with noise(DBSCAN)and the multi-scale geographically weighted regression(MGWR)model to explore similarities and differences in the spatial distribution patterns and influencing factors for urban and rural B&Bs on the Jiaodong Peninsula of China from 2010 to 2022.The results showed that:1)both urban and rural B&Bs in Jiaodong Peninsula went through three stages:a slow start from 2010 to 2015,rapid development from 2015 to 2019,and hindered development from 2019 to 2022.However,urban B&Bs demonstrated a higher development speed and agglomeration intensity,leading to an increasingly evident trend of uneven development between the two sectors.2)The clustering scale of both urban and rural B&Bs continued to expand in terms of quantity and volume.Urban B&B clusters characterized by a limited number,but a higher likelihood of transitioning from low-level to high-level clusters.While the number of rural B&B clusters steadily increased over time,their clustering scale was comparatively lower than that of urban B&Bs,and they lacked the presence of high-level clustering.3)In terms of development direction,urban B&B clusters exhibited a relatively stable pattern and evolved into high-level clustering centers within the main urban areas.Conversely,rural B&Bs exhibited a more pronounced spatial diffusion effect,with clusters showing a trend of multi-center development along the coastline.4)Transport emerged as a common influencing factor for both urban and rural B&Bs,with the density of road network having the strongest explanatory power for their spatial distribution.In terms of differences,population agglomeration had a positive impact on the distribution of urban B&Bs and a negative effect on the distribution of rural B&Bs.Rural B&Bs clustering was more influenced by tourism resources compared with urban B&Bs,but increasing tourist stay duration remains an urgent issue to be addressed.The findings of this study could provide a more precise basis for government planning and management of urban and rural B&B agglomeration areas.

Analysis of SNR for Acoustic Vector Sensor Linear Array in Volume and Surface-generated Noise Fields

Acoustic vector sensor consists of pressure and particle velocity sensors,which measure the three-dimensional acoustic particle velocity,as well as the pressure at one location at the same time.By preserving the amplitude and phase information of the pressure and particle velocity,they possess a number of advantages over traditional scalar sensors.Signal-to-noise ratio (SNR) gain (which is often called array gain) is one of such advantages and is always interested by all of us.But it is not unchangeable if the spatial correlation of the noise field varies.Much more important,it is difficult to be given if the noise becomes complex.In this paper,spatial correlation of the vector field of isotropic volume-noise and surface-generated noise has been introduced briefly.Based on the results,the combined SNR output of a vector linear array is investigated and the maximum gain is given in the specified noise.Computer simulation shows that the output of one array in the same noise is not the same in different gestures.And then we find the best gesture through SNR calculation and obtain the biggest gain,which has important meaning to guide how to deploy an array in practice.We also should use the array with respect to the characteristics of the real ambient noise,especially in anisotropic noise field.

Model/data comparison of typhoon-generated noise

Ocean noise recorded during a typhoon can be used to monitor the typhoon and investigate the mechanism of the wind- generated noise. An analytical expression for the typhoon-generated noise intensity is derived as a function of wind speed. A ＂bi-peak＂ structure was observed in an experiment during which typhoon-generated noise was recorded. Wind speed dependence and frequency dependence were also observed in the frequency range of 100 Hz-1000 Hz. The model/data comparison shows that results of the present model of 500 Hz and 1000 Hz are in reasonable agreement with the exper- imental data, and the typhoon-generated noise intensity has a dependence on frequency and a power-law dependence on wind speed.

Turbocharger Noise Prediction Using Broadband Noise Source Model

The noise prediction of the turbocharger is studied.The broadband noise source model is employed to predict the near-field noise of the turbocharger.The 3D software Solidworks is adopted to establish the model blades and inlet of turbocharger compressor,then this 3D model is introduced into the software CFD to calculate the flow-field under different inlet shapes,different blades shapes and different clearances between casing and impeller.On the base of the above simulation,the broadband noise source model is employed to calculate and analyze the near-field noise.The calculation shows that compressor static pressure values and the sound power values near the impeller outlet are the largest.Through the noise calculation by using broadband noise source model under different inlet shapes and blade shapes,we find that the noise level of the inlet of cylindrical and cone types are smaller.Compared with the current widespread used backward skewed or radial blades,there is little difference of the noise value of the inlet of the forward skewed blades.

A WYNER-ZIV VIDEO CODING METHOD UTILIZING MIXTURE CORRELATION NOISE MODEL

In Wyner-Ziv (WZ) Distributed Video Coding (DVC), correlation noise model is often used to describe the error distribution between WZ frame and the side information. The accuracy of the model can influence the performance of the video coder directly. A mixture correlation noise model in Discrete Cosine Transform (DCT) domain for WZ video coding is established in this paper. Different correlation noise estimation method is used for direct current and alternating current coefficients. Parameter estimation method based on expectation maximization algorithm is used to estimate the Laplace distribution center of direct current frequency band and Mixture Laplace-Uniform Distribution Model (MLUDM) is established for alternating current coefficients. Experimental results suggest that the proposed mixture correlation noise model can describe the heavy tail and sudden change of the noise accurately at high rate and make significant improvement on the coding efficiency compared with the noise model presented by DIStributed COding for Video sERvices (DISCOVER).

Extracting Acoustic Source Information of Shipping Noise for Dynamic Ambient Noise Modelling

Shipping traffic is one of the largest contributors to anthropogenic noise in the ocean. Noise generated by merchant ships elevates natural occurring ambient noise level by 20-30 dB in many areas of the world＇s ocean. In order to model the contributions of the noise generated by merchant ships to underwater ambient noise level correctly, a database that consists of the source levels as a function of frequency for different types of ships is essential. This paper describes the conceptual design, with an emphasis on the characteristics of shipping noise as sound sources, of a marine noise database. It was developed for providing necessary parameters for underwater ambient noise modelling. The parameters relevant to shipping noise modelling are organized in two catalogues： （l） source-receiver geometry related parameters, namely the coordinates of the ships at a given time period, as well as the sizes/types of the ships from which the noise source depths may be derived, and （2） acoustically relevant parameters, i.e., the acoustic SLs （source levels） at given frequencies. An example is presented here to demonstrate the efficacy of this database. The study area is a 117 × 55 km2 region off the coast of La Spezia, Italy, in the Mediterranean Sea.

Bark-Band Residual Noise Model for Parametric Audio Coding

A Bark-band residual noise model integrated with the human hearing mechanism is proposed to efficiently complement sinusoidal model in parametric audio coding. The time-varying spectrum of the residual noise is retrieved by Bark-scale piecewise constant magnitude estimates along with random phases. In the proposed noise model, Bark bands information is obtained by short-time FFT method and window overlap-add technique is exploited to remove boundary discontinuities. SVQ is also incorporated into parameter quantization process for the low bit-rate coding demand. Simulation results and informal listening tests show that when the sinusoidal model is combined with the Bark-band noise model, better synthesis audio quality can be achieved compared with the original sinusoidal modeling audio codec.

Equivalent Noise Models in Fiber Optical Communication Systems

An equivalent noise model of optical receiver amplifiers as shown in Fig.1 has been given in many fiber optical communication literatures. It is proved in this paper that this equivalent noise model is neither equivalent to the original one nor measurable. The main reason is that the position of the input impedance in this noise model is not the same with its in the typical noise model,but the same S vn , S in with the typical noise model are used. So the equivalent noise model above is wrong and is not fit to be taken into application.

A Review of a Study on Disc Brake Noise

With the development of the automotive industry, disc brake noise has become an issue of growing concern to the automotive industry and customers. In this paper, the types of disc brake noise have been discussed. After that, the theories and models that have been proposed as an explanation of brake squeal are reviewed. On the basis of these theories and models, some example simulations of disc brake squeal which use the Finite Element method and mathematical model have been introduced.

Progress and Research on Interconnects Crosstalk in Deep Submicron Technology

As develops in deep sub micron designs,the interconnect crosstalk becomes much more serious.Espe cially, the coupling inductance can not be ignored in gigahertz designs.So shield insertion is an efficient technique to reduce the inductive noise.In this paper,the characteristics of on chip mutual inductance (as well as self) for coplanar,micro stripline and stripline structures are introduced first.Then base on the coplanar interconnect structures,the effective coupling K eff model and the RLC explicit noise model are proposed respectively.The results of experiments show that these two models both have high fidelity.

Design and Implementation of a Wideband HF Channel Simulator

The paper presents a wideband high frequency (HF) channel simulator. The simulation consists of a software simulation of a direct-sequence spread-spectrum (DSSS) HF system with RAKE receiver. The validity of the HF channel simulator is demonstrated by the analysis of the scattering function and example results of the simulator. The performance of DS system under HF channel and noise are also presented. The simulation results and theoretical analysis show that a straightforward DS system may properly mitigate the intersymbol interference (ISI) distortion of the HF channel while perform badly under the HF noise and interference.

Multi-scale expression of spatial activity anomalies of earthquakes and its indicative significance on the space and time attributes of strong earthquakes

The noise modei based on 6 trous wavelet algorithm produces a multi-scale expression of image through the combination of wavelet transform and a testing modei of statistical significance. This kind of expression not only gives the formation and location of image structure on different scales, but also eliminates the influence of noise. Since the algorithm does not need any priori hypotheses, it is suitable for the data with complex structure. The research line is employed in this paper to analyze the spatial activity of earthquake. The method of how to recognize and describe the multi-scale space activity of earthquake is emphatically discussed in this paper. Taking typical sequences in Southwest China as research cases, we systematically study the structure characters of spatial activity of earthquake on different scales. Results show that multi-scale space structure to some extent possesses indicative effect on strong epicenters. And the foreshock anomalies of Songpan seismic sequence also reveal interesting pattern during the spatial-temporal evolvement.

Joint fuzzy background and adaptive foreground model for moving target detection

Moving target detection is one of the most basic tasks in computer vision.In conventional wisdom,the problem is solved by iterative optimization under either Matrix Decomposition(MD)or Matrix Factorization(MF)framework.MD utilizes foreground information to facilitate background recovery.MF uses noise-based weights to fine-tune the background.So both noise and foreground information contribute to the recovery of the background.To jointly exploit their advantages,inspired by two framework complementary characteristics,we propose to simultaneously exploit the advantages of these two optimizing approaches in a unified framework called Joint Matrix Decomposition and Factorization(JMDF).To improve background extraction,a fuzzy factorization is designed.The fuzzy membership of the background/foreground association is calculated during the factorization process to distinguish their contributions of both to background estimation.To describe the spatio-temporal continuity of foreground more accurately,we propose to incorporate the first order temporal difference into the group sparsity constraint adaptively.The temporal constraint is adjusted adaptively.Both foreground and the background are jointly estimated through an effective alternate optimization process,and the noise can be modeled with the specific probability distribution.The experimental results of vast real videos illustrate the effectiveness of our method.Compared with the current state-of-the-art technology,our method can usually form the clearer background and extract the more accurate foreground.Anti-noise experiments show the noise robustness of our method.

Effect of the span of Australian GPS coordinate time series in establishing an optimal noise model

The span of coordinate time series affects the determination of an optimal noise model. We analyzed position data recorded for 10 continuous Global Positioning System （GPS） sites from 1998.0 to mid-2009 on the Australian Plate to estimate the best noise model and thereafter obtain the true uncertainties of the velocity, employing the maximum likelihood estimation （MLE） method. MLE was employed to analyze the data in four ways. In the first two analyses, the noise was assumed to be a combination of flicker noise and white noise for the raw time series and spatially filtered time series. In the final two analyses, the spectral indices and amplitudes were simultaneously estimated for a power law noise plus white noise model for the raw time series and spatially filtered time series. We conclude that the noise model of GPS time series in Australia can be best described as the combination of flicker noise and white noise. Velocity uncertainties fall below -0.2 mm/yr when the time span exceeds -9.5 years. A comparison of noise amplitudes and maximum likelihood estimation values between the raw and spatially filtered time series suggests that traditional spatial filtering to remove common-mode errors might not be applicable to the raw time series of this region.

General noise support vector regression with non-constant uncertainty intervals for solar radiation prediction

General noise cost functions have been recently proposed for support vector regression(SVR). When applied to tasks whose underlying noise distribution is similar to the one assumed for the cost function, these models should perform better than classical -SVR. On the other hand, uncertainty estimates for SVR have received a somewhat limited attention in the literature until now and still have unaddressed problems. Keeping this in mind,three main goals are addressed here. First, we propose a framework that uses a combination of general noise SVR models with naive online R minimization algorithm(NORMA) as optimization method, and then gives nonconstant error intervals dependent upon input data aided by the use of clustering techniques. We give theoretical details required to implement this framework for Laplace, Gaussian, Beta, Weibull and Marshall–Olkin generalized exponential distributions. Second, we test the proposed framework in two real-world regression problems using data of two public competitions about solar energy. Results show the validity of our models and an improvement over classical -SVR. Finally, in accordance with the principle of reproducible research, we make sure that data and model implementations used for the experiments are easily and publicly accessible.