Industrial Fault Signals Propagation and Current Signature Analysis

Motor current signature analysis provides good results in laboratory environment. In real life situation, electrical machines usually share voltage and current from common terminals and would easily influence each other. This will result in considerable amount of interferences among motors and doubt in identity of fault signals. Therefore, estimating the mutual influence of motors will help identifying the original signal from the environmental noise. This research aims at modelling the propagation of signals that are caused by faults of induction motors in power networks. Estimating the propagation pattern of fault signal leads to a method to discriminate and identify the original source of major events in industrial networks. Simulation results show that source of fault could be identified using this approach with a higher certainty than anticipated output coming of any individual diagnosis.

Intra-Palm Propagation Signals as Suitable Biometrics for Successive Authentication

We propose the use of intra-palm propagation signals as biometrics. The intra-palm propagation signal is a signal that is propagated in the shallow part of the skin of a palm. In this paper, we prepare dedicated measuring devices and measure intra-palm propagation signals from twenty-one experimental subjects. We also evaluate the verification performance based on Euclidian distance or SVM （support vector machine）. The equal error rate in the case of SVM is aovroximatelv 24%.

Influence of the Gaussian colored noise and electromagnetic radiation on the propagation of subthreshold signals in feedforward neural networks

Iterative methods are used to simulate the in vitro feedforward neural networks in physiological experiments.Emissivity can be propagated to a minimum of ten groups.However,the discharge activity of each group will be more synchronized.The feedforward neural networks have a wide range of applications in machine learning,and the weight of synapses considerably influences the propagation of weak signals.Herein,we investigated the effect of Gaussian colored noise and electromagnetic radiation on the propagation of the subthreshold excitatory postsynaptic current signals in the input layer of the multilayer Izhikevich neural feedforward networks.In the absence of electromagnetic radiation,the excitatory postsynaptic current signal is stably propagated and amplified in multilayer feedforward neural networks under the optimal Gaussian colored noise strength or correlation time in the output layer of the network.Compared with the case in which there is no electromagnetic radiation,the presence of electromagnetic radiation slightly reduces the propagation of weak signals.Further,the time required to propagate the excitatory postsynaptic current signal to the output layer increases with the increasing feedback gain.The feedforward neural network considered in this study is a considerably simple model.More complex structures,such as backward connection and delayed feedback,can be observed in real biological systems.Hence,the next step will be to study more complex neural models with neuron models based on the physiological experimental data and compare them with real biological systems.Furthermore,the study of neural networks can be combined with an experimental study about the auditory nervous system of bats to understand the biological mechanism associated with the auditory system function of bats from two perspectives.

Signal Path Reckoning Localization Method in Multipath Environment

In the wireless localization application, multipath propagation seriously affects the localization accuracy. This paper presents two algorithms to solve the multipath problem. Firstly, we improve the Line of Possible Mobile Device(LPMD) algorithm by optimizing the utilization of the direct paths for single-bound scattering scenario. Secondly, the signal path reckoning method with the assistance of geographic information system is proposed to solve the problem of localization with multi-bound scattering paths. With the building model's idealization, the proposed method refers to the idea of ray tracing and dead reckoning. According to the rule of wireless signal reflection, the signal propagation path is reckoned using the measurements of emission angle and propagation distance, and then the estimated location can be obtained. Simulation shows that the proposed method obtains better results than the existing geometric localization methods in multipath environment when the angle error is controlled.

Propagation of quasi-periodic random sound pulse sequence signals in shallow-water waveguides

It is an efficient method to model ship radiated noise as quasi-periodic random sound pulse sequences. Based on the model, this paper discusses the characteristics change of ship noise after through shallow-water waveguides. Theoretical analysis and numerical simulation show that random waveguides and multi-path effects can bring much additional transmission loss for the noise line spectra, demonstrating the instability of ship radiated noise line spectra to a certain extent, and providing some theoretical support for advanced studies of ship radiated noise.

A Fuzzy-Logic Based Path Loss Model at 3.4 GHz for LTE Networks

Empirical and deterministic models have not proven to be effective in path loss predictions because of the problems of computational complexities, low accuracies, and inability to generalize. To solve these problems relating to path loss predictions, this article presents an optimal path loss propagation model developed at 3.4 GHz with the use of fuzzy logic. We introduced Fuzzy logic to accurately represent all forms of uncertainties in the data spectrum as the signal propagates from the transceiver to the receiver, thereby producing accurate results. Experimental data were collected across Cyprus at 3.4 GHz and compared with three existing path loss models. The fuzzy-logic path loss prediction model was then developed and compared with the experimental data and with each of the theoretical empirical models, the newly developed model predicted signal loss with the greatest accuracy as it gives the lowest root-mean-square error. The newly developed model is very efficient for signal propagation and path loss prediction.

Fault Detection for PMSM Motor Drive Systems by Monitoring Inverter Input Currents

This paper has proposed a fault detecting method for DC supplied permanent magnet synchronize motor(PMSM)drive systems by monitoring the drive DC input current.This method is based on the fault signal propagation from the torque disturbance on the motor shaft to the inverter input currents.The accuracy of this fault signal propagation is verified by the Matlab simulation and experiment tests with the emulated faulty conditions.The feasible of this approach is shown by the experimental test conducted by the Spectra test rig with the real gearbox fault.This detection scheme is also suitable for monitoring other drive components such as the power converter or the motor itself using only one set of current transducers mounted at the DC input side.

Leakage Current Estimation of CMOS Circuit with Stack Effect

Leakage current of CMOS circuit increases dramatically with the technologyscaling down and has become a critical issue of high performance system. Subthreshold, gate andreverse biased junction band-to-band tunneling (BTBT) leakages are considered three maindeterminants of total leakage current. Up to now, how to accurately estimate leakage current oflarge-scale circuits within endurable time remains unsolved, even though accurate leakage modelshave been widely discussed. In this paper, the authors first dip into the stack effect of CMOStechnology and propose a new simple gate-level leakage current model. Then, a table-lookup basedtotal leakage current simulator is built up according to the model. To validate the simulator,accurate leakage current is simulated at circuit level using popular simulator HSPICE forcomparison. Some further studies such as maximum leakage current estimation, minimum leakage currentgeneration and a high-level average leakage current macromodel are introduced in detail.Experiments on ISCAS85 and ISCAS89 benchmarks demonstrate that the two proposed leakage currentestimation methods are very accurate and efficient.

Adaptive access points deployment for indoor bluetooth positioning accuracy enhancement

The indoor positioning system based on fingerprint receives more and more attention due to its high positioning accuracy and time efficiency.In the existing positioning approaches,much consideration is given to the positioning accuracy improvement by using the angle of signal,but the optimization of access points(APs)deployment is ignored.In this circumstance,an adaptive APs deployment approach is proposed.First of all,the criterion of reference points(RPs)effective coverage is proposed,and the number of deployed APs in target environment is obtained by using the region partition algorithm and full coverage algorithm.Secondly,the wireless signal propagation model is established for target environment,and meanwhile based on the initial APs deployment,the simulation fingerprint database is constructed for the sake of establishing the discrimination function with respect to fingerprint database.Thirdly,the greedy algorithm is applied to optimize APs deployment.Finally,the extensive experiments show that the proposed approach is capable of achieving adaptive APs deployment as well as improving positioning accuracy.