Power Estimation of Acoustic Sources by Sensor Array Processing

A constant problem is to localize a number of acoustic sources, to separate their individual signals and to estimate their strengths in a propagation medium. An acoustic receiving array with signal processing algorithms is then used. The most widely used algorithm is the conventional beamforming algorithm but it has a very low resolution and high sidelobes that may cause a signal leakage problem. Several new signal processors for arrays of sensors are derived to evaluate the strengths of acoustic signals arriving at an array of sensors. In particular, we present the covariance vector estimator and the pseudoinverse of the array manifold matrix estimator. The covariance vector estimator uses only the correlations between sensors and the pseudoinverse of the array manifold matrix estimator operates with the minimum eigenvalues of the covariance matrix. Numerical and experimental results are presented.

Power Prediction of VLSI Circuits Using Machine Learning

The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit.A big database is needed to undertake important analytical work like statistical method,heat research,and IR-drop research that results in extended running times.This unit focuses on the assessment of test strength.Because of the enormous number of successful designs for currentmodels and the unnecessary time required for every test,maximum energy ratings with all tests cannot be achieved.Nevertheless,test safety is important for producing trustworthy findings to avoid loss of output and harm to the chip.Generally,effective power assessment is only possible in a limited sample of pre-selected experiments.Thus,a key objective is to find the experiments that might give the worst situations again for testing power.It offers a machine-based circuit power estimation(MLCPE)system for the selection of exams.Two distinct techniques of predicting are utilized.Firstly,to find testings with power dissipation,it forecasts the behavior of testing.Secondly,the changemovement and energy data are linked to the semiconductor design,identifying small problem areas.Several types of algorithms are utilized.In particular,the methods compared.The findings show great accuracy and efficiency in forecasting.That enables such methods suitable for selecting the worst scenario.

Distributed Photovoltaic Real-Time Output Estimation Based on Graph Convolutional Networks

The rapid growth of distributed photovoltaic(PV)has remarkable influence for the safe and economic operation of power systems.In view of the wide geographical distribution and a large number of distributed PV power stations,the current situation is that it is dificult to access the current dispatch data network.According to the temporal and spatial characteristics of distributed PV,a graph convolution algorithm based on adaptive learning of adjacency matrix is proposed to estimate the real-time output of distributed PV in regional power grid.The actual case study shows that the adaptive graph convolution model gives different adjacency matrixes for different PV stations,which makes the corresponding output estimation algorithm have higher accuracy.

Transfer Strategy for Power Output Estimation of Wind Farm at Planning Stage Based on a SVR Model

The conventional wind farm(WF)power generation modelling method highly relies on wind hindcast produced by record time-series data or numerical weather modelling.However,estimating production at future sites is challenging in the absence of local wind monitoring.To address this,a data-driven WF modelling and model transfer strategy is proposed in this work.It considers the challenge of how to transpose metered data from existing operational WFs to sites that might feature as a prospective site for a new WF.By modelling 14 WFs distributed across Scotland using a machine learning(ML)approach,this study proved it was possible to effectively model metered production at a site using modelled wind speed and direction.In addition,this study also found when the latitude difference between two WFs is less than 0.2 degrees and the distance is less than 5o km,two WFs in non-mountainous areas can share an ML model.The results of the shared ML model remain superior to the results of the given power curve from manufacturers,after adjusting the results by the ratio of the power curve in these two WFs.The WF model transfer strategy investigated in this work offered a novel approach to transposing WF production estimates to new sites and appeared to offer better value than simple power curves,which is of importance at the early planning stage for site selection,although it would likely not fully replace detailed micro-siting modelling which are well established in the industry.Index Terms-Machine learning,model transfer strategy,power curve,power output estimation,wind farm.

Second-Order Kalman Filtering Application to Fading Channels Supported by Real Data

The lack of effective techniques for estimation of shadow power in fading mobile wireless communication channels motivated the use of Kalman Filtering as an effective alternative. In this paper, linear second-order state space Kalman Filtering is further investigated and tested for applicability. This is important to optimize estimates of received power signals to improve control of handoffs. Simulation models were used extensively in the initial stage of this research to validate the proposed theory. Recently, we managed to further confirm validation of the concept through experiments supported by data from real scenarios. Our results have shown that the linear second-order state space Kalman Filter (KF) can be more accurate in predicting local shadow power profiles than the first-order Kalman Filter, even in channels with imposed non-Gaussian measurement noise.

Classical and modern power spectrum estimation for tune measurement in CSNS RCS

Precise measurement of betatron tune is required for good operating condition of CSNS RCS. The fractional part of betatron tune is important and it can be measured by analyzing the signals of beam position from the appointed BPM. Usually these signals are contaminated during the acquisition process, therefore several power spectrum methods are used to improve the frequency resolution. In this article classical and modern power spectrum methods are used. In order to compare their performance, the results of simulation data and IQT data from J-PARC RCS are discussed, It is shown that modern power spectrum estimation has better performance than the classical ones, though the calculation is more complex.

Dynamic modeling of ultra-precision fly cutting machine tool and the effect of ambient vibration on its tool tip response

The dynamic performances of an ultra-precision fly cutting machine tool(UFCMT)has a dramatic impact on the quality of ultra-precision machining.In this study,the dynamic model of an UFCMT was established based on the transfer matrix method for multibody systems.In particular,the large-span scale flow field mesh model was created;and the variation in linear and angular stiffness of journal and thrust bearings with respect to film thickness was investigated by adopting the dynamic mesh technique.The dynamic model was proven to be valid by comparing the dynamic characteristics of the machine tool obtained by numerical simulation with the experimental results.In addition,the power spectrum density estimation method was adopted to simulate the statistical ambient vibration excitation by processing the ambient vibration signal measured over a long period of time.Applying it to the dynamic model,the dynamic response of the tool tip under ambient vibration was investigated.The results elucidated that the tool tip response was significantly affected by ambient vibration,and the isolation foundation had a good effect on vibration isolation.

Symmetrical Processing of Interferogram and Spectrum Reconstruction in Interference Spectrometer

Because of its all-reflective layout based on the Fresnel double-mirror interference system, the newly developed Fourier transform imaging spectrometer has a very large spectral bandwidth ranged from a cut-off wavelength (related to the cut-off wave number σ max ) to far infrared. According to the signal's symmetry and wide-band characteristics, a simple method that can efficiently weaken the low frequency noise in the reconstructed spectrum is presented. Also, according to the symmetry, the eigenvector method is applied to the reconstruction of the spectrum.

Model Reduction by Generalized Falk Method for Efficient Dedicated to Professor Karl Stark Pister for his 95th birthday Field-Circuit Simulations

The Generalized Falk Method(GFM)for coordinate transformation,together with two model-reduction strategies based on this method,are presented for efficient coupled field-circuit simulations.Each model-reduction strategy is based on a decision to retain specific linearly-independent vectors,called trial vectors,to construct a vector basis for coordinate transformation.The reduced-order models are guaranteed to be stable and passive since the GFM is a congruence transformation of originally symmetric positive definite systems.We also show that,unlike the Pade-via-Lanczos(PVL)method,the GFM does not generate unstable positive poles while reducing the order´of circuit problems.Further,the proposed GFM is also faster when compared to methods of the type Lanczos(or Krylov)that are already widely used in circuit simulations for electrothermal and electromagnetic problems.The concept of response participation factors is introduced for the selection of the trial vectors in the proposed model-reduction methods.Further,we present methods to develop simple equivalent circuit networks for the field component of the overall field-circuit system.The implementation of these equivalent circuit networks in circuit simulators is discussed.With the proposed model-reduction strategies,significant improvement on the efficiency of the generalized Falk method is illustrated for coupled field-circuit problems.

Think Home: A Smart Home as Digital Ecosystem

This work brings all new and advanced technology which is proposed for refinement and improvement in the existing electrification system at domestic as well as commercial levels including hotels, commercial complexes, apartments, rented floors and rooms. This advanced module will not only convey means of luxury but will also accomplish real-time energy monitoring and cost es-timation. This developed module will rule out entire re-wiring and will be fruitful at places where installation of a new meter was a problem. The new system after installation will offer means of comfort to the consumer, elderly as well as handicapped and disabled people in operating electric load with ease and comfort. Apart from this, it would also benefit the apartment/hotel owner’s and business personnel who have rented their property or portion of property and face problems in calculating energy bill.

Detection of false data injection attacks on power systems using graph edge-conditioned convolutional networks

State estimation plays a vital role in the stable operation of modern power systems,but it is vulnerable to cyber attacks.False data injection attacks(FDIA),one of the most common cyber attacks,can tamper with measure-ment data and bypass the bad data detection(BDD)mechanism,leading to incorrect results of power system state estimation(PSSE).This paper presents a detection framework of FDIA for PSSE based on graph edge-conditioned convolutional networks(GECCN),which use topology information,node features and edge features.Through deep graph architecture,the correlation of sample data is effectively mined to establish the mapping relationship between the estimated values of measurements and the actual states of power systems.In addition,the edge-conditioned convolution operation allows processing data sets with different graph structures.Case studies are undertaken on the IEEE 14-bus system under different attack intensities and degrees to evaluate the performance of GECCN.Simulation results show that GECCN has better detection performance than convolutional neural networks,deep neural net-works and support vector machine.Moreover,the satisfactory detection performance obtained with the data sets of the IEEE 14-bus,30-bus and 118-bus systems verifies the effective scalability of GECCN.

The monotonic increasing relationship between average powers of CMOS VLSI circuits with and without delay and its applications

The authors theoretically describe the monotonic increasing relationship between average powers of a CMOS VLSI circuit with and without delay. The power of an ideal circuit without delay, which can be fast computed, has been used as the evaluation criterion for the power of a practical circuit with delay, which needs more computing time, in such fields as fast estimation for the average power and the maximum power, and fast optimization for the low test power. The authors propose a novel simulation approach that uses delay-free power to compact a long input vector pair sequence into a short sequence and then, uses the compacted one to fast simulate the average (or maximum) power for a CMOS circuit. In comparison with the traditional simulation approach that uses an un-compacted input sequence to simulate the average (or maximum) power, experiment results demonstrate that in the field of fast estimation for the average power, the present approach can be 6-10 times faster without significant loss in accuracy (less than 3.5% on average), and in the field of fast estimation for the maximum power, this approach can be 6? times faster without significant loss in accuracy (less than 5% on average). In the field of fast optimization for the test power, the authors propose a novel delay-free power optimization approach for the test power. Experiment results demonstrate that, in comparison with the approach of direct optimization and the approach of Hamming distance optimization, this approach is of the highest optimization efficiency because it needs shorter time (16.84%) to obtain a better optimization effect (reducing 35.11% test power).

An approach to predicting dynamic power dissipation of coupled interconnect network in dynamic CMOS logic circuits

In deep submicron (DSM) integrated circuits (IC), coupling capacitors between interconnects become dominant over grounded capacitors. As a result, the dynamic power dissipation of one node is no longer only in relation to the signal on that node, and it also depends on signals on its neighbor nodes through coupling capacitors. Thus, for their limitation in dealing with ca-pacitively coupled nets, past jobs on power estimation are facing rigorous challenges and need to be ameliorated. This paper proposes and proves a simple and fast approach to predicting dynamic power dissipation of coupled interconnect networks: a coupling capacitor in dynamic CMOS logic circuits is decoupled and mapped into an equivalent cell containing an XOR gate and a grounded capacitor, and the whole circuit after mapping, consuming the same power as the original one, could be easily managed by generally-used gate-level power estimation tools. This paper also investigates the correlation coefficient method (CCM). Given the signal probabilities and the correlation coefficients between signals, the dynamic power of interconnect networks can be calculated by using CCM. It can be proved that the decoupling method and CCM draw identical results, that is to say, the decoupling method implicitly preserves correlation properties between signals and there is no accuracy loss in the decoupling process. Moreover, it is addressed that the coupling capacitors in static CMOS circuits could be decoupled and mapped into an equivalent cell containing a more complicated logic block, and the power can be obtained by the probability method for dynamic CMOS logic circuits.

A review of cyber security risks of power systems:from static to dynamic false data attacks

With the rapid development of the smart grid and increasingly integrated communication networks,power grids are facing serious cyber-security problems.This paper reviews existing studies on the impact of false data injection attacks on power systems from three aspects.First,false data injection can adversely affect economic dispatch by increasing the operational cost of the power system or causing sequential overloads and even outages.Second,attackers can inject false data to the power system state estimator,and this will prevent the operators from obtaining the true operating conditions of the system.Third,false data injection attacks can degrade the distributed control of distributed generators or microgrids inducing a power imbalance between supply and demand.This paper fully covers the potential vulnerabilities of power systems to cyber-attacks to help system operators understand the system vulnerability and take effective countermeasures.

Scanning paths for estimating sound power of noise sources by sound intensity scanning method

A mathematical model of deterndulng sound power by using the scanning method is developed. It is assumed that the scanning speed is constant and the noise source is stationary The accuracy of estimating sound power along some simple paths on the surfaces such as rectangle, disc and hemisphere is analyzed. It is argued that the accuracy of estimating sound power is strongly depended on a suitable selection of scan path. The accurate estdriation of sound power can be made by scanning along some simple paths.

Minimum Density Power Divergence Estimator for Negative Binomial Integer-Valued GARCH Models

In this paper,we study a robust estimation method for the observation-driven integervalued time-series models in which the conditional probability mass of current observations is assumed to follow a negative binomial distribution.Maximum likelihood estimator is highly affected by the outliers.We resort to the minimum density power divergence estimator as a robust estimator and showthat it is strongly consistent and asymptotically normal under some regularity conditions.Simulation results are provided to illustrate the performance of the estimator.An application is performed on data for campylobacteriosis infections.

Design and Experiments of Model-free Compound Controller of Flight Simulator

A model-flee compound controller design method is proposed to achieve the wide frequency bandwidth requirement of flight simulators. The method based on quantitative feedback theory, acquires system uncertainty under different working conditions through closed-loop identification with power spectrum estimation. Then in controller designing, it makes a trade, off between the strict requirements for magnitude-frequency characteristics and those for phase-frequency characteristics of flight simulators, by converting the indices of magnitude-frequency characteristics of flight simulators into quantitative feedback theory-based tracking specification bounds and using feedforward controller to attain the required phase-flequency characteristics. Simulation and experimental results indicate that, when used to design inner flame controller of flight simulator, the proposed method can fulfill the requirements for wide frequency bandwidth indices. Compared with other controller design methods, it has the property of model-free and transparency.