Development and Application of a Power Law Constitutive Model for Eddy Current Dampers

Eddy current dampers (ECDs) have emerged as highly desirable solutions for vibration control due to theirexceptional damping performance and durability. However, the existing constitutive models present challenges tothe widespread implementation of ECD technology, and there is limited availability of finite element analysis (FEA)software capable of accurately modeling the behavior of ECDs. This study addresses these issues by developing anewconstitutivemodel that is both easily understandable and user-friendly for FEAsoftware. By utilizing numericalresults obtained from electromagnetic FEA, a novel power law constitutive model is proposed to capture thenonlinear behavior of ECDs. The effectiveness of the power law constitutive model is validated throughmechanicalproperty tests and numerical seismic analysis. Furthermore, a detailed description of the application process ofthe power law constitutive model in ANSYS FEA software is provided. To facilitate the preliminary design ofECDs, an analytical derivation of energy dissipation and parameter optimization for ECDs under harmonicmotionis performed. The results demonstrate that the power law constitutive model serves as a viable alternative forconducting dynamic analysis using FEA and optimizing parameters for ECDs.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

Intrusion Detection Using Ensemble Wrapper Filter Based Feature Selection with Stacking Model

The number of attacks is growing tremendously in tandem with the growth of internet technologies.As a result,protecting the private data from prying eyes has become a critical and tough undertaking.Many intrusion detection solutions have been offered by researchers in order to decrease the effect of these attacks.For attack detection,the prior system has created an SMSRPF(Stacking Model Significant Rule Power Factor)classifier.To provide creative instance detection,the SMSRPF combines the detection of trained classifiers such as DT(Decision Tree)and RF(Random Forest).Nevertheless,it does not generate any accuratefindings that are adequate.The suggested system has built an EWF(Ensemble Wrapper Filter)feature selection with SMSRPF classifier for attack detection so as to overcome this problem.The UNSW-NB15 dataset is used as an input in this proposed research project.Specifically,min–max normalization approach is used to pre-process the incoming data.The feature selection is then carried out using EWF.Based on the selected features,SMSRPF classifiers are utilized to detect the attacks.The SMSRPF is integrated with the trained classi-fiers such as DT and RF to create creative instance detection.After that,the testing data is classified using MCAR(Multi-Class Classification based on Association Rules).The SRPF judges the rules correctly even when the confidence and the lift measures fail.Regarding accuracy,precision,recall,f-measure,computation time,and error,the experimental findings suggest that the new system outperforms the prior systems.

A Thermal-Conscious Integrated Circuit Power Model and Its Impact on Dynamic Voltage Scaling Techniques

We propose a novel thermal-conscious power model for integrated circuits that can accurately predict power and temperature under voltage scaling. Experimental results show that the leakage power consumption is underestimated by 52 % if thermal effects are omitted. Furthermore, an inconsistency arises when energy and temperature are simultaneously optimized by dynamic voltage scaling. Temperature is a limiting factor for future integrated circuits,and the thermal optimization approach can attain a temperature reduction of up to 12℃ with less than 1.8% energy penalty compared with the energy optimization one.

A Slice Analysis-Based Bayesian Inference Dynamic Power Model for CMOS Combinational Circuits

To improve the accuracy and speed in cycle-accurate power estimation, this paper uses multiple dimensional coefficients to build a Bayesian inference dynamic power model. By analyzing the power distribution and internal node state, we find the deficiency of only using port information. Then, we define the gate level number computing method and the concept of slice, and propose using slice analysis to distill switching density as coefficients in a special circuit stage and participate in Bayesian inference with port information. Experiments show that this method can reduce the power-per-cycle estimation error by 21.9% and the root mean square error by 25.0% compared with the original model, and maintain a 700 ＋ speedup compared with the existing gate-level power analysis technique.

Allometric models for leaf area and leaf mass predictions across different growing seasons of elm tree(Ulmus japonica)

Convenient and effective methods to determine seasonal changes in individual leaf area (LA) and leaf mass (LM) of plants are useful in research on plant physiology and forest ecology. However, practical methods for estimating LA and LM of elm (Ulmus japonica) leaves in different periods have rarely been reported. We collected sample elm leaves in June, July and September. Then, we developed allometric models relating LA, LM and leaf parameters, such as leaf length (L) and width (W) or the product of L and W (LW). Our objective was to find optimal allometric models for conveniently and effectively estimating LA and LM of elm leaves in different periods. LA and LM were significantly correlated with leaf parameters (P < 0.05), and allometric models with LW as an independent variable were best for estimating LA and LM in each period. A linear model was separately developed to predict LA of elm leaves in June, July and September, and it yielded high accuracies of 93, 96 and 96%, respectively. Similarly, a specific allometric model for predicting LM was developed separately in three periods, and the optimal model form in both June and July was a power model, but the linear model was optimal for September. The accuracies of the allometric models in predicting LM were 88, 83 and 84% for June, July and September, respectively. The error caused by ignoring seasonal variation of allometric models in predicting LA and LM in the three periods were 1-4 and 16-59%, respectively.

Multilevel Power Modeling of Base Station and Its ICs

A new power estimation method is proposed for base station(BS) in this paper.Based on this method,a software platform for power estimation is developed.The proposed method models power consumption on different abstraction levels by splitting a typical base station into several basic components at different levels in the view of embedded system design.In particular,our focus is on baseband IC(Integrate Circuit) due to it's the dominant power consumer in small cells.Baseband power model is based on arithmetic computing costs of selected algorithms.All computing and storage costs are calibrated using algorithm complexity,hardware architecture,activity ratio,silicon technology,and overheads on all hierarchies.Micro architecture and IC technology are considered.The model enables power comparison of different types of base stations configured with different baseband algorithms,micro architectures,and ICs.The model also supports cellular operators in power estimation of different deployment strategies and transmission schemes.The model is verified by comparing power consumption with a real LTE base station.By exposing more configuration freedoms,the platform can be used for power estimation of current and future base stations.

Stability of GM(1,1) power model on vector transformation

The morbidity problem of the GM（1,1） power model in parameter identification is discussed by using multiple and rotation transformation of vectors. Firstly we consider the morbidity problem of the special matrix and prove that the condition number of the coefficient matrix is determined by the ratio of lengths and the included angle of the column vector, which could be adjusted by multiple and rotation transformation to turn the matrix to a well-conditioned one. Then partition the corresponding matrix of the GM（1,1） power model in accordance with the column vector and regulate the matrix to a well-conditioned one by multiple and rotation transformation of vectors, which completely solve the instability problem of the GM（1,1） power model. Numerical results show that vector transformation is a new method in studying the stability problem of the GM（1,1） power model.

Influence of lateral initial pressure on axisymmetric wave propagation in hollow cylinder based on first power hypo-elastic model

Influence of identical applied initial pressures on the radial surfaces of a hollow cylinder which is compose of materials with first power hypo-elastic constitutive model was investigated.The basic equations of the problem were built up based on the framework of piecewise homogeneous body model with the use of three-dimensional linearized theory of elastic waves in initially stressed bodies(TLTEWISB).With the method proposed previously,this problem was then solved numerically.Moreover,the dispersion group velocity of the lowest order mode with different initial pressures was also studied.It can be concluded that the initial pressure and the geometry parameters will induce considerable changes of different degrees in dispersive relation between phase velocity and wave number in opposite trend(positive in initial pressure and negative in thickness).

A power function model for simulating creep mechanical properties of salt rock

In this paper,a new micro-creep model of salt rock is proposed based on a linear parallel bonded model(LPBM)using the two-dimensional particle flow code(PFC2D).The power function weakening form is assumed to describe the variation of the parallel bonded diameter(PBD)over time.By comparing with the parallel-bonded stress corrosion(PSC)model,a smaller stress fluctuation and smoother creep strain−time curves can be obtained by this power function model at the same stress level.The validity and adaptability of the model to simulate creep deformation of salt rock are verified through comparing the laboratory creep test curves and the Burgers model fitting result.The numerical results reveal that this model can be capable of capturing the creep deformation and damage behavior from the laboratory observations.

APPROXIMATE POWER OF HETEROSCEDASTICITY TEST IN NONLINEAR MODELS WITH ARIMA（0,1,0） ERRORS

This paper presents an approach for estimating power of the score test, based on an asymptotic approximation to the power of the score test under contiguous alternatives. The method is applied to the problem of power calculations for the score test of heteroscedasticity in European rabbit data （Ratkowsky, 1983）. Simulation studies are presented which indicate that the asymptotic approximation to the finite-sample situation is good over a wide range of parameter configurations.

A Theoretical Model for the Asymmetric Transmission of Powerful Acoustic Wave in Double-Layer Liquids

A theoretical model which couples the oscillation of cavitation bubbles with the equation of an acoustic wave is utilized to describe the sound fields in double-layer liquids, which can be used to realize the asymmetric transmission of acoustic waves. Numerical simulations show that the asymmetry is related to the properties of the host liquids and the input acoustic wave. Asymmetry can be enhanced if the maximum number density or the ambient radius of the cavitation bubbles in the low cavitation threshold liquid increases. Moreover, the direction of rectification will be reversed if the amplitude of the input acoustic wave becomes high enough.

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.

Novel extended C-m models of flow stress for accurate mechanical and metallurgical calculations and comparison with traditional flow models

Here,we developed novel extended piecewise bilinear power law(C-m)models to describe flow stresses under broad ranges of strain,strain rate,and temperature for mechanical and metallurgical calculations during metal forming at elevated temperatures.The traditional C-m model is improved upon by formulating the material parameters C and m,defined at sample strains and temperatures as functions of the strain rate.The coefficients are described as a linear combination of the basis functions defined in piecewise patches of the sample strain and temperature domain.A comparison with traditional closed-form function flow models revealed that our approach using the extended piecewise bilinear C-m model is superior in terms of accuracy,ease of use,and adaptability;additionally,the extended C-m model was applicable to numerical analysis of mechanical,metallurgical,and microstructural problems.Moreover,metallurgy-related values can be calculated directly from the flow stress information.Although the proposed model was developed for materials at elevated temperatures,it can be applied over a broad temperature range.

Modeling Distortion Signals of Power Grid Based on Wiener-G Functionals

The uniform mathematical model of distortion signals in power grid has been setup with the theory of Wiener-G Functional. Firstly,the Matlab simulation models were established. Secondly,the Wiener kernel of power load was found based on the Gaussian white noise as input. And then the uniform mathematical model of the power grid signal was established according to the homogeneous of the same order of Wiener functional series. Finally,taking three typical distortion sources which are semiconductor rectifier,electric locomotive and electric arc furnace in power grid as examples,we have validated the model through the Matlab simulation and analyzed the simulation errors. The results show that the uniform mathematical model of distortion signals in power grid can approximation the actual model by growing the items of the series under the condition of the enough storage space and computing speed.

Prediction of a maximum pull-out load of anchor bolts using an optimal combination model

The mixed model of improved exponential and power function and unequal interval gray GM(1,1)model have poor accuracy in predicting the maximum pull-out load of anchor bolts.An optimal combination model was derived using the optimally weighted combination theory and the minimum sum of logarithmic squared errors as the objective function.Two typical anchor bolt pull-out engineering cases were selected to compare the performance of the proposed model with those of existing ones.Results showed that the optimal combination model was suitable not only for the slow P-s curve but also for the steep P-s curve.Its accuracy and stable reliability,as well as its prediction capability classification,were better than those of the other prediction models.Therefore,the optimal combination model is an effective processing method for predicting the maximum pull-out load of anchor bolts according to measured data.

Effect of Particle Phase Volume, Shape and Liquid Phase Concentrations on Rheological Properties of Large Particulate-Liquid Model Food Systems by Using Ball Measuring System

Rheological properties of large particulate-liquid model food systems were studied by using the BMS （ball measuring system）. The model food systems were composed of alginate gel particles （-10mm） and a gelatinised starch solution with 1% w/w sodium chloride as a liquid phase. The effects of particle phase volume （Ф, 0-0.60）, particle shapes （cube, sphere, rod and disc） and starch concentrations （3% and 5% w/w starch） were investigated. The power law model was successfully applied to characterize the flow properties of each system and the consistency K and power law index n were obtained. The K increased and n decreased with increasing # for samples at all particle shapes at 3% w/w starch in the liquid phase. The particle effect on the viscosity is further analysed by means of the Krieger-Dougherty model and the maximum packing fraction #,, and the intrinsic viscosity [η] were obtained in each system. The Фm, depended on the particle shape, as expected. The [7] value depended on particle shape and was largely in the order of 4.04 （cube）, 3.28 （disc）, 2.56 （sphere） and 2.32 （rod） at 3% w/w starch. The [η] also depended on starch concentration and was 1.1 at 5%,6 w/w starch in the liquid phase with spherical particles. The present results show successful application of BMS to study the rheological properties of large particulate liquid food systems at relatively small scale experiment （-0.5 L） and also that existing models for suspension rheology are applicable for such food systems to a great extend.

A New Model of China-US Big Power Relations： A Symbolic Sign in Foreign Affairs

To cope with the current crisis and tensions full-filled China-US relationship, Chinese President Xi Jinping put forward the concept of building a new model of China-US big power relations, which the US agrees. Yet the new model won a heated discussion. In China this new model was evaluated positively and optimistically, while in the US it was perceived as a strategic challenge or even a threat. In the present article, the author proposes that this new model of China-US big power relations is more like a symbolic sign in foreign affairs rather than a strategic challenge or a threat or an effective and workable mechanism at this moment, and meanwhile analyses this view from diachronic and semiotic perspectives. The analyses reveal that the new model functions as a symbolic sign, signifying to the world that conceptually the two big powers have a good and harmonious relationship.

A model solar energy power plant will be built in Shandong Province

A US-China jointly owned model solar energy power plant, with an ultimate installed capacity of 2 000 MW, will be built in Shandong Province.

Identification of Type of a Fault in Distribution System Using Shallow Neural Network with Distributed Generation

A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.