Energy Entropy On-Demand Multipath Routing Protocol for Mobile Ad Hoc Networks

This paper provides a critical review of energy entropy theory in Mobile Ad Hoc Networks (MANETs) and proposes an Energy Entropy on Ad Hoc On-demand Distance Vector Multipath (EEAODVM) routing protocol. The essential idea of the protocol is to find every route which can minimize the node residual energy in the process of selecting path. It balances individual node battery energy utilization and hence prolongs the entire network lifetime. The results of simulation show that, with the proposed EEAODVM routing protocol, packet delivery ratio, routing overhead ratio, average end-to-end delay, network's lifetime and minimal residual energy ratio can be improved in most of cases. It is an available approach for multipath routing decision.

Alcoholism Detection by Wavelet Energy Entropy and Linear Regression Classifier

Alcoholism is an unhealthy lifestyle associated with alcohol dependence.Not only does drinking for a long time leads to poor mental health and loss of self-control,but alcohol seeps into the bloodstream and shortens the lifespan of the body’s internal organs.Alcoholics often think of alcohol as an everyday drink and see it as a way to reduce stress in their lives because they cannot see the damage in their bodies and they believe it does not affect their physical health.As their drinking increases,they become dependent on alcohol and it affects their daily lives.Therefore,it is important to recognize the dangers of alcohol abuse and to stop drinking as soon as possible.To assist physicians in the diagnosis of patients with alcoholism,we provide a novel alcohol detection system by extracting image features of wavelet energy entropy from magnetic resonance imaging(MRI)combined with a linear regression classifier.Compared with the latest method,the 10-fold cross-validation experiment showed excellent results,including sensitivity 91.54±1.47%,specificity 93.66±1.34%,Precision 93.45±1.27%,accuracy 92.61±0.81%,F1 score 92.48±0.83%and MCC 85.26±1.62%.

Droplets diameter distribution using maximum entropy formulation combined with a new energy-based sub-model

The maximum entropy principle(MEP) is one of the first methods which have been used to predict droplet size and velocity distributions of liquid sprays. This method needs a mean droplets diameter as an input to predict the droplet size distribution. This paper presents a new sub-model based on the deterministic aspects of liquid atomization process independent of the experimental data to provide the mean droplets diameter for using in the maximum entropy formulation(MEF). For this purpose, a theoretical model based on the approach of energy conservation law entitled energy-based model(EBM) is presented. Based on this approach, atomization occurs due to the kinetic energy loss. Prediction of the combined model(MEF/EBM) is in good agreement with the available experimental data. The energy-based model can be used as a fast and reliable enough model to obtain a good estimation of the mean droplets diameter of a spray and the combined model(MEF/EBM) can be used to well predict the droplet size distribution at the primary breakup.

Robot’s Emotion Decision by Energy and Entropy Concepts

This study is to introduce concepts of energy and entropy to describe a robot＇s emoton decisien. It chooses the dimensional approach based on factors of pleasure and arousal for the merit of the interpolation between enotions. Especially, Circumplex model which has also two axes： pleasure and arousal is used. Besides, the model indicates how emotions are distributed in the two-dimensional plane. Then by the definition of psychodynamicsthe energy states （mental energy and physical energy） are matched to pleasure and arousal respectively that are the basis of Circumplex model. The mental energy is updated by the result of Prospect theory which measures the value of gain and loss as pleasure factor. And the physical energy is updated by the result of hedonic scaling which measures levels of arousal from pleasure computed by Prospect theory, and the result of intensity of stimuli. Then the energy states are fed by entropy. The feedback loop by entropy satisfies the 2nd law of thermodynamics. The energy states generated by stimuli and fed by entropy take a position in the plane of Circumplex model. Then distances between the current position and other emotions are cornputed to get a level of each emotion, proportional to the inverse of the distance.

A New Energy-Efficient and Reliable Protocol for Stochastic WSNs Based on Back-Pressure and Entropy of Residual Energy

In this paper, a new energy-efficient and reliable routing protocol is introduced for WSNs including a stochastic traffic generation model and a wakeup/sleep mechanism. Our objective is to improve the longevity of the WSNs by energy balancing but providing reliable packet transfer to the Base Station at the same time. The proposed protocol is based on the principle of the back-pressure method and besides the difference of backlogs, in order to optimize energy consumption, we use a cost function related to an entropy like function defined over the residual energies of the nodes. In the case of two-hop routing the optimal relay node is selected as the one which has maximum backlog difference and keeps the distribution of residual energy as close to uniform as possible where the uniformity is measured by the change of the entropy of the residual energy of the nodes. The protocol assumes Rayleigh fading model. Simulation results show that the proposed algorithm significantly improves the performance of traditional back-pressure protocol with respect to energy efficiency, E2E delay and throughput, respectively.

Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction

Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.

Research on the detecting methods of singularity in deformation signal based on two kinds of wavelet entropy

There are various influencing factors that affect the deformation observation, and deformation signals show differ- ent characteristics under different scales. Wavelet analysis possesses multi-scale property, and the information entropy has great representational capability to the complexity of information. By hamming window to the wavelet coefficients and windowed wavelet energy obtained by multi-resolution analysis （MRA）, it can be achieved to measure the wavelet time entropy （WTE） and wavelet energy entropy （WEE）. The paper established deformation signals, selected the parameters, and compared the sin- gularity detection ability and anti-noise ability of two kinds of wavelet entropy and applied them to the singularity detection at the GPS continuously operating reference stations. It is shown that the WTE performs well in weak singularity information de- tection in finite frequency components signals and the WEE is more suitable for detecting the singularity in the signals with complex, strong background noise.

Entropy change-induced elastic softening of lithiated materials

To understand the ＂elastic softening＂ of Li-Si alloys for the development of Li-ion batteries, the effect of stress-induced change of entropy on the mechanical properties of lithiated materials is examined within the theories of thermodynamics and linear elasticity, An approach is presented whereby the change of Gibbs free energy is governed by the change of the mixture entropy due to stress-induced migration of mobile atoms, from which the contribution of the change of the mixture entropy to the apparent elastic modulus of lithiated materials is determined. The reciprocal of the apparent elastic modulus of a lithiated material is a linear function of the concentration of mobile Li-atoms at a stress-free state and the square of the mismatch strain per unit mole fraction of mobile Li-atoms.

Method of Mechanical Fault Intelligent Diagnosis Based on Vibration Signal of High Voltage Circuit Breaker

Based on vibration signal of high voltage circuit breaker,a new method of intelligent fault diagnosis that wavelet packet extracts energy entropy which are used as characteristic vector of the support vector machine(SVM)to construct classifier for fault diagnosis is presented.The acceleration sensors are applied to collecting the vibration data of different states of high voltage circuit breakers based on self-made experimental platform in this method.The wavelet packet are fully applied to analyze the vibration signal and decompose vibration signal into three layers,and wavelet packet energy entropy of each frequency band are as the characteristic vector of circuit breaker failure mode.Then the intelligent diagnosis network is established on the basis of the support vector machine theory.It is verified that the method has a better capability of classification and a higher accuracy compared with the traditional neural network diagnosis method through distinguishing the three fault modes which are tripping device stuck,the vacuum arcing chamber fixed bolt looseness and too much friction force of the transmission mechanism of circuit breaker in this paper.

Thermodynamic properties of ZnSe under pressure and with variation in temperature

The thermodynamic properties of Zn Se are obtained by using quasi-harmonic Debye model embedded in Gibbscode for pressure range 0–10 GPa and for temperature range 0–1000 K. Helmholtz free energy, internal energy, entropy,Debye temperature, and specific heat are calculated. The thermal expansion coefficient along with Gruneisen parameter are also calculated at room temperature for the pressure range. It is found that internal energy is pressure dependent at low temperature, whereas entropy and Helmholtz free energy are pressure sensitive at high temperature. At ambient conditions,the obtained results are found to be in close agreement to available theoretical and experimental data.

Reconstruction of f(R) Gravity with Ordinary and Entropy-Corrected(m,n)-Type Holographic Dark Energy Model

In this assignment we will present a reconstruction scheme between f(R) gravity with ordinary and entropy corrected(m, n)-type holographic dark energy. The correspondence is established and expressions for the reconstructed f(R) models are determined. To study the evolution of the reconstructed models plots are generated. The stability of the calculated models are also investigated using the squared speed of sound in the background of the reconstructed gravities.

Planck’s Oscillators at Low Temperatures and Haken’s Perturbation Approach to the Quantum Oscillators Reconsidered

In the first step the extremal values of the vibrational specific heat and entropy represented by the Planck oscillators at the low temperatures could be calculated. The positions of the extrema are defined by the dimensionless ratios between the quanta of the vibrational energy and products of the actual temperature multiplied by the Boltzmann constant. It became evident that position of a local maximum obtained for the Planck’s average energy of a vibration mode and position of a local maximum of entropy are the same. In the next step the Haken’s time-dependent perturbation approach to the pair of quantum non-degenerate Schr?dinger eigenstates of energy is re-examined. An averaging process done on the time variable leads to a very simple formula for the coefficients entering the perturbation terms.

The study of disturbance sources energy size identification based on free energy theory

The influence of power system disturbance on safety and stability is serious. Therefore, it is very important to identify the disturbance quickly and accurately. However, until now there is no unilbrm standard evaluation of disturbances on the energy impact strength, also no appropriate method to quantitatively and quickly assess the size of the disturbance energy. In this paper, the relationship between energy enthalpy and free energy in thermodynamics field is introduced into the description of disturbance free energy. And a new method based on energy entropy theory is proposed. In order to analyze the energy characteristics of various types of disturbances in power systems, an amended method of energy entropy is proposed. The free energy of each disturbance is described. Finally, the proposed theory is validated using the standard IEEE 39-bus system. The disturbance free energy is calculated under 5 kinds of disturbances. The proposed method provides a new idea to analyze the disturbance propagation and describe the impact strength of disturbance.

Condition monitoring and fault diagnosis strategy of railway point machines using vibration signals

Condition monitoring of railway point machines is important for train operation safety and effectiveness.Referring to the fields of mechanical equipment fault detection,this paper proposes a fault detection and identification strategy of railway point machines via vibration signals.A comprehensive feature distilling approach by combining variational mode decomposition(VMD)energy entropy and time-and frequency-domain statistical features is presented,which is more effective than single type of feature.The optimal set of features was selected with ReliefF,which helps improve the diagnosis accuracy.Support vector machine(SVM),which is suitable for a small sample,is adopted to realize diagnosis.The diagnosis accuracy of the proposed method reaches 100%,and its effectiveness is verified by experiment comparisons.In this paper,vibration signals are creatively adopted for fault diagnosis of railway point machines.The presented method can help guide field maintenance staff and also provide reference for fault diagnosis of other equipment.

Acoustic emission characterization methods of damage modes identification on carbon fiber twill weave laminate

In order to get a deep understanding of composite failure mechanisms, the new advanced signal processing methodologies are established for the analysis of the large number of acoustic emission (AE) data obtained from the quasi-static tension test of carbon fiber twill weave composite. For this purpose, AE signals have been collected and post-processed for tension test, and are analyzed with three signal processing methods: Empirical Mode Decomposition (EMD), Hilbert-Huang Transform (HHT) and modified energy entropy algorithm. AE signals can be decomposed into a set of Intrinsic Mode Functions (IMF) components, results from this study reveal that the peak frequency of IMF components based on Fast Fourier Transform (FFT) corresponds to different damage mechanisms of composite. HHT of AE signals can clearly express the frequency distribution of IMF component in time-scale in different damage stages, and can calculate accurate instantaneous frequency for damage modes recognition. The energy entropy based on EMD is introduced to act as a new relevant descriptor of composite damage modes in order to improve the characterization and the discrimination of the damage mechanisms.

Fault line selection in cooperation with multi-mode grounding control for the floating nuclear power plant grid

The Floating nuclear power plant grid is composed of power generation,in-station power supply and external power delivery.To ensure the safety of the nuclear island,the in-station system adopts a special power supply mode,while the external power supply needs to be adapted to different types of external systems.Because of frequent single phase-ground faults and various fault forms,the fault line selection protection should be accurate,sensitive and adaptive.This paper presents a fault line selection method in cooperation with multi-mode grounding control.Based on the maximum united energy entropy ratio(MUEER),the optimal wavelet basis function and decomposition scale are adaptively chosen,while the fault line is selected by wavelet transform modulus maxima(WTMM).For high-impedance faults(HIFs),to enlarge the fault feature,the system grounding mode can be switched by the multi-mode grounding control.Based on the characteristic of HIFs,the fault line can be selected by comparing phase differences of zero-sequence current mutation and fault phase voltage mutation before and after the fault.Simulation results using MATLAB/Simulink show the effectiveness of the proposed method in solving the protection problems.

Study on the Algorithm of Vibration Source Identification Based on the Optical Fiber Vibration Pre-Warning System

One of the key technologies for optical fiber vibration pre-warning system （OFVWS） refers to identifying the vibration source accurately from the detected vibration signals. Because of many kinds of vibration sources and complex geological structures, the implement of identifying vibration sources presents some interesting challenges which need to be overcome in order to achieve acceptable performance. This paper mainly conducts on the time domain and frequency domain analysis of the vibration signals detected by the OFVWS and establishes attribute feature models including an energy information entropy model to identify raindrop vibration source and a fundamental frequency model to distinguish the construction machine and train or car passing by. Test results show that the design and selection of the feature model are reasonable, and the rate of identification is good.