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%.

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.

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.

移动自组织网络中能源信息熵的需求多路径路由协议(英文)

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.

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.

Quantitative Diagnosis of Fault Severity Trend of Rolling Element Bearings

The condition monitoring and fault diagnosis of rolling element bearings are particularly crucial in rotating mechanical applications in industry. A bearing fault signal contains information not only about fault condition and fault type but also the severity of the fault. This means fault severity quantitative analysis is one of most active and valid ways to realize proper maintenance decision. Aiming at the deficiency of the research in bearing single point pitting fault quantitative diagnosis, a new back-propagation neural network method based on wavelet packet decomposition coefficient entropy is proposed. The three levels of wavelet packet coefficient entropy（WPCE） is introduced as a characteristic input vector to the BPNN. Compared with the wavelet packet decomposition energy ratio input vector, WPCE shows more sensitive in distinguishing from the different fault severity degree of the measured signal. The engineering application results show that the quantitative trend fault diagnosis is realized in the different fault degree of the single point bearing pitting fault. The breakthrough attempt from quantitative to qualitative on the pattern recognition of rolling element bearings fault diagnosis is realized.

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.

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.

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.

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.