Research on Coupling Transfer Characteristics of Vibration Energy of Free Piston Linear Generator

In order to clarify the mechanism and main influencing factors of the vibration energy coupling transmission with a dual-piston structure,a thermodynamic and dynamic coupling model of the free piston linear generator(FPLG)was established.The system energy conversion,vibration energy coupling transmission,and influencing factors were studied in detail.The coupling transmission paths and the secondary influence mechanism from in-cylinder combustion on vibration energy transmission were obtained.In addition,the influence of the movement characteristics of the dual-piston on the vibration energy transmission was studied,and the typical parameter variation law was obtained,which provides theoretical guidance for the subsequent vibration reduction design of the FPLG.

Starting Control of Free Piston Stirling Linear Generator System Based on FOC

Aiming at the problem of poor system dynamic performance caused by low parameter matching in the coordinated control of Stirling engine and linear generator in the starting stage control of free piston Stirling linear generator system,a joint control method of free piston Stirling permanent magnet synchronous linear generator system based on field orientation control is proposed,based on the theoretical derivation of the mathematical model of the system and the principle of controller parameters setting,the simulation experiments of the system starting stage under several Stirling engine working conditions are carried out under simulation.The experimental results show that the stability and rapidity of the system are improved,and the dynamic response speed of generator parameters under different working conditions is accelerated,what fully verifies the correctness and effectiveness of the method.It provides an effective way to improve the control performance of the system and stabilize the power generation operation.

Time Series-, Time-Frequency- and Spectral Analyses of Sensor Measurements in an Offshore Wave Energy Converter Based on Linear Generator Technology

Inside the second experimental wave energy converter (WEC) launched at the Lysekil research site on the Swedish west coast in March 2009 a number of sensor systems were installed for measuring the mechanical performance of the WEC and its mechanical subsystems. One of the measurement systems was a set-up of 7 laser triangulation sensors for measuring relative displacement of the piston rod mechanical lead-through transmission in the direct drive. Two measurement periods, separated by 2.5 month, are presented in this paper. One measurement is made two weeks after launch and another 3 months after launch. Comparisons and correlations are made between different sensors measuring simultaneously. Noise levels are investigated. Filtering is discussed for further refinement of the laser triangulation sensor signals in order to separate noise from actual physical displacement and vibration. Measurements are presented from the relative displacement of the piston rod mechanical lead-through, from magnetic flux in the air gap, mechanical strain in the WEC structure, translator position and piston rod axial displacement and active AC power. Investigation into the measurements in the time domain with close-ups, in the frequency domain with Fast Fourier transform (FFT) and with time-frequency analysis with short time Fourier transform (STFT) is carried out to map the spectral content in the measurements. End stop impact is clearly visible in the time-frequency analysis. The FFT magnitude spectra are investigated for identifying the cogging bandwidth among other vibrations. Generator cogging, fluctuations in the damping force and in the Lorenz forces in the stator are distinguished and varies depending on translator speed. Vibrations from cogging seem to be present in the early measurement period while not so prominent in the late measurement period. Vibration frequencies due to wear are recognized by comparing with the noise at generator standstill and the vibration sources in the generator. It is concluded that a moving average is a sufficient filter in the time domain for further analysis of the relative displacement of the piston rod mechanical lead-through transmission.

Generating Electricity from Mechanical Vibrations:Optimization of Linear a Generator

Energy preservation is one of the key components in developing eco-friendly machines.In industry,the majority of machines lose a considerable amount of energy through mechanical vibrations.However,the wasted energy through vibration can be utilized as a renewable energy source to compensate for the overall energy loss.The research work presented in this paper discusses the ability to design a linear power generator utilizing Nd Fe-B magnets,which can generate energy through mechanical vibrations.A pilot model was developed and simulated to understand the efficiency and limitation.The results showed that the model could reduce 14% rotor bulk with a marginal impact on the current generation.

An experimental study of a single-piston free piston linear generator

Free piston linear generator(FPLG)is a promising range extender for the electrical vehicle with unparallel advantages,such as compact structure,higher system efficiency,and reduced maintenance cost.However,due to the lack of the mechanic crankshaft,the related piston motion control is a challenge for the FPLG which causes problems such as misfire and crash and limits its widespread commercialization.Aimed at resolving the problems as misfire,a single-piston FPLG prototype has been designed and manufactured at Shanghai Jiao Tong University(SJTU).In this paper,the development process and experimental validation of the related control strategies were detailed.From the experimental studies,significant misfires were observed at first,while the FPLG operated in natural-aspiration conditions.The root cause of this misfire was then identified as the poor scavenging process,and a compressed air source was leveraged to enhance the related scavenging pressure.Afterward,optimal control parameters,in terms of scavenging pressure,air-fuel equivalence ratio,and ignition position,were then calibrated in this charged-scavenging condition.Eventually,the FPLG prototype has achieved a continuous stable operation of over 1000 cycles with an ignition rate of 100%and a cycle-to-cycle variation of less than 0.8%,produced an indicated power of 2.8 kW with an indicated thermal efficiency of 26%and an electrical power of 2.5 kW with an overall efficiency of 23.2%.

Prediction of the Inductance in a Synchronous Linear Permanent Magnet Generator

This paper presents calculations of the varying inductances profile for a synchronous linear surface mounted permanent magnet generator in an ABC reference system. Calculations are performed by utilizing the reluctance term, known from analytic calculations and finite element method simulations. With the inductance term identified, the voltage difference between the generator’s no load and load voltage can be calculated and an external circuit can be designed for optimal use of the generator. Two different operation intervals of the linear generator are considered and the results are discussed. The result indicates that time costly finite element simulations can be replaced with simple analytical calculations for a surface mounted permanent magnet linear generator.

Numerical Simulation of a Spark Ignited Two-Stroke Free-Piston Engine Generator

A numerical program is built to simulate the performance of a spark ignited two-stroke free-piston engine coupled with a linear generator. The computational model combines a series of dynamic and thermodynamic equations that are solved simultaneously to predict the performances of the engines. The dynamic analysis performed consists of an evaluation of the frictional force and load force introduced by the generator. The thermodynamic analysis used a single zone model to describe the engine＇ s working cycle which includes intake, scavenging, compression, combustion and expansion, and to evaluate the effect of heat transfer based on the first law of thermodynamics and the ideal gas state equation. Because there is no crankshaft, a time based Wiebe equation was used to express the fraction of fuel burned in the combustion. The calculated results were validated by using the experimental data from another research group. The results indicate that the free-piston generator has some advantages over conventional engines.

Prediction and driving factors of forest fire occurrence in Jilin Province,China

Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie distribution.The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge.To address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos.The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data.After determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters.The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model.Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging data.The assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.

A New Type of Solution Method for the Generalized Linear Complementarity Problem over a Polyhedral Cone

This paper addresses the generalized linear complementarity problem （GLCP） over a polyhedral cone. To solve the problem, we first equivalently convert the problem into an affine variational inequalities problem over a closed polyhedral cone, and then propose a new type of method to solve the GLCP based on the error bound estimation. The global and R-linear convergence rate is established. The numerical experiments show the efficiency of the method.

Some inequalities for the generalized linear distortion function

In this paper, we establish several inequalities for the the generalized linear distortion function λ（a, K） by using the monotonicity and convexity of certain combinations λ（a, K）.

A New Conjugate Gradient Projection Method for Solving Stochastic Generalized Linear Complementarity Problems

In this paper, a class of the stochastic generalized linear complementarity problems with finitely many elements is proposed for the first time. Based on the Fischer-Burmeister function, a new conjugate gradient projection method is given for solving the stochastic generalized linear complementarity problems. The global convergence of the conjugate gradient projection method is proved and the related numerical results are also reported.

Preconditioned iterative methods for solving weighted linear least squares problems

A class of preconditioned iterative methods, i.e., preconditioned generalized accelerated overrelaxation （GAOR） methods, is proposed to solve linear systems based on a class of weighted linear least squares problems. The convergence and comparison results are obtained. The comparison results show that the convergence rate of the preconditioned iterative methods is better than that of the original methods. Furthermore, the effectiveness of the proposed methods is shown in the numerical experiment.

Noether's theory of generalized linear nonholonomic mechanical systems

By introducing the quasi-symmetry of the infinitesimal transformation of the transformation group Gr, the Noether＇s theorem and the Noether＇s inverse theorem for generalized linear nonholonomic mechanical systems are obtained in a generalized compound derivative space. An example is given to illustrate the application of the result.

Temporal Statistical Downscaling of Precipitation and Temperature Forecasts Using a Stochastic Weather Generator

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society （IRI）. In conjunction with the GLM （generalized linear modeling） weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts （the IRI format only specifies probabilities for three categories： below normal, near normal, and above normal） into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.

Asymptotic Normality of Multi-Dimension Quasi Maximum Likelihood Estimate in Generalized Linear Models withAdaptive Design

We study the quasi likelihood equation in Generalized Linear Models（GLM） with adaptive design ∑（i=1）^n xi（yi-h（x＇iβ））=0, where yi is a q=vector, and xi is a p×q random matrix. Under some assumptions, it is shown that the Quasi- Likelihood equation for the GLM has a solution which is asymptotic normal.

Investigating Performances of Some Statistical Tests for Heteroscedasticity Assumption in Generalized Linear Model: A Monte Carlo Simulations Study

In a linear regression model, testing for uniformity of the variance of the residuals is a significant integral part of statistical analysis. This is a crucial assumption that requires statistical confirmation via the use of some statistical tests mostly before carrying out the Analysis of Variance (ANOVA) technique. Many academic researchers have published series of papers (articles) on some tests for detecting variance heterogeneity assumption in multiple linear regression models. So many comparisons on these tests have been made using various statistical techniques like biases, error rates as well as powers. Aside comparisons, modifications of some of these statistical tests for detecting variance heterogeneity have been reported in some literatures in recent years. In a multiple linear regression situation, much work has not been done on comparing some selected statistical tests for homoscedasticity assumption when linear, quadratic, square root, and exponential forms of heteroscedasticity are injected into the residuals. As a result of this fact, the present study intends to work extensively on all these areas of interest with a view to filling the gap. The paper aims at providing a comprehensive comparative analysis of asymptotic behaviour of some selected statistical tests for homoscedasticity assumption in order to hunt for the best statistical test for detecting heteroscedasticity in a multiple linear regression scenario with varying variances and levels of significance. In the literature, several tests for homoscedasticity are available but only nine: Breusch-Godfrey test, studentized Breusch-Pagan test, White’s test, Nonconstant Variance Score test, Park test, Spearman Rank, Glejser test, Goldfeld-Quandt test, Harrison-McCabe test were considered for this study;this is with a view to examining, by Monte Carlo simulations, their asymptotic behaviours. However, four different forms of heteroscedastic structures: exponential and linear (generalize of square-root and quadratic structures) were injected into the residual part of the multiple linear regression models at different categories of sample sizes: 30, 50, 100, 200, 500 and 1000. Evaluations of the performances were done within R environment. Among other findings, our investigations revealed that Glejser and Park tests returned the best test to employ to check for heteroscedasticity in EHS and LHS respectively also White and Harrison-McCabe tests returned the best test to employ to check for homoscedasticity in EHS and LHS respectively for sample size less than 50.

Sieve MLE for Generalized Partial Linear Models with Type Ⅱ Interval-censored Data

This article concerded with a semiparametric generalized partial linear model （GPLM） with the type Ⅱ censored data. A sieve maximum likelihood estimator （MLE） is proposed to estimate the parameter component, allowing exploration of the nonlinear relationship between a certain covariate and the response function. Asymptotic properties of the proposed sieve MLEs are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. Moreover, the estimators of the unknown parameters are asymptotically normal and efficient, and the estimator of the nonparametric function has an optimal convergence rate.

Assessing the Effect of Climate Factors on Dengue Incidence via a Generalized Linear Model

Changes in climate factors such as temperature, rainfall, humidity, and wind speed are natural processes that could significantly impact the incidence of infectious diseases. Dengue is a widespread disease that has often been documented when it comes to the impact of climate change. It has become a significant concern, especially for the Malaysian health authorities, due to its rapid spread and serious effects, leading to loss of life. Several statistical models were performed to identify climatic factors associated with infectious diseases. However, because of the complex and nonlinear interactions between climate variables and disease components, modelling their relationships have become the main challenge in climate-health studies. Hence, this study proposed a Generalized Linear Model (GLM) via Poisson and Negative Binomial to examine the effects of the climate factors on dengue incidence by considering the collinearity between variables. This study focuses on the dengue hot spots in Malaysia for the year 2014. Since there exists collinearity between climate factors, the analysis was done separately using three different models. The study revealed that rainfall, temperature, humidity, and wind speed were statistically significant with dengue incidence, and most of them shown a negative effect. Of all variables, wind speed has the most significant impact on dengue incidence. Having this kind of relationships, policymakers should formulate better plans such that precautionary steps can be taken to reduce the spread of dengue diseases.

Sex modulates the outcome of subthalamic nucleus deep brain stimulation in patients with Parkinson's disease

There are many documented sex differences in the clinical course,symptom expression profile,and treatment response of Parkinson’s disease,creating additional challenges for patient management.Although subthalamic nucleus deep brain stimulation is an established therapy for Parkinson’s disease,the effects of sex on treatment outcome are still unclear.The aim of this retrospective observational study,was to examine sex differences in motor symptoms,nonmotor symptoms,and quality of life after subthalamic nucleus deep brain stimulation.Outcome measures were evaluated at 1 and 12 months post-operation in 90 patients with Parkinson’s disease undergoing subthalamic nucleus deep brain stimulation aged 63.00±8.01 years(55 men and 35 women).Outcomes of clinical evaluations were compared between sexes via a Student’s t-test and within sex via a paired-sample t-test,and generalized linear models were established to identify factors associated with treatment efficacy and intensity for each sex.We found that subthalamic nucleus deep brain stimulation could improve motor symptoms in men but not women in the on-medication condition at 1 and 12 months post-operation.Restless legs syndrome was alleviated to a greater extent in men than in women.Women demonstrated poorer quality of life at baseline and achieved less improvement of quality of life than men after subthalamic nucleus deep brain stimulation.Furthermore,Hoehn-Yahr stage was positively correlated with the treatment response in men,while levodopa equivalent dose at 12 months post-operation was negatively correlated with motor improvement in women.In conclusion,women received less benefit from subthalamic nucleus deep brain stimulation than men in terms of motor symptoms,non-motor symptoms,and quality of life.We found sex-specific factors,i.e.,Hoehn-Yahr stage and levodopa equivalent dose,that were related to motor improvements.These findings may help to guide subthalamic nucleus deep brain stimulation patient selection,prognosis,and stimulation programming for optimal therapeutic efficacy in Parkinson’s disease.