On the Trade-off between Power Consumption and Time Synchronization Quality for Moving Targets under Large-Scale Fading Effects in Wireless Sensor Networks

In this work we find a lower bound on the energy required for synchronizing moving sensor nodes in a Wireless Sensor Network (WSN) affected by large-scale fading, based on clock estimation techniques. The energy required for synchronizing a WSN within a desired estimation error level is specified by both the transmit power and the required number of messages. In this paper we extend our previous work introducing nodes’ movement and the average message delay in the total energy, including a comprehensive analysis on how the distance between nodes impacts on the energy and synchronization quality trade-off under large-scale fading effects.

Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.

Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China

BACKGROUND Hepatitis B(HB)and hepatitis C(HC)place the largest burden in China,and a goal of eliminating them as a major public health threat by 2030 has been set.Making more informed and accurate forecasts of their spread is essential for developing effective strategies,heightening the requirement for early warning to deal with such a major public health threat.AIM To monitor HB and HC epidemics by the design of a paradigmatic seasonal autoregressive fractionally integrated moving average(SARFIMA)for projections into 2030,and to compare the effectiveness with the seasonal autoregressive integrated moving average(SARIMA).METHODS Monthly HB and HC incidence cases in China were obtained from January 2004 to June 2023.Descriptive analysis and the Hodrick-Prescott method were employed to identify trends and seasonality.Two periods(from January 2004 to June 2022 and from January 2004 to December 2015,respectively)were used as the training sets to develop both models,while the remaining periods served as the test sets to evaluate the forecasting accuracy.RESULTS There were incidents of 23400874 HB cases and 3590867 HC cases from January 2004 to June 2023.Overall,HB remained steady[average annual percentage change(AAPC)=0.44,95%confidence interval(95%CI):-0.94-1.84]while HC was increasing(AAPC=8.91,95%CI:6.98-10.88),and both had a peak in March and a trough in February.In the 12-step-ahead HB forecast,the mean absolute deviation(15211.94),root mean square error(18762.94),mean absolute percentage error(0.17),mean error rate(0.15),and root mean square percentage error(0.25)under the best SARFIMA(3,0,0)(0,0.449,2)12 were smaller than those under the best SARIMA(3,0,0)(0,1,2)12(16867.71,20775.12,0.19,0.17,and 0.27,respectively).Similar results were also observed for the 90-step-ahead HB,12-step-ahead HC,and 90-step-ahead HC forecasts.The predicted HB incidents totaled 9865400(95%CI:7508093-12222709)cases and HC totaled 1659485(95%CI:856681-2462290)cases during 2023-2030.CONCLUSION Under current interventions,China faces enormous challenges to eliminate HB and HC epidemics by 2030,and effective strategies must be reinforced.The integration of SARFIMA into public health for the management of HB and HC epidemics can potentially result in more informed and efficient interventions,surpassing the capabilities of SARIMA.

Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways:Prediction and forecast.Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role.Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences.The time series analysis approach has the advantage of being easier to use(in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average).Still,it is limited in forecasting time,unlike the classical models such as Susceptible-Exposed-Infectious-Removed.Its applicability in forecasting comes from its better accuracy for short-term prediction.In its basic form,it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures(governments,companies,etc.).Instead,it estimates from the data directly.Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread;be it school closures,emerging variants,etc.It can be used in mortality or hospital risk estimation from new cases,seroprevalence studies,assessing properties of emerging variants,and estimating excess mortality and its relationship with a pandemic.

GPS measured static and kinematic offsets at near and far field of the 2011 Mw 9.0 Tohoku-Oki earthquake

The Mw 9.0 Tohoku-Oki earthquake that hit the mainland Japan on 11 th March, 2011 had resulted a devastating Tsunami due to an active thrusting between the Pacific and the North American Plates. Static and kinematic offsets at the offshore epicentre of the Mw 9.0 event remain unanswered and being investigated along with their near and far field limiting distances from the epicentre. Accordingly, offset measurements from 60 continuously operating IGS and GEONET GNSS stations were radially classified from the epicentre and interpreted with analytical models to find their linear offset decay rates. Co-and post-seismic static positional anomaly offsets of sixty days show almost all near field stations had strong or appreciable eastward or south eastward static shifts. Near stations(<250 km) showed both kinematic and static offsets. GEONET station ’0175’ showed maximum resultant static offset of-4.5 m, which diminishes approximately 1-2 cm at far sites like SMST and AIRA. Characteristic decay duration(’b’) of the mean kinematic co-seismic shift(’a’)of near field stations was 17.28 s during earthquake hours with an EW component shift >1.5 m. Spatial models of projected N-S static and kinematic offsets show their asymmetrical distributions around the epicentre with maximum model offset of-1.84 m displaced towards south at-45 km north of the epicentre. The Tohoku-Oki earthquake produced a resultant kinematic offset of-10.2 m towards East at its offshore epicentre;while the estimated near field static offset is ~9.82 m. However, both estimates are bigger than double the resultant offset measured value(~4.3 m) in the Japanese mainland using GPS. The difference in the kinematic and static near field offsets highlight that the near surface had elastic or in-elastic kinematic strain dissipation as against the lithospheric level viscoelastic static response, which resulted rapid kinematic strain release(1.12 cm/km)within the limiting radius of ~220 km from the Tohoku-Oki epicentre.

A study on the seismic behavior of a retrofitted building based on nonlinear static and dynamic analyses

This study describes the seismic performance of an existing five storey reinforced concrete building which represents the typical properties of low-rise non-ductile buildings in Turkey. The effectiveness of shear walls and the steel bracings in retrofitting the building was examined through nonlinear static and dynamic analyses. By using the nonlinear static analysis, retrofitted buildings seismic performances under lateral seismic load were compared with each other. Moreover, the performance points and response levels of the existing and retrofitting cases were determined by way of the capacity-spectrum method described in ATC-40 (1996). For the nonlinear dynamic analysis the records were selected to represent wide ranges of duration and frequency content. Considering the change in the stiffness and the energy dissipation capacities, the performance of the existing and retrofitted buildings were evaluated in terms of story drifts and damage states. It was found that each earthquake record exhibited its own peculiarities, dictated by frequency content, duration, sequence of peaks and their amplitude. The seismic performance of retrofitted buildings resulted in lower displacements and higher energy dissipation capacity depending mainly on the properties of the ground motions and the retrofitting strategies. Moreover, severe structural damage (irreparable or collapse) was observed for the existing building. However, buildings with retrofit alternatives exhibited lower damage levels changing from no damage to irreparable damage states.

Time series analysis of correlativity between pulmonary tuberculosis and seasonal meteorological factors based on theory of Human-Environmental Inter Relation

Objective:This paper aims to study the correlativity between the number of pulmonary tuberculosis (PTB) cases and seasonal meteorological factors in Beijing.Methods:Based on theory of Human-Environmental Inter Relation in Huangdi's Internal Classics,we adopted monthly cases of PTB in Beijing from 2004 to 2011,and established a Seasonal Autoregressive Integrated Moving Average (SARIMA) model.Using the cross-correlation function (CCF),we then analyzed the correlation between meteorological factors and number of infected patients.The related meteorological factors were subsequently integrated,to establish a Seasonal Autoregressive Integrated Moving Average with explanatory variables (SARIMAX) model,which was used to estimate and verify the number of PTB cases in 2012.Results:In this study,a SARIMA(0,1,1) (0,1,1)12 model was established;CCF analysis was used to reveal the correlativity between PTB and precipitation with 1 lag,relative humidity with 1 lag.Then,integrated with relative humidity with 1 lag (β =2.405,95% confidence interval:0.433-4.377),the SARIMAX prediction model was proved to be an accurate approach for predicting local situations of PTB occurrence.Conclusions:The occurrence of PTB is correlated with seasonal meteorological factors.Combining these factors,an exact prediction model can be established,to estimate of the number of PTB infected patients.

Ultraslow Relaxation Process of Static Light Scattering Intensity by Boron Oxide above the Glass Transition Temperature

The data describing of the light scattering intensity relaxation above the glass transition temperature by boron oxide obtained by using of the temperature jump methods are presented. It is found that the stabilization of the glass at 220℃ resulted in increasing of the anisotropic intensity at the same time the isotropic intensity is not practically unchangeable. It is shown that after the temperature jump to 330℃the intensity is characterized by the formation of a maximum. This effect is in compliance with the results obtained for the other oxide glasses. It is established that characteristic time of the relaxation of the maximum height is about two orders of magnitude more than the structural relaxation time for this temperature.

Performance analysis of high-static-low-dynamic stiffness vibration isolator with time-delayed displacement feedback

The displacement feedback with time delay considered is introduced in order to enhance the vibration isolation performance of a high-static-low-dynamic stiffness(HSLDS) vibration isolator. Such feedback is detailedly analyzed from the viewpoint of equivalent damping. Firstly, the primary resonance of the controlled HSLDS vibration isolator subjected to a harmonic force excitation is obtained based on the multiple scales method and further verified by numerical integration. The stability of the primary resonance is subsequently investigated. Then, the equivalent damping is defined to study the effects of feedback gain and time delay on primary resonance. The condition of jump avoidance is obtained with the purpose of eliminating the adverse effects induced by jumps. Finally, the force transmissibility of the controlled HSLDS vibration isolator is defined to evaluate its isolation performance. It is shown that an appropriate choice of feedback parameters can effectively suppress the force transmissibility in resonant region and reduce the resonance frequency. Furthermore, a wider vibration isolation frequency bandwidth can be achieved compared to the passive HSLDS vibration isolator.

Subcritical Crack-Growth and Lifetime Behavior of Glass and SiC under Static Load

Crack initiation and subcritical crack growth in glass sheet and SIC bar specimen under static loading were investigated to study the failure process. It has been demonstrated that the lifetime process of brittle materials involves three possible forms of crack growth: subcritical crack growth, partly subcritical crack growth and instantaneous fracture without subcritical crack growth. Curves of ν-K obtained in step-by-step static fatigue tests and in constant loading rate tests showed different trends for borosilicate glass sheet. α-SiC that is generally considered immune to mechanical fatigue effect and environmental attack was also tested under static loading and the lifetime was measured. The results showed that the threshold load to damage effect was over 80% of the initial strength for the SiC.

Research on measuring time constant of NANMAC thermocouple

The theory for measuring the time constant of thermocouple was introduced, and the method for measuring the time constant of NANMAC thermocouple by using dynamic calibration system of transient surface temperature sensor was proposed. In this system, static and dynamic calibrations were conducted for infrared detectors and thermocouples, and then both temperature-time curves were obtained. Since the frequency response of infrared detector is superior to that of calibrat- ed thermocouple, the values measured by infrared detectors are taken as true values. Through dividing the values measured with thermocouples by those with infrared detectors, a normalized curve was obtained, based on which the time constant of thermocouple was measured. With this method, the experiments were carried out with NANMAC thermocouple to obtain its time constant. The results show that the method for measuring the time constant is feasible and the dynamic calibration of thermocouples can be achieved at microsecond and millisecond level. This research has a certain reference value for research and application of NANMAC thermocouple temperature sensor.

基于Pyramid-Attention-U-Net深度学习模型的实时拓扑优化设计

拓扑优化设计可为现代工程提供具有卓越热学、力学及声学等多学科性能的创新型结构,移动变形组件法(Moving Morphable Components,MMC)以其独特的显示拓扑优化方法备受青睐,MMC法通过一系列可移动变形的显示组件间的移动、变形、重叠实现边界演化以完成结构优化的目的。该文利用椭圆形初始组件替代原有直线型骨架厚度二次变化组件进行拓扑优化。在减少设计变量次数的同时,可缩短一定的计算时间,但在实际计算中,随着初始组件单元数增加,中间迭代计算过程时间依旧相对较多,为精确实时获取拓扑优化构型,该文引入Pyramid-Attention-U-Net(PA-U-Net)深度学习模型加速优化设计,避免中间迭代计算过程。研究结果表明:该方法不仅在可忽略的计算时间内准确实时获取不同参数下的初始组件拓扑构型,而且准确率可达90.89%,高于其他深度学习网络模型。同时这种将深度学习与拓扑优化方法有机结合的形式在大型工程结构优化设计中具有广阔的应用前景。

Gross errors identification and correction of in-vehicle MEMS gyroscope based on time series analysis

This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.

Characteristics and dynamics analysis of Populus euphratica populations in the middle reaches of Tarim River

Populus euphratica Oliv. is widely distributed along the Tarim River. Maintaining stability of P. euphratica population is important to local development. This study explored the static life table, survivorship curves and four function curves (survival rate, cumulative mortality rate, mortality density, and hazard rate), and development index of P. euphratica population in the middle reaches of Tarim River. The results indicated that the age structure of P. euphratica population belonged to positive pyramidal type, which meant young age-class individuals occupied most populations. The number ofⅠ-Ⅱage classes accounted for 66.2% of whole population, and this indicated that there were abundant subsequent seedlings resources to support the growth of P. euphratica population in the middle reaches of Tarim River. The survivorship curve of P. euphratica belonged to the Deevey Ⅲ (concave-type) and the development index was 47.72%. Four function curves revealed that the individuals of P. euphratica sharply decreased at the initial stage and then leveled off at the late stage of survival curve. Time sequence prediction models predicted that the number of midlife individuals would increase in future 10, 20, 30 years, and P. euphratica population grew steadily as a result of rich saplings.

Effects of axial static stress on stress wave propagation in rock considering porosity compaction and damage evolution

A wave equation of rock under axial static stress is established using the equivalent medium method by modifying the Kelvin-Voigt model.The analytical formulas of longitudinal velocity,space and time attenuation coefficients and response frequency are obtained by solving the equation using the harmonic method.A series of experiments on stress wave propagation through rock under different axial static stresses have been conducted.The proposed models of stress wave propagation are then verified by comparing experimental results with theoretical solutions.Based on the verified theoretical models,the influences of axial static stress on longitudinal velocity,space and time attenuation coefficients and response frequency are investigated by detailed parametric studies.The results show that the proposed theoretical models can be used to effectively investigate the effects of axial static stress on the stress wave propagation in rock.The axial static stress influences stress wave propagation characteristics of porous rock by varying the level of rock porosity and damage.Moreover,the initial porosity,initial elastic modulus of the rock voids and skeleton,viscous coefficient and vibration frequency have significant effects on the P-wave velocity,attenuation characteristics and response frequency of the stress wave in porous rock under axial static stress.

Approximate and numerical analysis of nonlinear forced vibration of axially moving viscoelastic beams

Steady-state periodical response is investigated for an axially moving viscoelastic beam with hybrid supports via approximate analysis with numerical confirmation. It is assumed that the excitation is spatially uniform and temporally harmonic. The transverse motion of axially moving beams is governed by a nonlinear partial-differential equation and a nonlinear integro-partial-differential equation. The material time derivative is used in the viscoelastic constitutive relation. The method of multiple scales is applied to the governing equations to investigate primary resonances under general boundary conditions. It is demonstrated that the mode uninvolved in the resonance has no effect on the steady-state response. Numerical examples are presented to demonstrate the effects of the boundary constraint stiffness on the amplitude and the stability of the steady-state response. The results derived for two governing equations are qualitatively the same,but quantitatively different. The differential quadrature schemes are developed to verify those results via the method of multiple scales.

A simplified methd of evaluating the seismic performance of buildings

This paper presents a simplified method of evaluating the seismic performance of buildings. The proposed method is based on the transformation of a multiple degree of freedom (MDOF) system to an equivalent single degree of freedom (SDOF) system using a simple and intuitive process. The proposed method is intended for evaluating the seismic performance of the buildings at the intermediate stages in design, while a rigorous method would be applied to the final design. The performance of the method is evaluated using a series of buildings which are assumed to be located in Victoria in western Canada, and designed based on the upcoming version of the National Building Code of Canada which is due to be published in 2005. To resist lateral loads, some of these buildings contain reinforced concrete moment resisting frames, while others contain reinforced concrete shear walls. Each building model has been subjected to a set of site-specific seismic spectrum compatible ground motion records, and the response has been determined using the proposed method and the general method for MDOF systems. The results from the study indicate that the proposed method can serve as a useful tool for evaluation of seismic performance of buildings, and carrying out performance based design.

Numerical Simulation and Analysis of Electromagnetic Fields Induced by a Moving Ship Based on a Three-Layer Geoelectric Model

In this paper,we present a numerical simulation method of electromagnetic(EM)fields induced by a moving ship(EMFMS),which consist of both the shaft-rate EM field and the static EM field.The shaft-rate EM fields in the frequency domain are first obtained by solving the partial differential equations together with suitable boundary conditions,and then they are transformed into the time domain by using the inverse Fourier transform.Finally,the static fields are added to obtain the EM fields of a moving ship.The effects of the source current intensity and the source position on the EM fields of a moving ship are discussed in detail.A field example of EM response of a moving ship is presented and its characteristics are analyzed.

Key techniques for predicting the uncertain trajectories of moving objects with dynamic environment awareness

Emerging technologies of wireless and mobile communication enable people to accumulate a large volume of time-stamped locations,which appear in the form of a continuous moving object trajectory.How to accurately predict the uncertain mobility of objects becomes an important and challenging problem.Existing algorithms for trajectory prediction in moving objects databases mainly focus on identifying frequent trajectory patterns,and do not take account of the effect of essential dynamic environmental factors.In this study,a general schema for predicting uncertain trajectories of moving objects with dynamic environment awareness is presented,and the key techniques in trajectory prediction arc addressed in detail.In order to accurately predict the trajectories,a trajectory prediction algorithm based on continuous time Bayesian networks（CTBNs） is improved and applied,which takes dynamic environmental factors into full consideration.Experiments conducted on synthetic trajectory data verify the effectiveness of the improved algorithm,which also guarantees the time performance as well.