Analysis of reflected signal of quad rotor UAV based on model fitting in mobile communication system

In view of the many scenes of unmanned aerial vehicle(UAV)detection,a third-party signal source is used to design a receiver to monitor the UAV.It is of great significance to understand the reflection of the signal illuminating the UAV.Taking the communication base station(BS)signal as the third-party signal source,and considering the complete transmission link,reflection changes and loss fading of the communication signal,this study conducts model fitting for irregular UAV targets,simplifying complex targets into a combination of simple targets.Furthermore,the influence of the dielectric constant of the target surface and the signal irradiation angle on the signal reflection is analyzed.The analysis shows that the simulation results of this model fitting method are consistent with the results of other literature,which provides theoretical support for the detection of low and slow small targets such as UAVs.

Laboratory investigation on damping characteristics of homogeneous and stratified soil-ash system

In this study,the damping responses of uniform soil,equi-proportional fly ash,and local soil as a single unit were investigated.The large-strain cyclic triaxial tests were performed for the specimen compacted at the desired density(95%e99%of maximum dry density).The compacted specimens were tested under the loading frequency of 0.3e1 Hz with medium confinement of 70e100 kPa.Also,the unsymmetrical behavior of the hysteresis loop was analyzed using three different damping estimation approaches,i.e.symmetric hysteresis loop(SHL),asymmetric hysteresis loop(ASHL),and the modified American Society for Testing and Materials(ASTM)method.The outcome of the study shows for fly ash,local soil,and layered soil-ash,the ASHL technique has the highest damping value,followed by ASTM and then the SHL approach.The specimens prepared under high density and subjected to high confinement show low damping values.However,the specimens tested at high frequency exhibits high damping behavior.Similarly,the damping value of fly ash determined using the SHL and ASHL methods has a similar profile and reaches a maximum at 1%shear strain value before decreasing.The composite stratified deposit exhibits more dependency on relative compaction,confining pressure,and less on loading frequency.Based on the results,it is highly recommended to use the ASHL approach,especially under large strain conditions irrespective of soil type.The maximum damping ratio of stratified deposits is always in between the damping ratio of local soil and fly ash.The damping ratio of stratified soil and local soil is slightly larger than that of the other soils,although the damping ratio of fly ash is equivalent to that of the sand and clayey soil.These results may be helpful in the accurate determination of the damping properties of the layered soil-ash system that is required in the seismic response analysis.

Effects of dietary protein levels on the long-term growth response and fitting growth models of gibel carp(Carassius auratus gibelio)

A 41-wk growth trial was conducted to evaluate the effects of dietary protein levels on the long-term growth response and fitting growth models of gibel carp(Carassius auratus gibelio) with an initial body weight of 1.85 ± 0.17 g. The dietary protein levels were designed at 320(P32), 360(P36). 400(P40).and 440 g/kg(P44), respectively. The growth curves of the gibel carp for each group were fitted and analyzed with four nonlinear regression models(Gompertz. logistic. von Bertalanffy and Richards). The final body weights(mean ± SD) of the fish were 226 ± 6.231 ± 7.242 ± 2, and 236 ± 2 g for P32, P36, P40,and P44. respectively. Feed conversion ratio of P40 and P44 groups was significantly lower than that of P32 and P36 groups(P < 0.05). Productive protein value of P44 group was significantly lower than that of P32 and P36 groups, but not different from that of P40 group(P > 0.05). The growth response of the gibel carp for each group was the best fitted by Richards model with the lowest Chi^2, residual sum of squares and residual variance, then Gompertz and von Bertalanffy growth models, but the logistic model did not fit the data well justified by Chi^2 values. The optimal protein level(400 g/kg) prolonged the stage of fast growth and predicted the highest asymptotic weight, which was close to the harvest size in practice.

Highly Sensitive Ultrathin Flexible Thermoplastic Polyurethane/Carbon Black Fibrous Film Strain Sensor with Adjustable Scaffold Networks

In recently years,high-performance wearable strain sensors have attracted great attention in academic and industrial.Herein,a conductive polymer composite of electrospun thermoplastic polyurethane(TPU)fibrous film matrix-embedded carbon black(CB)particles with adjustable scaffold network was fabricated for high-sensitive strain sensor.This work indicated the influence of stereoscopic scaffold network structure built under various rotating speeds of collection device in electrospinning process on the electrical response of TPU/CB strain sensor.This structure makes the sensor exhibit combined characters of high sensitivity under stretching strain(gauge factor of 8962.7 at 155%strain),fast response time(60 ms),outstanding stability and durability(>10,000 cycles)and a widely workable stretching range(0–160%).This high-performance,wearable,flexible strain sensor has a broad vision of application such as intelligent terminals,electrical skins,voice measurement and human motion monitoring.Moreover,a theoretical approach was used to analyze mechanical property and a model based on tunneling theory was modified to describe the relative change of resistance upon the applied strain.Meanwhile,two equations based from this model were first proposed and offered an effective but simple approach to analyze the change of number of conductive paths and distance of adjacent conductive particles.

Automatic image segmentation method for cotton leaves with disease under natural environment

In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function Φ(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour(GAC) algorithm, Chan-Vese(C-V) algorithm and Local Binary Fitting(LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.

Optimization by Estimation of Distribution with DEUM Framework Based on Markov Random Fields

This paper presents a Markov random field （MRP） approach to estimating and sampling the probability distribution in populations of solutions. The approach is used to define a class of algorithms under the general heading distribution estimation using Markov random fields （DEUM）. DEUM is a subclass of estimation of distribution algorithms （EDAs） where interaction between solution variables is represented as an undirected graph and the joint probability of a solution is factorized as a Gibbs distribution derived from the structure of the graph. The focus of this paper will be on describing the three main characteristics of DEUM framework, which distinguishes it from the traditional EDA. They are： 1） use of MRF models, 2） fitness modeling approach to estimating the parameter of the model and 3） Monte Carlo approach to sampling from the model.

A derivation of the generalized Korf growth equation and its application

Based on the biological hypothesis of tree growth, the generalized Korf growth equation, was derived theoretically. From a standpoint of applications, the equation can be used in two ways associated with the power exponent ofp, and two types of growth equations: the Korf-A (p>1) and the Korf-B (O<p<1) were developed and between them, there is the Gompertz equation (p=1) to separate each other. All of the three types of equations are independent. It was concluded that the Korf-A equation could be used to describe the growth of trees, of which inflection point is between 0 andA/e, while the Korf-B equation with the inflection point betweenA/e andA could be applied to describe the biological population growth. It was found that the Korf-A equation had a better property in describing the growth process of a tree or a stand and its applications to predicting height growth and stand self-thinning showed general good fitness.

A Computer Software-Epitimulator~ for Simulating Temporal Dynamics of Plant Disease Epidemic Progress

The objective of the present study was to develop a computer software for simulating the temporal development of plant disease epidemics using Richards, logistic, Gompertz, monomolecular, and exponential functions, respectively, and for predicting disease with a fitted model. The software was programmed using Visual Basic （VB6.0） and packaged with the Wise Installation System. The Fibonacci （＇0.618＇） section strategy was used to find out the most appropriate value for the shape parameter （m） in Richards function simulation through looping procedures. The software program was repeatedly tested, debugged and edited until it was run through favorably and produced ideal outputs. It was named Epitimulator based on the phrase ＇epidemic time simulator＇ and has been registered by the National Copyright Department of China （Reg. no. 2007SR18489）. It can be installed and run on personal computers with all versions of Windows operational systems. Data of disease index and survey time are keyed in or imported from Access files. The output of fitted models and related data of parameters can be pasted into Microsoft Excel worksheet or into Word document for editing as required and the simulated disease progress curves can be stored in separate graphic files. After being finally tested and completed, Epitimulator was applied to simulate the epidemic progress of corn northern leaf blight （Exserohilum turcicum） with recorded data from field surveys of corn crops and the fitted models were output. Comparison of the simulation results showed that the disease progress was always best described by Richards function, which resulted in the most accurate simulation model. Result also showed that forecast of northern leaf blight development was highly accurate by using the computed progress model from Richards function.

Comparison of the Chapman-Richards Function with the Schnute Model in Stand growth

The Chapman-Richards Function and its two exception cases in applications were discussed and compared with the Schnute model in stand growth studies. Compared from all perspective, it was found that the Schnute model commonly used in foreitry was identical to the Chapman-Richards function. If some parameter in the Chapman-Richdrds Function was unconstraint, the function could also be very versatile to fit some exceptional growth curves, the fitted function should be identical to that the Schnute model.

Robust inversion analysis of local gravity anomalies caused by geological dislocation model of faults

Theoretical analysis and practical observations show that fault dislocations can change the gravity field around the fault. Gravity changes which were caused by the repeated dislocations over a long period of time were superimposed on the Bougeur gravity anomalies. These anomalies became the evidence of historical movement of fault as well as provide a way for the study of paleo earthquakes. This paper investigates inversion methods for the geological dislocation modeling of faults using the local Bouguer's gravity anomalies. To remove the effects of the irrelevant part of gravity anomalies to fault movements, we propose the robust nonlinear inversion method and set up the corresponding algorithm. Modeling examples indicate that the Marquardt's and Baye's least squares solutions depart from the true solution due to the attraction of gross errors in the data. The more seriously the data is contaminated, the more seriously the solutions are biased. In contrast, the proposed robust Marquardt's and Baye's inversion solutions can still maintain consistency with the solution without gross errors, even though 50 percent of the data is contaminated. This indicates that the proposed robust methods are effective. Using the proposed methods, we invert the geological dislocation models of the faults around the Erhai Lake in West Yunnan. The results show that the Northern Cangdong fault and the Erhai fault are normal dip slip faults with about 4 to 5 km dislocations; and that the Southern Cangdong fault has a less dip slip compared with the former two. A satisfactory fitting between the theoretical values of the inversion solution and the actual local gravity field is achievable.

Mathematical Modelling of the COVID-19 Epidemic in Northern Ireland in 2020

In this study, we investigate the dynamics of the COVID-19 epidemic in Northern Ireland from 1st March 2020 up to 25th December 2020, using several copies of a Susceptible-Exposed-Infectious-Recovered (SEIR) compartmental model, and compare it to a detailed publicly available dataset. We split the data into 10 time intervals and fit the models on the consecutive intervals to the cumulative number of confirmed positive cases on each interval. Using the fitted parameter estimates, we also provide estimates of the reproduction number. We also discuss the limitations and possible extensions of the employed model.

Seismicity acceleration model and its application to several earthquake regions in China

With the theory of subcritical crack growth, we can deduce the fundamental equation of regional seismicity acceleration model. Applying this model to intraplate earthquake regions, we select three earthquake subplates: North China Subplate, Chuan Dian Block and Xinjiang Subplate, and divide the three subplates into seven researched regions by the difference of seismicity and tectonic conditions. With the modified equation given by Sornette and Sammis (1995), we analysis the seismicity of each region. To those strong earthquakes already occurred in these region, the model can give close fitting of magnitude and occurrence time, and the result in this article indicates that the seismicity acceleration model can also be used for describing the seismicity of intraplate. In the article, we give the magnitude and occurrence time of possible strong earthquakes in Shanxi, Ordos, Bole Tuokexun, Ayinke Wuqia earthquake regions. In the same subplate or block, the earthquake periods for each earthquake region are similar in time interval. The constant α in model can be used to describe the intensity of regional seismicity, and for the Chinese Mainland, α is 0.4 generally. To the seismicity in Taiwan and other regions with complex tectonic conditions, the model does not fit well at present.

Estimating the Level of Asymptomatic COVID-19 Infections in Northern Ireland in 2020

The identification and understanding of COVID-19 potential routes of transmission are fundamental to informing policies and strategies to successfully control the outbreak. Various studies highlighted asymptomatic infections as one of the silent drivers of the epidemic. An accurate estimation of the asymptomatic cases and the understanding of their contribution to the spread of the disease could enhance the effectiveness of current control strategies, mainly based on the symptom onset, to curb transmission. We investigate the dynamics of the COVID-19 epidemic in Northern Ireland during the period 1st March 25th to December 2020 to estimate the proportion of the asymptomatic infections in the country. We extended our previous model to include the stage of the asymptomatic infection, and we implement the corresponding deterministic model using a publicly available dataset. We partition the data into 11 sets over the period of study and fit the model parameters on the consecutive intervals using the cumulative number of confirmed positive cases for each interval. Moreover, we assess numerically the impacts of uncertainty in testing and we provide estimates of the reproduction numbers using the fitted parameters. We found that the proportion of asymptomatically infectious subpopulations, in Northern Ireland during the period of study, ranged between 5% and 25% of exposed individuals. Also, the estimate of the basic reproduction number, R0, is 3.3089. The lower and upper estimates for herd immunity are (0.6181, 0.7243) suggesting that around 70% of the population of Northern Ireland should acquire immunity via infection or vaccination, which is in line with estimates reported in other studies.

Decoupled deep hough voting for point cloud registration

Estimating rigid transformation using noisy correspondences is critical to feature-based point cloud registration.Recently,a series of studies have attempted to combine traditional robust model fitting with deep learning.Among them,DHVR proposed a hough voting-based method,achieving new state-of-the-art performance.However,we find voting on rotation and translation simultaneously hinders achieving better performance.Therefore,we proposed a new hough voting-based method,which decouples rotation and translation space.Specifically,we first utilize hough voting and a neural network to estimate rotation.Then based on good initialization on rotation,we can easily obtain accurate rigid transformation.Extensive experiments on 3DMatch and 3DLoMatch datasets show that our method achieves comparable performances over the state-of-the-art methods.We further demonstrate the generalization of our method by experimenting on KITTI dataset.

A CONSTRAINED LEAST SQUARES FITTING TECHNIQUE FOR ARMA MODELING

Several ARMA modeling approaches are addressed. In these methods only part of a correlation sequence is employed for estimating parameters. It is satisfying, if the given correlation sequence is of real ARMA, since an ARMA process can be completely determined by part of its correlation se -quence. But for the case of a measured correlation sequence the whole sequence may be used to reduce the effect of error on model parameter estimation. In addition, these methods now do not guarantee a nonnegative spectral estimate. In view of the above-mentioned fact, a constrained least squares fitting technique is proposed which utilizes the whole measured correlation sequence and guarantees a nonnegative spectral estimate.

Applications of structural equation modeling(SEM)in ecological studies:an updated review

Aims:This review was developed to introduce the essential components and variants of structural equation modeling(SEM),synthesize the common issues in SEM applications,and share our views on SEM’s future in ecological research.Methods:We searched the Web of Science on SEM applications in ecological studies from 1999 through 2016 and summarized the potential of SEMs,with a special focus on unexplored uses in ecology.We also analyzed and discussed the common issues with SEM applications in previous publications and presented our view for its future applications.Results:We searched and found 146 relevant publications on SEM applications in ecological studies.We found that five SEM variants had not commenly been applied in ecology,including the latent growth curve model,Bayesian SEM,partial least square SEM,hierarchical SEM,and variable/model selection.We identified ten common issues in SEM applications including strength of causal assumption,specification of feedback loops,selection of models and variables,identification of models,methods of estimation,explanation of latent variables,selection of fit indices,report of results,estimation of sample size,and the fit of model.Conclusions:In previous ecological studies,measurements of latent variables,explanations of model parameters,and reports of key statistics were commonly overlooked,while several advanced uses of SEM had been ignored overall.With the increasing availability of data,the use of SEM holds immense potential for ecologists in the future.

A Discrete Bat Algorithm for Disassembly Sequence Planning

Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model(FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm(GA), particle swarm optimization(PSO) algorithm and differential mutation BA(DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms.

Empirical Research on User Acceptance of Mobile Searches

The mobile search, a combination of a web search engine and a mobile communication system, is viewed as the most influential application in the 3G era. Therefore, mobile search service providers are eager to know which factors most influence user acceptance of mobile searches. Based on the characteristics of mobile searches and a review of previous information technology acceptance research, this study integrates the task technology fit model and the unified theory of acceptance and use of technology model to develop a mobile search acceptance model and empirically tests this model. This study finds that, for mobile searches, the performance expectancy, social influence, and perceived cost all significantly influence use intention and the performance expectancy increases with the increasing user＇s experience and higher tasktechnology fit degree. The effort expectancy is found to not affect the use intention of mobile searches and the users＇ gender does not have a significant moderating effect on the use intention. The results are then used to develop suggestions for mobile search providers to promote their application and development.

A case study of development and application of a streamlined control and response modeling system for PM_(2.5)attainment assessment in China

This article describes the development and application of a streamlined air control and response modeling system with a novel response surface modeling-linear coupled fitting method and a new module to provide streamlined model data for PM_（2.5） attainment assessment in China.This method is capable of significantly reducing the dimensions required to establish a response surface model,as well as capturing more realistic response of PM_（2.5） to emission changes with a limited number of model simulations.The newly developed module establishes a data link between the system and the Software for Model Attainment Test—Community Edition（SMAT-CE）,and has the ability to rapidly provide model responses to emission control scenarios for SMAT-CE using a simple interface.The performance of this streamlined system is demonstrated through a case study of the Yangtze River Delta（YRD） in China.Our results show that this system is capable of reproducing the Community Multi-Scale Air Quality（CMAQ） model simulation results with maximum mean normalized error 〈 3.5%.It is also demonstrated that primary emissions make a major contribution to ambient levels of PM_（2.5） in January and August（e.g.,more than50%contributed by primary emissions in Shanghai）,and Shanghai needs to have regional emission control both locally and in its neighboring provinces to meet China＇s annual PM_（2.5）National Ambient Air Quality Standard.The streamlined system provides a real-time control/response assessment to identify the contributions of major emission sources to ambient PM_（2.5）（and potentially O_3 as well） and streamline air quality data for SMAT-CE to perform attainment assessments.

Bayesian inference for dynamical systems

Bayesian inference is a common method for conducting parameter estimation for dynamical systems.Despite the prevalent use of Bayesian inference for performing parameter estimation for dynamical systems,there is a need for a formalized and detailed methodology.This paper presents a comprehensive methodology for dynamical system parameter estimation using Bayesian inference and it covers utilizing different distributions,Markov Chain Monte Carlo(MCMC)sampling,obtaining credible intervals for parameters,and prediction intervals for solutions.A logistic growth example is given to illustrate the methodology.