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.

Fixed-bed column study for deep removal of copper(Ⅱ) from simulated cobalt electrolyte using polystyrene-supported 2-aminomethylpyridine chelating resin

This study presents the deep removal of copper (Ⅱ) from the simulated cobalt electrolyte using fabricated polystyrene-supported 2-aminomethylpyridine chelating resin (PS-AMP) in a fixed-bed.The effects of bed height (7.0–14.0 cm),feed flow rate (4.5–9.0 mL/min),initial copper (Ⅱ) concentration of the feed (250–1000 mg/L),feed temperature (25–40 ℃) and the value of pH (2.0–4.0) on the adsorption process of the PS-AMP resin were investigated.The experimental data showed that the PS-AMP resin can deeply eliminate copper (Ⅱ) from the simulated cobalt electrolyte.The bed height,feed flow rate,initial copper (Ⅱ) concentration of the feed,feed temperature and feed pH value which corresponded to the highest removal of copper (Ⅱ) were 7.0 cm with 35 mm of the column diameter,4.5 mL/min,40℃,1000 mg/L and 4.0,respectively.The breakthrough capacity,the saturated capacity of the column and the mass ratio of Cu/Co (g/g) in the saturated resin were correspondingly 16.51 mg/g dry resin,61.72 mg/g dry resin and 37.67 under the optimal experimental conditions.The copper (Ⅱ) breakthrough curves were fitted by the empirical models of Thomas,Yoon-Nelson and Adam-Bohart,respectively.The Thomas model was found to be the most suitable one for predicting how the concentration of copper (Ⅱ) in the effluent changes with the adsorption time.

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.

Gaussian process assisted coevolutionary estimation of distribution algorithm for computationally expensive problems

In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.

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.

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.

Parameterization of Tree and Shrub Stem Wood Density Adaptions to Multiple Climate and Soil Factor Gradients

Wood density(WD)is an important quality and functional trait of wood.However,despite the relationships between WD and abiotic factors being important to model or predict spatial distributions of functional traits,as well as responses of vegetation to climate changes,in current Earth system models or dynamic global vegetation models(ESMs/DGVMs),WD is often oversimplified,being defined as a globally uniform constant either for all plant functional types(PFTs)or for each individual PFT.Such oversimplifications may lead to simulation biases in the morphology of woody PFTs,as well as ecosystem transition and vegetation-atmosphere interactions.Moreover,existing conclusions about the relationships between WD and abiotic factors drawn from field observations remain mixed,making model parameterization improvements difficult.This study systematically investigated the influences of climate and soil factors on WD across various PFTs.Optimal fitting models for predicting WD within each PFT were then constructed by utilizing our collated global database of 138604 observations.For WDs of tree PFTs,climate emerges as a more influential factor than soil characteristics,whereas for shrub PFTs the effects of climate and soil are of equivalent significance.Across all six PFTs,correlation coefficients between predictions by fitting models and observed WD range from 0.49 to 0.93.The predicted and observed WD exhibit good agreement across climate space.It is expected that the incorporation of our research findings into DGVMs will improve the simulation of tree height and forest fractional coverage,particularly in the central forest areas and forest transition zones.

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.

SEIRS model for malaria transmission dynamics incorporating seasonality and awareness campaign

Malaria,a devastating disease caused by the Plasmodium parasite and transmitted through the bites of female Anopheles mosquitoes,remains a significant public health concern,claiming over 600,000 lives annually,predominantly among children.Novel tools,including the application of Wolbachia,are being developed to combat malaria-transmitting mosquitoes.This study presents a modified susceptible-exposed-infectious-recovered-susceptible(SEIRS)compartmental mathematical model to evaluate the impact of awareness-based control measures on malaria transmission dynamics,incorporating mosquito interactions and seasonality.Employing the next-generation matrix approach,we calculated a basic reproduction number(R0)of 2.4537,indicating that without robust control measures,the disease will persist in the human population.The model equations were solved numerically using fourth and fifth-order Runge-Kutta methods.The model was fitted to malaria incidence data from Kenya spanning 2000 to 2021 using least squares curve fitting.The fitting algorithm yielded a mean absolute error(MAE)of 2.6463 when comparing the actual data points to the simulated values of infectious human population(Ih).This finding indicates that the proposed mathematical model closely aligns with the recorded malaria incidence data.The optimal values of the model parameters were estimated from the fitting algorithm,and future malaria dynamics were projected for the next decade.The research findings suggest that social media-based awareness campaigns,coupled with specific optimization control measures and effective management methods,offer the most cost-effective approach to managing malaria.

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.

Species Abundance Distribution Patterns of a Toona ciliata Community in Xingdoushan Nature Reserve

With the goal of model fitting species abundance distribution patterns of the tree,shrub and herb layers of the natural Toona ciliata community in Xingdoushan Nature Reserve,Enshi Autonomous Prefecture,Hubei Province,we used the data collected from the field survey and employed different ecological niche models.The models tested were the broken stick model(BSM),the overlapping niche model(ONM)and the niche preemption model(NPM),as well as three statistic models,the log-series distribution model(LSD),the log-normal distribution model(LND)and the Weibull distribution model(WDM).To determine the fitted model most suitable to each layer,the fitting effects were judged by criteria of the lowest value of Akaike Information Criterion(AIC),Chi-square and the K-S values with no significant difference(P>0.05)between the theoretical predictions and observed species abundance distribution values.The result showed:(1)The fitting suitability and goodness of fit of the tree,shrub and herb layers by using the three ecological niche models were ranked as:NPM>BSM>ONM.Of the three statistical models,by accepting the fitting results of the three layers,WDM was the best fitting model,followed by LND.By rejecting the fitting tests of the herb layer,LSD had the worst fitting effect.The goodness of the statistical models was ranked as:WDM>LND>LSD.In general,the statistical models had better fitting results than the ecological models.(2)T.ciliata was the dominant species of the tree layer.The species richness and diversity of the herb layer were much higher than those of either the tree layer or the shrub layer.The species richness and diversity of the shrub layer were slightly higher than those of the tree layer.The community evenness accorded to the following order:herb>shrub>tree.Considering the fitting results of the different layers,different ecological niche models or statistical models with optimal goodness of fit and ecological significance can be given priority to in studying the species abundance distribution patterns of T.ciliata communities.

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.