基于无线局域网定位的误差关键因素分析与仿真

在基于无线局域网定位问题中,采样点间距、接入点的个数以及环境的干扰是影响定位平均误差的重要因素,但是对于这些参数的选取目前还没有系统的指导,主要根据经验来确定。提出了一个表示定位平均误差的数学模型,形式化地全面概括了定位问题中的关键因素。基于该模型,分析了采样点间距,接入点个数和环境与定位平均误差之间的关系。仿真结果表明采样点间距取1米即可满足定位的要求,选择多于3个接入点的定位对于弥补环境干扰,减小定位误差有显著的作用。

RSSI位置指纹的定位误差分析与仿真

为了确立RSSI位置指纹定位技术中定位误差与影响其主要参数的关系,提出了表示定位平均误差的数学模型,通过计算整个定位区域误差的期望,公式化地表达了接入点个数、接入点摆放位置、采样点间距、最邻近点个数各因素取值不同对定位精度的影响.通过对实验数据的统计分析,仿真结果表明:接入点个数取4个可满足定位要求,当选择多于5个接入点实现定位时,可以不考虑接入点摆放位置,采样点间距取1 m可满足定位要求,K的取值对定位精度影响不明显.

川南地区龙马溪组页岩高压甲烷等温吸附特征

准确测定页岩吸附气含量对于页岩气储层的评价和开发都具有重要的意义,但目前国内外学者在页岩甲烷等温吸附实验中对模型选择、吸附模式及吸附特征参数的认识上存在着差异,并且对于高压等温甲烷吸附特性的研究较少。为此,在利用N2/CO2气体低压等温吸附实验对四川盆地南部地区下志留统龙马溪组页岩孔隙结构特征进行分析的基础上,采用重量法高压甲烷等温吸附实验,选取SDR、Langmuir、BET等3种不同的吸附模型对吸附态甲烷含量进行计算,并对样品甲烷吸附特征进行研究。研究结果表明:①页岩在0~50 nm孔径区间内比表面积分布具有双峰特征,孔体积分布具有三峰特征,较之于中孔,微孔比表面积发育较好,而其孔体积和非均质性均弱于中孔(D1<D2);②3种模型中SDR和Langmuir模型的计算结果与实测值平均误差均小于6%,甲烷分子主要以单分子层与微孔充填吸附模式共存于页岩孔隙内;③在高压深埋藏情况下,温度是影响吸附态甲烷吸附量和密度值的主要因素,但热力学参数、孔隙结构、非均质性等也会对吸附态甲烷密度造成一定的影响;④低压阶段甲烷分子优先以单分子层形式吸附于吸附能较高、比表面积较大的孔径介于0.4~0.8 nm的微孔中,随后大部分甲烷分子以微孔充填与单分子层共存的形式吸附于孔径介于1.4~8.0 nm的微孔与中孔中,高压阶段极少部分甲烷以多分子层形式吸附于中孔及宏孔中。

Numerical simulation of the hydrodynamics within octagonal tanks in recirculating aquaculture systems

A three-dimensional numerical model was established to simulate the hydrodynamics within an octagonal tank of a recirculating aquaculture system. The realizable k-e turbulence model was applied to describe the flow, the discrete phase model （DPM） was applied to generate particle trajectories, and the governing equations are solved using the finite volume method. To validate this model, the numerical results were compared with data obtained from a full-scale physical model. The results show that： （1） the realizable k-e model applied for turbulence modeling describes well the flow pattern in octagonal tanks, giving an average relative error of velocities between simulated and measured values of 18% from contour maps of velocity magnitudes; （2） the DPM was applied to obtain particle trajectories and to simulate the rate of particle removal from the tank. The average relative error of the removal rates between simulated and measured values was 11%. The DPM can be used to assess the self-cleaning capability of an octagonal tank; （3） a comprehensive account of the hydrodynamics within an octagonal tank can be assessed from simulations. The velocity distribution was uniform with an average velocity of 15 cm/s; the velocity reached 0.8 m/s near the inlet pipe, which can result in energy losses and cause wall abrasion; the velocity in tank corners was more than 15 cm/s, which suggests good water mixing, and there was no particle sedimentation. The percentage of particle removal for octagonal tanks was 90% with the exception of a little accumulation of 〈5 mm particle in the area between the inlet pipe and the wall. This study demonstrated a consistent numerical model of the hydrodynamics within octagonal tanks that can be further used in their design and optimization as well as promote the wide use of computational fluid dynamics in aquaculture engineering.

Kinetic study of alkaline protease 894 for the hydrolysis of the pearl oyster Pinctada martensii

A new enzyme (alkaline protease 894) obtained from the marine extremophile Flavobacterium yellowsea (YS-80-122) has exhibited strong substrate-binding and catalytic activity, even at low temperature, but the characteristics of the hydrolysis with this enzyme are still unclear. The pearl oyster Pinctada martensii was used in this study as the raw material to illustrate the kinetic properties of protease 894. After investigating the intrinsic relationship between the degree of hydrolysis and several factors, including initial reaction pH, temperature, substrate concentration, enzyme concentration, and hydrolysis time, the kinetics model was established. This study showed that the optimal conditions for the enzymatic hydrolysis were an initial reaction pH of 5.0, temperature of 30°C, substrate concentration of 10% (w/v), enzyme concentration of 2 500 U/g, and hydrolysis time of 160 min. The kinetic characteristics of the protease for the hydrolysis of P. martensii were obtained. The inactivation constant was found to be 15.16/min, and the average relative error between the derived kinetics model and the actual measurement was only 3.04%, which indicated a high degree of fitness. Therefore, this study provides a basis for the investigation of the concrete kinetic characteristics of the new protease, which has potential applications in the food industry.

Artificial neural network approach for rheological characteristics of coal-water slurry using microwave pre-treatment

Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry(CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and k vary from 0.31 to 0.64 and 0.19 to 0.81 Pa·sn,respectively. This paper presents an artificial neural network(ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm(trainlm) was selected as the controlled ANN. Mean squared error(MSE) of 0.002 and coefficient of multiple determinations(R^2) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model.

A neural network method for estimating weighted mean temperature over China and adjacent areas

To improve the applicability of the global pressure and temperature 2 wet(GPT2w)model in estimating the weighted mean temperature in China and adjacent areas,the error compensation technology based on the neural network was proposed,and a total of 374800 meteorological profiles measured from 2006 to 2015 of 100 radiosonde stations distributed in China and adjacent areas were used to establish an enhanced empirical model for estimating the weighted mean temperature in this region.The data from 2016 to 2018 of the remaining 92 stations in this region was used to test the performance of the proposed model.Results show that the proposed model is about 14.9%better than the GPT2w model and about 7.6%better than the Bevis model with measured surface temperature in accuracy.The performance of the proposed model is significantly improved compared with the GPT2w model not only at different height ranges,but also in different months throughout the year.Moreover,the accuracy of the weighted mean temperature estimation is greatly improved in the northwestern region of China where the radiosonde stations are very rarely distributed.The proposed model shows a great application potential in the nationwide real-time ground-based global navigation satellite system(GNSS)water vapor remote sensing.

Simulation Model for Photosynthetic Production in Oilseed Rape

Photosynthetic production is a major determinant of final yield in crop plants. A simulation model was developed for canopy photosynthesis and dry matter accumulation in oilseed rape (Brassica napus L.) based on the ecophysiological processes and using a three-layer radiation balance scheme for calculating the radiation interception and absorption by the layers of flowers, pods, and leaves within the canopy. Gaussian integration method was used to calculate photosynthesis of the pod and leaf layers, and the daily total canopy photosynthesis was determined by the sum of photosynthesis from the two layers of green organs. The effects of physiological age, temperature, nitrogen, and water deficit on maximum photosynthetic rate were quantified. Maintenance and growth respiration were estimated to determine net photosynthetic production. Partition index of the shoot in relation to physiological development time was used to calculate shoot dry matter from plant biomass and shoot biomass loss because of freezing was quantified by temperature effectiveness. Testing of the model for dynamic dry matter accumulation through field experiments of different genotypes, sowing dates, and nitrogen levels showed good fit between the observed and simulated data, with an average root mean square error of 10.9% for shoot dry matter. Thus, the present model appears to be reliable for the prediction of photosynthetic production in oilseed rape.

Salting-out effect of ionic liquids on isobaric vapor-liquid equilibrium of acetonitrile-water system

This paper presents the vapor–liquid equilibrium(VLE) data of acetonitrile–water system containing ionic liquids(ILs) at atmospheric pressure(101.3 k Pa). Since ionic liquids dissociate into anions and cations, the VLE data for the acetonitrile + water + ILs systems are correlated by salt effect models, Furter model and improved Furter model. The overall average relative deviation of Furter model and improved Furter model is 5.43% and 4.68%, respectively. Thus the salt effect models are applicable for the correlation of IL containing systems. The salting-out effect theory can be used to explain the change of relative volatility of acetonitrile–water system.

Predication of plasma concentration of remifentanil based on Elman neural network

Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacokinetics parameters,nonlinear mixed effects model(NONMEM),has the abuses of tedious work and plenty of man-made jamming factors.The Elman feedback neural network was built.The relationships between the patients’plasma concentration of remifentanil and time,patient’age,gender,lean body mass,height,body surface area,sampling time,total dose,and injection rate through network training were obtained to predict the plasma concentration of remifentanil,and after that,it was compared with the results of NONMEM algorithm.In conclusion,the average error of Elman network is 6.34%,while that of NONMEM is 18.99%.The absolute average error of Elman network is 27.07%,while that of NONMEM is 38.09%.The experimental results indicate that Elman neural network could predict the plasma concentration of remifentanil rapidly and stably,with high accuracy and low error.For the characteristics of simple principle and fast computing speed,this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetic and pharmacodynamics.

Study on Residual Oil HDS Process with Mechanism Model and ANN Model

Based on the Residual Oil Hydrodesulfurization Treatment Unit (S-RHT), the n-order reaction kinetic model for residual oil HDS reactions and artificial neural network (ANN) model were developed to determine the sulfur content of hydrogenated residual oil. The established ANN model covered 4 input variables, 1 output variable and 1 hidden layer with 15 neurons. The comparison between the results of two models was listed. The results showed that the predicted mean relative errors of the two models with three different sample data were less than 5% and both the two models had good predictive precision and extrapolative feature for the HDS process. The mean relative error of 5 sets of testing data of the ANN model was 1.62%—3.23%, all of which were smaller than that of the common mechanism model (3.47%— 4.13%). It showed that the ANN model was better than the mechanism model both in terms of fitting results and fitting difficulty. The models could be easily applied in practice and could also provide a reference for the further research of residual oil HDS process.

Long-range precipitation forecasts using paleoclimate reconstructions in the western United States

Long-range precipitation forecasts are useful when managing water supplies.Oceanicatmospheric oscillations have been shown to influence precipitation.Due to a longer cycle of some of the oscillations,a short instrumental record is a limitation in using them for long-range precipitation forecasts.The influence of oscillations over precipitation is observable within paleoclimate reconstructions;however,there have been no attempts to utilize these reconstructions in precipitation forecasting.A data-driven model,KStar,is used for obtaining long-range precipitation forecasts by extending the period of record through the use of reconstructions of oscillations.KStar is a nearest neighbor algorithm with an entropy-based distance function.Oceanic-atmospheric oscillation reconstructions include the El Nino-Southern Oscillation(ENSO),the Pacific Decadal Oscillation(PDO),the North Atlantic Oscillation(NAO),and the Atlantic Multi-decadal Oscillation(AMO).Precipitation is forecasted for 20 climate divisions in the western United States.A 10-year moving average is applied to aid in the identification of oscillation phases.A lead time approach is used to simulate a one-year forecast,with a 10-fold cross-validation technique to test the models.Reconstructions are used from 1658-1899,while the observed record is used from 1900-2007.The model is evaluated using mean absolute error(MAE),root mean squared error(RMSE),RMSE-observations standard deviation ratio(RSR),Pearson's correlation coefficient(R),NashSutcliffe coefficient of efficiency(NSE),and linear error in probability space(LEPS) skill score(SK).The role of individual and coupled oscillations is evaluated by dropping oscillations in the model.The results indicate 'good' precipitation estimates using the KStar model.This modeling technique is expected to be useful for long-term water resources planning and management.

Evaluation and intercomparison of ozone simulations by Models-3/CMAQ and CAMx over the Pearl River Delta

Ozone pollution over the Pearl River Delta (PRD) in October 2004 has been simulated using the regional air quality models Models-3/CMAQ and CAMx. The results from both models were evaluated and compared with the observed concentrations from 12 monitoring stations. By integrated process rate analysis, the influences of different physical and chemical processes were quantified, and the causes of the deviations between the two models were investigated. Both CMAQ and CAMx repro- duced the magnitudes and variations of ozone at most stations over the PRD. The correlation coefficients (R) between the sim- ulated results and monitoring data were 0.73 for CMAQ and 0.74 for CAMx. The normalized mean bias (NMB) for CMAQ and CAMx over the 12 sites was ?8.5% and 8.8% on average, respectively. The normalized mean error (NME) for CMAQ and CAMx was 36.7% and 37.9%, respectively. The correlation between the results of two models was very high (R = 0.92), and their simulated ozone spatial distributions exhibited common features. But the values obtained using CMAQ simulation were about 17% lower than those obtained using CAMx on average. The results of simulations using the two models were not identical in certain regions, or for different types of monitoring stations. The differences in dry deposition, reaction parameters and vertical transport near the Pearl River Estuary can account for the discrepancies in the results obtained using the two models. In the upwind areas, the discrepancy in the boundary concentration of the finest nest was the main cause of the higher values obtained using CAMx compared with those obtained using CMAQ. There is a need for CAMx to provide more choices of dry deposition algo- rithms. Improvement of the calculation methods for photolysis rates would also improve the ozone simulation of CMAQ.

Simultaneous determination of brilliant blue and indigotine by derivative fluorescence spectrometry combined with WT-RBFNN

The mixed solutions of brilliant blue and indigotine are prepared and the fluorescence spectra of them are experimentally measured. The serious overlapping spectra of brilliant blue and indigotine are solved by means of the first-derivative fluorescence spectrometry. The wavelet coefficients, obtained by compressing the spectral data using wavelet transformation （WT）, are taken as inputs to establish the radial basis function neural network （RBFNN）. The neural network model can realize simultaneous determination of brilliant bFue and indigotine, and the mean relative errors of both compounds are 1.84% and 1.26%, respectively

aging structures in the presence of incomplete deterioration information

The performance of an aging structure is commonly evaluated under the framework of reliability analysis, where the uncertainties associated with the structural resistance and loads should be taken into account. In practical engineering, the probability distribution of resistance deterioration is often inaccessible due to the limits of available data, although the statistical parameters such as mean value and standard deviation can be obtained by fitting or empirical judgments. As a result, an error of structural reliability may be introduced when an arbitrary probabilistic distribution is assumed for the resistance deterioration. With this regard, in this paper, the amount of reliability error posed by different choices of deterioration distribution is investigated, and a novel approach is proposed to evaluate the averaged structural reliability under incomplete deterioration information, which does not rely on the probabilistic weight of the candidate deterioration models. The reliability for an illustrative structure is computed parametrically for varying probabilistic models of deterioration and different resistance conditions, through which the reliability associated with different deterioration models is compared, and the application of the proposed method is illustrated.