Statistical Model of Path Loss for Railway 5G Marshalling Yard Scenario

The railway mobile communication system is undergoing a smooth transition from the Global System for Mobile Communications-Railway(GSM-R)to the Railway 5G.In this paper,an empirical path loss model based on a large amount of measured data is established to predict the path loss in the Railway 5G marshalling yard scenario.According to the different characteristics of base station directional antennas,the antenna gain is verified.Then we propose the position of the breakpoint in the antenna propagation area,and based on the breakpoint segmentation,a large-scale statistical model for marshalling yards is established.

Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network

On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.

Anatomical changes in the somatosensory system after large sensory loss predict strategies to promote functional recovery after spinal cord injury

Among cases of spinal cord injury are injuries involving the dorsal column in the cervical spinal cord that interrupt the major cutaneous afferents from the hand to the cuneate nucleus（Cu）in the brainstem.Deprivation of touch and proprioceptive inputs consequently impair skilled hand use.

A new approach for prediction of the wear loss of PTA surface coatings using artificial neural network and basic,kernel-based,and weighted extreme learning machine

Wear tests are essential in the design of parts intended to work in environments that subject a part to high wear.Wear tests involve high cost and lengthy experiments,and require special test equipment.The use of machine learning algorithms for wear loss quantity predictions is a potentially effective means to eliminate the disadvantages of experimental methods such as cost,labor,and time.In this study,wear loss data of AISI 1020 steel coated by using a plasma transfer arc welding(PTAW)method with FeCrC,FeW,and FeB powders mixed in different ratios were obtained experimentally by some of the researchers in our group.The mechanical properties of the coating layers were detected by microhardness measurements and dry sliding wear tests.The wear tests were performed at three different loads(19.62,39.24,and 58.86 N)over a sliding distance of 900 m.In this study,models have been developed by using four different machine learning algorithms(an artificial neural network(ANN),extreme learning machine(ELM),kernel-based extreme learning machine(KELM),and weighted extreme learning machine(WELM))on the data set obtained from the wear test experiments.The R2 value was calculated as 0.9729 in the model designed with WELM,which obtained the best performance among the models evaluated.

Estimation of boundary parameters and prediction of transmission loss based upon ray acoustics

Estimation of boundary parameters and prediction of transmission loss using a coherent channel model based upon ray acoustics and sound propagation data collected in field experiments are presented. Comparison between the prediction results and the experiment data indicates that the adopted sound propagation model is valuable, both selection and estimation methods on boundary parameters are reasonable, and the prediction performance of transmission loss is favorable.

Some discussions on the prediction model of three-dimensional shock losses

Wennerstrom and Puterbaugh （1984） presented a model for predicting the shock loss in a compressor blade row that took into account the three-dimemionality of the shock surface. Both the measured data and the numerical solution show that the shock surface is almost normal to the relative flow on each S1 stream surface near the design flow condition. The shock surface is oblique in the spanwise direction because of the sweep of

The USLE soil erodibility nomograph revisited

The nomograph by Wischmeier et al.(1971)for calculating the K-factor in the USLE was extremely useful when there was low access to calculators.However,the generalised calculation of this factor requires the development of analytic procedures.This paper presents a detailed analysis of the nomograph and its underlying equation,which is applicable only when the silt plus very find sand fraction does not exceed 70%.We also examined the quality of fit on the nomograph of the adaptations to the equation that have been proposed,as a means of dealing with those areas where the original equation is not applicable.All models are shown to have areas where the fit is deficient or even unacceptable.Besides,the family of curves on the nomograph for the various values taken by the organic matter are not coincident with the mathematical function from which they presumably derive.The study also identifies those areas of the textural triangle in which the soils originally used in developing the USLE are located,with a view to according a lower predictive value to the contrasting areas in which calculations of the K-factor will necessarily be extrapolations.Finally,a new equation for calculating the K-factor is presented,which accurately reproduces the different sections of the nomograph,and allows the poorly functioning graph to be dispensed with.The paper ends with a link to a tool in R for simplifying the procedure for calculating the K-factor,taking into account varying situations of data availability.