PARAMETERS DETERMINATION METHOD OF PHASE-SPACE RECONSTRUCTION BASED ON DIFFERENTIAL ENTROPY RATIO AND RBF NEURAL NETWORK

Phase space reconstruction is the first step of recognizing the chaotic time series.On the basis of differential entropy ratio method,the embedding dimension opt m and time delay t are optimal for the state space reconstruction could be determined.But they are not the optimal parameters accepted for prediction.This study proposes an improved method based on the differential entropy ratio and Radial Basis Function(RBF)neural network to estimate the embedding dimension m and the time delay t,which have both optimal characteristics of the state space reconstruction and the prediction.Simulating experiments of Lorenz system and Doffing system show that the original phase space could be reconstructed from the time series effectively,and both the prediction accuracy and prediction length are improved greatly.

Investigation of the optimum differential gear ratio for real driving cycles by experiment design and genetic algorithm

Experiment statistical method and genetic algorithms based optimization method are used to obtain the optimum differential gear ratio for heavy truck that provides best fuel consumption when changing the working condition that affects its torque and speed range. The aim of the study is to obtain the optimum differential gear ratio with fast and accurate optimization calculation without affecting drivability characteristics of the vehicle according to certain driving cycles that represent the new working conditions of the truck. The study is carried on a mining dump truck YT3621 with 9 for- ward shift manual transmission. Two loading conditions, no load and 40 t, and four on road real driving cycles have been discussed. The truck powertrain is modeled using GT-drive, and DOE -post processing tool of the GT-suite is used for DOE analysis and genetic algorithm optimization.

Tunneling field effect transistors based on in-plane and vertical layered phosphorus heterostructures

Tunneling field effect transistors（TFETs） based on two-dimensional materials are promising contenders to the traditional metal oxide semiconductor field effect transistor, mainly due to potential applications in low power devices. Here,we investigate the TFETs based on two different integration types： in-plane and vertical heterostructures composed of two kinds of layered phosphorous（β-P and δ-P） by ab initio quantum transport simulations. NDR effects have been observed in both in-plane and vertical heterostructures, and the effects become significant with the highest peak-to-valley ratio（PVR）when the intrinsic region length is near zero. Compared with the in-plane TFET based on β-P and δ-P, better performance with a higher on/off current ratio of - 10-6 and a steeper subthreshold swing（SS） of - 23 mV/dec is achieved in the vertical TFET. Such differences in the NDR effects, on/off current ratio and SS are attributed to the distinct interaction nature of theβ-P and δ-P layers in the in-plane and vertical heterostructures.

Differential Approximation Algorithm of FSMVRP

Abstract We study the fleet size and mix vehicle routing problem with constraints on the capacity of each vehicle. The objective is to minimize the total cost including fixed utilization cost of vehicles and traveling cost by vehicles. We give differential approximation algorithms for the fleet size and mix vehicle routing problem （FSMVRP） with two kinds of vehicles, the capacities of which are respectively nlk and n2k, n2 〉 nl ≥ 1, k ≥ 1. Using existing theories for vehicle routing problems and feature of the algorithms represented in the paper, we also prove that the algorithms give（1-6n＋3/（n＋1）2k＋n＋1）differential approximation ratio for （k, nk） VRP, n 〉 1and （1-6n2＋3n/n1k＋n2k）2k）differential approximation ratio for （nlk, n2k）VRP, n2 〉 nl 〉 1.