A nonparametric analysis of energy environmental Kuznets Curve in Chinese Provinces

Energy resources are an important material foundation for the survival and development of human society, and the relationship between energy and economy is interactive and complementary. This paper analyzes the energy consumption–economic growth nexus in Chinese provinces using novel and recent nonparametric time-series as well as panel data empirical approaches. The dataset covers 30 provinces over the period of 1980–2018. The empirical analysis indicates the presence of a nonlinear functional form and smooth structural changes in most of the provinces. The nonparametric empirical analysis validates the presence of a nonlinear unit root problem in energy consumption and economic growth, and nonlinear cointegration between the variables. Additionally, the nonparametric panel cointegration test reports evidence of convergence in energy consumption and economic growth patterns across the provinces. The nonparametric regression analysis finds economic growth to have a positive effect, on average, on energy consumption in all provinces, except for Beijing. Further, the energy environmental Kuznets curve exists between economic growth and energy consumption in 20 out of 30 Chinese provinces. The Granger causality analysis reveals the presence of a mixed causal relationship between economic growth and energy consumption. The empirical findings have important implications for Chinese authorities in planning for improving energy efficiency, decoupling between economic growth and energy consumption, and reducing the environmental footprint of provinces.     

Performance parameters of an ejector-absorption heat transformer

Ejector-absorption heat transformers (EAHTs) are attractive for increasing a solar-pond's temperature and for recovering low-level waste-heat. Thermodynamic analysis of the performance of an EAHT is complicated due to the associated complex differential equations and simulation programs. This paper proposes the use of artificial neural-networks (ANNs) as a new approach to determine the performance parameters, as functions of only the working temperatures of the EAHT, which is used to increase the solar pond's temperature under various working conditions. Thus, this study is helpful in predicting the performance of an EAHT where the temperatures are known. Scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) learning algorithms and a logistic sigmoid transfer-function were used in the network. The best approach was investigated for performance parameters with developed software using various algorithms. The best statistical coefficients of multiple determinations (R²-values) equal 0.99995, 0.99997 and 0.99995 for the coefficient of performance (COP), exergetic coefficient of performance (ECOP) and circulation ratio (F), respectively obtained by the LM algorithm with seven neurons. In the comparison of performances, results obtained via analytic equations and by means of the ANN, the COP, ECOP and F for all working situations differ by less than 1.05%, 0.7% and 3.07%, respectively. These accuracies are acceptable in the design of the EAHT. The ANN approach greatly reduces the time required by design engineers to find the optimum solution. Apart from reducing the time required, it is possible to find solutions that make solar-energy applications more viable and thus more attractive to potential users. Also, this approach has the advantages of high computational speed, low cost for feasibility, rapid turn-around, which is especially important during iterative design phases, and ease of design by operators with little technical experience.     

增强现实光照方向估计方法评述与展望

光照一致性是增强现实中实现虚实融合的重要方面,实时地估计出户外场景中的光照方向,对实现户外场景光照一致性和增强真实感具有重要意义.针对光照方向估计问题,首先归纳了户外场景光照方向估计的特点,把现有的光照方向估计方法分为借助光测球等特殊标定物的估算方法、立体视觉法和人工智能法3类方法.然后总结了各类方法的基本原理和有代表性的研究工作,并且比较了各类方法的优缺点和适用范围.最后,根据实际应用中存在的问题和挑战,展望了户外增强现实系统光照方向估计的研究方向.     

Efficient FPGA implementation and verification of difference expansion based reversible watermarking with improved time and resource utilization

This paper presents Xilinx System Generator (XSG) model design for realization of reversible watermarking algorithm using Difference Expansion (DE) approach in System-On-Chip (SoC) Field Programmable Gate Array (FPGA) environment. The reversible watermarking is verified by taking a (4 × 4) sized test image and is applicable for larger sizes of cover images. The outcomes of the result demonstrate that the proposed structural design allows combining MATLAB-Simulink and XSG during graphical user interface for image processing applications. The superiority of the algorithm is justified by using comparative analysis with some well-known methods in both software and hardware environments. The method provides effectively higher PSNR at higher embedding capacity. It is also found that the method requires less time and hardware resources with throughput of 13.516 Mb/s at operational frequency of 80 MHz for real time implementation using FPGA.     

Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.     

一种基于CNNs电路模型的伪随机信号产生方法

随机数发生器广泛用于系统仿真、测试、信息安全、密码学等诸多领域,本文在介绍CNNs电路模型概念基础上,利用3元CNNs构造了混沌系统。针对该混沌系统系统变量随机性较差的情况,给出了系统变量局部放大和参数扰动的改进方案,得到了随机性能较好的变量,最后通过仿真分析证明了所提出方法的有效性。     

基于物联网的物流管理信息系统再造策略探讨

在分析我国物流管理信息系统现状的基础上,利用物联网的优势对现有物流信息系统进行再造,提出新型物流信息系统的再造策略,以实现安全、高效、可追溯的物流系统,对提高我国物流业信息化水平和物流服务水平具有重要参考价值。     

一种新型高精度温度控制方法

为控制环境温度剧烈变化,对于精密仪器设备对温度精度和系统稳定性要求也随之提高,提出了一种参数自学习比例-周期控温算法,能够自动辨识系统参数和控制参数,并具有自学习能力。当环境温度快速变化时,可以采用相应的控制策略,达到理想的控制效果,适用于控制系统参数未知的场合,同时对环境温度的变化具有较强的自适应能力。仿真结果表明,算法控制精度高、自适应能力强,确实能够满足精密仪器设备对温度的精度和稳定性的要求。