Dynamic Changes,Regional Differences,and Influencing Factorsof CO_2 Emission Performance in China

This paper proposes to use DEA models with undesirable outputs to construct the Malmquist index that can be use to investigate the dynamic changes of CO 2 emission performance.With the index,the authors have measured the CO 2 emission performance of 28 provinces and autonomous regions in China from 1996 to 2007;with the convergence theory and panel data regression model,the authors analyze the regional differences and the influencing factors.It is found that the performance of CO 2 emissions in China has been continuously improved mainly due to the technological progress,and the average improvement rate is 3.25%,with a cumulative improvement rate of 40.86%.In addition,the CO 2 emission performance varies across four regions.As a whole,the performance score of eastern China is the highest.The northeastern and central China has relatively lower performance scores,and the western China is relatively backward.The regional differences are decreasing,and the performance of CO 2 emissions is convergent.The influence of some factors on the performance of CO 2 emissions is significant,such as the level of economic development,the level of industrial structure,energy intensity,and ownership structure.The influence of some factors,such as opening-up to the outside world,on the performance of CO 2 emissions is not significant..

Global stabilizer of a general class of feedback nonlinear systems and its exponential convergence

We discuss the global stabilization procedure which renders a general class of feedback nonlinear systems exponential convergent, Our stabilizer consists of a nested saturation function, which is a nonlinear combination of saturation functions. Here we prove the exponential convergence of the stabilizer for the first time and give numerical examples to illustrate the efficiency of the result given above,

Fault detection and accommodation via neural network and variable structure control

This paper proposes a novel idea that classifies faults into two different kinds： serious faults and small faults, and treats them with different strategies respectively. A kind of artificial neural network （ANN） is proposed for detecting serious faults, and variable structure （VS） model-following control is constructed for accommodating small faults. The proposed framework takes both advantages of qualitative way and quantitative way of fault detection and accommodation. Moreover, the uncertainty case is investigated and the VS controller is modified. Simulation results of a remotely piloted aircraft with control actuator failures illustrate the performance of the developed algorithm.

The Kinematics Analysis of the Hydraulic Mine-clearing Mechanical Manipulator

Through analyzing the kinematics of the hydraulic manipulator operating system, according to the rules for seting up the D-H coordinate system, the generalized coordinate of the manipulator system is established. The rotating and moving joints are selected from the mechanism as joint variables. Each generalized transformation matrix of joints is worked out. The kinematics equation at the finger end of the manipulator is calculated. The obverse solution for the manipulator is gained. The geometrical operating parameters and primary technical specification of the manipulator system are simulated through the computer. The simulative result has shown that the manipulator operating system meets the working task requirements. This research provides theoretical basis for optimizing structural parameters of the manipulator operating. So it also is justified the feasibility for mechanical manipulators to be used in the engineering equipment platform of the hydraulic excavator.

Multimodel-based flight control system reconfiguration control in the presence of input constraints

In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model switching.The μ-modification is introduced in the model reference architecture to construct the adaptive controller.The proof of stability is based on the candidate Lyapunov function,while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals.Simulation results illustrate the efficiency of the proposed method.

Investigation of optical reflectance from different animal vertebra along the fixation trajectory of pedicle screw in frequency domain

Accurate placement of pedicle screw(PS)is crucial in spinal surgery.Developing new real-time intra-operative monitoring and navigation methods is an important direction of clinical appli-cation research.In this paper,we studied the spectrum along the fixation trajectory of PS in frequency domain to tackle the accuracy problem.Fresh porcine vertebrae,bovine vertebrae and ovine vertebrae were measured with the near-infrared spectrum(NIR)device to obtain the reflected spectrum from the vertebrae.Along the fixation trajectory of PS,average energy from different groups was calculated and used for identifying different tissues and compared to achieve the optimal recognition factor.Compared with the time domain approach,the frequency domain method could divide the spectra measured at different tissue points into different groups more stably and accurately,which could serve as a new method to assist the PS insertion.The results gained from this study are significant to the development of hi-tech medical instruments with independent intellectual property rights.

Visualizing veins from color skin images using convolutional neural networks

Intravenous cannulation is the most important phase in medical practices.Currently,limited literature is available about visibility of veins and the characteristics of patients associated with difficult intravenous access.In modern medical treatment,a major challenge is locating veins for patients who have difficult venous access.Presently,some products of vein locators are available in the market to improve vein access,but they need auxiliary equipment such as near infrared(NIR)illumination and camera,which add weight and cost to the devices,and cause inconveniences to daily medical care.In this paper,a vein visualization algorithm based on the deep learning method was proposed.Based on a group of synchronous RGB/NIR arm images,a convolutional neural network(CNN)model was designed to implement the mapping from RGB to NIR images,where veins can be detected from skin.The model has a simple structure and less optimization parameters.A color transfer scheme was also proposed to make the network adaptive to the images taken by smartphone in daily medical treatments.Comprehensive experiments were conducted on three datasets to evaluate the proposed method.Subjective and objective evaluations showed the effec-tiveness of the proposed method.These results indicated that the deep learning-based method can be used for visualizing veins in medical care applications.