Active Compensation Technology for the Target Measurement Error of Two-Axis Electro-Optical Measurement Equipment.

For two-axis electro-optical measurement equipment, there are many error sources in parts manufacturing, assembly, sensors, calibration, and so on, which cause some random errors in the final measurement results of the target. In order to eliminate the random measurement error as much as possible and improve the measurement accuracy, an active compensation technique for target measurement error is proposed in this paper. Firstly, the error formation mechanism and error transfer model establishment of the two-axis electro-optical measurement equipment were studied, and based on that, three error compensation and correction methods were proposed: the least square (LS)-based error compensation method, adaptive Kalman filter(AKF)-based error correction method, and radial basis function neural network (RBFNN)-based error compensation method.

Variation in Wheat Quality and Starch Structure under Granary Conditions during Long-Term Storage.

As a globally distributed cereal, wheat is an essential part of the daily human dietary structure. Various changes in nutrient composition and starch structure can reflect the quality of wheat. In this study, we carried out a series of measurements to reveal the levels of wheat quality during long-term storage.

Research on the Line of Sight Stabilization Control Technology of Optronic Mast under High Oceanic Condition and Big Swaying Movement of Platform.

To realize high-performance line of sight (LOS) stabilization control of the optronic mast under high oceanic conditions and big swaying movements of platforms, a composite control method based on an adaptive radial basis function neural network (RBFNN) and sliding mode control (SMC) is proposed. The adaptive RBFNN is used to approximate the nonlinear and parameter-varying ideal model of the optronic mast, so as to compensate for the uncertainties of the system and reduce the big-amplitude chattering phenomenon caused by excessive switching gain in SMC. The adaptive RBFNN is constructed and optimized online based on the state error information in the working process; therefore, no prior training data are required.

A novel catalase mimicking nanocomposite of Mn(II)-poly-L-histidine-carboxylated multi walled carbon nanotubes and the application to hydrogen peroxide sensing.

In this work, a novel enzyme-mimicking nanocomposite of Mn(II)-poly-L-histidine (PLH) functionalized carboxylated multi walled carbon nanotubes (CMWCNTs) was designed and synthesized. Based on the catalase-like activity of the nanocomposite, a non-enzymatic hydrogen peroxide (HO) biosensor was then established and explored for HO electrochemical detection. The nanocomposite was characterized by Fourier transform infrared spectra, Raman spectroscopy, and transmission electron microscopy.

Copper (II)-ploy-L-histidine functionalized multi walled carbon nanotubes as efficient mimetic enzyme for sensitive electrochemical detection of salvianic acid A.

In this work, carboxylated multi walled carbon nanotubes (CMWCNTs) were firstly prepared and functionalized with poly-L-histidine (PLH), which were then chelated with copper (II) ions to from the nanocomposites of Cu(II)-PLH-CMWCNTs. The nanocomposites could be exploited as an efficient mimic enzyme for sensitive electrochemical detection of salvianic acid A (SAA). Cu(II)-PLH-CMWCNTs owned good charge transfer property and excellent synergetic catalytic effect between the overoxidized imidazole groups and the copper redox-active units.