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. According to the theoretical analysis and numerical simulation comparison, the proposed RBFNN-based error compensation method was identified as the optimal error compensation method, which can approximate the random error space surface more precisely, so that a more accurate error compensation value can be obtained, and in order to improve the measurement accuracy with higher precision. Finally, the experimental results proved that the proposed active compensation technology was valid in engineering applicability and could efficiently enhance the measurement accuracy of the two-axis electro-optical measurement equipment.

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. We found that the deterioration of wheat was apparent after two years of storage: (1) the content of fatty acid increased from 12.47% to 29.02%; (2) the malondialdehyde content increased to 37.46%; (3) the conductivity significantly increased from 35.71% to 46.79%; and (4) other indexes, such as the amylopectin content, peak viscosity, and disintegration rate, increased noticeably during storage. Moreover, SEM images revealed a certain degree of damage on the surface of starch granules, and an X-ray diffraction (XRD) analysis showed A-type crystalline starch of wheat. Additionally, FTIR spectra suggested that the ratio of amylose and amylopectin decreased with a decreasing content of amylose and increasing content of amylopectin. The ratio of amylose and amylopectin can lead to variations in wheat machining characteristics. Therefore, wheat should be kept at an average of 20 °C with safe water content for less than two years to maintain reasonable quality.

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. At the same time, a saturation function is used to replace the sign function for the time-varying hydrodynamic disturbance torque and the friction disturbance torque, which further reduce the chattering phenomenon of the system. The asymptotic stability of the proposed control method has been proven by the Lyapunov stability theory. The applicability of the proposed control method is validated by a series of simulations and experiments.

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 (H2O2) biosensor was then established and explored for H2O2 electrochemical detection. The nanocomposite was characterized by Fourier transform infrared spectra, Raman spectroscopy, and transmission electron microscopy. Due to the enlarged effective surface area and the efficient electrocatalytic activity of the Mn(II)-PLH redox-active units, the obtained Mn(II)-PLH-CMWCNT electrode showed excellent electrocatalytic performance toward H2O2 disproportionation. Under the selected optimum conditions, the prepared biosensor exhibited highly sensitive response toward H2O2, and the response current had a good linear relationship between the response currents and H2O2 concentrations in the range of 0.002-1.0 mM, a low detection limit of 0.5 µM and a sensitivity of 464.18 µA mM-1 cm-2. With the good stability, reproducibility and selectivity, the proposed biosensor was successfully applied to the determination of H2O2 in real-life samples, and showed satisfactory results. In summary, the Mn(II)-PLH-CMWCNT nanocomposite could be a promising enzyme-mimicking nanomaterial for the researches of electrocatalysis, biosensing and relevant fields.

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. Therefore, highly sensitive electrochemical response to SAA was achieved under optimum experimental conditions. A good linear relationship between differential pulse voltammetry (DPV) peak current and the SAA concentration was established in the range of 0.4-1000 µM. A low detection limit of 0.037 µM and a sensitivity of 0.27 µA µM-1 cm-2 were achieved. The developed biosensor also had advantages of good repeatability, stability and high selectivity, thus, it was successfully applied to the determination of SAA in real samples with satisfactory results, which may have great potential for further exploitation of electroanalysis applications.