There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

There is a growing demand for stretchable strain sensors because of their potential applications in various emerging fields, such as human motion detection, health monitoring, wearable electronics, and soft robotic skin. Recently, strain sensors based on flexible conductive polymer composites (FCPCs) composed of conductive materials and a stretchable elastomer have received intensive attention owing to their high stretchability, good flexibility, excellent durability, tunable strain sensing behaviors, and ease of processing. Here, we systematically summarize the recent progress of stretchable strain sensors based on FCPCs. This review covers the classification and sensing mechanisms as well as the influence of multiple factors on the sensing behaviors of FCPC based strain sensors with detailed examples.

An overview of stretchable strain sensors from conductive polymer nanocomposites

To improve the dispersion of graphene oxide (GO) nanosheets in sizing agent and to enhance the interfacial adhesion between GO and epoxy, GO nanosheets were chemically modified with cyanuric chloride (TCT) and diethylenetriamine (DETA). The functionalized GO (i.e. GO-TCT-DETA) with different contents was introduced into the carbon fiber (CF) interface by sizing process. The uniform distribution of GO sheets on CF surface and the enhancement of surface roughness were obtained. Moreover, significant enhancements (i.e., 104.2%, 100.2%, and 78.3%) of interfacial shear strength (IFSS), interlaminar shear strength (ILSS), and flexural properties were achieved in the composites with only 1.0 wt% GO-TCT-DETA sheets introduced in the fiber sizing. The GO-TCT-DETA in the interface region enhanced the stress being transferred effectively and the local stress concentrations being relieved. This study indicates that the utilization of functionalized GO is one of the alternative approaches for controlling the fiber-matrix interface and improving the mechanical properties of CF epoxy composites.

Reinforcing carbon fiber epoxy composites with triazine derivatives functionalized graphene oxide modified sizing agent

Robust dynamic surface trajectory tracking control for a quadrotor UAV via extended state observer

Dual-optimization for a MEMS-INS/GPS system during GPS outages based on the cubature Kalman filter and neural networks

To improve the seamless navigation ability of an integrated Global Positioning System (GPS)/inertial navigation system in GPS-denied environments, a hybrid navigation strategy called the self-learning square-root- cubature Kalman filter (SL-SRCKF) is proposed in this article. The SL-SRCKF process contains two innovative steps: 1) it provides the traditional SRCKF with a self-learning ability, which means that navigation system observations can be provided continuously, even during long-term GPS outages; and 2) the relationship between the current Kalman filter gains and the optimal estimation error is established, which means that the optimal estimation accuracy can be improved by error compensation. The superiority of the proposed SL-SRCKF strategy is verified via experimental results and prominent advantages of this approach include: 1) the SL-SRCKF comprises two cycle filtering systems that work in a tightly coupled mode, and this allows more accurate error correction results to be obtained during GPS outages; 2) the system's error prediction ability is effectively improved by introducing a long short-term memory, which provides much better performance than other neural networks, such as random forest regression or the recursive neural network; and 3) under different (30, 60, and 100 s) GPS outage conditions, the long-term stability of SL-SRCKF is much better than that of other error correction approaches.

Seamless GPS/Inertial Navigation System Based on Self-Learning Square-Root Cubature Kalman Filter

This paper presents a bandwidth expansion method for dual-mass microelectromechanical system (MEMS) gyroscopes based on the pole–zero temperature compensation method. When the sense loop operates under open conditions, the mechanical sensitivity of the gyroscope structure conflicts with the bandwidth and is governed by the frequency difference between the drive and the sense modes (min {Δ ω 1, Δ ω 2}), which is proven to change with temperature during the experiment. The pole–zero temperature compensation proportional controller (PZTCPC) is proposed to expand the bandwidth under different temperatures based on the pole–zero compensation method. The force rebalancing combs stimulation method (FRCSM) is used to achieve accurate gyroscope bandwidth characteristics. The mechanical bandwidth of the gyroscope is proven to be approximately 13 Hz when the sense-mode loop is open, and the simulation results show that the PZTCPC method expands the bandwidth to greater than 91.7 Hz after the sense-mode loop is closed. The FRCSM experiments indicate that gyroscope bandwidth is expanded to 95 Hz at –40 °C, 94 Hz at 20 °C and 92 Hz at 60 °C, while the bandwidths at –40, 20, and 60 °C are all 93 Hz with the turntable method. The experimental curves match the simulation curves well and verify the simulation results. The new limiting condition of the closed-loop bandwidth is the trough generated by conjugate zeros, formed by superposition of in-phase and anti-phase sense modes.

Pole-Zero Temperature Compensation Circuit Design and Experiment for Dual-Mass MEMS Gyroscope Bandwidth Expansion

This paper proposed three methods to compensate the temperature energy influence drift of the MEMS vibration gyroscope, including radial basis function neural network (RBF NN), RBF NN based on genetic algorithm (GA), and RBF NN based on GA with Kalman filter (KF). Three-axis MEMS vibration gyroscope (Gyro X, Gyro Y, and Gyro Z) output data are compensated and analyzed in this paper. The experimental results proved the correctness of these three methods, and MEMS vibration gyroscope temperature energy influence drift is compensated effectively. The results indicate that, after RBF NN-GA-KF method compensation, the bias instability of Gyros X, Y, and Z improves from 139°/h, 154°/h, and 178°/h to 2.9°/h, 3.9°/h, and 1.6°/h, respectively. And the angle random walk of Gyros X, Y, and Z was improved from 3.03°/h1/2, 4.55°/h1/2, and 5.89°/h1/2 to 1.58°/h1/2, 2.58°/h1/2, and 0.71°/h1/2, respectively, and the drift trend and noise characteristic are optimized obviously.

https://downloads.hindawi.com/journals/sv/2018/2830686.pdf

Huiliang Cao

Papers

Robust dynamic surface trajectory tracking control for a quadrotor UAV via extended state observer

Dual-optimization for a MEMS-INS/GPS system during GPS outages based on the cubature Kalman filter and neural networks

Seamless GPS/Inertial Navigation System Based on Self-Learning Square-Root Cubature Kalman Filter

Pole-Zero Temperature Compensation Circuit Design and Experiment for Dual-Mass MEMS Gyroscope Bandwidth Expansion

Temperature Energy Influence Compensation for MEMS Vibration Gyroscope Based on RBF NN-GA-KF Method