A fast tracking method for magnetic abnormalities using distributed Overhauser magnetometer system based on genetic algorithm.

Magnetic anomaly detection technologies have been widely used for tracking moving targets. In this paper, we present a fast-tracking method for magnetic abnormalities using a distributed Overhauser magnetometer system based on the genetic algorithm. Our proposed framework of the Overhauser magnetometer system employs multiple sensors to eliminate background interference, and the genetic algorithm efficiently solves magnetic anomaly data without requiring the derivation of the objective function. Test platforms were built to evaluate the distributed Overhauser magnetometer system and the genetic algorithm. Results from the natural outdoor magnetism laboratories showed that the noise of our presented magnetometers was below 0.134 nT. The optimal factors for solution precision and effectiveness in the genetic algorithm were obtained from the simulation. Moreover, the outdoor tracking experiments indicated that the proposed method could accurately and quickly detect the moving ferromagnetic object within 6.9% maximum positioning error in 0.55 m, and the tracking precision of the object velocity can get 5.88% maximum error in 4.33 km/h.

Muscle Fatigue Analysis With Optimized Complementary Ensemble Empirical Mode Decomposition and Multi-Scale Envelope Spectral Entropy.

The preprocessing of surface electromyography (sEMG) signals with complementary ensemble empirical mode decomposition (CEEMD) improves frequency identification precision and temporal resolution, and lays a good foundation for feature extraction. However, a mode-mixing problem often occurs when the CEEMD decomposes an sEMG signal that exhibits intermittency and contains components with a near-by spectrum into intrinsic mode functions (IMFs). This paper presents a method called optimized CEEMD (OCEEMD) to solve this problem. The method integrates the least-squares mutual information (LSMI) and the chaotic quantum particle swarm optimization (CQPSO) algorithm in signal decomposition. It uses the LSMI to calculate the correlation between IMFs so as to reduce mode mixing and uses the CQPSO to optimize the standard deviation of Gaussian white noise so as to improve iteration efficiency. Then, useful IMFs are selected and added to reconstruct a de-noised signal. Finally, considering that the IMFs contain abundant frequency and envelope information, this paper extracts the multi-scale envelope spectral entropy (MSESEn) from the reconstructed sEMG signal. Some original sEMG signals, which were collected from experiments, were used to validate the methods. Compared with the CEEMD and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the OCEEMD effectively suppresses mode mixing between IMFs with rapid iteration. Compared with approximate entropy (ApEn) and sample entropy (SampEn), the MSESEn clearly shows a declining tendency with time and is sensitive to muscle fatigue. This suggests a potential use of this approach for sEMG signal preprocessing and the analysis of muscle fatigue.

An improved equivalent-input-disturbance approach for PMSM drive with demagnetization fault.

Closed-loop torque control system of permanent magnet synchronous motor (PMSM) drive is highly applied in industrial applications. However, high temperature and other external factors lead to permanent magnet (PM) demagnetization fault of the motor, which inevitably brings barriers to precise torque control. This paper presents a rejection method for demagnetization based on an improved equivalent-input-disturbance (EID) approach. A system model, which contains demagnetization fault, is first constructed. Then, an equivalent-input-demagnetization on the control input channel is used to describe the model. An improved EID approach is designed to reject the effect of the demagnetization fault. In particular, an improved sliding-mode observer rather than the Luenberger observer in a conventional EID theory is designed to estimate the equivalent-input-demagnetization. This greatly enhances the response speed and accuracy of the estimation and rejection, and then effectively improves the accuracy of torque tracking. Finally, the step change and the ramp change of a demagnetization are introduced into a hardware-in-the-loop experiment, separately. Comparisons with other methods show the effectiveness of the method.

Study on a practical robotic follower to support home oxygen therapy patients--questionnaire-based concept evaluation by the patients-.

Home oxygen therapy (HOT) is a medical treatment for the patients suffering from severe lung diseases. Although walking outdoors is recommended for the patients to maintain physical strength, the patients always have to carry a portable oxygen supplier which is not sufficiently light weight for this purpose. Our ultimate goal is to develop a mobile robot to carry an oxygen tank and follow a patient in an urban outdoor environment. We have proposed a mobile robot with a tether interface to detect the relative position of the foregoing patient. In this paper, we report the questionnaire-based evaluation about the two developed prototypes by the HOT patients. We conduct maneuvering experiments, and then obtained questionnaire-based evaluations from the 20 patients. The results show that the basic following performance is sufficient and the pulling force of the tether is sufficiently small for the patients. Moreover, the patients prefer the small-sized prototype for compactness and light weight to the middle-sized prototype which can carry larger payload. We also obtained detailed requests to improve the robots. Finally the results show the general concept of the robot is favorably received by the patients.