Good control of blood glucose levels significantly reduces vascular complications in diabetes. In this paper, a theoretical analysis of the control of blood glucose levels in diabetic individuals is undertaken using a simple mathematical model of the dynamics of glucose and insulin interaction in the blood system developed by Bergman et al. A PID controller based on BP neural network is used as a better alternative to robustly stabilize the blood glucose level of a diabetic patient. For comparison purpose, a classical PID controller has been designed. The aim of the control performance was assessed in terms of its ability to track a setpoint (70 mg/dL) form initial state of hyperglycemia, as well as in response to a meal disturbance. The MATLAB simulation is performed to manifest the theoretical analysis. The development reported in this paper is expected to simplify the insulin delivery, thereby enhancing the quality of life of the patient.

The randomness and fuzziness that exist in rolling bearings when faults occur result in uncertainty in acquisition signals and reduce the accuracy of signal feature extraction. To solve this problem, this study proposes a new method in which cloud model characteristic entropy (CMCE) is set as the signal characteristic eigenvalue. This approach can overcome the disadvantages of traditional entropy complexity in parameter selection when solving uncertainty problems. First, the acoustic emission signals under normal and damage rolling bearing states collected from the experiments are decomposed via ensemble empirical mode decomposition. The mutual information method is then used to select the sensitive intrinsic mode functions that can reflect signal characteristics to reconstruct the signal and eliminate noise interference. Subsequently, CMCE is set as the eigenvalue of the reconstructed signal. Finally, through the comparison of experiments between sample entropy, root mean square and CMCE, the results show that CMCE can better represent the characteristic information of the fault signal.

This work discusses the use of an Evolvable Proportional-Integral-Derivative (PID) controller that consists of an evolvable PID controller hardware whose gains can be set by Evolutionary Computation techniques, such as Genetic Algorithms in water level control system. Due to PID controllers' widespread use in industry, tuning procedures for them are always a topic of interest. An evolutionary immune inspired algorithm, named Immune Genetic Algorithm (IGA), is used for tuning the controller so that closed-loop step response specifications are satisfied. Experiments with different processes indicate that the gains obtained through Immune Genetic Algorithm (IGA) may provide better responses than those obtained by the classical Ziegler-Nichols method. The PID Controller based on IGA is realized by the configuration software and Lab VIEW. The experiment results show that the water level controlled by the PID controller can response fast and reach the stable state quickly in comparison with that controlled by the conventional PID controller.

In order to improve fringe precision of a self-mixing signal, a simple and effective method based on an equivalent wavelength self-mixing interferometer is presented. And a linearization fringe counting method is proposed for equivalent wavelength self-mixing interferometry to quickly reconstruct target displacement. The validity of the proposed method was demonstrated by means of simulated signals and confirmed by several experimental measurements for both harmonic and aleatory target displacement with a fringe resolution of $\sim 125\mathrm{nm}$.

Originally, a rolling bearing, as a key part in rotating machinery, is a cyclic symmetric structure. When a fault occurs, it disrupts the symmetry and influences the normal operation of the rolling bearing. To accurately identify faults of rolling bearing, a novel method is proposed, which is based enhancing the mode characteristics of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). It includes two parts: the first is the enhancing decomposition of CEEMDAN algorithm, and the second is the identified method of intrinsic information mode (IIM) of vibration signal. For the first part, the new mode functions (CIMFs) are obtained by combing the adjacent intrinsic mode functions (IMFs) and performing the corresponding Fast Fourier Transform (FFT) to strengthen difference feature among IMFs. Then, probability density function (PDF) is used to estimate FFT of each CIMF to obtain overall information of frequency component. Finally, the final intrinsic mode functions (FIMFs) are obtained by proposing identified method of adjacent PDF based on geometrical similarity (modified Hausdorff distance (MHD)). FIMFs indicate the minimum amount of mode information with physical meanings and avoid interference of spurious mode in original CEEMDAN decomposing. Subsequently, comprehensive evaluate index (Kurtosis and de-trended fluctuation analysis (DFA)) is proposed to identify IIM in FIMFs. Experiment results indicate that the proposed method demonstrates superior performance and can accurately extract characteristic frequencies of rolling bearing.