Earticle

A fourth order Butterworth active-RC complex filter with tuning circuits for wireless sensor networks (WSN) is presented in this paper. A method to synthesize an active-RC complex filter is proposed. A tuning system for the complex filter is designed, which is based on tuning the bit number of capacitors bank to compensate RC variations. The operational amplifier (OPA) is designed, which the gain bandwidth (GBW) is about 380MHz, the gain of the DC is 58.1dB, the phase margin is 58 and the power dissipation of the operational amplifier is about 0.6mW. The complex filter is implemented in BCD 0.18 um CMOS process. The complex filter has a bandwidth of 2 MHz, and centre frequency is 2MHz. The results of simulation show that the complex filter image rejection ratio is -48.2dB, pass-band gain is 0.379dB, the power consumption is about 4.98mW.

The microgrid systems play a vital role in reducing the impact of renewable generation effect on the main grid. Microgrid systems can achieve grid-tied operation and isolated operation modes with fast switching and isolation functions. The reliability of power supply can be further achieved through the microgrid by reforming a mesh configuration. However, the protection of the network is a challenging task due to the bi-directional power flow and the change of the network structure. In this paper, an adaptive -model and simulation of the solar photovoltaic microgrid differential current protection scheme -are presented. Differential scheme denotes a very credible method to secure the protection zone. The protection system that is under consideration behaves the most important part of the power system. Current protection relay scheme plays an important role in the protection of transmission lines, because it is sufficient and reliable. This paper discusses (dual slope) differential relay parameters of the system with various fault situation states. Therefore, the protection scheme must identify the fault zone for rapid separation to reduce the damage of equipment. Our simulation results show that the proposed method is effective and can reduce the percentage of errors.

The modular multilevel converter (MMC) has been widely researched in the past decade for its superior performance in high voltage high power applications. However, applying MMC into electric motor drive systems is a intrinsically challenging problem because the capacitor voltages fluctuate in a wide range when the converter works at low output frequencies. A novel model predictive vector control (MPVC) scheme is presented to ensure the output phase currents’ quality of MMC for entire frequency range. In this scheme, model predictive control (MPC) is adopted to track the phase currents’ reference values and balance the capacitor voltages as far as possible. By replacing the conventional inner current control loops and pulse width modulation (PWM) module with MPC controllers, the vector control oriented by rotor flux is constructed to meet the requirements of drive systems. Simulations of a MMC drive with an induction motor are carried out using the Matlab/Simulink. The simulation waveforms demonstrate that the drive system achieves satisfied steady and dynamic performance.

For the motion control problem of a SWATH ship, a terminal sliding mode controller is designed based on extended state observer (ESO). Firstly, considering the motion control problem under random wave disturbances, the nonlinear model of SWATH ship with compound disturbances is established. Then, considering the parameters perturbation, complexity and randomness of the wave, the control system is divided into inner loop observer and outer loop controller respectively. An ESO is implemented to estimate and compensate the compound disturbances in the terminal sliding mode controller. At last, the asymptotical stability is achieved based on Lyapunov theory. Simulations show the effectiveness of the proposed method. Linear ESO can estimate the compound disturbances successfully, meanwhile, the terminal sliding mode controller can enable the system states converge quickly.

This paper considers the global practical tracking problem by state feedback for a class of high-order non-linear systems with more general uncertainties, to which the existing control methods are inapplicable. We successfully propose a new tracking control design scheme for the system studied by introducing sign function and necessarily modifying the method of adding a power integrator. It is shown that the designed controller guarantees that all states of the resulting closed-loop system are globally bounded and the tracking error remains prescribed arbitrarily small after a finite time.

Optimal sensor placement (OSP) techniques are of vital importance for the monitoring of large-scale structures or complex mechanical systems. In order to overcome the defects of slow convergence speed and easily falling into local optimum in monkey algorithm (MA), a new MA, called chaotic monkey algorithm (CMA), is proposed by introducing chaos search strategy to implement the OSP. In this algorithm, the initial monkeys are generated by using chaos variable and binary coding to enhance the global search capability, and a greedy strategy is adopted to improve the efficiency of local search. Numerical experiments are conducted on the sensor placement of a suspension bridge. The results verify that the new CMA can solve the OSP problem well and has better search capability than MA.

With the rapid development of mobile devices and IT technologies, protecting personal information is becoming an important issue, and safety must be seriously considered. However, while smartphones in use today continue to have security vulnerabilities in the authentication stage, so far no complimentary security technology has been firmly prepared to address it. Authentication is the process of confirming the truth of an attribute of an entity. As intelligent mobile devices are getting popular, the role of authentication has become more critical as a mean to control access to one’s banking account or personal information. In this paper, user authentication is made more secure by using two-factor authentication with the hybrid recognition method. The core elements of the authentication technology are using the hybrid face recognition method employing dual-stage holistic and local feature-based recognition algorithms. This enhanced the recognition rate and recognition time by adapting weighted sub-region and diverse Garbor scale in the two-factor authentication system. To demonstrate the practical efficiency and validity of the proposed method, the experiments are conducted and the experimental results are compared with the existing methods.

Non-volatile memory (NVM)such as phase change memory (PCM) has emerged as an attractive type of next-generation memories. NVMcan be incorporated into memory hierarchy to solve the problems of current DRAM instorage density and energy consumption.NVM is byte addressable and its latency is close to main memory, it exhibits higher density and lower idle power consumption than DRAM. However, NVM haslimited write endurance and asymmetriclatency and energy consumption for read and write operations. Therefore, hybrid memory systems involving both NVM and DRAM have been proposed for better utilizing NVM and DRAM. In this paper, we present a novel hybrid memory management schemecalled W-HCLOCK (Write-aware Hybrid CLOCK) that aims to keep a high hit ratio and minimize writes to NVM. Particularly, we introduce the concept of Recent-Rewrite-Distance (RRD), and combine it with recency of write operations to estimate the write-hotness of pages. Then, weplacewrite-intensive pages in DRAM to reduce NVM writes. In addition, we keepadditional information about the writes to evicted pages to avoid unnecessary migrations between DRAM and PCM. We conduct simulation experiments on four synthetic traces and one real OLTP trace. The results suggest the efficiency of our proposal.

Brushless DC motor (BLDCM) has been widely used in industries because of its advantages. BLDCM control system is a nonlinear, multi-variable, strong-coupling system. It is difficult to get satisfied control performance with conventional PID controller. By using self-learning and adaptive ability of neural network to unknown information, an intelligent PID control method based on adaptive radial basis function (RBF) neural network is proposed. Connection weight of neural network is updated according to the motor speed and phase current. The duty ratio of pulse width modulation (PWM) is adjusted to regulate the speed of BLDCM. The effectiveness of the proposed control method was validated with simulation and experiment. Simulation and experiment results showed that compared with the conventional PID controller, the proposed controller has less overshoot, faster response speed, stronger ability of anti-disturbance.