MEC-Based Jamming-Aided Anti-Eavesdropping with Deep Reinforcement Learning for WBANs

Wireless body area network (WBAN) suffers secure challenges, especially the eavesdropping attack, due to constraint resources. In this article, deep reinforcement learning (DRL) and mobile edge computing (MEC) technology are adopted to formulate a DRL-MEC-based jamming-aided anti-eavesdropping (DMEC-JAE) scheme to resist the eavesdropping attack without considering the channel state information. In this scheme, a MEC sensor is chosen to send artificial jamming signals to improve the secrecy rate of the system. Power control technique is utilized to optimize the transmission power of both the source sensor and the MEC sensor to save energy. The remaining energy of the MEC sensor is concerned to ensure routine data transmission and jamming signal transmission. Additionally, the DMEC-JAE scheme integrates with transfer learning for a higher learning rate. The performance bounds of the scheme concerning the secrecy rate, energy consumption, and the utility are evaluated. Simulation results show that the DMEC-JAE scheme can approach the performance bounds with high learning speed, which outperforms the benchmark schemes.

A new high speed charge and high efficiency Li-Ion battery charger interface using pulse control technique

A new Li-Ion battery charger interface (BCI) using pulse control (PC) technique is designed and analyzed in this paper. Thanks to the use of PC technique, the main standards of the Li-Ion battery charger, i.e. fast charge, small surface area and high efficiency, are achieved. The proposed charger achieves full charge in forty-one minutes passing by the constant current (CC) charging mode which also included the start-up and the constant voltage mode (CV) charging mode. It designed, simulated and layouted which occupies a small size area 0.1 mm2 by using Taiwan Semiconductor Manufacturing Company 180 nm complementary metal oxide semi-conductor technology (TSMC 180 nm CMOS) technology in Cadence Virtuoso software. The battery voltage VBAT varies between 2.9 V to 4.35 V and the maximum battery current IBAT is 2.1 A in CC charging mode, according to a maximum input voltage VIN equal 5 V. The maximum charging efficiency reaches 98%.

Novel Motor-Kinetic-Energy-Based Power Pulsation Buffer Concept for Single-Phase-Input Electrolytic-Capacitor-Less Motor-Integrated Inverter System

The motor integration of singe-phase-supplied Variable-Speed Drives (VSDs) is prevented by the significant volume, short lifetime, and operating temperature limit of the electrolytic capacitors required to buffer the pulsating power grid. The DC-link energy storage requirement is eliminated by using the kinetic energy of the motor as a buffer. The proposed concept is called the Motor-Integrated Power Pulsation Buffer (MPPB), and a control technique and structure are detailed that meet the requirements for nominal and faulted operation with a simple reconfiguration of existing controller blocks. A 7.5 KW, motor-integrated hardware demonstrator validated the proposed MPPB concept and loss models for a scroll compressor drive used in auxiliary railway applications. The MPPB drive with a front-end CISPR 11/Class A EMI filter, PFC rectifier stage, and output-side inverter stage achieved a power density of 0.91 KW L−1 (15 W in−3). The grid-to-motor-shaft efficiency exceeded 90% for all loads over 5 kW or 66% of nominal load, with a worst-case loss penalty over a conventional system of only 17%.

Research on cyber-physical system control strategy under false data injection attack perception

This paper investigates the security control problem of the cyber-physical system under false data injection attacks. A model predictive switching control strategy based on attack perception is proposed to compensate for the untrusted sequence of data caused by false data injection attacks. First, the binary attack detector is applied whether the system has suffered the attack. If the attack occurs, multistep correction is carried out for the future data according to the previous time data, and the waiting period [Formula: see text] is set. The input and output sequence of the controller is reconstructed, and the system is modeled as a constant time-delay switched system. Subsequently, the Lyapunov methods and average-dwell time are combined to provide sufficient conditions for the asymptotical stability of closed-loop switched system. Finally, the simulation of the networked first-order inverted pendulum model reveals that the control technique can efficiently suppress the influence of the attacks.

Improving the Stability of Islanded DC Microgrid with Constant Power Loads

The AC power system is leading due to its established standards. The depleting thread of fossil fuels, the significant increase in cost and the alarming environmental situation raises concerns. An Islanded DC microgrid, due to its novel characteristics of being able to withstand faulty conditions, has increased the reliability, accuracy, ease of integration, and efficiency of the power system. Renewable energy sources, characteristically DC, have wide usability in a distributive network and, accordingly, less circuitry and conversion stages are required, eliminating the need of reactive power compensation and frequency sync. Constant power loads (CPLs) are the reason for instability in the DC microgrid. Various centralized stability techniques have been proposed in the literature; however, the grid system collapses if there is a fault. To compensate, an efficient distributive control architecture, i.e., droop control method is proposed in this research. The significant advantage of using the droop control technique includes easy implementation, high reliability and flexibility, a reduced circulating current, a decentralized control with local measurements, the absence of a communication link and, thus, it is economic. Moreover, it offers local control for each individual power source in the microgrid. To investigate the stability of the islanded DC microgrid with constant power loads using the droop control technique, a small signal model of the islanded DC microgrid was developed in MATLAB/Simulink. Simulations were carried out to show the efficiency of the proposed controller and analyze the stability of the power system with constant power loads.

An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-based Systems

An explainable intelligent framework for cyber anomaly mitigation of cyber-physical inverter-based systems is presented.<div><br></div><div>Smart inverter-based microgrids essentially constitute an extensive communication layer that makes them vulnerable to cyber anomalies. The distributed cooperative controllers implemented at the secondary control level of such systems exchange information among physical nodes using the cyber layer to meet the control objectives. The cyber anomalies targeting the communication network may distort the normal operation therefore, an effective cyber anomaly mitigation technique using an artificial neural network (ANN) is proposed in this paper. The intelligent anomaly mitigation control is modeled using adynamic recurrent neural network that employs a nonlinear autoregressive network with exogenous inputs. The effects of false data injection to the distributed cooperative controller at the secondary control level are considered. The training data for designing the neural network are generated by multiple simulations of the designed microgrid under various operating conditions using MATLAB/Simulink. The neural network is trained offline and tested online in the simulated microgrid. The proposed technique is applied as secondary voltage and frequency control of distributed cooperative control-based microgrid to regulate the voltage under various operating conditions. The performance of the proposed control technique is verified by injecting various types of false data injection-based cyber anomalies. The proposed ANN-based secondary controller maintained the normal operation of microgrid in the presence of cyber anomalies as demonstrated by real-time simulations on a real-time digital simulator OPAL-RT.<br></div>

Pulse density modulation control-based multi-load handling three leg inverter for cooking applications

Purpose Induction heating applications aided by power electronic control have become very attractive in the recent past. For cooking applications, power electronics circuits are very suitable to feed power to multi loads with an appropriate control technique. The purpose of this paper is to develop a three leg inverter to feed power to three loads simultaneously and independently. Design/methodology/approach Pulse density modulation control technique is used to control the output power independently with constant switching frequency. Findings Multi-load handling converter with independent power control is achieved with reduced number of switching devices (two switches/per load) with simple control strategy. Originality/value The proposed system is simulated in MATLAB/Simulink, and the thermal analysis is carried out in COMSOL multi-physics software. The hardware realisation is performed for a 1 kW prototype with 20 kHz switching frequency and 10 kHz pulse density modulation frequency. PIC16F877A microcontroller is used to validate the experimental results for various values of control signals (DPDM). The simulation and experimental results are in good agreement and validates the developed system.

Quality factor control of mechanical resonators using variable phononic bandgap on periodic microstructures

The quality factor (Q-factor) is an important parameter for mechanical resonant sensors, and the optimal values depend on its application. Therefore, Q-factor control is essential for microelectromechanical systems (MEMS). Conventional methods have some restrictions, such as additional and complicated equipment or nanoscale dimensions; thus, structural methods are one of the reasonable solutions for simplifying the system. In this study, we demonstrate Q-factor control using a variable phononic bandgap by changing the length of the periodic microstructure. For this, silicon microstructure is used because it has both periodicity and a spring structure. The bandgap change is experimentally confirmed by measuring the Q-factors of mechanical resonators with different resonant frequencies. The bandgap range varies depending on the extended structure length, followed by a change in the Q-factor value. In addition, the effects of the periodic structure on the Q-factor enhancement and the influence of stress on the structural length were evaluated. Although microstructures can improve the Q-factors irrespective of periodicity; the result of the periodic microstructure is found to be efficient. The proposed method is feasible as the novel Q-factor control technique has good compatibility with conventional MEMS.

An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-based Systems

An explainable intelligent framework for cyber anomaly mitigation of cyber-physical inverter-based systems is presented.<div><br></div><div>Smart inverter-based microgrids essentially constitute an extensive communication layer that makes them vulnerable to cyber anomalies. The distributed cooperative controllers implemented at the secondary control level of such systems exchange information among physical nodes using the cyber layer to meet the control objectives. The cyber anomalies targeting the communication network may distort the normal operation therefore, an effective cyber anomaly mitigation technique using an artificial neural network (ANN) is proposed in this paper. The intelligent anomaly mitigation control is modeled using adynamic recurrent neural network that employs a nonlinear autoregressive network with exogenous inputs. The effects of false data injection to the distributed cooperative controller at the secondary control level are considered. The training data for designing the neural network are generated by multiple simulations of the designed microgrid under various operating conditions using MATLAB/Simulink. The neural network is trained offline and tested online in the simulated microgrid. The proposed technique is applied as secondary voltage and frequency control of distributed cooperative control-based microgrid to regulate the voltage under various operating conditions. The performance of the proposed control technique is verified by injecting various types of false data injection-based cyber anomalies. The proposed ANN-based secondary controller maintained the normal operation of microgrid in the presence of cyber anomalies as demonstrated by real-time simulations on a real-time digital simulator OPAL-RT.<br></div>