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This article concerns the theoretical analysis and mechanical verification of input-to-state stability (ISS) for nonautonomous discrete-time switched systems. To start with, based on a bounded function and the average dwell time, we successively propose less conservative sufficient conditions for uniform input-to-state stability, global uniform asymptotic input-to-state stability, and global uniform exponential input-to-state stability of nonautonomous switched nonlinear systems. Then, for systems with zero inputs, we apply our bounded function and average dwell time based method to further relax the sufficient conditions for their uniform stability, global uniform asymptotic stability, and global uniform exponential stability. Particularly, we propose a linear semidefinite programming based computable approach for mechanical verification of our current theoretical results for the rational (and even certain nonrational) nonautonomous switched systems. Note that our theoretical results and mechanical approach are both illustrated by examples.

Blockchain technique, with the novelties of decentralization, smart contract, security and cooperative autonomy, is expected to play great effects on promoting the development of energy local networks (ELNs). This paper presents an automated demand response (ADR) framework for decentralized scheduling and secure peer-to-peer (P2P) trading among energy storage systems in ELNs. Different from most existing works that trade electricity over long distances and through complex meshes, this proposed work performs decentralized and automated demand response through energy sharing of P2P executors. We explore for the first time the benefits of a promising blockchain to conduct the overall ADR framework and increase the P2P trading security. To achieve decentralized scheduling without relying on a central entity, a price-incentive noncooperative game theoretic model is introduced to produce equilibrium solutions for energy storage systems. Moreover, we develop a schedulable ability evaluation system to match trading pairs involving buying and selling nodes. On this basis, a state-machine-driven smart contract mechanism is built to realize P2P trading without reliance on a trusted third party. To illustrate the implementation details of the ADR method, a distributed algorithm is designed. Case studies are provided to verify the effectiveness of the proposed method.

Inspired by the idea of multiple Lyapunov functions ( $\mathtt {MLFs}$ ), we use piecewise continuous scalar functions to investigate the stabilizability of time-varying switched systems. Starting with time-varying switched linear systems, we first combine the idea of $\mathtt {MLFs}$ with the existence of asymptotically (exponentially, uniformly exponentially) stable functions to provide necessary and sufficient conditions for their asymptotic (exponential, uniform exponential) stabilizability. Compared to traditional differential Lyapunov inequalities, we release the requirement on negative definiteness of the derivatives of $\mathtt {MLFs}$ . Successively, the above results are extended to time-varying switched nonlinear systems. Then, two illustrative examples are given to show the applicability of our theoretical results. In the end, we consider the computation issue of our current results for a special class of nonautonomous switched systems, i.e., rational nonautonomous switched systems.

In this paper, a hybrid model that considers both accuracy and efficiency is proposed to predict photovoltaic (PV) power generation. To achieve this, improved forward feature selection is applied to obtain the optimal feature set, which aims to remove redundant information and obtain related features, resulting in a significant improvement in forecasting accuracy and efficiency. The prediction error is irregularly distributed. Thus, a bias compensation–long short-term memory (BC–LSTM) network is proposed to minimize the prediction error. The experimental results show that the new feature selection method can improve the prediction accuracy by 0.6% and the calculation efficiency by 20% compared to using feature importance identification based on LightGBM. The BC–LSTM network can improve accuracy by 0.3% using about twice the time compared with the LSTM network, and the hybrid model can further improve prediction accuracy and efficiency based on the BC–LSTM network.

In this work, we proposed a high resolution D-shaped photonic crystal fiber (PCF) surface plasmon resonance (SPR) sensor based on gold grating. Gold grating is introduced to modulate the resonance wavelength and enhance the refractive index (RI) sensitivity. Structure parameters of PCF and gold grating are analyzed by finite element method (FEM) for optimizing the SPR sensor. The simulation results indicate that air hole pitch, air hole diameter and gold thickness and grating constant have little influence on the sensitivity of refractive index, which reduces the requirement of precise processing. For improving the resolution of RI sensing, two-feature (2F) interrogation method which combines wavelength interrogation and amplitude interrogation is used and the maximum theoretical resolution of SPR sensor reaches to 5.98*10-6 RIU in range of 1.36-1.38 and the wavelength sensitivity reaches to 3340nm/RIU. The proposed SPR sensor shows potential applications on developing a high-sensitivity, real time and fast-response SPR-RI sensor., Research Article

Direct processing of raw high-dimensional data such as images and video by machine learning systems is impractical both due to prohibitive power consumption and the “curse of dimensionality,” which makes learning tasks exponentially more difficult as dimension increases. Deep machine learning (DML) mimics the hierarchical presentation of information in the human brain to achieve robust automated feature extraction, reducing the dimension of such data. However, the computational complexity of DML systems limits large-scale implementations in standard digital computers. Custom analog or mixed-mode signal processors have been reported to yield much higher energy efficiency than DSP [1-4], presenting the means of overcoming these limitations. However, the use of volatile digital memory in [1-3] precludes their use in intermittently-powered devices, and the required interfacing and internal A/D/A conversions add power and area overhead. Nonvolatile storage is employed in [4], but the lack of learning capability requires task-specific programming before operation, and precludes online adaptation.

Multilevel-cell (MLC) phase change memory (PCM) usually evaluated in the program method, delay or write energy while the endurance characteristic has not been focused on. In this paper, we exploit a staircase-up program and verify method in the current-driven 4 Mb PCM chip to achieve MLC storage. The direct results of the change in the R–I characteristic and resistance distribution during the cycles are displayed. According to the measurement, the PCM device shows four separable resistance levels after 106 operation cycles with a decreasing resistance of full-crystalline state. And the resistance drift and read disturb which affect the cell reliability also are briefly tested. The MLC device shows a good performance where four resistance levels can be separated well after 1000 s and the read endurance under 0.3 V is retained up to 109 cycles.

A novel time-domain bulk-tuned offset cancellation technique is applied to a low-power high-precision dynamic comparator to reduce its input-referred offset with minimal additional power consumption and delay. The design has been fabricated in a commercially available 0.5-μm process. Measurement results of 10 circuits show a reduction of offset standard deviation from 5.415 mV to 50.57 μV, improved by a factor of 107.1. The offset cancellation scheme does not introduce observable offset or noise, and can achieve fast and robust convergence with a wide range of common mode input. Operating at a supply of 5 V and clock frequency of 200 kHz, the comparator together with the OC circuitry consumes 4.65 μW of power, or 23 pJ of energy per comparison.

An ultra-low-power tunable bump circuit is presented. It incorporates a novel wide-input-range tunable pseudo-differential transconductor linearised using the drain resistances of saturated transistors. Measurement results show that the transconductor has a 5 V differential input range with

Establishing the schemes of accurate and computationally efficient performance estimation and fault diagnosis for turbofan engines has become a new research focus and challenges. It is able to increase reliability and stability of turbofan engine and reduce the life cycle costs. Accurate estimation of turbofan engine performance counts on thoroughly understanding the components’ performance, which is described by component characteristic maps and the fault of each component can be regarded as the change of characteristic maps. In this paper, a novel method based on a Levenberg–Marquardt (LM) algorithm is proposed to enhance the fidelity of the performance estimation and the credibility of the fault diagnosis for the turbofan engine. The presented method utilizes the LM algorithm to figure out the operating point in the characteristic maps, preparing for performance estimation and fault diagnosis. The accuracy of the proposed method is evaluated for estimating performance parameters in the transient case with Rayleigh process noise and Gaussian measurement noise. The comparison among the extended Kalman filter (EKF) method, the particle filter (PF) method and the proposed method is implemented in the abrupt fault case and the gradual degeneration case and it has been shown that the proposed method has the capability to lead to more accurate result for performance estimation and fault diagnosis of turbofan engine than current popular EKF and PF diagnosis methods.

The on-board sensor fault detection and isolation (FDI) system is essential to guarantee the reliability and safety of an aero engine. In this paper, a novel online sequential extreme learning machine with memory principle (MOS-ELM) is proposed for detecting, isolating, and reconstructing the fault sensor signal of aero engines. In many practical online applications, the sequentially coming data chunk usually possesses a characteristic of timeliness, and the overdue training data may mislead the subsequent learning process. The proposed MOS-ELM can improve the training process by introducing the concept of memory principle into the online sequential extreme learning machine (OS-ELM) to tackle the timeliness of the data chunk. Simulations on some time series problems and some benchmark databases show that MOS-ELM performs better in generalization performance, stability, and prediction accuracy than OS-ELM. The experiment results of the MOS-ELM-based sensor fault diagnosis system also verify the excellent generalization performance of MOS-ELM and indicate the effectiveness and feasibility of the developed diagnosis system.