Distributed Dynamic Event-Triggered Consensus Protocol for General Linear Multiagent Systems Without Accurate System Information.

This study focuses on distributed event-triggered consensus control under the scenario where only inaccurate agent model information is available. By designing a novel triggering error, a distributed dynamic event-triggered consensus (DETC) protocol is proposed for multiagent systems (MASs) with general linear dynamics over digraphs, without accurate a priori information of agent models. To improve the efficiency of the dynamic triggering law, a mixed triggering threshold is designed with a resilient function integrated to further enlarge interevent intervals. Within the proposed DETC protocol, the computational cost is significantly reduced especially for MASs with nonsparse and high-dimensional agent system matrices. In addition, for each individual agent, the states of neighboring agents used for triggering detections or controller updates are required in an on-demand (instead of continuous) way, which preserves communication resources and facilitates practical implementation. The feasibility of the designed DETC protocol is corroborated by rigorous theoretical analysis on consensus convergence and Zeno behavior exclusion. Finally, simulations are shown to demonstrate the effectiveness of the studied theory.

Flexible integrated sensor with asymmetric structure for simultaneously 3D tactile and thermal sensing.

The human body detects tactile stimuli through a combination of pressure force and temperature signals via various cutaneous receptors. The development of a multifunctional artificial tactile perception system has potential benefits for future robotic technologies, human-machine interfaces, artificial intelligence, and health monitoring devices. However, constructing systems beyond simple pressure sensing capabilities remains challenging. Here, we propose an artificial flexible and ultra-thin (50 µ m) skin system to simultaneously capture 3D tactile and thermal signals, which mimics the human tactile recognition process using customized sensor pairs and compact peripheral signal-converting circuits. The 3D tactile sensors have a flower-like asymmetric structure with 5-ports and 4 capacitive elements in pairs. Differential and average signals would reveal the curl and amplitude values of the fore field with a resolution of 0.18/mm. The resistive thermal sensors are fabricated with serpentine lines and possess stable heat-sensing performance (165 mV/°C) under shape deformation conditions. Real-time monitoring of the skin stimuli is displayed on the user interface and stored on mobile clients. This work offers broad capabilities relevant to practical applications ranging from assistant prosthetics to artificial electronic skins.

Single Image Video Prediction with Auto-Regressive GANs.

In this paper, we introduce an approach for future frames prediction based on a single input image. Our method is able to generate an entire video sequence based on the information contained in the input frame. We adopt an autoregressive approach in our generation process, i.e., the output from each time step is fed as the input to the next step. Unlike other video prediction methods that use "one shot" generation, our method is able to preserve much more details from the input image, while also capturing the critical pixel-level changes between the frames. We overcome the problem of generation quality degradation by introducing a "complementary mask" module in our architecture, and we show that this allows the model to only focus on the generation of the pixels that need to be changed, and to reuse those that should remain static from its previous frame. We empirically validate our methods against various video prediction models on the UT Dallas Dataset, and show that our approach is able to generate high quality realistic video sequences from one static input image. In addition, we also validate the robustness of our method by testing a pre-trained model on the unseen ADFES facial expression dataset. We also provide qualitative results of our model tested on a human action dataset: The Weizmann Action database.

Novel method to operation conditions identification of high-order power converters.

INTRODUCTION: Intermittent solar energy causes different operational modes of power converters including continuous current modes (CCMs) and discontinuous current modes (DCMs), which need appropriate control strategies and parameters assignment to ensure the functionality of the overall solar energy power generation system. Hence, it is important to identify suitable operation modes for a high-order converter system. However, for a high-order power converter (HOPC), traditional time-domain analysis method and bifurcation analysis are inapplicable, since this requires comprehensive analysis and sophisticated control design. OBJECTIVES: To improve reliability and reduce mathematical complexity, this paper focuses on the operation mode derivation of HOPCs to well identify its boundary conditions and provide industry standards for converter applications. METHODS: With complex operation modes, 3-Z-network converter is analysed as a typical example and its derivations of boundary conditions are elaborated. In detail, the equilibrium points and boundary conditions of each operation modes are first derived; then with the guidance of boundary conditions, unexpected operation modes can be avoided by parameters reassignment. RESULTS: Simulations and experimentation on the newly established system prototype are conducted to validate the effectiveness of the proposed approach. It demonstrates that the theoretical and experimental boundary conditions are in good agreement. CONCLUSION: This paper provides equilibrium points and boundary conditions, and obtains deeper insights into the behaviors of the 3-Z-network converter. The derivations of four operation modes and the boundary condition of each mode has been conducted and provided for the large-signal averaged model of the converter, which provides guidance for engineers to adjust the system parameters so as to realize required operation modes. Simulation and experimentation have verified the accuracy and effectiveness of the proposed identified operation boundaries.

A novel 3D non-autonomous system with parametrically excited abundant dynamics and bursting oscillations.

In this paper, a novel non-autonomous chaotic system with rich dynamical behaviors is proposed by introducing parametric excitation to a Lorenz-like system, and the effect of the initial value of the excitation system on the resulting system dynamics is then thoroughly investigated. The attractors resulting from the proposed chaotic system will enter different oscillating states or have topological change when the initial value varies. Furthermore, some novel bursting oscillations and bifurcation mechanism are revealed. Stability and bifurcation of the proposed 3D non-autonomous system are comprehensively investigated to analyze the causes of the observed dynamics through a range of analytical methods, including bifurcation diagram, Lyapunov exponent spectrum, and sequence and phase diagrams. Software simulation and hardware experimentation are conducted in this study, which verify the dynamic behaviors of the proposed chaotic system. This study will create a new perspective and dimension of perceiving non-autonomous chaotic systems and exploring their applicability in real-world engineering applications.

Dynamic behaviors of hyperbolic-type memristor-based Hopfield neural network considering synaptic crosstalk.

Crosstalk phenomena taking place between synapses can influence signal transmission and, in some cases, brain functions. It is thus important to discover the dynamic behaviors of the neural network infected by synaptic crosstalk. To achieve this, in this paper, a new circuit is structured to emulate the Coupled Hyperbolic Memristors, which is then utilized to simulate the synaptic crosstalk of a Hopfield Neural Network (HNN). Thereafter, the HNN's multi-stability, asymmetry attractors, and anti-monotonicity are observed with various crosstalk strengths. The dynamic behaviors of the HNN are presented using bifurcation diagrams, dynamic maps, and Lyapunov exponent spectrums, considering different levels of crosstalk strengths. Simulation results also reveal that different crosstalk strengths can lead to wide-ranging nonlinear behaviors in the HNN systems.

A complex network theory analytical approach to power system cascading failure-From a cyber-physical perspective.

The modern electric power grid is evolving rapidly into such a state that distributed controllers and two-way energy and information flow are replacing the traditional paradigm of electricity distribution and energy management. Therefore, a power grid coupled with a communication network is playing a pivotal role in establishing modern electric power systems. Previous cascading failure analysis in power systems focused more on the physical network, while falling short of investigations on the coupling effect of interdependency of the integrated electricity and communication networks, i.e., cyber-physical power systems. To address such a pressing issue, this study introduces a novel stochastic cascading failure model, considering the interdependency between the cyber network and power network. A multiagent system and a novel protection relay model are incorporated into the proposed model. To apply the proposed analytical method, a test power system, the IEEE 68-bs power system, is used to study the impacts of a range of interdependencies and cyber network topological structures on the cascading failure. Simulation results show the necessity and effects of consideration of cyber communication network when investigating power system cascading failures. The study also provides useful information on robustness and vulnerability of a particular power grid, given different communication topologies and interdependencies.