Continuous Time Domain Research Articles

Fully analytical model of heating networks for integrated energy systems

The wide promotion of electric heating has necessitated the combined analysis of the electric power system (EPS) and the district heating network (DHN) for economic and secure improvement. However, the flexibility of DHN brought by thermal dynamics is governed by partial differential equations (PDE), which is difficult to be accurately quantified. The traditional numerical methods usually solve the problem based on discretization, but the extra complexity and numerical oscillation inevitably occur. To address the problem, this paper proposes a fully analytical method (FAM) to describe the thermal dynamics based on a bilateral characteristic line method (CLM), avoiding inaccurate state estimation and operational strategy design. A FAM-based equivalent model is then developed to quantify the relationship between thermal sources and demands and reduce model complexity. Finally, the expression of average temperature is derived to reformulate the optimal energy flow (OEF) problem in the heat and electricity integrated energy system (HE-IES), which accurately reflects the influence of thermal dynamics in a continuous-time domain. Case studies indicate that the proposed FAM significantly improves the analysis accuracy, stability, and efficiency over the traditional numerical methods in simulation and optimization.

Anisotropic GPMP2: A Fast Continuous-Time Gaussian Processes Based Motion Planner for Unmanned Surface Vehicles in Environments With Ocean Currents

In the past decade, there is an increasing interest in the deployment of unmanned surface vehicles (USVs) for undertaking ocean missions in dynamic, complex maritime environments. The success of these missions largely relies on motion planning algorithms that can generate optimal navigational trajectories to guide a USV. Apart from minimising the distance of a path, when deployed a USVs’ motion planning algorithms also need to consider other constraints such as energy consumption, the affected of ocean currents as well as the fast collision avoidance capability. In this paper, we propose a new algorithm named anisotropic GPMP2 to revolutionise motion planning for USVs based upon the fundamentals of GP (Gaussian process) motion planning (GPMP, or its updated version GPMP2). Firstly, we integrated the anisotropy into GPMP2 to make the generated trajectories follow ocean currents where necessary to reduce energy consumption on resisting ocean currents. Secondly, to further improve the computational speed and trajectory quality, a dynamic fast GP interpolation is integrated in the algorithm. Finally, the new algorithm has been validated on a WAM-V 20 USV in a ROS environment to show the practicability of anisotropic GPMP2. Note to Practitioners—The work reported in this article will be significant for USVs to conduct missions in complex, dynamic maritime environments where various obstacles and time-varying ocean currents exit. We develop this novel motion planning algorithm based on Gaussian process and optimise the trajectory using probabilistic inferences. The new algorithm can generate collision free trajectories that also minimise the influences caused by adverse ocean currents in a highly efficient way. In addition, the planning has been undertaken in a continuous-time domain making the generated trajectory have a guaranteed smoothness and readily feasible for autopilots to track. We use a coastal area with time-varying vortexes to present a challenging practical maritime environment. The presented algorithm integrates the available information about a fluid field regarding energy consumption and hazard level, along with the density of obstacles to plan a navigational route efficiently. To increase the practical performance of the proposed method, diverse models for generating ocean currents need to be developed in the future to tackle unpredictable situations.

Transformer-enhanced Hawkes process with decoupling training for information cascade prediction

The ability to model the information diffusion process and predict its size is crucial to understanding information propagation mechanism and is useful for many applications such as popularity prediction and fake news detection. Recent research works have attempted to address the problem of information cascade prediction using two basic paradigms: (1) sequential methods, e.g., recurrent neural networks (RNNs), and (2) graph learning techniques to retain the topological information and enable consideration of structural relationships among diffusion participants. However, existing models consider the topological and temporal features separately, falling short of simulating their entanglement in the diffusion process. As a consequence, since the evolving directed acyclic graph (DAG) of information diffusion is intrinsically coupled with both topological and temporal dependencies, there is a loss of cross-domain information. In this paper, we propose a transformer enhanced Hawkes process (Hawkesformer), which links the hierarchical attention mechanism to Hawkes self-exciting point process for information cascade prediction. Specifically, we extend traditional Hawkes process with a topological horizon and efficiently acquire knowledge from continuous-time domain. A two-level attention architecture is used to parameterize the intensity function of Hawkesformer. At the first-level, we disentangle the primary and non-primary paths to simulate the coupled topological and temporal information for capturing the global dependencies between the nodes in a graph. At the second-level, a local pooling attentive module is proposed to embed the cascade evolution rate for modeling short-term outbreaks. Extensive experiments on two real-world datasets demonstrate the significant performance improvements of Hawkesformer over existing state-of-the-art models.

Active Disturbance Rejection Control of a Magnetic Screw Motor for High Tracking Performance

A magnetic screw motor integrated by a magnetic screw and a permanent magnet synchronous motor offers high thrust density without friction and loss. However, its internal structure is complicated, resulting in difficulty to control its linear displacement to track the given signal in time and even getting out of control due to interference. In this article, to improve the tracking performance and anti-interference performance of the system, an active disturbance rejection controller (ADRC) is adopted to improve the current loop and makes up for nonlinear regional control in the closed-loop control system. To ensure the stability of the proposed scheme in the entire operation range, the design and the stability analysis are carried out in the continuous-time domain. The novel control algorithm enhances the flexibility of the magnetic screw motor by optimizing the nonlinear function in ADRC. In addition, to highlight the high-performance control of the ADRC for the magnetic screw motor, sliding mode control and PI control are added to compare with it. Finally, the reciprocating motion control is added to show the diversity of the functions of the magnetic screw motor, which verifies the effectiveness and feasibility of the control algorithm.

Linking Transformer to Hawkes Process for Information Cascade Prediction (Student Abstract)

Information cascade is typically formalized as a process of (simplified) discrete sequence of events, and recent approaches have tackled its prediction via variants of recurrent neural networks. However, the information diffusion process is essentially an evolving directed acyclic graph (DAG) in the continuous-time domain. In this paper, we propose a transformer enhanced Hawkes process (Hawkesformer), which links the hierarchical attention mechanism with Hawkes process to model the arrival stream of discrete events continuously. A two-level attention architecture is used to parameterize the intensity function of Hawkesformer, which captures the long-term dependencies between nodes in graph and better embeds the cascade evolution rate for modeling short-term outbreaks. Experimental results demonstrate the significant improvements of Hawkesformer over the state-of-the-art.

Semi-time-dependent stabilization for a class of continuous-time impulsive switched linear systems

In this paper, the stability and stabilization problems of a class of impulsive switched linear systems with mode-dependent persistent dwell-time (MPDT) switching are investigated in continuous-time domain. The proposed switching law is more general, which not only covers the widely probed dwell-time (DT) and average dwell-time (ADT) as special cases but also extends the persistent dwell time (PDT) in the literature. By invoking novel modal partition time-dependent multiple Lyapunov-like functions, the stability criteria for the systems under MPDT switching in nonlinear settings are established as a first attempt, upon which the desired mode-dependent and semi-time-dependent (STD) stabilizing controllers for continuous-time impulsive switched linear systems are designed. It is demonstrated that the control performance is improved as both the length of admissible MPDT and the number of time partition segment are added. Finally, a numerical example is provided to illustrate the potential of the obtained theoretical results.

Networked control design for an engine throttle valve system

In this paper, the design and the implementation of a stabilising feedback law for an engine throttle valve system are investigated while taking into account the digital communication over the vehicle network. In particular, the digital communication between sensors, controller, and actuators is studied using the event-triggered theory. Considering the sampled-data nature of the closed-loop system, the emulation approach is used to synthesise the control law to stabilise the plant in the continuous-time domain. Next, we provide a systematic methodology to derive, both, time-triggering and event-triggering rules such that the closed-loop stability is preserved. The approach is illustrated by simulation on the dynamic model of the throttle valve system.

An information theory approach to network evolution models

Abstract A novel Markovian network evolution model is introduced and analysed by means of information theory. It will be proved that the model, called network evolution chain, is a stationary and ergodic stochastic process. Therefore, the asymptotic equipartition property can be applied to it. The model’s entropy rate and typical sequences are also explored. Extracting particular information from the network and methods to simulate network evolution in the continuous time domain are discussed. Additionally, the Erdős–Rényi network evolution chain is introduced as a subset of our model with the additional property of its stationary distribution matching the Erdős–Rényi random graph model. The stationary distributions of nodes and graphs are calculated for this subset alongside its entropy rate. The simulation results at the end of the article back up the proved theorems and calculated values.

Formula omitted] control for continuous-time Markov jump nonlinear systems with piecewise-affine approximation

This paper investigates the problem of stability, H∞ performance analysis, and H∞ control for continuous-time Markov jump nonlinear systems, where the nonlinear subsystems are approximated by the piecewise-affine technique. The proposed Markov jump piecewise-affine systems contain different modes and regions, both of which are determined by Markov chains and piecewise-affine partitions, respectively. A new admissible adjacent region switching paths (AARSPs) algorithm is proposed for the first time in the continuous-time domain to decrease the conservatism of the complete adjacent region switching paths (CARSPs) algorithm. This new algorithm optimizes the path selection conditions of the next instantaneous time region switching in the CARSPs algorithm, and effectively reduces the computational complexity and the conservatism of the CARSPs algorithm. Furthermore, a state-feedback piecewise-linear controller is designed by means of the ellipsoidal outer approximation estimation method, such that the corresponding closed-loop system is stochastically stable and has a guaranteed H∞ performance index. Finally, the effectiveness and practicability of both the AARSPs algorithm and the piecewise-linear control strategy are fully demonstrated via two illustrative examples including a class of tunnel diode circuit systems.

Modeling, Simulation and Development of Grid-Connected Voltage Source Converter with Selective Harmonic Mitigation: HiL and Experimental Validations

This paper elaborates on a development technique for the grid-connected voltage source converter (VSC). We propose a simulation technique in the MATLAB/Simulink environment that emulates the operation of the discrete-time controlled grid-connected VSC. The switched-circuit modeling approach is used for simulation of the power stage in the continuous-time domain with the physical unit scale. The discrete-time control algorithm is implemented in an interpreted MATLAB function in the per-unit scale, which synchronizes with the switching period. Such a control algorithm is easily translated into the C language for programing of the 32-bit C2000 DSP controller with the same regulators’ parameters. The proposed platform was validated with a hardware-in-the-loop real-time simulator and with a 5-kVA 3-phase LCL-filtered grid-connected VSC. The discrete-time control scheme was implemented in the synchronous reference frame control with proportional-integral with multi-resonant controllers at harmonic orders 6th and 12th for suppression of the grid voltage harmonic orders 5th, 7th, 11th, and 13th. The experimental results closely agreed with the simulation results. The experimental grid currents complied with the IEEE 1547 standard thanks to the multi-resonant controllers. The proposed platform provides a smooth transition from implementation to a near-commercial prototype with a low investment cost in simulation and rapid prototyping tools. A MATLAB/Simulink VSC model is provided as an attachment of this paper.

Practical Considerations for Continuous Time-Domain Cerebrovascular Reactivity Indices in Traumatic Brain Injury: Do Scaling Errors in Parent Signals Matter?

Literature pertaining to traumatic brain injury care involves the mediation and control of secondary brain injury mechanisms, chief among these is cerebral autoregulation. Cerebral autoregulation is frequently assessed through surrogate measures of cerebrovascular reactivity. An important aspect to acknowledge when calculating cerebrovascular reactivity indices is the linearity within two-parent bio-signals or variables. We highlighted the concept of linearity in raw parent bio-signals used for the calculation of the cerebrovascular reactivity index and what potential implications linearity carries for index derivation. Key of which is that the initial differencing or location of the pressure probes does not influence linear methods of cerebral reactivity calculations so long as the slow-wave vasogenic changes are being recorded.

Hybrid Swapped Battery Charging and Logistics Dispatch Model in Continuous Time Domain

Electric vehicles (EVs) have attracted enormous attention in recent years due to their potentials in mitigating energy crisis and air pollutions. However, the long battery charging time and lack of sufficient charging infrastructure highly restrict the popularization of EVs. In this context, it is promising to establish battery charging and swapping systems (BCSSs) based on the concept of battery swapping services. To optimally achieve the combined operation of BCSSs, this paper proposes a hybrid swapped battery charging and logistics dispatch model in continuous time domain. Identifying the special structure of the mathematical models of the two problems, this paper innovatively formulated the swapped battery charging strategy as the rectangle packing problem and the battery logistics model as the vehicle routing problem. The two models are closely linked by the delivery time of transporting the well-charged batteries from battery charging stations to battery swapping stations. A hybrid optimal operation model of BCSSs is further formulated as a mixed-integer linear programming model by incorporating the interaction between the battery charging and battery logistics. Finally, case studies are conducted on several BCSSs and numerical results validate the effectiveness of the proposed model.

Digital Current Controller With a Novel Active Damping Design for IPMSM

Internal model control gets wide attentions in electric drives for its outstanding robustness. Underdamped oscillations however, always can be seen in the synchronous <i>d</i>- and <i>q</i>- axis components of the stator currents if without proper extra damping elements included, especially under the low sampling to fundamental frequency ratios. The reasons for these oscillations are analyzed in the discrete-time domain other than as usually done in the continuous-time domain. Following that, a distinctive active damping matrix is proposed for the current closed-loop control of interior permanent magnet synchronous motors (IPMSMs). As a result, the anti-disturbance capability is enhanced, allowing the oscillations, often seen in the <i>d</i>- and <i>q</i>-axis current components, suppressed very well. Compared with other damping methods, the typical PI structure is still retained, which is desired on the point of engineering view. At last, the analysis as well as the damping effectiveness is validated through experiments.

Event–Triggered Control for Systems on Non–Uniform Time Domains Using Measure Chain Theory

This brief introduces the concept of measure chain theory in order to design event–triggered controllers for systems evolving on arbitrary time domains. The event–triggering mechanism is derived from input–to–state (ISS) stable Lyapunov functions defined on measure chains. These results not only unify existing event–triggered controller design procedures for systems on continuous and discrete time domains, but also extend them to non–homogeneous time domains. Numerical simulations for systems on different measure chains illustrate the effectiveness of the designed event–triggered controllers.

A 18.7 TOPS/W Mixed-Signal Spiking Neural Network Processor With 8-bit Synaptic Weight On-Chip Learning That Operates in the Continuous-Time Domain

We present a mixed-signal spiking neural networks processor with 8-bit synaptic weight on-chip learning in 40 nm CMOS that consists of a 10k mixed-signal synapse circuit and 100 analog leaky integrate-and-fire (LIF) neuron circuits. The processor has no clock signal except in peripheral circuits for I/O, and neuron and synapse circuits can operate asynchronously in the continuous-time domain, just like biological neurons. We demonstrate the energy efficiency of 6.24–18.7 TOPS/W in a multitarget spike learning task.

Delta system transfer function model for IEMI time domain test

Intentional electromagnetic interference (IEMI) is a serious threat to the stable operation of computer and other intelligent terminal equipment. For testing research on IEMI, it is very important to build a stable and reliable test system model for accurate analysis of test data. In the time-domain test of IEMI, the z-domain discrete system transfer function is usually used to describe the response characteristics of the test system. However, with the change of sampling frequency, the parameters of the z-domain model should change accordingly, which leads to inconvenience in modeling the test system. In this paper, a δ-domain transfer function model of the test system is constructed by the transformation from z-domain. And the δ-domain model has better stability at high sampling rate and can be approximately equivalent to the system transfer function in the continuous-time domain.

Tri‐transformer Hawkes process via dot‐product attention operations with event type and temporal encoding

AbstractAsynchronous event sequences widely exist in the real world, such as social networks, electronic medical records, financial data, and genome analysis. For modeling asynchronous event sequences in the continuous time domain, point process has become the underpinning. In the initial research stage, Hawkes process is widely used because it can capture the self‐triggering and mutual triggering modes between different events in a variety of point process functions. In recent years, due to the development of neural networks, deep point process (also known as neural point process) can learn models with the stronger fitting ability and reduce the dependence on prior knowledge by using the powerful capacity of neural networks. The proposal of the transformer Hawkes process (THP) has led to a huge performance improvement, so a new climax of the transformer‐based deep Hawkes process is set off. However, THP does not make full use of the event and temporal information underlying the asynchronous event sequence, meanwhile, if we simply take the event type encoding and temporal encoding as the sequence encoding, a single transformer may suffer from learning bias. In order to circumvent these problems, we propose a tri‐transformer Hawkes process model (TTHP), in which the event and temporal information are introduced to the dot‐product attention operations as auxiliary information to form different multihead attention, respectively, and are utilized to build three heterogeneous learners. A series of well‐designed experiments on synthetic and real‐world datasets validate the effectiveness of the proposed TTHP.

Continuous Time-Domain Cerebrovascular Reactivity Metrics and Discriminate Capacity for the Upper and Lower Limits of Autoregulation: A Scoping Review of the Animal Literature.

Over a wide range of systemic arterial pressures, cerebral blood flow (CBF) is regulated fairly constantly by the cerebral vessels in a process termed cerebral autoregulation (CA), which is depicted by the Lassen autoregulatory curve. After traumatic brain injury (TBI), CA can get impaired and these impairments manifest in changes of the Lassen autoregulatory curve. Continuous surrogate metrics of pressure-based CA, termed cerebrovascular reactivity (CVR) metrics, evaluate the relationship between slow vasogenic fluctuations in a driving pressure for cerebral blood flow, and the most commonly studied and utilized measures are based in the time domain and have been increasingly applied in bedside TBI care and have sparked the investigation of individualized cerebral perfusion pressure targets. However, not all CVR metrics have been validated as true measures of autoregulation in the pre-clinical setting. We reviewed all available pre-clinical animal literature that assessed the association between continuous time-domain metrics of CVR and some aspect of the Lassen autoregulatory curve. All 15 articles found associated the evaluated continuous metrics to the lower limit of autoregulation curve whereas none looked at the upper limit. Most of the evaluated metrics showed the ability to discriminate the lower limit of autoregulation with various methods of perturbation. Further work is required to evaluate the utility of such surrogate measures against the upper limit of autoregulation, while also providing validation to the existing literature supporting specific indices and their ability to discriminate the lower limit.

Nostradamus: A novel event propagation prediction approach with spatio-temporal characteristics in non-Euclidean space

The prediction of event propagation has received extensive attention from the knowledge discovery community for applications such as virus spread analytics, social network analysis, earthquake location prediction, and typhoon tracking. The data describing these phenomena are multidimensional asynchronous event data that affect each other and show complex dynamic patterns in the continuous-time domain. Unlike the discrete characteristics formed by sampling at equal intervals of asynchronous time series, the timestamps of asynchronous events are in the continuous-time field. The study of these dynamic processes and the mining of their potential correlations provide a foundation for applying event propagation prediction, traceability, and causal inference at both the micro and macro levels.Most of the existing methods represent data as being embedded in the Euclidean space. However, when embedding a real-world graph with a tree-likeliness graph, Euclidean space cannot visually represent a graph. Inspired by the characteristics of hyperbolic space, we propose a model called Nostradamus to capture the propagation of the events of interest from historical events in a graph via the hyperbolic graph neural Hawkes process with Spatio-temporal characteristics. The Nostradamus’ core concept is to integrate the Hawkes process’s conditional intensity function with a hyperbolic graph convolutional recurrent neural network.

A Comparison of Papoulis and Chebyshev Filters in the Continuous Time Domain

A Comparison of Papoulis and Chebyshev Filters in the Continuous Time Domain