Physical Systems Research Articles

Design concept of intelligent integrated control system for neutral beam injection

Abstract Due to the specificity of neutral beam injection (NBI) system, the control of its actual physical system is achieved by the integrated control system (ICS). The current NBI ICS adopts a distributed design structure to balance the system load, which is also the mainstream system design and architecture model of ICS around the world. However, from a practical point of view, the distributed system architecture is not perfect. In the long run, upgrading the system intelligence and automation capabilities is an inevitable choice for the future, so it is necessary to explore ways to transform and upgrade the ICS. At present, Internet of Things (IoT), as an important part of the new generation information technology, can realize the control of everything through data exchange. This is because on the one hand, IoT has wide compatibility and powerful scenario-based capabilities, it not only has the advantages and characteristics of distributed design, but also can pull the NBI subsystems into the same level scenario and lay the foundation for the further construction of digital NBI; on the other hand, the intervention of artificial intelligence makes IoT have some new typical characteristics such as intelligent sensing, ubiquitous connectivity, precise control, digital modeling, real-time analysis and iterative optimization, which is enough to pull the current NBI ICS into a new intelligent control era. Finally, it is worth mentioning that due to its inherent design structure and functional characteristics, ICS tends to be broadly generic, so it is not exclusively used for NBI operation in nuclear fusion, and it can provide some insight into other application areas.

Explainable AI in Manufacturing and Industrial Cyber–Physical Systems: A Survey

This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts.

Application of Industry 4.0 and digital twin in the field of smart healthcare

There is an increasing need for preventive health care as well as precise diagnosis and tailored treatment of different diseases in recent years. Providing customized treatment for each patient and maximizing accuracy and efficiency are main goals of a good healthcare system. This thesis explores the integration of digital twin technology with Industry 4.0 in healthcare. Digital twins create virtual representations of physical systems, enabling real-time monitoring and tailored treatments. Key elements include the living human body, IoT, digital twin, cloud computing, and simulation. The architecture comprises data acquisition, data munging, data storage, data simulation with analytics, and user access layers. Creating a digital twin involves precise 3D modeling, representing the entire body or specific organs. A case study demonstrates real-time monitoring using wearable sensors for blood pressure, blood sugar, and heart rate. Data is transmitted, integrated with the digital twin, and accessible via a website with alarms for abnormal readings. While Industry 4.0 and digital twin adoption in healthcare is evolving, the architecture serves as a reference. It offers real-time data for diagnosis and treatment, with potential for advanced simulations. Challenges include automated data systems and privacy concerns. Despite limitations, this integration holds promise for precision medicine and personalized healthcare.

Design your own universe: a physics-informed agnostic method for enhancing graph neural networks

AbstractPhysics-informed Graph Neural Networks have achieved remarkable performance in learning through graph-structured data by mitigating common GNN challenges such as over-smoothing, over-squashing, and heterophily adaption. Despite these advancements, the development of a simple yet effective paradigm that appropriately integrates previous methods for handling all these challenges is still underway. In this paper, we draw an analogy between the propagation of GNNs and particle systems in physics, proposing a model-agnostic enhancement framework. This framework enriches the graph structure by introducing additional nodes and rewiring connections with both positive and negative weights, guided by node labeling information. We theoretically verify that GNNs enhanced through our approach can effectively circumvent the over-smoothing issue and exhibit robustness against over-squashing. Moreover, we conduct a spectral analysis on the rewired graph to demonstrate that the corresponding GNNs can fit both homophilic and heterophilic graphs. Empirical validations on benchmarks for homophilic, heterophilic graphs, and long-term graph datasets show that GNNs enhanced by our method significantly outperform their original counterparts.

A Review of Differential Equations that is applicable to Differential Difference Equations

Mathematical modeling relies heavily on differential equations because they offer a robustframework for studying and understanding dynamical systems. However, the study of differential differenceequations (DDEs) has evolved as a subfield within differential equations due to the increasing prevalence ofsystems with discrete time steps and delayed effects. The purpose of this work is to present a synopsisof research on differential equations that may be used in the analysis of differential difference equations.The review starts with a thorough introduction to ODEs and PDEs (ordinary and partial differentialequations). Fundamental ideas, analytical approaches, and numerical strategies for solving thesecontinuous-time problems are discussed. ODEs and PDEs are highlighted for their importance insimulating a wide range of physical, biological, and engineering systems.

119 Addressing inequalities in participation: Developing an inclusive sport and physical activity system across Wales, UK

Abstract Purpose Sport and physical activity participation rates remain low and are particularly apparent in areas of deprivation, among girls and women, those with a disability and minority ethnic populations. In collaboration with Sport Wales (the Welsh Government sponsored body for sport in Wales), this project seeks to facilitate the development of an inclusive sport and physical activity system. Its aim is to identify key organisations and potential leaders where participation in sport and physical activity is low and engage them in finding solutions to these challenges. A specific objective of the project is to use insight and engagement from under-represented groups to drive physical activity and sport participation, amongst children and young people, forwards. Project description The project, undertaken in two phases, was commissioned by Sport Wales. It commenced in September 2023 and is due to finish in late 2024. Phase one includes a social network analysis via an online survey to map the existing sport and physical activity network in Wales. Both a formal and informal distribution strategy for the survey was adopted. The survey seeks to ensure sufficient responses to map the network of key stakeholders and identify previously unknown organisations and individuals. The mapping should enhance our understanding of the current network and identify potential new leaders. Phase two includes the development of a national stakeholder engagement group, made up of the organisations and individuals identified in phase one. This group will act as an advisory group to Sport Wales and produce an engagement plan to inform more effective strategies to target inequalities and under-representation in sport and physical activity participation. A process evaluation is embedded into the project. Conclusions and implications Identifying the organisations and leaders in sport and physical activity in areas where participation is low, and actively engaging them in a leadership group reflects a new approach. It demonstrates a commitment by a national organisation to foster collaboration and seek alternative solutions. The project has the potential to be innovative by developing new ways of working and providing solutions to address ongoing and significant challenges in sport and physical activity participation.

A Transformative Topological Representation for Link Modeling, Prediction and Cross-Domain Network Analysis.

Many complex social, biological, or physical systems are characterized as networks, and recovering the missing links of a network could shed important lights on its structure and dynamics. A good topological representation is crucial to accurate link modeling and prediction, yet how to account for the kaleidoscopic changes in link formation patterns remains a challenge, especially for analysis in cross-domain studies. We propose a new link representation scheme by projecting the local environment of a link into a "dipole plane", where neighboring nodes of the link are positioned via their relative proximity to the two anchors of the link, like a dipole. By doing this, complex and discrete topology arising from link formation is turned to differentiable point-cloud distribution, opening up new possibilities for topological feature-engineering with desired expressiveness, interpretability and generalization. Our approach has comparable or even superior results against state-of-the-art GNNs, meanwhile with a model up to hundreds of times smaller and running much faster. Furthermore, it provides a universal platform to systematically profile, study, and compare link-patterns from miscellaneous real-world networks. This allows building a global link-pattern atlas, based on which we have uncovered interesting common patterns of link formation, i.e., the bridge-style, the radiation-style, and the community-style across a wide collection of networks with highly different nature.

A novel method of steady-state modeling for self-excited induction generators in distributed generation system based on transient space-vector model

Abstract The paper proposes a novel mathematic model for steady states represented by the complex-domain transient space vectors of SEIGs in the stationary coordinate system, where the physical systems are extended to be analyzed by applying the previous approach of holistic analysis based on the stability theorem of Lyapunov. The motion analysis of mechanisms gives analytical formulas of operating points at steady states by solving the novel steady-state model analytically. Good agreement between former numerically computed results and analytically computed results by the analytic formulas proves the validity and effectiveness of the proposed novel model, with extensive engineering referenced and applicable value.

GPTFF: A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials

This study introduces a novel artificial intelligence (AI) force field, namely a graph-based pre-trained transformer force field (GPTFF), which can simulate arbitrary inorganic systems with good precision and generalizability. Harnessing a large trove of the data and the attention mechanism of transformer algorithms, the model can accurately predict energy, atomic force, and stress with mean absolute error (MAE) values of 32 meV/atom, 71 meV/Å, and 0.365 GPa, respectively. The dataset used to train the model includes 37.8 million single-point energies, 11.7 billion force pairs, and 340.2 million stresses. We also demonstrated that the GPTFF can be universally used to simulate various physical systems, such as crystal structure optimization, phase transition simulations, and mass transport. The model is publicly released with this paper, enabling anyone to use it immediately without needing to train it.

Complexity Measures in the Tight-Binding Model

Abstract The deformation of a wave packet is a significant topic in classical and quantum mechanics. Understanding this phenomenon is relevant in the study of various physical systems. In this work, we characterize the evolution of a highly localized wave packet in a tight-binding lattice. We investigate the behavior of the probability distribution associated with the wave packet and the accompanying complexity measures. We take information entropy, disequilibrium, disorder, and complexity measures to describe the localization-delocalization process from a highly localized initial pulse, showing the particles moving in a lattice. The main result is obtained from the entropy definition (Logarithmic and Linear) and the inverse of the participant ratio to describe the expected localization-delocalization process, evoking two definitions of Complexity: CLMC and CSDL .

Improved prediction of critical currents for multi-pancake coil across variable temperature regions

With the increasing performance of the second-generation high-temperature superconducting (2G-HTS) tapes, the technology of all-REBa2Cu3O7-δ (REBCO, RE: rare earth) superconducting magnets has developed rapidly. However, the 2G-HTS magnet with hybrid cryocoolers constantly adjusts its operating temperature according to the actual demands, which involves the critical current when the magnet crosses variable temperature regions. In this paper, a modified ideal model based on the homogenization model is proposed, which can predict the critical current of 2G-HTS magnets more consistently. The in-field properties of REBCO tapes in variable temperature regions were tested by the physical property measurement system. Furthermore, the improved model was combined with the in-field properties of REBCO tapes to obtain the critical currents of a 2G-HTS multi-pancake (MP) coil at 20 K, 30 K, 65 K, and 77 K. The critical current of a multi-pancake coil at 77 K was experimentally verified. The modified ideal model uses the actual current density, which results are closer to the experimental results with an agreement of 97.3%. This new method proposed in this work is significant in quickly predicting critical currents for 2G-HTS magnets across variable temperature regions.

A novel family of asymmetric generalized passive voltage‐controlled memristive system

AbstractResearch on the physical memristive system is vital for realizing widespread applications of the memristor in practical engineering. In this article, a family of the asymmetric generalized passive voltage‐controlled memristive system (AGPVCMS) is proposed; also the mathematical model of the AGPVCMS is established and its remarkable memristive characteristics are analysed. Especially, the AGPVCMS only consists of the diode bridge and LR filter, however it can enhance its asymmetry as well as display various asymmetrical pinched hysteresis loops in the voltage–current plane, via expediently changing the number of the diodes on different bridge arms. In addition, the effectiveness of the AGPVCMS is verified by the consistency between theoretical and experimental results.

Promoting digital twin technology application for process industry: A novel generation modelling platform and its implementations

AbstractOne key concept of Industry 4.0 is the industrial digital twin (DT)—a virtual replica of physical assets, processes, and systems. DTs optimise operations, enhance productivity, and facilitate innovation across various industries, including manufacturing, energy, healthcare, and transportation. This paper presents a multi‐domain and multi‐time scale modelling and simulation platform, featuring built‐in model libraries that represent diverse physical, chemical, and behavioural properties. A comprehensive system modelling approach and a closed‐loop industrial control system based on DTs are developed, showcasing advanced control, optimisation, and system security research capabilities. The architecture and advantages of the novel generation modelling platform are detailed, with specific applications highlighted, underscoring its contributions to the process industry.

Dreaming of electrical waves: Generative modeling of cardiac excitation waves using diffusion models.

Electrical waves in the heart form rotating spiral or scroll waves during life-threatening arrhythmias, such as atrial or ventricular fibrillation. The wave dynamics are typically modeled using coupled partial differential equations, which describe reaction-diffusion dynamics in excitable media. More recently, data-driven generative modeling has emerged as an alternative to generate spatio-temporal patterns in physical and biological systems. Here, we explore denoising diffusion probabilistic models for the generative modeling of electrical wave patterns in cardiac tissue. We trained diffusion models with simulated electrical wave patterns to be able to generate such wave patterns in unconditional and conditional generation tasks. For instance, we explored the diffusion-based (i) parameter-specific generation, (ii) evolution, and (iii) inpainting of spiral wave dynamics, including reconstructing three-dimensional scroll wave dynamics from superficial two-dimensional measurements. Furthermore, we generated arbitrarily shaped bi-ventricular geometries and simultaneously initiated scroll wave patterns inside these geometries using diffusion. We characterized and compared the diffusion-generated solutions to solutions obtained with corresponding biophysical models and found that diffusion models learn to replicate spiral and scroll wave dynamics so well that they could be used for data-driven modeling of excitation waves in cardiac tissue. For instance, an ensemble of diffusion-generated spiral wave dynamics exhibits similar self-termination statistics as the corresponding ensemble simulated with a biophysical model. However, we also found that diffusion models produce artifacts if training data are lacking, e.g., during self-termination, and "hallucinate" wave patterns when insufficiently constrained.

Complete synchronization of two spirals by a messenger wave in a reaction diffusion system

Synchronization phenomena are ubiquitous in nature. They can be observed in physical, chemical, and biological systems. In the present study, we examine synchronization phenomena in chemical reaction–diffusion systems in the experimental Belousov–Zhabotinsky reaction. We study how two counter-rotating spirals pinned to unexcitable heterogeneities separated by a wall interact with a third free spiral of higher frequency. We found that the latter, which we call the messenger wave, synchronizes the two non-interacting pinned spirals. We also carried out numerical simulations of a model system with Barkley’s reaction–diffusion equations and found corroborating results.

A Cyber–Physical System for Real-Time Physiological Data Monitoring and Analysis

A Cyber–Physical System for Real-Time Physiological Data Monitoring and Analysis

Robust Adaptive Fuzzy Control for Second-Order Euler-Lagrange Systems With Uncertainties and Disturbances via Nonlinear Negative-Imaginary Systems Theory.

Ensuring robust and precise tracking control in the presence of uncertain multi-input-multi-output (MIMO) system dynamics and environmental variations is a significant challenge in the field of robust and adaptive control theory. While fuzzy control strategies have demonstrated good tracking performance in normal conditions, designing and tuning fuzzy controllers can be a challenging task in highly uncertain environments. In this study, we investigate a novel approach that combines robust nonlinear negative-imaginary (NI) systems theory with a self-adaptive fuzzy control scheme and the Lyapunov synthesis to develop a robust adaptive negative-imaginary-fuzzy (RANIF) control scheme. We optimize the critical parameters of the proposed fuzzy system using a self-tuning technique with a proportional-derivative sliding manifold. Furthermore, unlike the existing adaptive fuzzy control methods, we propose a small number of membership functions and systematically derive the fuzzy rules by employing Lyapunov, nonlinear NI, and dissipativity theories, which simplify the tuning process, work out the matter of "explosion of complexity," and reduce computational complexity. We demonstrate the global stability of the closed-loop system using nonlinear NI theory. To evaluate the effectiveness of our proposed approach, we present simulation results for two examples involving uncertain MIMO second-order Euler-Lagrange systems. These systems, known for their capacity to represent a diverse range of practical physical systems, serve as suitable testbeds for our methodology. Our results show that RANIF outperforms other control methods, such as nonlinear strictly NI-Fuzzy, fuzzy-logic control, model predictive control, and conventional PID control, in terms of robustness to disturbances and inestimable faults, trajectory tracking performance, and computational complexity.

On the Equations of Motion of Some Physical Systems

On the Equations of Motion of Some Physical Systems

Synchronization control of cyber–physical systems with time-varying dynamics under denial-of-service attacks

In this paper, the problem of synchronization control of cyber–physical systems with time-varying dynamics under Denial-of-Service attacks is studied. The communication channels of the synchronization protocol between the subsystems in the cyber–physical systems may be injected into DoS attacks. To counteract the adverse effects of these attacks on synchronization, a target reference model is initially created for each subsystem to mimic its standard operational dynamics. Subsequently, utilizing this reference model, an adaptive compensator is proposed. The unified synchronization protocol guarantees that the synchronization error remains minimal through the adjustment of specific parameters, even under DoS attacks. To showcase the efficiency of this approach, an illustrative numerical simulation example is conducted.

Suppression and synchronization of chaos in uncertain time-delay physical system

Suppression and synchronization of chaos in uncertain time-delay physical system