Physical Systems Research Articles

Machine Learning Memory Kernels as Closure for Non-Markovian Stochastic Processes.

Finding the dynamical law of observable quantities lies at the core of physics. Within the particular field of statistical mechanics, the generalized Langevin equation (GLE) comprises a general model for the evolution of observables covering a great deal of physical systems with many degrees of freedom and an inherently stochastic nature. Although formally exact, GLE brings its own great challenges. It depends on the complete history of the observables under scrutiny, as well as the microscopic degrees of freedom, all of which are often inaccessible. We show that these drawbacks can be overcome by adopting elements of machine learning from empirical data, in particular coupling a multilayer perceptron (MLP) with the formal structure of GLE and calibrating the MLP with the data. This yields a powerful computational tool capable of describing noisy complex systems beyond the realms of statistical mechanics. It is exemplified with a number of representative examples from different fields: from a single colloidal particle and particle chains in a thermal bath to climatology and finance, showing in all cases excellent agreement with the actual observable dynamics. The new framework offers an alternative perspective for the study of nonequilibrium processes opening also a new route for stochastic modeling.

Design and development of robotic technology through microcontroller system with machine learning techniques

Through the integration of AI and IoT, the digital twin transforms industrial sectors by virtually portraying physical systems. Simulation and the application of lifecycle management improve decision-making. In this paper, a virtual prototype system a digital twin framework that integrates robotic devices is proposed. Created using debugging platforms, they track every robotic activity, supported by real-time microcontroller structural design systems. Machine learning methods are the fundamental engine of the digital twin system. Because of this connection, robotic actions may be seamlessly controlled and monitored, guaranteeing effectiveness and adaptability in changing contexts. Robotics has advanced significantly with the combination of digital twin technology, machine learning, and microcontroller systems, offering improved performance and versatility in a range of applications.

VelogCPS: A safe blockchain network for cyber-physical systems leveraging block verifiers

Non-functional requirements related to safety, security, and timeliness have made cyber–physical systems (CPS) initially reluctant to their integration with blockchain technology. Despite the multiple advantages of blockchain like improved data security and traceability, the main reasons that have slowed down its adoption in CPS still remain. Examples of these are the inherent overhead of accessing the distributed ledger and the security incidents that a number of blockchain networks have suffered since its inception. This paper presents VelogCPS, a novel middleware that guarantees that logic and data managed by blockchain networks of cyber–physical systems is verified and generated by a legitimate source. Thus, VelogCPS avoids a kind of security incidents that impact the authenticity and integrity of the logic and data managed in blockchain networks. By authenticity we refer to provenance authenticity of the involved smart contracts, i.e., the perfect matching between the source-code and a corresponding advertised smart-contract logic. This middleware ensures that the entities that participate to a CPS use solely authentic logic. For this, our approach leverages block verification services and enforces them through the operation workflow. As a result, the middleware guarantees that the CPS participants use and share authentic logic. Our approach is validated by providing an implementation on a real blockchain network, employing actual smart contract verifier logic, and analysing the temporal behavior of the overall system operations; this ensures its utility for CPS and IoT.

Cascade Failure Modeling and Resilience Analysis of Mine Cyber Physical Systems Under Deliberate Attacks

Cascade Failure Modeling and Resilience Analysis of Mine Cyber Physical Systems Under Deliberate Attacks

Proximity-induced gapless superconductivity in two-dimensional Rashba semiconductor in magnetic field

Two-dimensional semiconductor-superconductor heterostructures form the foundation of numerous nanoscale physical systems. However, measuring the properties of such heterostructures, and characterizing the semiconductor in-situ is challenging. A recent experimental study by [Phys. Rev. Lett. 128, 107701 (2022)] was able to probe the semiconductor within the heterostructure using microwave measurements of the superfluid density. This work revealed a rapid depletion of superfluid density in semiconductor, caused by the in-plane magnetic field which in presence of spin-orbit coupling creates so-called Bogoliubov Fermi surfaces. The experimental work used a simplified theoretical model that neglected the presence of non-magnetic disorder in the semiconductor, hence describing the data only qualitatively. Motivated by experiments, we introduce a theoretical model describing a disordered semiconductor with strong spin-orbit coupling that is proximitized by a superconductor. Our model provides specific predictions for the density of states and superfluid density. Presence of disorder leads to the emergence of a gapless superconducting phase, that may be viewed as a manifestation of Bogoliubov Fermi surface. When applied to real experimental data, our model showcases excellent quantitative agreement, enabling the extraction of material parameters such as mean free path and mobility, and estimating gg-tensor after taking into account the orbital contribution of magnetic field. Our model can be used to probe in-situ parameters of other superconductor-semiconductor heterostructures and can be further extended to give access to transport properties.

Synthesis of covert command attackers at the supervisory layer of cyber-physical systems

In this paper, we investigate the problem of command attacker synthesis in cyber–physical systems at the supervisory control layer. The attacker attempts to inflict damage or reach a blocking state, while undetected by the supervisor. Specially, we propose the so-called Command Attack Structure (CAS) to capture attack processes and describe the interaction between the attacker and the supervisory control system. To address the races between attack events and commands or internal events of the control system during the interaction, we propose the conditions of strictly race-free and loosely race-free, which the attacker must adhere to. Moreover, we design pruning algorithms to achieve covert and race-free CASs, and ultimately obtain successful command attackers. Lastly, we also consider combined attacks, which involve multiple attack operations on a single command. Then the combined CAS is designed to model and analyze such scenarios.

Air tightness detection method in parallel for aerospace: mechanical design, system modeling and optimum control

To address the problems of high cost, complex operation, and low efficiency of existing equipment for aerospace, a simple but efficient multichannel air tightness detection device is designed. A physical system model is constructed for the process of inflation, deflation, and inflation with leakage. The expression between the inflation/deflation rate and the valve area is obtained. Through modeling the intersection and rotation of spatial curves, the relationship between the valve area and the valve core angle is derived, and the optimal valve core angle calculation method is proposed to achieve precise rate control of inflation and deflation. Simulation experiments and numerical analysis demonstrate the superiority of the proposed system scheme, the effectiveness of the control method, as well as the accuracy of the mathematical model. For the tested object of 0.3 L, the target pressure of 400 KPa, and the inflation and deflation rate of 75 Kpa/min, by using four channels with a 10 s interval for sequential operation and adjusting valves every 60 s, the efficiency of inflation and deflation is more than 3.5 times that of a single device with a single circuit. Its performance is not much different from real-time automatic control equipment, but the economy is greatly improved.

Approaching Electroencephalographic Pathological Spikes in Terms of Solitons

A delicate balance between dissipative and nonlinear forces allows traveling waves termed solitons to preserve their shape and energy for long distances without steepening and flattening out. Solitons are so widespread that they can generate both destructive waves on oceans’ surfaces and noise-free message propagation in silica optic fibers. They are naturally observed or artificially produced in countless physical systems at very different coarse-grained scales, from solar winds to Bose–Einstein condensates. We hypothesize that some of the electric oscillations detectable by scalp electroencephalography (EEG) could be assessed in terms of solitons. A nervous spike must fulfill strict mathematical and physical requirements to be termed a soliton. They include the proper physical parameters like wave height, horizontal distance and unchanging shape; the appropriate nonlinear wave equations’ solutions and the correct superposition between sinusoidal and non-sinusoidal waves. After a thorough analytical comparison with the EEG traces available in the literature, we argue that solitons bear striking similarities with the electric activity recorded from medical conditions like epilepsies and encephalopathies. Emerging from the noisy background of the normal electric activity, high-amplitude, low-frequency EEG soliton-like pathological waves with relatively uniform morphology and duration can be observed, characterized by repeated, stereotyped patterns propagating on the hemispheric surface of the brain over relatively large distances. Apart from the implications for the study of cognitive activities in the healthy brain, the theoretical possibility to treat pathological brain oscillations in terms of solitons has powerful operational implications, suggesting new therapeutical options to counteract their detrimental effects.

Real-time cyber/physical interplay in scheduling for peak load optimisation in Cyber–Physical Energy Systems

This paper is about reducing the power consumption of Cyber–Physical Energy Systems (CPESs) composed of many loads through the usage of a scheduling technique inspired by the real-time-computing domain; the electric-load coordination guarantees a more efficient operation of the entire system by avoiding unnecessary concurrent activation of loads and thus limiting the peak load. We represent the power loads themselves as “physical” components and the computing devices that coordinate them as “cyber” components and formally derive the relationship between the operations in cyber and physical domains as the interplay between the schedules enforced on each component: indeed, the schedule of the loads – generated by combining a two-dimensional bin-packing and an optimal multi-processor real-time scheduling algorithm – influences the timing of the processing tasks that are dedicated to the activation/deactivation of loads themselves. We also consider non-schedulable loads by introducing a policy to cope with the presence of such loads. Numerical simulations and experiments confirm the good performance of the proposed peak load reduction method. The usage of real-time scheduling in this context provides inherent resource optimisation by limiting the number of concurrent loads that are active at the same time, thus directly reducing the overall peak load; moreover, thanks to the limited computational complexity of the algorithms, it scales to large systems, overcoming the scalability issues of common optimisation methods. Numerical simulations and experiments confirm the good performance of the proposed peak load reduction method.

Learning Traveling Solitary Waves Using Separable Gaussian Neural Networks.

In this paper, we apply a machine-learning approach to learn traveling solitary waves across various physical systems that are described by families of partial differential equations (PDEs). Our approach integrates a novel interpretable neural network (NN) architecture, called Separable Gaussian Neural Networks (SGNN) into the framework of Physics-Informed Neural Networks (PINNs). Unlike the traditional PINNs that treat spatial and temporal data as independent inputs, the present method leverages wave characteristics to transform data into the so-called co-traveling wave frame. This reformulation effectively addresses the issue of propagation failure in PINNs when applied to large computational domains. Here, the SGNN architecture demonstrates robust approximation capabilities for single-peakon, multi-peakon, and stationary solutions (known as "leftons") within the (1+1)-dimensional, b-family of PDEs. In addition, we expand our investigations, and explore not only peakon solutions in the ab-family but also compacton solutions in (2+1)-dimensional, Rosenau-Hyman family of PDEs. A comparative analysis with multi-layer perceptron (MLP) reveals that SGNN achieves comparable accuracy with fewer than a tenth of the neurons, underscoring its efficiency and potential for broader application in solving complex nonlinear PDEs.

A numerical approach for nonlinear transmission line analysis with bidirectional coupling to lumped-element and particle-in-cell models

In this article, an iterative method for nonlinear transmission lines (TLs) based on the Lax-Wendroff method has been established to describe a bidirectional coupling model of nonlinear lumped-element models, nonlinear transmission line models, and particle-in-cell (PIC) models. The proposed self-consistent global circuit model is achieved through the introduction of the charge conservation equation at the upper plate as the Robin boundary condition and its coupling with the PIC method used for plasma simulation. The effectiveness of the proposed model is validated through five numerical experiments, including nonlinear transmission line coupling to lumped circuits, and the most general case of plasma-TLs-lumped elements model (LEM) coupling systems. This non-linear global circuit coupling model provides a powerful tool for simulating the complex behavior of various physical systems, including but not limited to capacitively coupled plasmas, vacuum electronic devices, and Z-pinches.

A physics-driven and machine learning-based digital twinning approach to transient thermal systems

Purpose In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system. Design/methodology/approach To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop. Findings The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems. Originality/value The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

A Cyber–Physical System Based on Digital Twin and 3D SCADA for Real-Time Monitoring of Olive Oil Mills

Cyber–physical systems involve the creation, continuous updating, and monitoring of virtual replicas that closely mirror their physical counterparts. These virtual representations are fed by real-time data from sensors, Internet of Things (IoT) devices, and other sources, enabling a dynamic and accurate reflection of the state of the physical system. This emphasizes the importance of data synchronization, visualization, and interaction within virtual environments as a means to improve decision-making, training, maintenance, and overall operational efficiency. This paper presents a novel approach to a cyber–physical system that integrates virtual reality (VR)-based digital twins and 3D SCADA in the context of Industry 4.0 for the monitoring and optimization of an olive mill. The methodology leverages virtual reality to create a digital twin that enables immersive data-driven simulations for olive mill monitoring. The proposed CPS takes data from the physical environment through the existing sensors and measurement elements in the olive mill, concentrates them, and exposes them to the virtual environment through the Open Platform Communication United Architecture (OPC-UA) protocol, thus establishing bidirectional and real-time communication. Furthermore, in the proposed virtual environment, the digital twin is interfaced with the 3D SCADA system, allowing it to create virtual models of the process. This innovative approach has the potential to revolutionize the olive oil industry by improving operational efficiency, product quality, and sustainability while optimizing maintenance practices.

A hybrid multi-attribute decision-making model for the systematic evaluation of exoticism-themed retail spaces from the perspective of consumer experience

Previous studies have provided abundant evidence confirming the significant impact of the physical environment in retail spaces themed with exoticism on brand establishment, consumer attraction, and stimulation of consumption behavior. However, there has been limited research employing systematic approaches to examine the influence of physical environmental elements on consumer experiences in Exoticism-themed retail spaces. Therefore, this study aims to propose a comprehensive and systematic evaluation framework from the perspective of consumer experience. It integrates the Caption Evaluation Method (CEM), Kano model, and Decision Making Trial and Evaluation Laboratory (DEMATEL) technique into a Multiple Attribute Decision Making (MADM) model. This study selects cases from China to demonstrate the superiority of the model. The research findings suggest that in the development and renovation process of integrating exoticism elements into retail environments, more attention should be paid to infrastructure such as street layout and facilities. Real plants, in particular, have the greatest primary influence on other elements. Theoretically, it offers a novel perspective on decision-making concerning the enhancement of the physical environment system in Exoticism-themed retail spaces. Practically, the analytical model developed in this study can provide decision-making insights and experiential references for similar themed retail spaces globally.

Implementation of Manufacturing Execution System (MES) to Increase Productivity and Competitiveness in the Manufacturing Sector

Abstract: Industry 4.0's underlying digital technologies are already essential in helping manufacturing businesses, especially small and medium-sized enterprises (SMEs), to adapt to the dynamics of the new normal. Manufacturing execution system (MES) serves as the factory information hub and is made possible by internet of things (IoT) ready devices that generate data and cyber physical systems that control the interconnection between equipment and computational assets for industries to achieve sustained competitiveness and address post- pandemic market volatility by utilizing real-time information. This paper focuses on the implementation for MES with the help of three case studies. Three case studies were used to empirically test the theories put out on MES to provide an overview of "How MES is efficient in loss reduction” through findings from cross-case synthesis of the data that was gathered.

Off-Axis Co-Optical Path Large-Range Line Scanning Chromatic Confocal Sensor

This article proposes a line scanning chromatic confocal sensor to solve the problem of limited chromatic confocal measurement due to the small measurement range and low measurement efficiency in the industrial inspection process. To obtain an extensive dispersion range, the advantages of a simple single-axis structure are combined with the advantages of a large luminous flux of a biaxial structure. Considering large-scale measurement, our sensor uses off-axis rays to limit the illumination path and imaging path to the same optical path structure. At the same time, the field of view is expanded, and a symmetrical structure is adopted to provide a compact optical path and improve space utilization. The simulation and physical system test results shows that the sensor scanning line length is 12.5 mm, and the axial measurement range in the 450 nm to 750 nm band is better than 20 mm. The axial resolution of the detector is ±1 µm combined with the subpixel centroid extraction data processing method, and the maximum allowable tilt angle for specular reflection samples is ±7°. The thicknesses of transparent standard flat glass and the wet collagen membrane are measured. The maximum average error is 1.3 µm, and the relative error is within 0.7%. The constructed sensor is of great significance for rapidly measuring the three-dimensional profile, flatness, and thickness in the fields of transparent biological samples, optics, micromechanics, and semiconductors.

Adaptive RBF Neural Network Tracking Control of Stochastic Nonlinear Systems with Actuators and State Constraints

This paper investigates the adaptive neural network (NN) tracking control problem for stochastic nonlinear systems with multiple actuator constraints and full-state constraints. The issue of system full-state constraints is tackled by a generalized barrier Lyapunov function (GBLF), and the output constraints of the system are considered to be in the form of time-varying functions, which are more in line with the needs of real physical systems. The NN approximation technique is utilized to overcome the influence of the uncertainty term on controller design due to randomness. Based on the backstepping technique, a neural adaptive fixed-time tracking control strategy is designed. Under the designed control strategy, the tracking accuracy of the controlled system can reach the expectation in a fixed time. The multi-actuator constraints are converted into a generalized mathematical model to simplify the controller design process. Using the characteristics of the hyperbolic tangent function, a new function called practical virtual control signal is designed using the virtual control signal as the input. Due to the saturation constraint property of the hyperbolic tangent function, it is theoretically ensured that no state of the system exceeds the constraints through to the new form of the virtual controller. Using the adaptive controller constructed in this paper, the controlled system is semi-global fixed-time stabilized in probability (SGFSP). Finally, the effectiveness of the proposed control strategy is further verified by simulation examples.

Digital twin for credit card fraud detection: opportunities, challenges, and fraud detection advancements

Credit cards are widely used for payments due to their convenience and broad acceptance. Their popularity comes with the critical challenge of safeguarding personal and payment information from fraud and unauthorized access. Robust security measures are crucial to maintaining trust and confidence among users. In response to this pressing issue, this paper focuses on credit card fraud detection, its challenges, and innovative solutions using digital twins and blockchain. This research highlights the importance of understanding and reducing credit card fraud to protect consumers and financial institutions. The study provides a detailed overview of credit card fraud analysis and categorizes its different types to clarify the threat landscape. It introduces a new digital twin approach to improve fraud detection. Digital twins are virtual replicas of physical systems that show promise for enhancing anomaly detection and behavioral analysis for more precise and timely fraud identification. In addition, the paper examines blockchain-enabled federated learning (BFL) as a decentralized method that uses blockchain’s security features to improve collaborative learning. By merging digital twins with federated learning (FL), the study presents a dynamic strategy for identifying known and emerging fraud patterns effectively. These advanced technologies represent a significant step forward in combating credit card fraud. Overall, the research not only focuses on creating more robust fraud detection systems but also emphasizes the importance of continuous innovation and adaptation to enhance financial security measures.

Construction of Athletes' Physical Condition Monitoring and Analysis System Using Biometrics Recognition Technology

Biometrics recognition technology utilizes unique biological characteristics such as fingerprints, iris patterns, facial features, or voiceprints to identify and authenticate individuals. Through advanced algorithms and pattern recognition techniques, biometric systems capture and analyze these physiological or behavioral traits to verify a person's identity. This technology offers high levels of security and accuracy, making it valuable for various applications including access control, time and attendance tracking, border security, and digital payments. The construction of an athletes' physical condition monitoring and analysis system utilizing biometric recognition technology represents a significant advancement in sports science. By integrating cutting-edge biometric sensors and recognition algorithms, this system provides real-time insights into athletes' physiological parameters and performance metrics. Athlete performance monitoring and analysis play pivotal roles in optimizing training strategies, preventing injuries, and enhancing overall athletic performance. In this paper, we propose a novel approach leveraging the Hybrid Multi-Instance Ensemble Classifier (HMIEC) combined with biometric recognition technology to accurately classify athlete data and assess their physical condition. Our study explores the effectiveness of HMIEC across multiple biometric modalities, including ECG-based, fingerprint-based, and facial recognition, in identifying athletes and monitoring their physical parameters. Through a series of experiments and analyses, demonstrate HMIEC's superior classification performance compared to other classifiers, with high accuracy, sensitivity, specificity, and area under the curve (AUC). The reductions in heart rate from 75 to 65 beats per minute, increase in oxygen saturation levels from 98% to 99%, decreases in blood pressure readings from 120/80 mmHg to 110/70 mmHg, and enhancements in flexibility from 30 to 35 centimeters, muscle strength from 100 lbs to 120 lbs, and endurance capacity from 20 to 25 minutes.

Designing Variational Ansatz for Quantum‐Enabled Simulation of Non‐Unitary Dynamical Evolution ‐ An Excursion into Dicke Supperradiance

AbstractAdaptive Variational Quantum Dynamics (AVQD) algorithms offer a promising approach to providing quantum‐enabled solutions for systems treated within the purview of open quantum dynamical evolution. In this study, the unrestricted‐vectorization variant of AVQD is employed to simulate and benchmark various non‐unitarily evolving systems. Exemplification of how construction of an expressible ansatz unitary and the associated operator pool can be implemented to analyze examples such as the Fenna–Matthews–Olson complex (FMO) and even the permutational invariant Dicke model of quantum optics. Furthermore, an efficient decomposition scheme is shown for the ansatz used, which can extend its applications to a wide range of other open quantum system scenarios in near future. In all cases the results obtained are in excellent agreement with exact numerical computations that bolsters the effectiveness of this technique. The successful demonstrations pave the way for utilizing this adaptive variational technique to study complex systems in chemistry and physics, like light‐harvesting devices, thermal, and opto‐mechanical switches, to name a few.