Real-world Systems Research Articles

Joint Fault Diagnosis of IGBT and Current Sensor in LLC Resonant Converter Module Based on Reduced Order Interval Sliding Mode Observer

LLC resonant converters have emerged as essential components in DC charging station modules, thanks to their outstanding performance attributes such as high power density, efficiency, and compact size. The stability of these converters is crucial for vehicle endurance and passenger experience, making reliability a top priority. However, malfunctions in the switching transistor or current sensor can hinder the converter’s ability to maintain a resonant state and stable output voltage, leading to a notable reduction in system efficiency and output capability. This article proposes a fault diagnosis strategy for LLC resonant converters utilizing a reduced-order interval sliding mode observer. Initially, an augmented generalized system for the LLC resonant converter is developed to convert current sensor faults into generalized state vectors. Next, the application of matrix transformations plays a critical role in decoupling open-circuit faults from the inverter system’s state and current sensor faults. To achieve accurate estimation of phase currents and detection of current sensor faults, a reduced-order interval sliding mode observer has been designed. Building upon the estimation results generated by this observer, a diagnostic algorithm featuring adaptive thresholds has been introduced. This innovative algorithm effectively differentiates between current sensor faults and open switch faults, enhancing fault detection accuracy. Furthermore, it is capable of localizing faulty power switches and estimating various types of current sensor faults, thereby providing valuable insights for maintenance and repair. The robustness and effectiveness of the proposed fault diagnosis algorithm have been validated through experimental results and comparisons with existing methods, confirming its practical applicability in real-world inverter systems.

Advanced Design Validation Planning Techniques for Commercial Vehicle E-Axles

<div>Design validation plays a crucial role in the overall cost and time allocation for product development. This is especially evident in high-value manufacturing sectors like commercial vehicle electric drive systems or e-axles, where the expenses related to sample procurement, testing complexity, and diverse requirements are significant.</div> <div>Validation methodologies are continuously evolving to encompass new technologies, yet they must be rigorously evaluated to identify potential efficiencies and enhance the overall value of validation tests. Simulation tools have made substantial advancements and are now widely utilized in the development phase. The integration of simulation-based or simulation-supported validation processes can streamline testing timelines and sample quantities, all the while upholding quality standards and minimizing risks when compared to traditional methods. This study examines various scenarios where the implementation of advanced techniques has led to a reduction in the e-axle design validation plan (DVP), particularly in relation to the start of production (SOP) decision-making quality gates.</div> <div>Many DVPs incorporate tests in a specific order to enhance the validation impact of each sample and to guarantee that preconditioning tests are implemented when necessary. An instance of preconditioning is subjecting a system to thermal cycling. Thermal cycle preconditioning ensures that the sample accurately represents a real-world system when undergoing sealing-related tests, such as ingress protection testing according to ISO 20653 [<span>1</span>]. Assessing the necessary thermal cycling to “bed-in” a sample would be highly elaborate using a physical sample, but it is a straightforward aspect to extract from modern finite element analysis (FEA). A case study will be presented to evaluate the minimum required thermal cycling and determine the cost savings for the DVP.</div> <div>Furthermore, the validation supported by simulations will be showcased in the context of accelerated lifetime testing. Often, accelerating such tests necessitates a balance between the distribution of damages and the overall testing duration. By incorporating safety factors from simulations as an initial input, the distribution of damages can be adjusted to allow for some over-testing on more secure components, thereby reducing the overall testing time.</div> <div>Efficiency assessment will additionally be considered for virtual validation due to the complexity of the measurement and data acquisition, contributing to measurement uncertainty. Furthermore, simulation-based validation will be considered for the case of lubrication testing, which can be hampered by a largely qualitative assessment criteria.</div> <div>Overall, these scenarios will be evaluated for their potential to streamline the overall DVP process in terms of testing time, sample quantity, and critical path duration. This underscores the potential benefits of leveraging advanced DVP techniques for cost and time savings.</div>

Stepanov-like Pseudo S-Asymptotically (ω,c)-Periodic Solutions of a Class of Stochastic Integro-Differential Equations

The study of long-term behavior in stochastic systems is critical for understanding the dynamics of complex processes influenced by randomness. This paper addresses the existence and uniqueness of Stepanov-like pseudo S-asymptotically (ω,c)-periodic solutions for a class of stochastic integro-differential equations. These equations model systems where the interplay between deterministic and stochastic components dictates the overall dynamics, making periodic analysis essential. The problem addressed in this study is the lack of a comprehensive framework to describe the periodic behavior of such systems in noisy environments. To tackle this, we employ advanced techniques in stochastic analysis, fixed-point theorems and the properties of L1- and L2-convolution kernels to establish conditions for the existence and uniqueness of mild solutions under these extended periodicity settings. The methodology involves leveraging the decay properties of the operator kernels and the boundedness of stochastic integrals to ensure well-posedness. The major outputs of this study include novel results on the existence, uniqueness and stability of Stepanov-like pseudo S-asymptotically (ω,c)-periodic solutions, along with illustrative example demonstrating their applicability in real-world stochastic systems.

Susceptible-infected-recovered-susceptible processes competing on simplicial complexes

Contagions do not exist in isolation but in the presence of other propagating quantities, with which they may compete. Furthermore, higher-order interactions (interactions involving more than two nodes) are widely represented in real-world systems. We propose a stochastic model for susceptible-infected-recovered-susceptible (SIRS) processes competing in a simplicial complex that accommodates higher-order interactions. We also propose deterministic microscopic Markov chain (MMC) and mean-field (MF) forms of this model, which we analyze to reveal conditions for the persistence of each infection. We verify our analysis via numerical simulations, which also unveil critical mass effects, discontinuous phase transitions, and bistability. The stochastic model exhibits eight distinct classes of dependence of final state on initial conditions. All eight of these classes can be approximately reproduced with MMC, but only seven are seen under MF. The class observed in MMC and approximated in the stochastic model, but missing from MF, involves three distinct steady states for a single set of parameter values. However, in the absence of higher-order interactions MF exhibits an additional class, not observed in MMC or the stochastic model, corresponding to an infinite spectrum of steady states, in which the ratio of infection levels remains at its initial level. Both MMC and MF models can exhibit sustained oscillations in which one contagion disappears while the other tends to a limit cycle. Published by the American Physical Society 2024

Applying closed-loop equations for kinematic analysis of an eight-bar walking mechanism

Bionic mechanisms are used in constructing robotic systems that perform complex tasks similar to the movement of animals. With advancements in scientific research and breakthroughs in simulation techniques and computational methods, the design of these systems has become increasingly detailed and refined. This paper proposes using the closed-loop method to analyze the operational characteristics of a wheelless walking mechanism designed to traverse complex terrains. In addition to evaluating fundamental factors affecting system operation, the study determines kinematic parameters such as leg trajectory, movement velocity, and tilt angle. The results demonstrate the effectiveness of numerical methods in analyzing and simulating real-world systems, thus extending the use of computational tools for analyzing similar linkages.

A hybrid dynamic graph neural network framework for real-time anomaly detection

ABSTRACT The timely and robust detection of anomalies is essential for resilient and secure operations of critical water infrastructures against operational faults or malicious actions. However, real-world systems exhibit diverse and evolving spatiotemporal relationships among their components, posing an intricate challenge in anomaly detection task. This study proposes a hybrid dynamic graph neural network (GNN) that jointly maps long- and short-term spatiotemporal relationships in multivariate data streams. Those relationships are encoded via a hybrid graph, comprising an optimally learned static subgraph for persistent structural relationships and a complementary dynamic subgraph for dynamically shifting relationships. Additionally, an attention mechanism captures time-varying relational importances and shifts the model's focus towards significant relationships, while minimising contributions of less importance to the final outputs. The proposed architecture is showcased through a synthetic case study of a water distribution system with multivariate data streams from both on-site and soft sensors, and shows strong detection and localisation accuracy against all scenarios of operational faults and malicious actions explored. A comparative analysis with an equivalent static graph model indicates that the addition of the hybrid dynamic component enhances detection accuracy and reduces false alarm rates through more robust characterisation of behaviours, thus allowing actionable insights for more resilient and secure operations.

Adaptive connectivity control in networked multi-agent systems: A distributed approach.

Effective communication is crucial for the performance and collaboration within cooperative networked multi-agent systems. However, existing literature lacks comprehensive solutions for dynamically monitoring and adjusting communication topologies to balance connectivity and energy efficiency. This study addresses this gap by proposing a distributed approach for estimating and controlling system connectivity over time. We introduce a modified consensus protocol where agents exchange local assessments of communication link quality, enabling the estimation of a global weighted adjacency matrix without requiring centralized information. The system's connectivity is measured using the second smallest eigenvalue of the communication graph Laplacian, commonly referred to as algebraic connectivity. Additionally, we enhance the consensus protocol with an adaptive mechanism to expedite convergence, irrespective of system size or structure. Furthermore, we present an analytical method for connectivity control based on the Fiedler vector approximation, facilitating the addition or removal of communication links. This method adjusts control parameters to accommodate minor variations in link quality while reconfiguring the network in response to significant changes. Notably, it identifies and eliminates energy-consuming yet non-contributory links, improving long-term connectivity efficiency. Simulation experiments across diverse scenarios and the number of agents validate the efficacy of our proposed algebraic connectivity estimation and tracking strategy. Results demonstrate robust connectivity maintenance against external disturbances and agent failures, underscoring the practical utility of our approach for real-world multi-agent systems.

Efficient multi-agent reinforcement learning HVAC power consumption optimization

This work proposed a novel reinforcement learning (RL) approach deep deterministic policy gradient with multi-stabilization network (MADDPG-MSN). It enhanced the sample efficiency of RL in heating, ventilation, and air conditioning (HVAC) power consumption optimization. Employing the multi-stabilization network trick, MADDPG-MSN efficiently learned to balance temperature control and power consumption with a limited number of interactions, expanding the potential of RL in the power consumption optimization of real-world HVAC systems. Evaluated by the simulated data center scenario, it reduced 28% power usage without compromising temperature control capability compared with the traditional model-predictive controller. In the real-world air conditioner testing, it demonstrated a similar control performance of indoor temperature to the built-in controller while consuming 32% less power.

Siamese neem tree as a natural preservative: Chemical profile, antioxidant properties, and antibacterial efficacy against foodborne pathogens and spoilage bacteria

The objective of the present study was to investigate the effect of various ethanol in water solvent systems (0, 25, 50, 75 and 100% v/v) on the extraction crude yield, total phenolic content, total flavonoid content, and chemical profile of Azadirachta indica (Siamese neem tree) leaf extracts. In addition, the antioxidant and antimicrobial properties were investigated using in vitro assays. Crude yield was highest for the water extract (5.78 g/100 g DW) and decreased with increased ethanol. Total phenolic and flavonoid contents were highest in the 75% ethanol extract (26.95 mg GAE/g crude and 23.73 mg QE/g crude, respectively). GC-MS analysis of the 75% ethanol extract revealed 33 bioactive compounds, accounting for 96.33% of the total area. Significant components included benzene, 1,4-bis(phenylmethyl)- (15.05%), and naphthalene, 1,6-dimethyl- (10.98%); also present were sterols, fatty acids, hydrocarbons, aromatic, and heterocyclic compounds, indicating a complex chemical profile. In antioxidant assays, the 75% ethanol extract revealed the highest DPPH radical scavenging activity (IC50: 143.03 mg/L), reducing power (IC50: 378.69 mg/L), and lipid peroxidation (IC50: 526.25 mg/L). However, the absolute ethanol extract exhibited both the highest metal chelating activity (IC50: 382.60 mg/L) and the strongest antimicrobial activity, inhibiting all 11 tested foodborne pathogenic and spoilage bacteria. The lowest minimum inhibitory concentration (MIC) of 25 mg/mL was observed for Staphylococcus aureus, Aeromonas hydrophila, Pseudomonas fluorescens, Latilactobacillus sakei, Streptococcus sp., and Lactococcus cremoris, with inhibition zone diameters of 27.21, 28.79, 28.19, 17.60, 15.09 and 11.20 mm, respectively. Similarly, the lowest minimum bactericidal concentration (MBC) of 25 mg/mL was obtained for S. aureus and L. sakei, indicating potent antibacterial effects against these species. Future studies should include direct comparative analyses of neem extracts and synthetic preservatives to evaluate further their relative efficacy and applicability in real-world food systems.

MINN: Learning the Dynamics of Differential-Algebraic Equations and Application to Battery Modeling.

The concept of integrating physics-based and data-driven approaches has become popular for modeling sustainable energy systems. However, the existing literature mainly focuses on the data-driven surrogates generated to replace physics-based models. These models often trade accuracy for speed but lack the generalizability, adaptability, and interpretability inherent in physics-based models, which are often indispensable in modeling real-world dynamic systems for optimization and control purposes. We propose a novel machine learning architecture, termed model-integrated neural networks (MINN), that can learn the physics-based dynamics of general autonomous or non-autonomous systems consisting of partial differential-algebraic equations (PDAEs). The obtained architecture systematically solves an unsettled research problem in control-oriented modeling, i.e., how to obtain optimally simplified models that are physically insightful, numerically accurate, and computationally tractable simultaneously. We apply the proposed neural network architecture to model the electrochemical dynamics of lithium-ion batteries and show that MINN is extremely data-efficient to train while being sufficiently generalizable to previously unseen input data, owing to its underlying physical invariants. The MINN battery model has an accuracy comparable to the first principle-based model in predicting both the system outputs and any locally distributed electrochemical behaviors but achieves two orders of magnitude reduction in the solution time.

A velocity-domain MAPPO approach for perimeter defensive confrontation by USV groups

This study investigates the utilization of Multi-Agent Deep Reinforcement Learning (MADRL) in Perimeter Defensive Confrontation (PDC) by USV groups. We propose a novel approach called Velocity-Domain Multi-Agent Proximal Policy Optimization (VD-MAPPO), which integrates the MAPPO framework with velocity-domain confrontation analysis. It addresses the challenge of sparse rewards by introducing a velocity-domain advantage estimation model to predict advantageous velocities for short-term action assessment. The incorporation of velocity-domain expressions in the advantage estimation model enables the consideration of kinematic constraints, enhancing alignment with real-world USV systems. Additionally, the velocity-domain analysis unifies action and immediate reward spaces within the velocity domain, thereby promoting stability and convergence during network training. Through extensive experiments in adversarial naval warfare scenarios, we demonstrate the efficacy of VD-MAPPO, showcasing its superiority over traditional distance-based MADRL approaches.

Learning in practice: reinforcement learning-based traffic signal control augmented with actuated control

ABSTRACT Most Reinforcement Learning (RL) based Traffic Signal Control (TSC) models are trained in simulation platforms, which inevitably suffer from the performance degradation after deployment due to the mismatch between the traffic simulator and the real-world traffic system. Toward the real-world training of TSC agents, this study integrates the actuated control into the RL-based TSC framework as a defense mechanism, enabling the traffic system to remain resilient to suboptimal actions of agent during the real-world training process. The introduction of actuated control can significantly reduce the green time wastes due to unreasonable timing plans. The numerical results demonstrate that the proposed approach can reduce interferences to the traffic system operation during the real-world training of TSC agents, particularly at the early stage of agent learning, thereby promoting the practical deployment of RL-based TSC models. Meanwhile, agents can converge to better signal policies with the help of defense mechanism.

Advanced threshold signature schemes leveraging isogeny-based cryptography

This paper investigates the use of threshold signature schemes in isogeny-based cryptosystems. By leveraging the distinct features of isogeny graphs, we propose a protocol that offers high security and practical efficiency, making it ideal for distributed ledger technologies and secure multi-party computations. Our scheme is resistant to quantum attacks and maintains minimal computational and communication overheads. We present an extensive analysis of the security and performance of our protocol, emphasizing its relevance to real-world cryptographic systems. MSC 2020: 94A60, 11G07, 68M07.

Distributed voltage control for multi-feeder distribution networks considering transmission network voltage fluctuation based on robust deep reinforcement learning

In the multi-feeder distribution network, the power balance between photovoltaics generations and load demands across regions is more complex. To solve the above problems, this paper proposes a multi-agent distributed voltage control strategy based on robust deep reinforcement learning to reduce voltage deviation. The whole multi-feeder distribution network is divided into a main agent and several sub-agents, and a multi-agent distributed voltage control model considering the transmission network voltage fluctuations and the corresponding power fluctuations is established. Based on the information uploaded by sub-agents, the main agent models the uncertainty of the transmission network voltage fluctuations and the corresponding power fluctuations as a disturbance to the state, and a RDRL method is employed to determine the tap position of on-load tap changer. Furtherly, each sub-agent uses the second-order cone relaxation technique to adjust the reactive power outputs of the inverters on each feeder. The effectiveness of the proposed method has been verified in two real-world multi-feeder systems. The results show that the proposed method can achieve millisecond-level decision-making, with a voltage deviation only 1.28 % higher than the global optimal results, achieving near-optimal control. The proposed method also demonstrates robustness in handling transmission network uncertainties and partial measurement loss.

Hysteresis in a generalized Kuramoto model with a first-order approximate coupling function and inhomogeneous coupling strengths

We investigate hysteresis in a generalized Kuramoto model with identical oscillators, focusing on coupling strength inhomogeneity, which results in oscillators being coupled to others with varying strength, and a first-order approximation of general coupling functions. With the coupling function and the coupling strength inhomogeneity, each oscillator acquires an effective intrinsic frequency proportional to its individual coupling strength. This is analogous to the positive coupling strength-frequency correlation introduced explicitly or implicitly in some previous models with nonidentical oscillators that show explosive synchronization and hysteresis. Through numerical simulations and analysis using truncated Gaussian, uniform, and truncated power-law coupling strength distributions, we observe that the system can exhibit abrupt phase transitions and hysteresis. The distribution of coupling strengths significantly affects the hysteresis regions within the parameter space of the coupling function. Additionally, numerical simulations of models with weighted networks including a brain network confirm the existence of hysteresis due to the first-order approximate coupling function and coupling strength inhomogeneity, suggesting the broad applicability of our findings to complex real-world systems.

Integration of BIM and GIS for the Digitization of the Built Environment

The integration of Building Information Modelling (BIM) and Geographic Information Systems (GIS) is a growing reality in the building production sector. Through this integration, it is possible to improve the efficiency of management, maintenance, use and planning of conservation operations, providing an integrated and dynamic vision of the built environment. Simultaneous exchange of BIM-GIS elements in a shared environment facilitates information access and optimizes processes like requalification, activity planning, safety and sustainable urban design. Two alternative strategies are proposed for the multidisciplinary approach, using advanced technologies to acquire, process and manage detailed and georeferenced data. The first one is an open-source environment to guarantee flexibility, customization and accessibility. The second option, in a closed-source environment, provides advanced functionalities and dedicated support. Both require careful planning, detailed analysis and collaboration between the disciplines of architecture, engineering and geoinformatics. The study transcends theoretical analysis by exploring practical implications for real-world systems integration, examining their advantages, limitations and potential synergies in terms of flexibility, security and sustainability. This will enable a more efficient and comprehensive management of the architectural heritage and the built environment, contributing to its preservation and enhancement in the context of the digital transition in a future perspective of smart cities.

Existence Results and Trajectory Controllability of Conformable Hilfer Fractional Neutral Stochastic Integro-differential Equations

This manuscript commits to analyzing the existence and uniqueness of mild solution and trajectory controllability of conformable Hilfer fractional neutral stochastic integro-differential system with infinite delay through Lipschitz, growth conditions and properties of semigroup theory, controllability theory, stochastic analysis techniques, and Banach fixed point theorem with example. This manuscript mainly focuses on fractional system provide the mathematical foundation to understand and control complex, real-world systems with memory, and randomness.

Evolutionary dynamics in stochastic nonlinear public goods games

Understanding the evolution of cooperation in multi-player games is of vital significance for natural and social systems. An important challenge is that group interactions often lead to nonlinear synergistic effects. However, previous models mainly focus on deterministic nonlinearity, where synergy or discounting effects occur under specific conditions, not accounting for uncertainty and stochasticity in real-world systems. Here, we develop a probabilistic framework to study the cooperative behavior in stochastic nonlinear public goods games. Through both analytical treatment and Monte Carlo simulations, we provide a comprehensive understanding of social dilemmas with stochastic nonlinearity in both well-mixed and structured populations. We find that increasing the degree of nonlinearity makes synergy more advantageous when competing with discounting, thereby promoting cooperation. Furthermore, we show that network reciprocity loses effectiveness when the probability of synergy is small. Moreover, group size exhibits nonlinear effects on group cooperation regardless of the underlying structure. Our findings thus provide insights into how stochastic nonlinearity influences the emergence of prosocial behavior.

Reliability analysis of (r,s)-out-of-n multi-state systems using copulas

PurposeThis study evaluates the reliability of a multi-state system (MSS) with n components, each having two s-dependent components via copulas.Design/methodology/approachThe study employs copula functions to model dependencies between components in an MSS. Specifically, it analyzes a (1,1)-out-of-n three-state system using Frank and Clayton copulas for reliability evaluation. A simulation-based case study of a micro-inverter solar panel system is also conducted using the Farlie–Gumbel–Morgenstern (FGM) copula.FindingsThe study finds that incorporating component dependencies significantly impacts the reliability of multi-state systems. Using Frank and Clayton copulas, the analysis shows how dependency structures alter system performance compared to independent models. The case study on a micro-inverter solar panel system, using the FGM copula, demonstrates that real-world systems with dependent components exhibit different performances. Also some effects of dependence parameters on the performance characteristics of the system such as mean residual lifetime and mean past lifetime are also examined.Originality/valueThis study is original in its use of copula functions to evaluate the performance of multi-state systems, particularly focusing on a (1,1)-out-of-n three-state system with dependent components. By applying Frank and Clayton copulas, the research advances reliability analysis by considering component dependencies, often overlooked in traditional models. Additionally, a case study on a micro-inverter solar panel system using the FGM copula highlights the practical application of these methods.

Transient and Steady-State Evaluation of Distributed Generation in Medium-Voltage Distribution Networks

As power generation systems with increasingly higher capacities are interconnected with distribution networks, a pressing need arises for a thorough analysis of their integration and the subsequent impacts on medium-voltage lines. This study conducts a comprehensive evaluation, encompassing both steady-state and transient behaviours, leading to a holistic assessment of a real-world biogas generation system integrated into a medium-voltage network. Although the methodology does not introduce revolutionary concepts, its detailed application on a real feeder under various operating conditions adds practical value to the existing body of knowledge. The methodology explores various aspects, including voltage profiles, load capacity, power losses, short-circuit currents, and protection coordination in steady-state conditions. Additionally, a transient analysis is performed to examine the system’s response under fault conditions. This systematic approach provides a deep understanding of the system’s behaviour across diverse operational scenarios, enriching the field with practical insights. The key contributions of this study include identifying the effects of distributed generation systems (DGSs) on short-circuit currents, protection coordination, and defining voltage levels that briefly exceed the CBEMA quality curve. The benefits of incorporating a generation system into a distribution network are discussed from various technical perspectives. In a peak demand scenario, with a 1.72 MW generation capacity, the phase current experiences a notable reduction of 35.78%. Concurrently, the minimum peak demand voltage increases from 12.62 to 12.83 kV compared to a nominal voltage of 12.7 kV. Furthermore, the contribution of the generation system to the short-circuit current remains minimal, staying below 4% even under the most adverse conditions. However, our findings reveal that voltage levels exceed the upper limit of the CBEMA quality curve briefly during a single-phase fault with generation, which could potentially damage electronic equipment connected to the grid. Nonetheless, the likelihood of encountering a single-phase grounding fault with zero resistance remains low.