arXiv subjects

Ruizhe Yang

Publications and source records attributed to Ruizhe Yang.

8 recordsLinked to original sources

arXiv2026

Model-Driven Discipline for Multi-Agent LLMs: Requirement-to-Verification Generation of Traceable System Models

Software complexity is a long-standing challenge for system engineers. Model-Driven Engineering (MDE) addresses it by treating models as first-class artefacts, but a typical MDE process spans many tools and produces heterogeneous models of different system aspects, making traceability, maintenance, and change management difficult. We propose RADIANT, an engineering methodology that combines MDE with Multi-Agent Large Language Models (LLMs) for complete model-based system development, with a focus on safety-critical systems. From a carefully specified requirement model, RADIANT automatically generates heterogeneous models across engineering phases -- a concept model, a domain-specific modelling language, a conforming system model, and a behaviour model -- together with executable, element-level traceability links, on top of which it provides exact, automated change-impact analysis. Generated behaviour models are translated into CSP and formally verified (e.g.\ for deadlock freedom and convergence) with a counterexample-driven repair loop. Evaluating RADIANT across three LLMs, we find that the multi-agent decomposition reliably improves the \emph{syntactic validity} of the generated formal artefacts over a single-agent baseline -- and their \emph{executability} where the model's code generation permits -- while gains in semantic accuracy are model-dependent. A six-participant study shows an order-of-magnitude ($10$--$15\times$) reduction in development time, and the unmodified pipeline transfers to a second domain.

cs.SE↗

arXiv2026

Spatially and Temporally Resolved Mapping of Contact Electrification on Stand-Alone Ultrathin Glass Materials via Kelvin Probe Force Microscopy

Contact electrification (CE) remains a critical challenge in advanced material technologies where uncontrolled surface charging can compromise manufacturability, reliability, and performance in practical applications. Ultrathin glass with micrometer-scale thickness is a state-of-the-art specialty oxide material for flexible touchscreens in next-generation electronic devices. Here, we visualize and quantify CE-induced surface charges on ultrathin glass using sideband-mode Kelvin probe force microscopy (KPFM). Nanoscale atomic force microscopy (AFM) probes are used to scan and induce triboelectric charges on stand-alone glass surfaces under ultra-pure N$_2$ conditions. Time-dependent measurements reveal that surface charges on a 30~$\mu$m-thick glass sample decay from 4.47~V to 0.37~V over 240~minutes. Furthermore, electrostatic charges are found to exhibit capacitor-like discharging behavior primarily through the bulk material, yielding a long relaxation time constant of approximately 41~minutes. This behavior differs from the lateral surface discharging observed in thermally grown SiO$2$ thin films reported previously. A self-capacitance analytical model is developed to estimate the corresponding surface charge density ($\sigma$), yielding comparable values of 136.26~$\pm$~16.25~$\mu$C/m$^2$ at 30~$\mu$m and 131.44~$\pm$~28.41~$\mu$C/m$^2$ at 100~$\mu$m. Additionally, external bias applied to AFM tips can be used to enhance, suppress, or invert the intrinsic CE response of glass materials.

physics.app-ph↗

arXiv2025

Adaptive Multi-Objective Bayesian Optimization for Capacity Planning of Hybrid Heat Sources in Electric-Heat Coupling Systems of Cold Regions

The traditional heat-load generation pattern of combined heat and power generators has become a problem leading to renewable energy source (RES) power curtailment in cold regions, motivating the proposal of a planning model for alternative heat sources. The model aims to identify non-dominant capacity allocation schemes for heat pumps, thermal energy storage, electric boilers, and combined storage heaters to construct a Pareto front, considering both economic and sustainable objectives. The integration of various heat sources from both generation and consumption sides enhances flexibility in utilization. The study introduces a novel optimization algorithm, the adaptive multi-objective Bayesian optimization (AMBO). Compared to other widely used multi-objective optimization algorithms, AMBO eliminates predefined parameters that may introduce subjectivity from planners. Beyond the algorithm, the proposed model incorporates a noise term to account for inevitable simulation deviations, enabling the identification of better-performing planning results that meet the unique requirements of cold regions. What's more, the characteristics of electric-thermal coupling scenarios are captured and reflected in the operation simulation model to make sure the simulation is close to reality. Numerical simulation verifies the superiority of the proposed approach in generating a more diverse and evenly distributed Pareto front in a sample-efficient manner, providing comprehensive and objective planning choices.

eess.SY↗

arXiv2024

Noise-Aware Bayesian Optimization Approach for Capacity Planning of the Distributed Energy Resources in an Active Distribution Network

The growing penetration of renewable energy sources (RESs) in active distribution networks (ADNs) leads to complex and uncertain operation scenarios, resulting in significant deviations and risks for the ADN operation. In this study, a collaborative capacity planning of the distributed energy resources in an ADN is proposed to enhance the RES accommodation capability. The variability of RESs, characteristics of adjustable demand response resources, ADN bi-directional power flow, and security operation limitations are considered in the proposed model. To address the noise term caused by the inevitable deviation between the operation simulation and real-world environments, an improved noise-aware Bayesian optimization algorithm with the probabilistic surrogate model is proposed to overcome the interference from the environmental noise and sample-efficiently optimize the capacity planning model under noisy circumstances. Numerical simulation results verify the superiority of the proposed approach in coping with environmental noise and achieving lower annual cost and higher computation efficiency.

cs.NE↗

arXiv2024

MESC: Re-thinking Algorithmic Priority and/or Criticality Inversions for Heterogeneous MCSs

Modern Mixed-Criticality Systems (MCSs) rely on hardware heterogeneity to satisfy ever-increasing computational demands. However, most of the heterogeneous co-processors are designed to achieve high throughput, with their micro-architectures executing the workloads in a streaming manner. This streaming execution is often non-preemptive or limited-preemptive, preventing tasks' prioritisation based on their importance and resulting in frequent occurrences of algorithmic priority and/or criticality inversions. Such problems present a significant barrier to guaranteeing the systems' real-time predictability, especially when co-processors dominate the execution of the workloads (e.g., DNNs and transformers). In contrast to existing works that typically enable coarse-grained context switch by splitting the workloads/algorithms, we demonstrate a method that provides fine-grained context switch on a widely used open-source DNN accelerator by enabling instruction-level preemption without any workloads/algorithms modifications. As a systematic solution, we build a real system, i.e., Make Each Switch Count (MESC), from the SoC and ISA to the OS kernel. A theoretical model and analysis are also provided for timing guarantees. Experimental results reveal that, compared to conventional MCSs using non-preemptive DNN accelerators, MESC achieved a 250x and 300x speedup in resolving algorithmic priority and criticality inversions, with less than 5\% overhead. To our knowledge, this is the first work investigating algorithmic priority and criticality inversions for MCSs at the instruction level.

cs.AR↗

arXiv2024

Towards an Extensible Model-Based Digital Twin Framework for Space Launch Vehicles

The concept of Digital Twin (DT) is increasingly applied to systems on different levels of abstraction across domains, to support monitoring, analysis, diagnosis, decision making and automated control. Whilst the interest in applying DT is growing, the definition of DT is unclear, neither is there a clear pathway to develop DT to fully realise its capacities. In this paper, we revise the concept of DT and its categorisation. We propose a DT maturity matrix, based on which we propose a model-based DT development methodology. We also discuss how model-based tools can be used to support the methodology and present our own supporting tool. We report our preliminary findings with a discussion on a case study, in which we use our proposed methodology and our supporting tool to develop an extensible DT platform for the assurance of Electrical and Electronics systems of space launch vehicles.

cs.SE↗

arXiv2024

ACCESS: Assurance Case Centric Engineering of Safety-critical Systems

Assurance cases are used to communicate and assess confidence in critical system properties such as safety and security. Historically, assurance cases have been manually created documents, which are evaluated by system stakeholders through lengthy and complicated processes. In recent years, model-based system assurance approaches have gained popularity to improve the efficiency and quality of system assurance activities. This becomes increasingly important, as systems becomes more complex, it is a challenge to manage their development life-cycles, including coordination of development, verification and validation activities, and change impact analysis in inter-connected system assurance artifacts. Moreover, there is a need for assurance cases that support evolution during the operational life of the system, to enable continuous assurance in the face of an uncertain environment, as Robotics and Autonomous Systems (RAS) are adopted into society. In this paper, we contribute ACCESS - Assurance Case Centric Engineering of Safety-critical Systems, an engineering methodology, together with its tool support, for the development of safety critical systems around evolving model-based assurance cases. We show how model-based system assurance cases can trace to heterogeneous engineering artifacts (e.g. system architectural models, system safety analysis, system behaviour models, etc.), and how formal methods can be integrated during the development process. We demonstrate how assurance cases can be automatically evaluated both at development and runtime. We apply our approach to a case study based on an Autonomous Underwater Vehicle (AUV).

cs.SE↗

arXiv2009

Flavor Asymmetry of the Nucleon Sea and W Boson Production

The advantage and feasibility of using $W$-boson production to extract unique information on the flavor asymmetry of the $\bar u$ and $\bar d$ sea-quark distributions in the proton are examined. The $W^+$ and $W^-$ production cross section ratios in $p+p$ collisions are shown to be sensitive to the $\bar d/ \bar u$ ratios, and they are free from charge-symmetry-breaking and nuclear-binding effects. The feasibility for measuring these ratios at the RHIC and LHC proton-proton colliders, as well as the expected sensitivity to the $\bar d/ \bar u$ ratios, are also presented.

nucl-ex↗