Search Results (1,491)

In embodied-intelligence Industrial Internet of Things (IIoT), multi-AGV intelligent warehousing requires continuous processing of latency-sensitive tasks, such as environmental perception, inventory monitoring, and anomaly detection. Due to limited onboard computing capability and energy capacity, purely local execution can hardly satisfy real-time requirements, whereas fully cloud-based processing may incur excessive transmission delay and backhaul overhead. To address this issue, this paper investigates the joint optimization of AGV service-point migration and task offloading under a cloud-edge-end collaborative architecture. Considering the impact of service-point selection on wireless access, MEC resources, movement delay, and energy consumption, as well as the effect of offloading decisions on transmission, computation, and AGV-side energy cost, a dual-time-scale optimization model is formulated to minimize the long-term accumulated system delay while satisfying task latency and AGV energy constraints. To solve the resulting mixed discrete problem, a DPSO-MAPPO algorithm is proposed, where DPSO searches service-point plans satisfying movement and conflict constraints at the slow time scale, and MAPPO learns coordinated multi-AGV offloading policies at the fast time scale. The delay and energy feedback further enables coordination between the two types of decisions. Simulation results show that the proposed algorithm converges stably, reduces system delay by 13.55% compared with benchmark algorithms, and improves total energy consumption and energy-violation control. Full article

In the context of the “dual-carbon” target, the large-scale integration of renewable energy sources leads to an increased risk of transient voltage instability at the high voltage direct current (HVDC) transmission receiving end. The HVDC transmission system possesses fast and accurate power regulation capability. After a fault occurs near the inverter station, reducing the DC current enables the reactive power from the compensation devices to be released and injected into the receiving-end power grid, thereby providing emergency voltage support for the receiving-end grid. To reduce control costs, an optimization model constrained by transient voltage violation is established, and the DC current modulation is acquired via an online solution. To maintain system stability and meet the requirements of online applications, it is crucial to rapidly solve the optimization model based on the grid operating mode and contingency information to update the emergency control strategy table in the special protection system (SPS). Conventional global orthogonal collocation (GOC) and adaptive orthogonal collocation (AOC)-based solution methods transform the optimization model in the continuous time domain into a nonlinear programming (NLP) problem for solution, which addresses the low efficiency of traditional rolling optimization. However, the GOC- and AOC-based solution methods improve the discretization accuracy of the model by pursuing global uniform densification of collocation points, making it difficult to balance solution accuracy and solution efficiency. To this end, this paper proposes an efficient interval partition dynamic adaptive orthogonal collocation (IP-DAOC)-based solution method. Firstly, the overall optimization time window is interval-partitioned into multiple initial intervals, and an interval-partitioned transient voltage stability emergency control optimization model is established. Furthermore, the interval length and the number of collocation points are dynamically adjusted according to the curvature of interpolation polynomials at collocation points in different intervals. Finally, after interval adjustment, the dynamic equations discretized in adjacent intervals are made continuous by reconstructing the differential matrix. This solution method reduces the total number of collocation points, thereby decreasing the scale of the NLP problem and narrowing the search space, significantly improving solution efficiency while ensuring solution accuracy. To verify the effectiveness of the proposed solution method, simulations are carried out on a modified IEEE 14-bus system. The results are compared with those of the traditional GOC- and AOC-based solution methods, which further demonstrate the superiority of the proposed solution method. Full article

Small unmanned aerial vehicles are difficult short-range radar targets because their millimeter-wave radar cross-sections often fall between −10 and −25 dBsm. This paper presents a modular analytical and simulation-based benchmark of a cascaded 77 GHz TDM-MIMO FMCW radar with 12 transmitters and 16 receivers, yielding a 192-element virtual ULA over a 40 m instrumented range. The framework is organized around the main counter-UAV sensing functions: range–Doppler processing first evaluates target observability and provides range–Doppler gates; Doppler-dependent TDM phase compensation is then required before virtual-array snapshots are formed for DoA estimation; and a separate long-dwell single-transmitter branch evaluates micro-Doppler separability using handcrafted features and a nearest-centroid Mahalanobis classifier. Four benchmarks are considered: detection under Swerling fluctuation models, residual TDM phase error caused by Doppler quantization, DoA estimation under an idealized far-field snapshot model, and micro-Doppler separability among UAV and bird classes. Under Swerling I, targets with a mean RCS of $-10$ dBsm or larger maintain detection probability above 0.9 throughout the 40 m window, whereas the $-20$ and $-25$ dBsm classes fall below that level at about 28 m and 21 m. In the far-field DoA benchmark, TLS-ESPRIT gives the lowest conditional RMSE and remains about 13–14 dB above the subarray CRLB at moderate SNR; however, these angular results are reference ceilings because the short-range operating region violates the full-aperture far-field condition and because residual TDM phase error can be severe without accurate compensation. In the micro-Doppler benchmark, birds exceed 95% per-class accuracy at 20 dB total SNR, but overall four-class accuracy saturates near 72–75% and UAV-only three-class accuracy near 63%, with most confusion between the micro-quadrotor and fixed-wing classes. This study therefore identifies architecture-specific performance margins and limitations before measured-data field validation, rather than claiming complete deployment-level performance. Full article

The proposed methodology combines analytical control laws with adaptive mechanisms and machine-learning-assisted modules based on regression trees, random forests, and extreme gradient boosting (XGBoost). Machine learning models are employed to approximate unknown nonlinear dynamics, compensate disturbances, and adjust controller parameters, while the overall control structure is constrained by Lyapunov stability conditions. This ensures that the inclusion of data-driven components does not violate the fundamental requirement of system stability. The effectiveness of the proposed approach is evaluated through simulation experiments across three operating modes with varying degrees of nonlinearity and dynamic complexity. The results show that hybrid models incorporating ensemble machine learning methods improved performance compared with the analytical and adaptive baselines examined. XGBoost-based control achieves the lowest error values and the highest level of Lyapunov stability compliance (up to 99.3%). The main contribution of this study lies in the development of a unified synthesis framework in which machine learning is not used as a standalone control strategy but as a machine-learning-assisted support mechanism integrated into a theoretically grounded control architecture. The proposed approach provides a balance between adaptability, accuracy, and rigorous stability guarantees, suggesting potential applicability to simulation-based and offline-assisted control design tasks, while real-time embedded implementation requires additional computational optimization and validation. Full article

Autoregressive models with exogenous variables (ARX) constitute a fundamental class of dynamic regression models used extensively for time series analysis across a wide range of applications. A pervasive limitation of the existing Bayesian analyses of ARX models is their near-exclusive reliance on the Gaussian error assumption, which is routinely violated in empirical applications exhibiting heavy-tailed innovations, distributional outliers, or excess kurtosis. To address this deficiency, we develop a rigorous Bayesian estimation framework for these models whose errors are drawn from the scale-mixtures of normal (SMN) family, which is a rich, symmetric, heavy-tailed class of distributions. Exploiting the hierarchical stochastic representation of the SMN family through observation-specific latent scale-mixing variables, the ARX model is embedded in an augmented data structure that restores Gaussian conditional structure. Under three distinct prior formulations—namely, normal-gamma, Zellner’s g-prior, and Jeffreys’ prior—we derive closed-form full conditional posterior distributions for the ARX coefficient vector and the error scale parameter, which follow multivariate normal and inverse-gamma distributions, respectively. In addition, for the SMN-specific shape parameters, we derive the full conditional posteriors for each distribution in the family, and some of them are non-standard distributions handled by embedding Metropolis-Hastings steps within the Gibbs sampler. The resulting hybrid MCMC algorithm is validated through a comprehensive simulation study spanning three ARX model configurations and all three SMN special cases. A real macroeconomic application to US consumer price inflation demonstrates the practical utility of the framework, confirming heavy-tailed residuals and yielding precise, well-calibrated posterior estimates. Full article

Wavelength stability of distributed feedback (DFB) semiconductor lasers in dense wavelength division multiplexing (DWDM) systems hinges on sub-millikelvin temperature regulation, a task complicated by the nonlinear, multi-node dynamics of the thermoelectric cooler (TEC) and the purely reactive nature of conventional proportional–integral–derivative (PID) control. We present a physics-informed neural network (PINN) built around a residual correction architecture for hybrid feedforward–feedback TEC temperature control. Rather than penalizing physics-residual violations in the loss function, the architecture wires a simplified one-node thermal model directly into the network graph as a frozen baseline. A trainable branch then learns only the residual mismatch. Temporal lag features are appended to the input so that the network can reconstruct unmeasured internal thermal states from the cold-side temperature history, which proves essential for overcoming the partial-observability bottleneck inherent in multi-node TEC packages. Ablation experiments on a high-fidelity three-node TEC simulator show that all model variants (PINN, physics-feature-augmented NN, and pure NN) exceed R² = 0.993 when trained on the full dataset, yet the PINN’s advantage becomes pronounced under data scarcity. At a 3% training budget, it reaches R² = 0.966 versus 0.930 for the pure NN, implying an approximately 5.4× reduction in the data needed to reach a given accuracy target. In closed-loop validation, the PINN+PID hybrid settles 60% faster than standalone PID. Tracking RMSE drops by 69%, and peak disturbance deviation falls by 74%, across step, multi-setpoint, and current-perturbation scenarios. All results reported here are obtained in simulations. Experimental validation on physical DFB-TEC hardware is left to future work. Full article

The high penetration of renewable energy has exposed integrated energy systems (IES) to stronger source-load uncertainties. Traditional scheduling methods that primarily pursue economic optimality often fail to account for system regulation margins, which may lead to excessive charging and discharging of energy storage systems, frequent fluctuations in unit output, and insufficient supply–demand matching capability under uncertain operating scenarios. To address these issues, this paper proposes a Flex-TD3 optimal scheduling method for IESs with embedded flexibility rules. First, a regional IES model incorporating photovoltaic generation, wind power, micro-gas turbines, gas boilers, electric chillers, waste heat recovery units, heat exchangers, and battery energy storage systems is established to describe the coupling relationships among electricity, heat, cooling, and gas flows, as well as the operational constraints of key devices. Second, active regulation flexibility indicators are constructed from the perspectives of system upward regulation capability, downward regulation capability, energy storage state health, and electro-thermal decoupling regulation margin. A comprehensive flexibility score is then formulated to characterize the system’s capability to cope with renewable energy fluctuations and load disturbances under the current operating state. Third, the flexibility indicators are embedded into the state space and reward function of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, and a rule-based physical feasibility mapping mechanism is introduced to modify the raw scheduling actions generated by the agent according to device operational constraints, thereby enhancing the physical consistency and operational safety of the scheduling strategy. Case study results show that, compared with traditional optimal scheduling methods, the proposed method achieves better overall performance in terms of training convergence speed, operational economy, and scheduling stability. It can effectively reduce system operating costs, improve renewable energy accommodation capability, and decrease renewable energy curtailment, supply shortages, and constraint violations. Under uncertain scenarios involving renewable energy prediction errors, load disturbances, and high renewable energy penetration, the proposed method still maintains favorable scheduling performance, demonstrating its effectiveness and robustness. Full article

High penetration of distributed photovoltaic (PV) generation has intensified voltage violations and stochastic voltage fluctuations in distribution networks, while existing voltage–var control methods still have limitations in terms of communication dependence, scalability, and edge deployment. To address these issues, this paper proposes a stability-constrained voltage–var self-optimizing control method for distribution networks based on the Bandit-Guided Online Self-Tuning Group Relative Policy Optimization (BOST-GRPO) algorithm. First, based on the LinDistFlow linearized power-flow model, a communication-free, decentralized, and locally observable reinforcement learning control environment is constructed, enabling each node to independently generate reactive power regulation commands using only local voltage measurements. Second, a contraction-mapping-based stability constraint is embedded into the policy output layer, theoretically guaranteeing the local exponential convergence of nodal voltage deviations around the equilibrium point and reducing the risk of voltage instability caused by overly aggressive policy actions. Meanwhile, device capacity constraints are incorporated into the policy output through a tanh-based action mapping, ensuring the physical feasibility of control commands. On this basis, BOST-GRPO realizes the online self-tuning of key hyperparameters within a single training process through a Bandit-guided mechanism, thereby avoiding the repeated training overhead caused by traditional offline hyperparameter tuning. Simulation results on the IEEE 33-bus system show that the proposed method outperforms benchmark reinforcement learning algorithms in final test cost, voltage deviation suppression, steady-state error, and regulation speed. Further tests under sensitivity matrix mismatch, different initial voltage disturbance intensities, and the extended IEEE 69-bus system demonstrate that the proposed method achieves good robustness and scalability. Full article

Autonomous driving systems (ADSs) are reliable only when heterogeneous sensors, estimation algorithms, and safety mechanisms are engineered as a single coherent safety-critical measurement system rather than as loosely coupled modules. Production stacks integrate cameras, LiDAR, automotive radar, and GNSS/IMU, yet deployment remains constrained by modality-specific failure modes, calibration and synchronization drift, and out-of-distribution (OOD) conditions that violate modeling assumptions. These limitations induce overconfidence and downstream decision errors whenever planning assumes certainty sharper than sensing can justify. This survey introduces a sensor-centric framework linking measurement physics, uncertainty propagation, fusion integrity, safety assurance, and risk-aware planning and control. We formalize what each modality physically measures; unify probabilistic, evidential, and conformal uncertainty representations; analyze filtering, factor-graph, BEV, transformer, and state-space fusion architectures with an emphasis on robustness and graceful degradation; and generalize aviation-style integrity concepts (RAIM/ARAIM) to multi-modal autonomy. The distinctive contribution is a single sensor-to-assurance throughline in which every uncertainty representation is tied to its measurement physics, every fusion architecture is evaluated against an explicit integrity-monitoring requirement generalized from RAIM/ARAIM, and every safety-standard clause is mapped to a concrete architectural mechanism. We map these mechanisms onto ISO 26262, ISO 21448 (SOTIF), ISO/PAS 8800, ANSI/UL 4600, and the UNECE framework, and connect perception uncertainty to decision-making through chance-constrained MPC and formal safety filters (RSS, CBF). Industry case studies and emerging V2X and generative-simulation approaches close the loop to deployable safety arguments. Full article

SYNGAP1-related intellectual disability presents a therapeutic paradox where genetic rescue is highly effective in neonates but limited in adults, suggesting that deficiency represents a developmental trajectory violation rather than a static biochemical defect. By synthesizing molecular, biophysical, and clinical evidence, this review proposes the “Synaptic Clock” framework, redefining SynGAP1 as a critical developmental regulator. We hypothesize that SynGAP1 operates through a strictly ordered temporal sequence: Phase I (Scaffold Assembly) utilizes the α1 isoform and phase separation to establish the structural postsynaptic density, while Phase II (Catalytic Refinement) involves isoform switching to enable activity-dependent plasticity and homeostatic scaling. This model characterizes synaptic maturation as a biophysical transition from a fluid scaffold to a consolidated gel, potentially marking the biological closure of structural rescue windows. Based on this hypothesized temporal mapping, we establish a phase-stratified therapeutic roadmap—transitioning from early-stage “reset” strategies like gene replacement to late-stage “refinement” and “compensation” via pharmacological and neuromodulatory interventions. Ultimately, validating phase-specific biomarkers, including gamma oscillations and isoform stoichiometry, is essential for shifting from generic interventions toward precision, phase-matched medicine for neurodevelopmental timing. Full article

The rapid proliferation of cloud data centers has intensified concerns over carbon emissions, energy efficiency, and sustainability. Virtual machine (VM) placement is a pivotal control lever, yet existing methods rarely couple carbon intensity signals with computationally tractable multi-objective optimization. In this paper, we propose CASO (Carbon-Aware Surrogate-Guided Optimization), a novel framework that integrates an online adaptive Radial Basis Function (RBF) surrogate model with a self-adaptive hybrid PSO-DE swarm optimizer for real-time VM placement in geo-distributed edge cloud environments. CASO simultaneously minimizes carbon emissions, energy consumption, SLA violation rate, and network latency under strict host capacity and Quality-of-Service (QoS) constraints. Three key innovations differentiate CASO: (i) an online surrogate update mechanism that refines fitness approximations incrementally as workload patterns evolve; (ii) a carbon intensity weighting scheme anchored to real-time Grid Emission Factor (GEF) signals; and (iii) an adaptive parameter controller that autonomously tunes swarm exploration–exploitation trade-offs without hand-crafting. Experiments on the publicly available Alibaba Cluster Trace (cluster-trace-v2026-GenAI) dataset within a CloudSim-Plus environment show that CASO reduces carbon emissions by up to 31.4%, energy consumption by 27.9%, and SLA violations by 18.8% compared to the strongest baseline while converging 3.8× faster than the strongest baseline (ADEDL). Full article

This paper investigates uncertainty-resilient stabilization of an inverted pendulum on a cart (IPOC) using an interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy model and an LQR-based control framework. The IPOC dynamics are represented as a weighted combination of local linear subsystems, where interval firing strengths derived from upper and lower membership functions capture modeling uncertainties. An LQR state-feedback controller is designed for each subsystem, and the final control input is obtained by blending the local controllers according to the normalized firing strengths. To analyze stability, an LMI-based verification condition is established as a sufficient condition for the subsystem LQR controllers. Simulation results show that this condition is satisfied only in a limited operating region, while the closed-loop system can still remain stable even when the condition is violated, demonstrating the reduced conservatism and flexibility of the proposed approach. Furthermore, comparisons with the conventional PDC structure confirm that the proposed method provides greater design flexibility and enables a trade-off between robustness and transient-state performance. Full article

Digital trust in online interactions is commonly established through mechanisms such as decentralized identifiers (DIDs), verifiable credentials (VCs), and digital wallets. While these technologies support the correctness of individual components, they do not by themselves establish that an interaction as a whole is trustworthy. This limitation arises because real-world interactions consist of sequences of dependent steps, where inconsistencies may arise even when each step is locally valid. In this paper, we introduce the concept of executable trust, which models trust as a verifiable property of execution across complete interaction sequences. We formalize interactions as chains of TrustEvidence objects that capture step-level validity, constraint satisfaction, and cross-step dependencies. Based on this model, we show that step-level correctness alone is insufficient to characterize interaction-level trust under the stated execution assumptions. We further clarify the definition-induced modular structure of interaction-level trust and use a local failure-witness characterization to connect the formal model with scenario-based validation. We also present the Executable Trust Architecture (ETA), a five-layer architecture that operationalizes the proposed model through components for evidence generation, constraint enforcement, secure communication, and auditability. The feasibility of the approach is examined through scenario-based evaluation covering key trust properties—authenticity, integrity, privacy, and accountability—across nine scenarios comprising 68 test cases. The evaluation illustrates cases in which cross-step violations that pass conventional step-level verification are reflected as failures of ETA’s sequence-aware trust conditions under the evaluated assumptions. Full article

Bandstructure methods occupy a central place in the physics of nanostructures, and the multiband $\mathit{k}·\mathit{p}$ theory of Luttinger, Kohn, and Kane has served as one of the most widely used computational frameworks for modelling electronic states and energies in low-dimensional semiconductor systems for several decades. Despite its broad success, the theory harbours a fundamental mathematical difficulty that has been largely overlooked: the multiband Luttinger–Kohn Hamiltonians are non-elliptic partial differential operators for the overwhelming majority of common III–V and III-nitride crystalline materials, a fact that violates the axiomatic requirements of quantum mechanics and is the root cause of the long-standing problem of spurious solutions. In this paper, we derive ellipticity conditions rigorously for the $6\times 6$, $8\times 8$, and $14\times 14$ zinc-blende Hamiltonians, demonstrating that non-ellipticity affects a substantially larger class of materials than previously reported. We develop and justify a systematic parameter rescaling procedure for the $8\times 8$ Kane Hamiltonian and obtain admissible parameter sets for GaAs, AlAs, InAs, GaP, AlP, InP, GaSb, AlSb, InSb, GaN, AlN, and InN. The inversion-asymmetry parameter B is shown to play an essential and previously unrecognized role in maintaining ellipticity, and it is used to optimize the bandstructure fit of the rescaled parameter sets. Analysis of several known $14\times 14$ models reveals structural sources of non-ellipticity, pointing to the need for a revision of perturbative assumptions regarding out-of-basis band contributions. The consistent parametrization framework developed here provides the rigorous mathematical foundation required by inverse design methodologies, AI-enhanced electronic structure calculations, and data-driven multifidelity approaches in nanoscience and nanotechnology. Full article

Possible violations of the no-signaling constraint in Weinberg-type nonlinear extensions motivate the question of whether entanglement could, under suitable conditions, become a resource for operational signaling. A minimal binary model is introduced in which, in each run, a sender selects a binary input bit and a receiver locally records a binary output bit. Signaling is defined operationally as a dependence of the local output statistics on the remote input and is summarized by a single channel parameter that can be estimated from data. An estimator and a corrected confidence interval are then introduced to assess this dependence quantitatively, while transmission reliability is expressed through the minimum decision error. On this basis, a conservative criterion is formulated, using an upper bound on the error and a threshold fixed in advance, to characterize a near-identity channel regime. Minimal reporting requirements are also proposed to document the conditions under which artifacts and classical leakage may reasonably be excluded. Full article