arXiv subjects

Zhou He

Publications and source records attributed to Zhou He.

13 recordsLinked to original sources

arXiv2026

VISA: A Structured Description Protocol for Agent-Based Simulation Models Towards Machine Reproducibility

Agent-based models (ABMs) are difficult to reproduce: their behavior is spread across prose narratives, platform-specific code, and implicit assumptions, so that two readers routinely reconstruct different models from the same documentation. We present VISA, a structured, symbol-based description protocol that specifies a model in eight interconnected tables---four at the agent level (Agent, Variable, Sensing, Internal Function) and four at the model level (Associated Data, Input/Output, Schedule, Validation)---under the principle of minimality with completeness. VISA makes a model machine-parseable and unambiguous via two artifacts: nineteen executable consistency rules that turn model validity into a checkable property, and three reusable LLM-executable skills (authoring, checking, and code generation) that operationalize the full author--check--code--reproduce loop. We validate the protocol on three external, independently authored ABMs spanning three platforms: we reproduce two cross-language (NetLogo to Python) directly from their VISA specifications, and we capture a third, an industrial AnyLogic model, in eight tables (passing all nineteen rules) while honestly demarcating where reproduction is blocked by a proprietary movement library and unavailable data---itself a transparency contribution. VISA moves the reproduction barrier from the model, where it is invisible, to a named, localized dependency, where it is actionable.

cs.MA↗

arXiv2026

Simulation-Optimization of Systems of Optimizers: Exploiting the Inner Optimization's Geometry

We study simulation-optimization of systems of optimizers (SOSO): agent-based simulations in which every agent solves a structured optimization - a linear program (LP), mixed-integer program, or dynamic program - at every decision epoch. Such systems arise in supply chains, electricity markets, and logistics, yet standard simulation optimization treats the simulation as a black box, discarding the inner optimization's geometry. We formalize SOSO and develop a framework that converts this geometry into computational advantage. We prove that the inner LP's optimal basis and dual variables propagate through the dynamics to yield an exact, unbiased, single-replication infinitesimal perturbation analysis (IPA) gradient of the outer objective, with measure-zero basis changes as the only obstructions. We derive a common-random-numbers covariance bound governed by a computable basis-disagreement count from the forward simulation. We further prove that IPA variance grows exponentially with feedback depth, formalizing the bullwhip effect, and introduce surrogate-as-decision with an error budget unifying LP-horizon and reinforcement-learning surrogates under one bound. We compose these into PRIME, a stochastic-approximation solver integrating the IPA gradient, adaptive step sizing, and multi-start spatial diversification. On six testbeds spanning the SOSO taxonomy, PRIME achieves the best or tied-best optimality gap at equal budget with near-zero oscillation and narrow seed-to-seed variance; IPA yields a ~2000x per-replication variance reduction over independent finite differences, and common random numbers cut paired-difference variance by over 10,000x in a 1,000-SKU, six-distribution-center supply chain. These results establish exploiting embedded-optimization geometry as a practically significant direction for simulation optimization.

math.OC↗

arXiv2026

When Is Heterogeneous Distance-Decay Facility Location Tractable? A Structural Classification, Exact Methods, and a Real-World Study

We study continuous planar facility location in which a demand point's captured value decays with distance, with the per-point decay scale varying across points. This heterogeneity is ubiquitous yet underexploited, and one nearest-facility objective unifies decay, clustering, and median goals, containing k-means, the Weber/p-median problem, and maximum covering as special cases. We make four contributions. (i) A tractability classification: the discrete objective is always monotone submodular, so the (1-1/e) greedy guarantee holds regardless of decay shape or heterogeneity, and the continuous cooperative objective is concave if and only if the decay is concave in distance; the clip max(0,d) in common coverage specifications is what destroys concavity, and the classification is tight. (ii) An exact discrete method: the candidate-discretized maximum-cover MIP has an empirically tight LP relaxation (~0% gap) and is solved by branch-and-bound in seconds for n <= 500. (iii) A force-as-gradient / large-neighborhood-search heuristic, within 0.5% of the discrete optimum, that outperforms the (1-1/e) greedy, Cooper-style alternating location-allocation, particle swarm optimization, and weighted k-means (30/30 per-instance wins at K=30, p<10^-9) and is competitive with bespoke solvers on k-means, Weber/p-median, and shape-demand instances. (iv) A real-world study: on 592,667 urban-delivery orders, ignoring the calibrated decay variation loses up to 9.7% of captured demand and relocates facilities by up to 37% of the map; a retail dataset calibrates the decay as exponential with scale R ~ 1.4 km.

cs.MA↗

arXiv2026

Dynamic Scheduling for Flexible Manufacturing Systems Based on Multi-Agent Deep Reinforcement Learning and Petri Nets

This paper investigates dynamic scheduling for flexible manufacturing systems (FMSs) subject to dynamic events, such as new order arrivals, temporary order cancellations, and machine failures. Traditional methods often face significant challenges in achieving real-time responsiveness under such conditions. To address this issue, the scheduling problem is formulated as a Markov decision process (MDP) with timed Petri nets, where the future evolution of the system depends exclusively on the current marking and the subsequently executed transitions, independent of historical trajectories. The state space and action space of the MDP are constructed using the notion of basis reachability graph (a compact state space representation) of Petri nets to alleviate the state explosion problem, thereby accelerating model training convergence. Meanwhile, a hierarchical dense reward function is constructed by integrating stepwise guidance with terminal evaluation. Then, a multi-agent proximal policy optimization algorithm is employed for model training under the centralized training and decentralized execution paradigm to improve scheduling efficiency. Numerical experiments are conducted involving typical dynamic events, and the results demonstrate that the proposed method can effectively handle dynamic events and achieve superior scheduling performance compared with conventional approaches.

eess.SY↗

arXiv2025

Non-interference analysis of bounded labeled Petri nets

This paper focuses on a fundamental problem on information security of bounded labeled Petri nets: non-interference analysis. As in hierarchical control, we assume that a system is observed by users at different levels, namely high-level users and low-level users. The output events produced by the firing of transitions are also partitioned into high-level output events and low-level output events. In general, high-level users can observe the occurrence of all the output events, while low-level users can only observe the occurrence of low-level output events. A system is said to be non-interferent if low-level users cannot infer the firing of transitions labeled with high-level output events by looking at low-level outputs. In this paper, we study a particular non-interference property, namely strong non-deterministic non-interference (SNNI), using a special automaton called SNNI Verifier, and propose a necessary and sufficient condition for SNNI.

cs.FL↗

arXiv2025

Scheduling of Flexible Manufacturing Systems Based on Place-Timed Petri Nets and Basis Reachability Graphs

Scheduling is a key decision-making process to improve the performance of flexible manufacturing systems. Place-timed Petri nets provide a formal method for graphically modeling and analyzing such systems. By generating reachability graphs and combining intelligent search algorithms, operation sequences from the initial state to the target state can be found for the underlying system. However, the reachability graph grows exponentially with the system size increases, which is the main challenge of existing methods for scheduling large systems. To this end, we develop an efficient improved beam search algorithm to optimize the makespan based on a compact representation of reachability graph called basis reachability graph. The key idea behind the proposed method is to form a state together with the basis markings and its corresponding transition sequences, and evaluate the cost of the state based on the resource idle time. Experimental results are conducted on several benchmark systems which show that the developed method improves the search efficiency while ensuring the quality of the solution compared with existing methods.

eess.SY↗

arXiv2025

Optimal Task and Motion Planning for Autonomous Systems Using Petri Nets

This study deals with the problem of task and motion planning of autonomous systems within the context of high-level tasks. Specifically, a task comprises logical requirements (conjunctions, disjunctions, and negations) on the trajectories and final states of agents in certain regions of interest. We propose an optimal planning approach that combines offline computation and online planning. First, a simplified Petri net system is proposed to model the autonomous system. Then, indicating places are designed to implement the logical requirements of the specifications. Building upon this, a compact representation of the state space called extended basis reachability graph is constructed and an efficient online planning algorithm is developed to obtain the optimal plan. It is shown that the most burdensome part of the planning procedure may be removed offline, thanks to the construction of the extended basis reachability graph. Finally, series of simulations are conducted to demonstrate the computational efficiency and scalability of our developed method.

eess.SY↗

arXiv2024

FDA Jamming Against Airborne Phased-MIMO Radar-Part I: Matched Filtering and Spatial Filtering

Phased multiple-input multiple-output (Phased-MIMO) radar has received increasing attention for enjoying the advantages of waveform diversity and range-dependency from frequency diverse array MIMO (FDA-MIMO) radar without sacrificing coherent processing gain through partitioning transmit subarray. This two-part series proposes a framework of electronic countermeasures (ECM) inspired by frequency diverse array (FDA) radar, called FDA jamming, evaluating its effectiveness for countering airborne phased-MIMO radar. This part introduces the principles and categories of FDA jammer and proposes the FDA jamming signal model based on the two cases of phased-MIMO radar, phased-array (PA) radar and FDA-MIMO radar. Moreover, the effects of FDA jamming on matched filtering and spatial filtering of PA and FDA-MIMO radar are analyzed. Numerical results verify the theoretical analysis and validate the effectiveness of the proposed FDA jamming in countering phased-MIMO radar.

eess.SP↗

arXiv2024

FDA Jamming Against Airborne Phased-MIMO Radar-Part II: Jamming STAP Performance Analysis

The first part of this series introduced the effectiveness of frequency diverse array (FDA) jamming through direct wave propagation in countering airborne phased multiple-input multiple-output (Phased-MIMO) radar. This part focuses on the effectiveness of FDA scattered wave (FDA-SW) jamming on the space-time adaptive processing (STAP) for airborne phased-MIMO radar. Distinguished from the clutter signals, the ground equidistant scatterers of FDA-SW jamming constitute an elliptical ring, whose trajectory equations are mathematically derived to further determine the spatial frequency and Doppler frequency. For the phased-MIMO radar with different transmitting partitions, the effects of jamming frequency offset of FDA-SW on the clutter rank and STAP performance are discussed. Theoretical analysis provides the variation interval of clutter rank and the relationship between the jamming frequency offset and the improvement factor (IF) notch of phased-MIMO-STAP. Importantly, the requirements of jamming frequency offset for both two-part applications are discussed in this part. Numerical results verify these mathematical findings and validate the effectiveness of the proposed FDA jamming in countering the phased-MIMO radar.

eess.SP↗

arXiv2019

3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation

Recent advancements in medical image segmentation techniques have achieved compelling results. However, most of the widely used approaches do not take into account any prior knowledge about the shape of the biomedical structures being segmented. More recently, some works have presented approaches to incorporate shape information. However, many of them are indeed introducing more parameters to the segmentation network to learn the general features, which any segmentation network is able learn, instead of specifically shape features. In this paper, we present a novel approach that seamlessly integrates the shape information into the segmentation network. Experiments on human brain MRI segmentation demonstrate that our approach can achieve a lower Hausdorff distance and higher Dice coefficient than the state-of-the-art approaches.

eess.IV↗

arXiv2018

A DAQ Prototype for Front-end Waveform Digitization in Intensive Electromagnetic Field Circumstance

A front-end waveform digitization data acquisition system prototype for pulsed magnetic field generator in inertial confinement fusion is described. The pulse magnetic field is created by discharging a high-voltage capacitor through a small wire-wound coil, and the Rogowski coil is used to measure the discharge current which can describe the corresponding magnetic field waveforms. The prototype is designed to measure the signal instead of the oscilloscope through a long-distance coaxial, which is greatly affected by electromagnetic interference caused by high-power loser. The outfield test result shows that the prototype can has a comparable performance as the oscilloscope for pulse magnetic field measurement.

physics.ins-det↗

arXiv2017

Online human aggregation under pressure moves beyond preferential attachment

There is a significant amount of online human activity which is either clandestine or illicit in nature, and hence where individuals operate under fear of exposure or capture. Yet there is little theoretical understanding of what models best describe the resulting dynamics. Here we address this gap, by analyzing the evolutionary dynamics of the supporters behind the 95 pro-ISIS online communities (i.e. self-organized social media groups) that appeared recently on a global social media site. We show that although they do not follow a conventional (i.e. size-based) preferential attachment (PA) model, their dynamical evolution can be explained by a new variant that we introduce here, which we refer to as active attraction model (AA). This AA model takes into account the locality and group heterogeneity which undoubtedly feature in humans' online behavior under pressure, but which are not contained in conventional PA models. The AA model captures both group-specific and macroscopic observations over all size ranges -- as opposed to just the tail for large groups or groups' initial growth -- suggesting that heterogeneity and locality play a crucial role in the dynamics of online extremist support. We derive approximate expressions for the group size distributions in two simple systems that involve simultaneously the mechanisms of group joining (governed by either PA or AA), group leaving, and account banning, and show how these processes influence the group size distributions. We believe this work will serve in helping understand a broad spectrum of online human activities which are either clandestine or illicit in nature, and hence where individuals operate under fear of exposure or capture.

physics.soc-ph↗

arXiv2017

Asymmetric access to information impacts the power-law exponent in networks

The preferential attachment (PA) process is a popular theory for explaining network power-law degree distributions. In PA, the probability that a new vertex adds an edge to an existing vertex depends on the connectivity of the target vertex. In real-world networks, however, each vertex may have asymmetric accessibility to information. Here we address this issue using a new network-generation mechanism that incorporates asymmetric accessibility to upstream and downstream information. We show that this asymmetric information accessibility directly affects the power-law exponent, producing a broad range of values that are consistent with observations. Our findings shed new light on the possible mechanisms in three important real-world networks: a citation network, a hyperlink network, and an online social network.

physics.soc-ph↗