arXiv subjects

Sudchai Boonto

Publications and source records attributed to Sudchai Boonto.

3 recordsLinked to original sources

arXiv2026

Cognitive Flexibility as a Latent Structural Operator for Bayesian State Estimation

Deep stochastic state-space models enable Bayesian filtering in nonlinear, partially observed systems but typically assume a fixed latent structure. When this assumption is violated, parameter adaptation alone may result in persistent belief inconsistency. We introduce \emph{Cognitive Flexibility} (CF) as a representation-level operator that selects latent structures online via an innovation-based predictive score, while preserving the Bayesian filtering recursion. Structural mismatch is formalized as irreducible predictive inconsistency under fixed structure. The resulting belief--structure recursion is shown to be well posed, to exhibit a structural descent property, and to admit finite switching, with reduction to standard Bayesian filtering under correct specification. Experiments on latent-dynamics mismatch, observation-structure shifts, and well-specified regimes confirm that CF improves predictive accuracy under a mismatch while remaining non-intrusive when the model is correctly specified.

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arXiv2026

Cognitive-Flexible Control via Latent Model Reorganization with Predictive Safety Guarantees

Learning-enabled control systems must maintain safety when system dynamics and sensing conditions change abruptly. Although stochastic latent-state models enable uncertainty-aware control, most existing approaches rely on fixed internal representations and can degrade significantly under distributional shift. This letter proposes a \emph{cognitive-flexible control} framework in which latent belief representations adapt online, while the control law remains explicit and safety-certified. We introduce a Cognitive-Flexible Deep Stochastic State-Space Model (CF--DeepSSSM) that reorganizes latent representations subject to a bounded \emph{Cognitive Flexibility Index} (CFI), and embeds the adapted model within a Bayesian model predictive control (MPC) scheme. We establish guarantees on bounded posterior drift, recursive feasibility, and closed-loop stability. Simulation results under abrupt changes in system dynamics and observations demonstrate safe representation adaptation with rapid performance recovery, highlighting the benefits of learning-enabled, rather than learning-based, control for nonstationary cyber--physical systems.

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arXiv2022

Modeling the Material-Inventory Transportation Problem Using Multi-Objective Optimization

In the era of industry 4.0, procurement in supply chain management is the key to developing information management systems. It directly affects production planning failure. In this case, it is the process to prepare and confirming the material inventory is in the ordinal stages and be able to produce the products in any production line. In terms of industrial informatics, it can provide information management approaches for leveraging data sharing between factories. The multiobjective optimization will be enabled by integrating material inventory, production planning and monitoring, and transportation planning collaboration. The material-inventory transportation problem is the virtual factory situation when production plan failure occurs. It becomes the cost to transport material between each factory and the distribution to clients. In this study, the question of the material-inventory transportation problem is: How can we transport other materials from one factory into another factory? This study proposed a model to find out about the adjustment of material inventory through transportation. The objective of this model is to minimize the whole production cost and total transportation cost.

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