Systems and Control

All posts under category "Systems and Control"

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A Neural Network-Based Real-time Casing Collar Recognition System for Downhole Instruments

A Neural Network-Based Real-time Casing Collar Recognition System for Downhole Instruments

Accurate downhole positioning is critical in oil and gas operations but is often compromised by signal degradation in traditional surface-based Casing Collar Locator (CCL) monitoring. To address this, we present an in-situ, real-time collar recognition system using embedded neural network. We introduce lightweight Collar Recognition Nets (CRNs) optimized for resource-constrained ARM Cortex-M7 microprocessors. By leveraging temporal and depthwise separable convolutions, our most compact model reduces computational complexity to just 8,208 MACs while maintaining an F1 score of 0.972. Hardware validation confirms an average inference latency of 343.2 μs, demonstrating that robust, autonomous signal processing is feasible within the severe power and space limitations of downhole instrumentation.

paper research
Probability-Aware Parking Selection

Probability-Aware Parking Selection

Current parking navigation systems often underestimate total travel time by failing to account for the time spent searching for a parking space, which significantly affects user experience, mode choice, congestion, and emissions. To address this issue, this paper introduces the probability-aware parking selection problem, which aims to direct drivers to the best parking location rather than straight to their destination. An adaptable dynamic programming framework is proposed for decision-making based on probabilistic information about parking availability at the parking lot level. Closed-form analysis determines when it is optimal to target a specific parking lot or explore alternatives, as well as the expected time cost. Sensitivity analysis and three illustrative cases are examined, demonstrating the model s ability to account for the dynamic nature of parking availability. Acknowledging the financial costs of permanent sensing infrastructure, the paper provides analytical and empirical assessments of errors incurred when leveraging stochastic observations to estimate parking availability. Experiments with real-world data from the US city of Seattle indicate this approach s viability, with mean absolute error decreasing from 7% to below 2% as observation frequency grows. In data-based simulations, probability-aware strategies demonstrate time savings up to 66% relative to probability-unaware baselines, yet still take up to 123% longer than direct-to-destination estimates.

paper research
Reliable Grid Forecasting State Space Models for Safety-Critical Energy Systems

Reliable Grid Forecasting State Space Models for Safety-Critical Energy Systems

Accurate grid load forecasting is safety-critical under-predictions risk supply shortfalls, while symmetric error metrics can mask this operational asymmetry. We introduce an operator-legible evaluation framework -- Under-Prediction Rate (UPR), tail Reserve$_{99.5}^{ %}$ requirements, and explicit inflation diagnostics (Bias$_{24h}$/OPR) -- to quantify one-sided reliability risk beyond MAPE. Using this framework, we evaluate state space models (Mamba variants) and strong baselines on a weather-aligned California Independent System Operator (CAISO) dataset spanning Nov 2023--Nov 2025 (84,498 hourly records across 5 regional transmission areas) under a rolling-origin walk-forward backtest. We develop and evaluate thermal-lag-aligned weather fusion strategies for these architectures. Our results demonstrate that standard accuracy metrics are insufficient proxies for operational safety models with comparable MAPE can imply materially different tail reserve requirements (Reserve$_{99.5}^{ %}$). We show that explicit weather integration narrows error distributions, reducing the impact of temperature-driven demand spikes. Furthermore, while probabilistic calibration reduces large-error events, it can induce systematic schedule inflation. We introduce Bias/OPR-constrained objectives to enable auditable trade-offs between minimizing tail risk and preventing trivial over-forecasting.

paper research
Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees

Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees

We propose a scalable reachability-based framework for probabilistic, data-driven safety verification of unknown nonlinear dynamics. We use Koopman theory with a neural network (NN) lifting function to learn an approximate linear representation of the dynamics and design linear controllers in this space to enable closed-loop tracking of a reference trajectory distribution. Closed-loop reachable sets are efficiently computed in the lifted space and mapped back to the original state space via NN verification tools. To capture model mismatch between the Koopman dynamics and the true system, we apply conformal prediction to produce statistically-valid error bounds that inflate the reachable sets to ensure the true trajectories are contained with a user-specified probability. These bounds generalize across references, enabling reuse without recomputation. Results on high-dimensional MuJoCo tasks (11D Hopper, 28D Swimmer) and 12D quadcopters show improved reachable set coverage rate, computational efficiency, and conservativeness over existing methods.

paper research

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