arXiv subjects

Masako Kishida

Publications and source records attributed to Masako Kishida.

At least 19 recordsLinked to original sources

arXiv2026

Alignment-Free Nanometric Optical Metrology Enabled by Structured Light

Advances in the semiconductor industry are driven by the development of increasingly compact devices featuring intricate etched geometries, the characterization of which essentially requires ultraprecise, label-free, and real-time metrology. However, non-destructive and alignment-free optical metrology of sub-wavelength structures with nanometric resolution remains a major challenge. Here, we demonstrate a novel single-shot, label-free, and alignment-free optical metrology approach for determining the 1D position of sub-wavelength nanostructures, achieving lambda/110 (7.2 nm) precision. The high precision benefits from utilizing structured illuminations of Laguerre-Gaussian (LG) or Hermite-Gaussian (HG) beams, and the AI analyzing method can retrieve the information when such structured light interacts with sub-wavelength objects. Instead of relying on phase singularities in superoscillatory microscopy, our approach leverages spatially distributed phase jumps in HG and LG beams interacting with the nanostructures, providing an alignment-robust solution to the challenges in optical metrology. Such an alignment-free, non-destructive, and high-precision metrology technique enables real-time machine vision, semiconductor inspection, and advanced manufacturing.

physics.optics↗

arXiv2026

Receding-Horizon Maximum-Likelihood Estimation of Neural-ODE Dynamics and Thresholds from Event Cameras

Event cameras emit asynchronous brightness-change events where each pixel triggers an event when the last event exceeds a threshold, yielding a history-dependent measurement model. We address online maximum-likelihood identification of continuous-time dynamics from such streams. The latent state follows a Neural ODE and is mapped to predicted log-intensity through a differentiable state-to-image model. We model events with a history-dependent marked point process whose conditional intensity is a smooth surrogate of contrast-threshold triggering, treating the contrast threshold as an unknown parameter. The resulting log-likelihood consists of an event term and a compensator integral. We propose a receding-horizon estimator that performs a few gradient steps per update on a receding horizon window. For streaming evaluation, we store two scalars per pixel (last-event time and estimated log-intensity at that time) and approximate the compensator via Monte Carlo pixel subsampling. Synthetic experiments demonstrate joint recovery of dynamics parameters and the contrast threshold, and characterize accuracy--latency trade-offs with respect to the window length.

eess.SY↗

arXiv2025

Distributionally Robust Safety Verification of Neural Networks via Worst-Case CVaR

Ensuring the safety of neural networks under input uncertainty is a fundamental challenge in safety-critical applications. This paper builds on and expands Fazlyab's quadratic-constraint (QC) and semidefinite-programming (SDP) framework for neural network verification to a distributionally robust and tail-risk-aware setting by integrating worst-case Conditional Value-at-Risk (WC-CVaR) over a moment-based ambiguity set with fixed mean and covariance. The resulting conditions remain SDP-checkable and explicitly account for tail risk. This integration broadens input-uncertainty geometry-covering ellipsoids, polytopes, and hyperplanes-and extends applicability to safety-critical domains where tail-event severity matters. Applications to closed-loop reachability of control systems and classification are demonstrated through numerical experiments, illustrating how the risk level $\varepsilon$ trades conservatism for tolerance to tail events-while preserving the computational structure of prior QC/SDP methods for neural network verification and robustness analysis.

cs.LG↗

arXiv2025

Reference-Free Iterative Learning Model Predictive Control with Neural Certificates

In this paper, we propose a novel reference-free iterative learning model predictive control (MPC). In the proposed method, a certificate function based on the concept of Control Lyapunov Barrier Function (CLBF) is learned using data collected from past control executions and used to define the terminal set and cost in the MPC optimization problem at the current iteration. This scheme enables the progressive refinement of the MPC's terminal components over successive iterations. Unlike existing methods that rely on mixed-integer programming and suffer from numerical difficulties, the proposed approach formulates the MPC optimization problem as a standard nonlinear program, enabling more efficient online computation. The proposed method satisfies key MPC properties, including recursive feasibility and asymptotic stability. Additionally, we demonstrate that the performance cost is non-increasing with respect to the number of iterations, under certain assumptions. Numerical experiments including the simulation with PyBullet confirm that our control scheme iteratively enhances control performance and significantly improves online computational efficiency compared to the existing methods.

eess.SY↗

arXiv2025

Clustering-based Recurrent Neural Network Controller synthesis under Signal Temporal Logic Specifications

Autonomous robotic systems require advanced control frameworks to achieve complex temporal objectives that extend beyond conventional stability and trajectory tracking. Signal Temporal Logic (STL) provides a formal framework for specifying such objectives, with robustness metrics widely employed for control synthesis. Existing optimization-based approaches using neural network (NN)-based controllers often rely on a single NN for both learning and control. However, variations in initial states and obstacle configurations can lead to discontinuous changes in the optimization solution, thereby degrading generalization and control performance. To address this issue, this study proposes a method to enhance recurrent neural network (RNN)-based control by clustering solution trajectories that satisfy STL specifications under diverse initial conditions. The proposed approach utilizes trajectory similarity metrics to generate clustering labels, which are subsequently used to train a classification network. This network assigns new initial states and obstacle configurations to the appropriate cluster, enabling the selection of a specialized controller. By explicitly accounting for variations in solution trajectories, the proposed method improves both estimation accuracy and control performance. Numerical experiments on a dynamic vehicle path planning problem demonstrate the effectiveness of the approach.

eess.SY↗

arXiv2024

Risk-Aware Control of Discrete-Time Stochastic Systems: Integrating Kalman Filter and Worst-case CVaR in Control Barrier Functions

This paper proposes control approaches for discrete-time linear systems subject to stochastic disturbances. It employs Kalman filter to estimate the mean and covariance of the state propagation, and the worst-case conditional value-at-risk (CVaR) to quantify the tail risk using the estimated mean and covariance. The quantified risk is then integrated into a control barrier function (CBF) to derive constraints for controller synthesis, addressing tail risks near safe set boundaries. Two optimization-based control methods are presented using the obtained constraints for half-space and ellipsoidal safe sets, respectively. The effectiveness of the obtained results is demonstrated using numerical simulations.

math.OC↗

arXiv2024

Introducing Graph Learning over Polytopic Uncertain Graph

This extended abstract introduces a class of graph learning applicable to cases where the underlying graph has polytopic uncertainty, i.e., the graph is not exactly known, but its parameters or properties vary within a known range. By incorporating this assumption that the graph lies in a polytopic set into two established graph learning frameworks, we find that our approach yields better results with less computation.

eess.SP↗

arXiv2023

A Risk-Aware Control: Integrating Worst-Case CVaR with Control Barrier Function

This paper proposes a risk-aware control approach to enforce safety for discrete-time nonlinear systems subject to stochastic uncertainties. We derive some useful results on the worst-case Conditional Value-at-Risk (CVaR) and define a discrete-time risk-aware control barrier function using the worst-case CVaR. On this basis, we present optimization-based control approaches that integrate the worst-case CVaR into the control barrier function, taking into account both safe set and tail risk considerations. In particular, three types of safe sets are discussed in detail: half-space, polytope, and ellipsoid. It is shown that control inputs for the half-space and polytopic safe sets can be obtained via quadratic programs, while control inputs for the ellipsoidal safe set can be computed via a semidefinite program. Through numerical examples of an inverted pendulum, we compare its performance with existing methods and demonstrate the effectiveness of our proposed controller.

math.OC↗

arXiv2023

Bayesian Meta-Learning on Control Barrier Functions with Data from On-Board Sensors

In this paper, we consider a way to safely navigate the robots in unknown environments using measurement data from sensory devices. The control barrier function (CBF) is one of the promising approaches to encode safety requirements of the system and the recent progress on learning-based approaches for CBF realizes online synthesis of CBF-based safe controllers with sensor measurements. However, the existing methods are inefficient in the sense that the trained CBF cannot be generalized to different environments and the re-synthesis of the controller is necessary when changes in the environment occur. Thus, this paper considers a way to learn CBF that can quickly adapt to a new environment with few amount of data by utilizing the currently developed Bayesian meta-learning framework. The proposed scheme realizes efficient online synthesis of the controller as shown in the simulation study and provides probabilistic safety guarantees on the resulting controller.

eess.SY↗

arXiv2023

Greedy Synthesis of Event- and Self-Triggered Controls with Control Lyapunov-Barrier Function

This paper addresses the co-design problem of control inputs and execution decisions for event- and self-triggered controls subject to constraints given by the control Lyapunov function and control barrier function. The proposed approach computes the control input in a way that allows for longer inter-execution intervals, which distinguishes it from many existing event- and self-triggered controllers or control Lyapunov-barrier function controllers. The proposed approach guarantees lower bounds on the minimum inter-execution times. The effectiveness of the proposed approach is demonstrated and compared with existing approaches using a numerical example.

eess.SY↗

arXiv2022

Neural Controller Synthesis for Signal Temporal Logic Specifications Using Encoder-Decoder Structured Networks

In this paper, we propose a control synthesis method for signal temporal logic (STL) specifications with neural networks (NNs). Most of the previous works consider training a controller for only a given STL specification. These approaches, however, require retraining the NN controller if a new specification arises and needs to be satisfied, which results in large consumption of memory and inefficient training. To tackle this problem, we propose to construct NN controllers by introducing encoder-decoder structured NNs with an attention mechanism. The encoder takes an STL formula as input and encodes it into an appropriate vector, and the decoder outputs control signals that will meet the given specification. As the encoder, we consider three NN structures: sequential, tree-structured, and graph-structured NNs. All the model parameters are trained in an end-to-end manner to maximize the expected robustness that is known to be a quantitative semantics of STL formulae. We compare the control performances attained by the above NN structures through a numerical experiment of the path planning problem, showing the efficacy of the proposed approach.

eess.SY↗

arXiv2022

Learning-based Symbolic Abstractions for Nonlinear Control Systems

Symbolic models or abstractions are known to be powerful tools for the control design of cyber-physical systems (CPSs) with logic specifications. In this paper, we investigate a novel learning-based approach to the construction of symbolic models for nonlinear control systems. In particular, the symbolic model is constructed based on learning the un-modeled part of the dynamics from training data based on state-space exploration, and the concept of an alternating simulation relation that represents behavioral relationships with respect to the original control system. Moreover, we aim at achieving safe exploration, meaning that the trajectory of the system is guaranteed to be in a safe region for all times while collecting the training data. In addition, we provide some techniques to reduce the computational load, in terms of memory and computation time, of constructing the symbolic models and the safety controller synthesis, so as to make our approach practical. Finally, a numerical simulation illustrates the effectiveness of the proposed approach.

eess.SY↗

arXiv2022

Risk-Aware Stability, Ultimate Boundedness, and Positive Invariance

This paper introduces the notions of stability, ultimate boundedness, and positive invariance for stochastic systems in the view of risk. More specifically, those notions are defined in terms of the worst-case Conditional Value-at-Risk (CVaR), which quantifies the worst-case conditional expectation of losses exceeding a certain threshold over a set of possible uncertainties. Those notions allow us to focus our attention on the tail behavior of stochastic systems in the analysis of dynamical systems and the design of controllers. Furthermore, some event-triggered control strategies that guarantee ultimate boundedness and positive invariance with specified bounds are derived using the obtained results and illustrated using numerical examples.

math.OC↗

arXiv2021

Instabilizability Conditions for Continuous-Time Stochastic Systems Under Control Input Constraints

In this paper, we investigate constrained control of continuous-time linear stochastic systems. We show that for certain system parameter settings, constrained control policies can never achieve stabilization. Specifically, we explore a class of control policies that are constrained to have a bounded average second moment for Ito-type stochastic differential equations with additive and multiplicative noise. We prove that in certain settings of the system parameters and the bounding constant of the control constraint, divergence of the second moment of the system state is inevitable regardless of the initial state value and regardless of how the control policy is designed.

eess.SY↗

arXiv2021

Temporal Deep Unfolding for Nonlinear Maximum Hands-off Control

This paper proposes a computational technique based on "deep unfolding" to solving the finite-time maximum hands-off control problem for discrete-time nonlinear stochastic systems. In particular, we seek a sparse control input sequence that stabilizes the system such that the expected value of the square of the final states is small by training a deep neural network. The proposed technique is demonstrated by a numerical experiment.

math.OC↗

arXiv2021

Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning

Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation. Recently, we also observe a rapidly growing interest in applying machine learning (ML) to support different adaptation mechanisms. While MAPE and CT have particular characteristics and strengths to be applied independently, in this paper, we are concerned with the question of how these approaches are related with one another and whether combining them and supporting them with ML can produce better adaptive systems. We motivate the combined use of different adaptation approaches using a scenario of a cloud-based enterprise system and illustrate the analysis when combining the different approaches. To conclude, we offer a set of open questions for further research in this interesting area.

cs.SE↗

arXiv2020

Optimal Resource Allocation for Dynamic Product Development Process via Convex Optimization

Resource allocation is an essential aspect of successful Product Development (PD). In this paper, we formulate the dynamic resource allocation of the PD process as a convex optimization problem. Specially, we build and solve two variants of this issue: the budget-constrained problem and the performance-constrained problem. By using convex optimization, we propose a framework to optimally solve large problem instances at a relatively small computational cost. The solutions to both problems exhibit similar trends regarding resource allocation decisions and performance evolution. Furthermore, we show that the product architecture affects resource allocation, which in turn affects the performance of the PD process. By introducing centrality metrics for measuring the location of the modules and design rules within the product architecture network, we find that resource allocation decisions correlate to their metrics. These results provide simple, but powerful, managerial guidelines for efficiently designing and managing the PD process. Finally, for validating the model and its results, we introduce and solve two design case studies for a mechanical manipulator and for an automotive appearance design process.

eess.SY↗

arXiv2020

Deep Learning-Based Average Consensus

In this study, we analyzed the problem of accelerating the linear average consensus algorithm for complex networks. We propose a data-driven approach to tuning the weights of temporal (i.e., time-varying) networks using deep learning techniques. Given a finite-time window, the proposed approach first unfolds the linear average consensus protocol to obtain a feedforward signal-flow graph, which is regarded as a neural network. The edge weights of the obtained neural network are then trained using standard deep learning techniques to minimize consensus error over a given finite-time window. Through this training process, we obtain a set of optimized time-varying weights, which yield faster consensus for a complex network. We also demonstrate that the proposed approach can be extended for infinite-time window problems. Numerical experiments revealed that our approach can achieve a significantly smaller consensus error compared to baseline strategies.

math.OC↗