Insup Lee

Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction

BMC Medical Informatics and Decision Making · 2021 · 166 citations

BACKGROUND: Blood glucose (BG) management is crucial for type-1 diabetes patients resulting in the necessity of reliable artificial pancreas or insulin infusion systems. In recent years, deep learning techniques have been utilized for a more accurate BG level prediction system. However, continuous glucose monitoring (CGM) readings are susceptible to sensor errors. As a result, inaccurate CGM readings would affect BG prediction and make it unreliable, even if the most optimal machine learning mod…

Verisig 2.0: Verification of Neural Network Controllers Using Taylor Model Preconditioning

Lecture notes in computer science · 2021 · 56 citations

Abstract This paper presents Verisig 2.0, a verification tool for closed-loop systems with neural network (NN) controllers. We focus on NNs with tanh/sigmoid activations and develop a Taylor-model-based reachability algorithm through Taylor model preconditioning and shrink wrapping. Furthermore, we provide a parallelized implementation that allows Verisig 2.0 to efficiently handle larger NNs than existing tools can. We provide an extensive evaluation over 10 benchmarks and compare Verisig 2.0 ag…

Verifying the Safety of Autonomous Systems with Neural Network Controllers

ACM Transactions on Embedded Computing Systems · 2020 · 49 citations

This article addresses the problem of verifying the safety of autonomous systems with neural network (NN) controllers. We focus on NNs with sigmoid/tanh activations and use the fact that the sigmoid/tanh is the solution to a quadratic differential equation. This allows us to convert the NN into an equivalent hybrid system and cast the problem as a hybrid system verification problem, which can be solved by existing tools. Furthermore, we improve the scalability of the proposed method by approxima…

Assured Runtime Monitoring and Planning: Toward Verification of Neural Networks for Safe Autonomous Operations

IEEE Robotics & Automation Magazine · 2020 · 16 citations

Autonomous systems operating in uncertain environments under the effects of disturbances and noises can reach unsafe states even while using finetuned controllers and precise sensors and actuators. To provide safety guarantees on such systems during motion planning operations, reachability analysis (RA) has been demonstrated to be a powerful tool. RA, however, suffers from computational complexity, especially when dealing with intricate systems characterized by high-order dynamics, making it har…

IdentityKD: Identity-wise Cross-modal Knowledge Distillation for Person Recognition via mmWave Radar Sensors

2024-12-03 · 4 citations

Recent advancements in person recognition have raised concerns about identity privacy leaks.Gait recognition through millimeterwave radar provides a privacy-centric method.However, it is challenged by lower accuracy due to the sparse data these sensors capture.We are the first to investigate a cross-modal method, Iden-tityKD, to enhance gait-based person recognition with the assistance of facial data.IdentityKD involves a training process using both gait and facial data, while the inference stag…