235 citations · 531 across the 38 of their papers we have counts for
42 papers
Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning
Mirco Theile, Harald Bayerlein, Marco Caccamo +1
Coverage path planning (CPP) is a critical problem in robotics, where the goal is to find an efficient path that covers every point in an area of interest. This work addresses the…
Floorplet: Performance-aware Floorplan Framework for Chiplet Integration
Shixin Chen, Shanyi Li, Zhen Zhuang +5
A chiplet is an integrated circuit that encompasses a well-defined subset of an overall system's functionality. In contrast to traditional monolithic system-on-chips (SoCs), chiple…
A generic physics-informed neural network-based framework for reliability assessment of multi-state systems
Taotao Zhou, Xiaoge Zhang, Enrique Lopez Droguett +1
In this paper, we leverage the recent advances in physics-informed neural network (PINN) and develop a generic PINN-based framework to assess the reliability of multi-state systems…
Class-wise Thresholding for Robust Out-of-Distribution Detection
Matteo Guarrera, Baihong Jin, Tung-Wei Lin +3
We consider the problem of detecting OoD(Out-of-Distribution) input data when using deep neural networks, and we propose a simple yet effective way to improve the robustness of sev…
Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data
Hari Prasanna Das, Ryan Tran, Japjot Singh +4
At the onset of a pandemic, such as COVID-19, data with proper labeling/attributes corresponding to the new disease might be unavailable or sparse. Machine L…
Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments
Abdus Salam Azad, Edward Kim, Qiancheng Wu +4
The capability of a reinforcement learning (RL) agent heavily depends on the diversity of the learning scenarios generated by the environment. Generation of diverse realistic scena…
Hypercontracts
Inigo Incer, Albert Benveniste, Alberto Sangiovanni-Vincentelli +1
Contract theories have been proposed to formally support distributed and decentralized system design while ensuring safe system integration. In this paper we propose hypercontracts…
A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions
Corvin Deboeser, Jordan Ivanchev, Thomas Braud +3
This paper introduces the SEAD framework that simplifies the process of designing and describing autonomous vehicle platooning manoeuvres. Although a large body of research has bee…
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving
Jay Shenoy, Edward Kim, Xiangyu Yue +4
Safely interacting with humans is a significant challenge for autonomous driving. The performance of this interaction depends on machine learning-based modules of an autopilot, suc…
Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation
Sicheng Zhao, Xuanbai Chen, Xiangyu Yue +7
Thanks to large-scale labeled training data, deep neural networks (DNNs) have obtained remarkable success in many vision and multimedia tasks. However, because of the presence of d…
Scenic: A Language for Scenario Specification and Data Generation
Daniel J. Fremont, Edward Kim, Tommaso Dreossi +4
We propose a new probabilistic programming language for the design and analysis of cyber-physical systems, especially those based on machine learning. Specifically, we consider the…
The Quotient in Preorder Theories
Íñigo X. Íncer Romeo, Leonardo Mangeruca, Tiziano Villa +1
Seeking the largest solution to an expression of the form A x <= B is a common task in several domains of engineering and computer science. This largest solution is commonly called…
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
Sicheng Zhao, Xiangyu Yue, Shanghang Zhang +8
Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many applications, it is prohibitively e…
Generalizing Fault Detection Against Domain Shifts Using Stratification-Aware Cross-Validation
Yingshui Tan, Baihong Jin, Qiushi Cui +2
Incipient anomalies present milder symptoms compared to severe ones, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions. Th…
Using Ensemble Classifiers to Detect Incipient Anomalies
Baihong Jin, Yingshui Tan, Albert Liu +3
Incipient anomalies present milder symptoms compared to severe ones, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions. Th…
Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults?
Baihong Jin, Yingshui Tan, Yuxin Chen +2
Intermediate-Severity (IS) faults present milder symptoms compared to severe faults, and are more difficult to detect and diagnose due to their close resemblance to normal operatin…
Industrial Control via Application Containers:Maintaining determinism in IAAS
Florian Hofer, Martin Sehr, Alberto Sangiovanni-Vincentelli +1
Industry 4.0 is changing fundamentally data collection, its storage and analysis in industrial processes, enabling novel application such as flexible manufacturing of highly custom…
ODRE Workshop: Probabilistic Dynamic Hard Real-Time Scheduling in HPC
Florian Hofer, Martin A. Sehr, Alberto Sangiovanni-Vincentelli +1
Industry 4.0 is changing fundamentally the way data is collected, stored and analyzed in industrial processes. While this change enables novel application such as flexible manufact…
Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults
Baihong Jin, Yingshui Tan, Yuxin Chen +1
The Monte Carlo dropout method has proved to be a scalable and easy-to-use approach for estimating the uncertainty of deep neural network predictions. This approach was recently ap…
Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data
Xiangyu Yue, Yang Zhang, Sicheng Zhao +3
We propose to harness the potential of simulation for the semantic segmentation of real-world self-driving scenes in a domain generalization fashion. The segmentation network is tr…
Industrial Control via Application Containers: Migrating from Bare-Metal to IAAS
Florian Hofer, Martin A. Sehr, Antonio Iannopollo +3
We explore the challenges and opportunities of shifting industrial control software from dedicated hardware to bare-metal servers or cloud computing platforms using off the shelf t…
An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing
Baihong Jin, Yingshui Tan, Alexander Nettekoven +4
We present a novel unsupervised deep learning approach that utilizes the encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial…
A Formalization of Robustness for Deep Neural Networks
Tommaso Dreossi, Shromona Ghosh, Alberto Sangiovanni-Vincentelli +1
Deep neural networks have been shown to lack robustness to small input perturbations. The process of generating the perturbations that expose the lack of robustness of neural netwo…
A tractable ellipsoidal approximation for voltage regulation problems
Pan Li, Baihong Jin, Ruoxuan Xiong +3
We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation. The novelty of o…