Bingqing Chen

Bingqing Chen
Carnegie Mellon University | CMU · Department of Civil and Environmental Engineering

Doctor of Engineering

About

22
Publications
5,307
Reads
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153
Citations
Citations since 2016
22 Research Items
153 Citations
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20162017201820192020202120220204060

Publications

Publications (22)
Conference Paper
Full-text available
Reinforcement learning (RL) was first demonstrated to be a feasible approach to controlling heating, ventilation, and air conditioning (HVAC) systems more than a decade ago. However, there has been limited progress towards a practical and scalable RL solution for HVAC control. While one can train an RL agent in simulation, it is not cost-effective...
Conference Paper
Full-text available
Demand flexibility is increasingly important for power grids. Careful coordination of thermostatically controlled loads (TCLs) can modulate energy demand, decrease operating costs, and increase grid resiliency. We propose a novel distributed control framework for the Coordination Of HeterOgeneous Residential Thermostatically controlled loads (COHOR...
Preprint
Full-text available
Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing. At the same time, existing racing simulation frameworks struggle in capturing realism, with respect to visual rendering, vehicular dynamics, and task objectives, inhibiting...
Conference Paper
Full-text available
While reinforcement learning (RL) is gaining popularity in energy systems control, its real-world applications are limited due to the fact that the actions from learned policies may not satisfy functional requirements or be feasible for the underlying physical system. In this work, we propose PROjected Feasibility (PROF), a method to enforce convex...
Preprint
Full-text available
Racing demands each vehicle to drive at its physical limits , when any safety infraction could lead to catastrophic failure. In this work, we study the problem of safe reinforcement learning (RL) for autonomous racing, using the vehicle's ego-camera view and speed as input. Given the nature of the task, autonomous agents need to be able to 1) ident...
Preprint
Full-text available
We present the results of our autonomous racing virtual challenge, based on the newly-released Learn-to-Race (L2R) simulation framework, which seeks to encourage interdisciplinary research in autonomous driving and to help advance the state of the art on a realistic benchmark. Analogous to racing being used to test cutting-edge vehicles, we envisio...
Preprint
Full-text available
Anomaly detection has many important applications, such as monitoring industrial equipment. Despite recent advances in anomaly detection with deep-learning methods, it is unclear how existing solutions would perform under out-of-distribution scenarios, e.g., due to shifts in machine load or environmental noise. Grounded in the application of machin...
Preprint
Full-text available
Computer vision-based damage detection using remote cameras and unmanned aerial vehicles (UAVs) enables efficient and low-cost bridge health monitoring that reduces labor costs and the needs for sensor installation and maintenance. By leveraging recent semantic image segmentation approaches, we are able to find regions of critical structural compon...
Preprint
Full-text available
To be viable for safety-critical applications, such as autonomous driving and assis-tive robotics, autonomous agents should adhere to safety constraints throughout the interactions with their environments. Instead of learning about safety by collecting samples, including unsafe ones, methods such as Hamilton-Jacobi (HJ) reachability compute safe se...
Conference Paper
Full-text available
Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing. At the same time, existing racing simulation frameworks struggle in capturing realism, with respect to visual rendering, vehicular dynamics, and task objectives , inhibiting...
Article
Full-text available
Training and validating algorithms in a simulation testbed can accelerate research and applications of optimal control of residential loads to improve energy flexibility and grid resilience. We developed an open-source simulation environment, AlphaBuilding ResCommunity, that can be used to train and validate algorithms to control a single thermosta...
Preprint
Full-text available
While reinforcement learning (RL) is gaining popularity in energy systems control, its real-world applications are limited due to the fact that the actions from learned policies may not satisfy functional requirements or be feasible for the underlying physical system. In this work, we propose PROjected Feasibility (PROF), a method to enforce convex...
Preprint
Full-text available
Alternating current optimal power flow (AC-OPF) is one of the fundamental problems in power systems operation. AC-OPF is traditionally cast as a constrained optimization problem that seeks optimal generation set points whilst fulfilling a set of non-linear equality constraints -- the power flow equations. With increasing penetration of renewable ge...
Conference Paper
Full-text available
We present an initial study of off-policy evaluation (OPE), a problem prerequisite to real-world reinforcement learning (RL), in the context of building control. OPE is the problem of estimating a policy's performance without running it on the actual system, using historical data from the existing controller. It enables the control engineers to ens...
Article
Full-text available
We propose Gnu-RL: a novel approach that enables real-world deployment of reinforcement learning (RL) for building control and requires no prior information other than historical data from existing Heating Ventilation and Air Conditioning (HVAC) controllers. In contrast with existing RL agents, which are opaque to expert interrogation and need ampl...
Preprint
Full-text available
Demand flexibility is increasingly important for power grids. Careful coordination of thermostatically controlled loads (TCLs) can modulate energy demand, decrease operating costs, and increase grid resiliency. We propose a novel distributed control framework for the Coordination Of HeterOgeneous Residential Thermostatically controlled loads (COHOR...
Preprint
Full-text available
Demand flexibility is increasingly important for power grids, in light of growing penetration of renewable generation. Careful coordination of thermostatically controlled loads (TCLs) can potentially modulate energy demand, decrease operating costs, and increase grid resiliency. However, it is challenging to control a heterogeneous population of TC...
Conference Paper
Full-text available
Non-intrusive load monitoring (NILM) is the set of algorithmic techniques for inferring the operational states of individual appliances in a household given the aggregate electrical measurements at a single point of instrumentation. Most successful techniques to-date approach the problem from a supervised learning perspective and thus rely on label...
Conference Paper
Full-text available
We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle traveling over a bridge to assess bridge health. Motivated by indirect sensing methods’ benefits, such as low-cost and low-maintenance, vehicle-vibration-based bridge health monit...
Preprint
Full-text available
We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle trav-eling over a bridge to assess bridge health. Motivated by indirect sensing methods' benefits, such as low-cost and low-maintenance, vehicle-vibration-based bridge health moni...

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Projects

Projects (3)
Project
- Democratize research on autonomous racing by releasing a open-source, high-fidelity simulation environment for Formula-style racing; - We envision autonomous racing to serve as a particularly challenging proving ground for safe learning algorithms, as racing demands each vehicle to drive at its physical limits, when any safety infraction could lead to catastrophic failures.
Project
Develop enabling methods for practical deployment of RL for building control, such as: - Initialize a policy with historical data through imitation learning - Estimate a policy’s performance without running it on the actual system, i.e. off-policy evaluation - Learn on the real buildings sample efficiently through model-based RL
Project
Deep reinforcement learning algorithms have seen an increased interest and have demonstrated human expert level performance in other domains, e.g., computer games. Research in the building and cities domain has been fragmented and with focus on different problems and using a variety of frameworks. The purpose of this Workshop is to build a growing community around this exciting topic, provide a platform for discussion for future research direction, and share common frameworks.