Data privacy concerns in the power systems sector significantly complicate the sharing and integration of sensitive operational data among various independent entities. This challenge is particularly pronounced when d...
详细信息
ISBN:
(数字)9798350390421
ISBN:
(纸本)9798350390438
Data privacy concerns in the power systems sector significantly complicate the sharing and integration of sensitive operational data among various independent entities. This challenge is particularly pronounced when developing system-wide mathematical models, as the reluctance to share sub-system models parametrized by sensitive data hinders effective system analysis and decision-making. To address this issue, this work introduces the application of Physics-Informed Neural Networks (PINNs) to develop surrogate models that accurately replicate power system dynamics without exposing sensitive data, enabling privacy-preserving model sharing. By embedding physical laws into the training process, PINNs utilize both available data and inherent system physics, making them particularly suitable for modeling complex dynamics. We propose a framework for model development, including dataset generation and integration of the physics knowledge during PINN training. As a proof-of-concept, we apply this framework to a simplified Single Machine Infinite Bus (SMIB) system. Case studies demonstrate that the trained PINN model closely follows ground-truth dynamics and consistently achieves higher accuracy compared to generic neural networks, highlighting the potential for accurate, privacy-preserving model sharing and system-wide dynamic simulations in power systems.
This paper presents an integrated multiscopic cyber-physical-social system (CPSS) for evaluating physical and cognitive aspects in an independent block-design test (BDT). First, we utilize a hand tracker to extract ph...
详细信息
For emerging quantum networks to coexist with the classical internet, they must conform to existing specifications. We examine the architectural model of the internet, especially physical layer requirements, and we pr...
Machine Learning (ML) systems require representative and diverse datasets to accurately learn the objective task. In supervised learning data needs to be accurately annotated, which is an expensive and error-prone pro...
详细信息
Across the world, the lack of proper healthcare resources in rural areas significantly impacts the physical and economic well-being of individuals living there. There is a need for multi-faceted solutions to help alle...
详细信息
ISBN:
(数字)9798350378931
ISBN:
(纸本)9798350378948
Across the world, the lack of proper healthcare resources in rural areas significantly impacts the physical and economic well-being of individuals living there. There is a need for multi-faceted solutions to help alleviate healthcare issues in these areas. Medibot is a robot which monitors the health of individuals in these areas and takes appropriate measures. Patients visit Medibot for daily non-invasive monitoring of their vitals and then receive updated guidance or are connected with a healthcare professional for immediate support. The robot provides an easy way to get continuous care for those with limited access to healthcare resources or facilities.
Across the world, the lack of proper healthcare resources in rural areas significantly impacts the physical and economic well-being of individuals living there. There is a need for multi-faceted solutions to help alle...
详细信息
The inclusion of technologies such as telepractice, and virtual reality in the field of communication disorders has transformed the approach to providing healthcare. This research article proposes the employment of si...
详细信息
In communication restricted environments, a multi-robot system can be deployed to either: i) maintain constant communication but potentially sacrifice operational efficiency due to proximity constraints or ii) allow d...
In communication restricted environments, a multi-robot system can be deployed to either: i) maintain constant communication but potentially sacrifice operational efficiency due to proximity constraints or ii) allow disconnections to increase environmental coverage efficiency, challenges on how, when, and where to reconnect (rendezvous problem). In this work we tackle the latter problem and notice that most state-of-the-art methods assume that robots will be able to execute a predetermined plan; however system failures and changes in environmental conditions can cause the robots to deviate from the plan with cascading effects across the multi-robot system. This paper proposes a coordinated epistemic prediction and planning framework to achieve consensus without communicating for exploration and coverage, task discovery and completion, and rendezvous applications. Dynamic epistemic logic is the principal component implemented to allow robots to propagate belief states and empathize with other agents. Propagation of belief states and subsequent coverage of the environment is achieved via a frontier-based method within an artificial physics-based framework. The proposed framework is validated with both simulations and experiments with unmanned ground vehicles in various cluttered environments.
In applications such as search and rescue or disaster relief, heterogeneous multi-robot systems (MRS) can provide significant advantages for complex objectives that require a suite of capabilities. However, within the...
In applications such as search and rescue or disaster relief, heterogeneous multi-robot systems (MRS) can provide significant advantages for complex objectives that require a suite of capabilities. However, within these application spaces, communication is often unreliable, causing inefficiencies or outright failures to arise in most MRS algorithms. Many researchers tackle this problem by requiring all robots to either maintain communication using proximity constraints or assuming that all robots will execute a predetermined plan over long periods of disconnection. The latter method allows for higher levels of efficiency in a MRS, but failures and environmental uncertainties can have cascading effects across the system, especially when a mission objective is complex or time-sensitive. To solve this, we propose an epistemic planning framework that allows robots to reason about the system state, leverage heterogeneous system makeups, and optimize information dissemination to disconnected neighbors. Dynamic epistemic logic formalizes the propagation of belief states, and epistemic task allocation and gossip is accomplished via a mixed integer program using the belief states for utility predictions and planning. The proposed framework is validated using simulations and experiments with heterogeneous vehicles.
This paper describes an experimental and numerical optimization procedure for off-line extraction of parameters for unsymmetrical, single-phase induction machines with capacitor-start operation. In addition to permitt...
详细信息
暂无评论