pwc-archive/papers-with-abstracts huggingface dataset:
Low carbon optimal scheduling of integrated energy system considering waste heat utilization under the coordinated operation of incineration power plant and P2G
Authors: Limeng Wang, Shuo Wang, Na Wang, Yuze Ma, Yang Li
Date: 2024-09-11 00:00:00
Tasks: FLUE, Scheduling

In order to improve energy utilization and reduce carbon emissions, this paper presents a comprehensive energy system economic operation strategy of Incineration power plant Power-to-gas (P2G) with waste heat recovery. First, consider the coordinated operation of Incineration power plant - P2G, introduce the refined Power-to-gas two-stage operation process, add Hydrogen fuel cells on the basis of traditional Power-to-gas to reduce the energy ladder loss, and recycle the Methanation reaction heat; Secondly, in order to improve the energy utilization efficiency of Incineration, it is considered to install a waste heat recovery device containing a water source heat pump to recover the waste heat of flue gas and consume some electric energy, sourced from wind power, and add a CO2 separation device to combine the recovered CO2 with P2G to synthesize CH4 to achieve carbon recycling. Finally, within the framework of a tiered carbon trading mechanism an IES optimization model for electricity-heat with the goal of minimizing the system operating cost is constructed, and the GUROBI modeling optimization engine is used to solve this model. The results verify the effectiveness of the model.
Analytical Optimized Traffic Flow Recovery for Large-scale Urban Transportation Network
Authors: Sicheng Fu, Haotian Shi, Shixiao Liang, Xin Wang, Bin Ran
Date: 2024-09-05 00:00:00

The implementation of intelligent transportation systems (ITS) has enhanced data collection in urban transportation through advanced traffic sensing devices. However, the high costs associated with installation and maintenance result in sparse traffic data coverage. To obtain complete, accurate, and high-resolution network-wide traffic flow data, this study introduces the Analytical Optimized Recovery (AOR) approach that leverages abundant GPS speed data alongside sparse flow data to estimate traffic flow in large-scale urban networks. The method formulates a constrained optimization framework that utilizes a quadratic objective function with l2 norm regularization terms to address the traffic flow recovery problem effectively and incorporates a Lagrangian relaxation technique to maintain non-negativity constraints. The effectiveness of this approach was validated in a large urban network in Shenzhen's Futian District using the Simulation of Urban MObility (SUMO) platform. Analytical results indicate that the method achieves low estimation errors, affirming its suitability for comprehensive traffic analysis in urban settings with limited sensor deployment.
High Performance Three-Terminal Thyristor RAM with a P+/P/N/P/N/N+ Doping Profile on a Silicon-Photonic CMOS Platform
Authors: Changseob Lee, Ikhyeon Kwon, Anirban Samanta, Siwei Li, S. J. Ben Yoo
Date: 2024-09-11 00:00:00

3T TRAM with doping profile (P+PNPNN+) is experimentally demonstrated on a silicon photonic platform. By using additional implant layers, this device provides excellent memory performance compared to the conventional structure (PNPN). TCAD is used to reflect the physical behavior, and the high-speed memory operations are described through the model.
Orthogonal Mode Decomposition for Finite Discrete Signals
Authors: Ning li, Lezhi Li
Date: 2024-09-11 00:00:00

In this paper, an orthogonal mode decomposition method is proposed to decompose ffnite length real signals on both the real and imaginary axes of the complex plane. The interpolation function space of ffnite length discrete signal is constructed, and the relationship between the dimensionality of the interpolation function space and its subspaces and the band width of the interpolation function is analyzed. It is proved that the intrinsic mode is actually the narrow band signal whose intrinsic instantaneous frequency is always positive (or always negative). Thus, the eigenmode decomposition problem is transformed into the orthogonal projection problem of interpolation function space to its low frequency subspace or narrow band subspace. Different from the existing mode decomposition methods, the orthogonal modal decomposition is a local time-frequency domain algorithm. Each operation extracts a speciffc mode. The global decomposition results obtained under the precise deffnition of eigenmodes have uniqueness and orthogonality. The computational complexity of the orthogonal mode decomposition method is also much smaller than that of the existing mode decomposition methods.
Bio-Eng-LMM AI Assist chatbot: A Comprehensive Tool for Research and Education
Authors: Ali Forootani, Danial Esmaeili Aliabadi, Daniela Thraen
Date: 2024-09-11 00:00:00
Tasks: Chatbot, Image Generation, Information Retrieval, RAG, Response Generation, Retrieval, Retrieval-augmented Generation, Text Summarization

This article introduces Bio-Eng-LMM AI chatbot, a versatile platform designed to enhance user interaction for educational and research purposes. Leveraging cutting-edge open-source Large Language Models (LLMs), Bio-Eng-LMM operates as a sophisticated AI assistant, exploiting the capabilities of traditional models like ChatGPT. Central to Bio-Eng-LMM is its implementation of Retrieval Augmented Generation (RAG) through three primary methods: integration of preprocessed documents, real-time processing of user-uploaded files, and information retrieval from any specified website. Additionally, the chatbot incorporates image generation via a Stable Diffusion Model (SDM), image understanding and response generation through LLAVA, and search functionality on the internet powered by secure search engine such as DuckDuckGo. To provide comprehensive support, Bio-Eng-LMM offers text summarization, website content summarization, and both text and voice interaction. The chatbot maintains session memory to ensure contextually relevant and coherent responses. This integrated platform builds upon the strengths of RAG-GPT and Web-Based RAG Query (WBRQ) where the system fetches relevant information directly from the web to enhance the LLMs response generation.
Decentralized Control of Multi-Agent Systems Under Acyclic Spatio-Temporal Task Dependencies
Authors: Gregorio Marchesini, Siyuan Liu, Lars Lindemann, Dimos V. Dimarogonas
Date: 2024-09-08 00:00:00

We introduce a novel distributed sampled-data control method tailored for heterogeneous multi-agent systems under a global spatio-temporal task with acyclic dependencies. Specifically, we consider the global task as a conjunction of independent and collaborative tasks, defined over the absolute and relative states of agent pairs. Task dependencies in this form are then represented by a task graph, which we assume to be acyclic. From the given task graph, we provide an algorithmic approach to define a distributed sampled-data controller prioritizing the fulfilment of collaborative tasks as the primary objective, while fulfilling independent tasks unless they conflict with collaborative ones. Moreover, communication maintenance among collaborating agents is seamlessly enforced within the proposed control framework. A numerical simulation is provided to showcase the potential of our control framework.
Distributionally Robust Stochastic Data-Driven Predictive Control with Optimized Feedback Gain
Authors: RuiQi Li, John W. Simpson-Porco, Stephen L. Smith
Date: 2024-09-09 00:00:00

We consider the problem of direct data-driven predictive control for unknown stochastic linear time-invariant (LTI) systems with partial state observation. Building upon our previous research on data-driven stochastic control, this paper (i) relaxes the assumption of Gaussian process and measurement noise, and (ii) enables optimization of the gain matrix within the affine feedback policy. Output safety constraints are modelled using conditional value-at-risk, and enforced in a distributionally robust sense. Under idealized assumptions, we prove that our proposed data-driven control method yields control inputs identical to those produced by an equivalent model-based stochastic predictive controller. A simulation study illustrates the enhanced performance of our approach over previous designs.
Safe and Stable Connected Cruise Control for Connected Automated Vehicles with Response Lag
Authors: Yuchen Chen, Gabor Orosz, Tamas G. Molnar
Date: 2024-09-10 00:00:00

Controlling connected automated vehicles (CAVs) via vehicle-to-everything (V2X) connectivity holds significant promise for improving fuel economy and traffic efficiency. However, to deploy CAVs and reap their benefits, their controllers must guarantee their safety. In this paper, we apply control barrier function (CBF) theory to investigate the safety of CAVs implementing connected cruise control (CCC). Specifically, we study how stability, connection architecture, and the CAV's response time impact the safety of CCC. Through safety and stability analyses, we derive stable and safe choices of control gains, and show that safe CAV operation requires plant and head-to-tail string stability in most cases. Furthermore, the reaction time of vehicles, which is represented as a first-order lag, has a detrimental effect on safety. We determine the critical value of this lag, above which safe CCC gains do not exist. To guarantee safety even with lag while preserving the benefits of CCC, we synthesize safety-critical CCC using CBFs. With the proposed safety-critical CCC, the CAV can leverage information from connected vehicles farther ahead to improve its safety. We evaluate this controller by numerical simulation using real traffic data.
Probabilistically safe controllers based on control barrier functions and scenario model predictive control
Authors: Allan Andre do Nascimento, Antonis Papachristodoulou, Kostas Margellos
Date: 2024-09-10 00:00:00
Tasks: Model Predictive Control

Control barrier functions (CBFs) offer an efficient framework for designing real-time safe controllers. However, CBF-based controllers can be short-sighted, resulting in poor performance, a behaviour which is aggravated in uncertain conditions. This motivated research on safety filters based on model predictive control (MPC) and its stochastic variant. MPC deals with safety constraints in a direct manner, however, its computational demands grow with the prediction horizon length. We propose a safety formulation that solves a finite horizon optimization problem at each time instance like MPC, but rather than explicitly imposing constraints along the prediction horizon, we enforce probabilistic safety constraints by means of CBFs only at the first step of the horizon. The probabilistic CBF constraints are transformed in a finite number of deterministic CBF constraints via the scenario based methodology. Capitalizing on results on scenario based MPC, we provide distribution-free, \emph{a priori} guarantees on the system's closed loop expected safety violation frequency. We demonstrate our results through a case study on unmanned aerial vehicle collision-free position swapping, and provide a numerical comparison with recent stochastic CBF formulations.
A Hardened CO$_2$ Sensor for In-Ground Continuous Measurement in a Perennial Grass System
Authors: Bobby Schulz, Bryan Runck, Andrew Hollman, Ann Piotrowski, Eric Watkins
Date: 2024-09-10 00:00:00

Carbon dioxide levels below the soil surface are an important measurement relating to plant health, especially for plants such as perennial grasses in northern climates where ice encasement can occur over winter. In such cases, the CO$_2$ levels can build up and become toxic. This is likely a significant contributor to turfgrass death over winter; however, there is an insufficient amount of data regarding this phenomenon in large part due to the lack of effective sensors. Many off the shelf CO$_2$ sensors exist, but they are not sufficiently hardened for in ground deployment over winter. As a result, the only options currently available are very costly automated gas samplers or manual sampling at intervals with laboratory testing -- a process that results in a limited number of data points and is labor intensive. To combat this problem we have taken an established NDIR CO$_2$ sensor and hardened it for use in winter and ice encased environments to allow for continuous automated sampling of subsurface CO$_2$ levels to better understand ice encasement damage in perennial grass systems.
Equilibria and Their Stability Do Not Depend on the Control Barrier Function in Safe Optimization-Based Control
Authors: Yiting Chen, Pol Mestres, Jorge Cortes, Emiliano Dall'Anese
Date: 2024-09-10 00:00:00

Control barrier functions (CBFs) play a critical role in the design of safe optimization-based controllers for control-affine systems. Given a CBF associated with a desired ``safe'' set, the typical approach consists in embedding CBF-based constraints into the optimization problem defining the control law to enforce forward invariance of the safe set. While this approach effectively guarantees safety for a given CBF, the CBF-based control law can introduce undesirable equilibrium points (i.e., points that are not equilibria of the original system); open questions remain on how the choice of CBF influences the number and locations of undesirable equilibria and, in general, the dynamics of the closed-loop system. This paper investigates how the choice of CBF impacts the dynamics of the closed-loop system and shows that: (i) The CBF does not affect the number, location, and (local) stability properties of the equilibria in the interior of the safe set; (ii) undesirable equilibria only appear on the boundary of the safe set; and, (iii) the number and location of undesirable equilibria for the closed-loop system do not depend of the choice of the CBF. Additionally, for the well-established safety filters and controllers based on both CBF and control Lyapunov functions (CLFs), we show that the stability properties of equilibria of the closed-loop system are independent of the choice of the CBF and of the associated extended class-K function.
Self-calibrated Microring Weight Function for Neuromorphic Optical Computing
Authors: J. Garcia-Echeverria, D. Musat, A. Mahsafar, K. R. Mojaver, D. Rolston, G. Cowan, O. Liboiron-Ladouceur
Date: 2024-09-10 00:00:00

This paper presents a microring resonator-based weight function for neuromorphic photonic applications achieving a record-high precision of 11.3 bits and accuracy of 9.3 bits for 2 Gbps input optical signals. The system employs an all-analog self-referenced proportional-integral-derivative (PID) controller to perform real-time temperature stabilization within a range of up to 60 degree Celsius. A self-calibrated weight function is demonstrated for a range of 6 degree Celsius with a single initial calibration and minimal accuracy and precision degradation. By monitoring the through and drop ports of the microring with variable gain transimpedance amplifiers, accurate and precise weight adjustment is achieved, ensuring optimal performance and reliability. These findings underscore the system's robustness to dynamic thermal environments, highlighting the potential for high-speed reconfigurable analog photonic networks.
On Epistemic Properties in Discrete-Event Systems: A Uniform Framework and Its Applications
Authors: Bohan Cui, Ziyue Ma, ShaoYuan Li, Xiang Yin
Date: 2024-09-10 00:00:00

In this paper, we investigate the property verification problem for partially-observed DES from a new perspective. Specifically, we consider the problem setting where the system is observed by two agents independently, each with its own observation. The purpose of the first agent, referred to as the low-level observer, is to infer the actual behavior of the system, while the second, referred to as the high-level observer, aims to infer the knowledge of Agent 1 regarding the system. We present a general notion called the epistemic property capturing the inference from the high-level observer to the low-level observer. A typical instance of this definition is the notion of high-order opacity, which specifies that the intruder does not know that the system knows some critical information. This formalization is very general and supports any user-defined information-state-based knowledge between the two observers. We demonstrate how the general definition of epistemic properties can be applied in different problem settings such as information leakage diagnosis or tactical cooperation without explicit communications. Finally, we provide a systematic approach for the verification of epistemic properties. Particularly, we identify some fragments of epistemic properties that can be verified more efficiently.
Autoencoder-Based and Physically Motivated Koopman Lifted States for Wind Farm MPC: A Comparative Case Study
Authors: Bindu Sharan, Antje Dittmer, Yongyuan Xu, Herbert Werner
Date: 2024-09-10 00:00:00
Tasks: Model Predictive Control

This paper explores the use of Autoencoder (AE) models to identify Koopman-based linear representations for designing model predictive control (MPC) for wind farms. Wake interactions in wind farms are challenging to model, previously addressed with Koopman lifted states. In this study we investigate the performance of two AE models: The first AE model estimates the wind speeds acting on the turbines these are affected by changes in turbine control inputs. The wind speeds estimated by this AE model are then used in a second step to calculate the power output via a simple turbine model based on physical equations. The second AE model directly estimates the wind farm output, i.e., both turbine and wake dynamics are modeled. The primary inquiry of this study addresses whether any of these two AE-based models can surpass previously identified Koopman models based on physically motivated lifted states. We find that the first AE model, which estimates the wind speed and hence includes the wake dynamics, but excludes the turbine dynamics outperforms the existing physically motivated Koopman model. However, the second AE model, which estimates the farm power directly, underperforms when the turbines' underlying physical assumptions are correct. We additionally investigate specific conditions under which the second, purely data-driven AE model can excel: Notably, when modeling assumptions, such as the wind turbine power coefficient, are erroneous and remain unchecked within the MPC controller. In such cases, the data-driven AE models, when updated with recent data reflecting changed system dynamics, can outperform physics-based models operating under outdated assumptions.
Analysis of a Simple Neuromorphic Controller for Linear Systems: A Hybrid Systems Perspective
Authors: E. Petri, K. J. A. Scheres, E. Steur, W. P. M. H. Heemels
Date: 2024-09-10 00:00:00

In this paper we analyze a neuromorphic controller, inspired by the leaky integrate-and-fire neuronal model, in closed-loop with a single-input single-output linear time-invariant system. The controller consists of two neuron-like variables and generates a spiking control input whenever one of these variables reaches a threshold. The control input is different from zero only at the spiking instants and, hence, between two spiking times the system evolves in open-loop. Exploiting the hybrid nature of the integrate-and-fire neuronal dynamics, we present a hybrid modeling framework to design and analyze this new controller. In the particular case of single-state linear time-invariant plants, we prove a practical stability property for the closed-loop system, we ensure the existence of a strictly positive dwell-time between spikes, and we relate these properties to the parameters in the neurons. The results are illustrated in a numerical example.
Upstream Allocation of Bidirectional Load Demand by Power Packetization
Authors: Shiu Mochiyama, Kento Hiwatashi, Takashi Hikihara
Date: 2024-09-04 00:00:00
Tasks: Management

The power packet dispatching system has been studied for power management with strict tie to an accompanying information system through power packetization. In the system, integrated units of transfer of power and information, called power packets, are delivered through a network of apparatuses called power packet routers. This paper proposes upstream allocation of a bidirectional load demand represented by a sequence of power packets to power sources. We first develop a scheme of power packet routing for upstream allocation of load demand with full integration of power and information transfer. The routing scheme is then proved to enable packetized management of bidirectional load demand, which is of practical importance for applicability to, e.g., electric drives in motoring and regenerating operations. We present a way of packetizing the bidirectional load demand and realizing the power and information flow under the upstream allocation scheme. The viability of the proposed methods is demonstrated through experiments.
APEX: Attention on Personality based Emotion ReXgnition Framework
Authors: Ruijie Fang, Ruoyu Zhang, Elahe Hosseini, Chongzhou Fang, Mahdi Eslaminehr, Setareh Rafatirad, Houman Homayoun
Date: 2024-09-10 00:00:00
Tasks: Emotion Recognition

Automated emotion recognition has applications in various fields, such as human-machine interaction, healthcare, security, education, and emotion-aware recommendation/feedback systems. Developing methods to analyze human emotions accurately is essential to enable such diverse applications. Multiple studies have been conducted to explore the possibility of using physiological signals and machine-learning techniques to evaluate human emotions. Furthermore, internal factors such as personality have been considered and involved in emotion recognition. However, integrating personality that is user specific within traditional machine-learning methods that use user-agnostic large data sets has become a critical problem. This study proposes the APEX: attention on personality-based emotion recognition framework, in which multiple weak classifiers are trained on physiological signals of each participant's data, and the classification results are reweighed based on the personality correlations between corresponding subjects and test subjects. Experiments have been conducted on the ASCERTAIN dataset, and the results show that the proposed framework outperforms existing studies.
Advanced Energy-Efficient System for Precision Electrodermal Activity Monitoring in Stress Detection
Authors: Ruoyu Zhang, Ruijie Fang, Elahe Hosseini, Chongzhou Fang, Ning Miao, Houman Homayoun
Date: 2024-09-09 00:00:00

This paper presents a novel Electrodermal Activity (EDA) signal acquisition system, designed to address the challenges of stress monitoring in contemporary society, where stress affects one in four individuals. Our system focuses on enhancing the accuracy and efficiency of EDA measurements, a reliable indicator of stress. Traditional EDA monitoring solutions often grapple with trade-offs between sensor placement, cost, and power consumption, leading to compromised data accuracy. Our innovative design incorporates an adaptive gain mechanism, catering to the broad dynamic range and high-resolution needs of EDA data analysis. The performance of our system was extensively tested through simulations and a custom Printed Circuit Board (PCB), achieving an error rate below 1\% and maintaining power consumption at a mere 700$\mu$A under a 3.7V power supply. This research contributes significantly to the field of wearable health technology, offering a robust and efficient solution for long-term stress monitoring.