pwc-archive/papers-with-abstracts huggingface dataset:
Measurements and System Identification for the Characterization of Smooth Muscle Cell Dynamics
Authors: Dilan Ozturk, Pepijn Saraber, Kevin Bielawski, Alessandro Giudici, Leon Schurgers, Koen Reesink, Maarten Schoukens
Date: 2024-09-25 00:00:00

Biological tissue integrity is actively maintained by cells. It is essential to comprehend how cells accomplish this in order to stage tissue diseases. However, addressing the complexity of a cell's system of interrelated mechanisms poses a challenge. This necessitates a well-structured identification framework and an effective integration of measurements. Here we introduce the use of state-of-the-art frequency-domain system identification techniques combined with an indentation measurement platform to analyze the underlying mechanisms from the perspective of control system theory. The ultimate goal is to explore how mechanical and biological factors are related in induced Pluripotent Stem Cell-derived vascular smooth muscle cells. We study on the frequency-domain analysis for the investigation and characterization of cellular dynamics of smooth muscle cells from the measured data. The measurement model in this study exploits the availability of human tissue and samples, enabling fundamental investigations of vascular tissue disease. This approach using human cell lines holds significant potential to decrease the necessity for animal-based safety and efficacy studies. The focus of this review is to investigate the cellular dynamics underlying the myogenic response and to demonstrate the practicability of employing a nano-indentation measurement setup for the broadband frequency-domain characterization of induced Pluripotent Stem Cell-derived vascular smooth muscle cells.
Adaptive Single-Terminal Fault Location for DC Microgrids
Authors: Vaibhav Nougain, Sukumar Mishra, Joan-Marc Rodriguez-Bernuz, Adria Junyent-Ferre, Aditya Shekhar, Aleksandra Lekic
Date: 2024-09-25 00:00:00

Identifying faulty lines and their accurate location is key for rapidly restoring distribution systems. This will become a greater challenge as the penetration of power electronics increases, and contingencies are seen across larger areas. This paper proposes a single terminal methodology (i.e., no communication involved) that is robust to variations of key parameters (e.g., sampling frequency, system parameters, etc.) and performs particularly well for low resistance faults that constitute the majority of faults in low voltage DC systems. The proposed method uses local measurements to estimate the current caused by the other terminals affected by the contingency. This mimics the strategy followed by double terminal methods that require communications and decouples the accuracy of the methodology from the fault resistance. The algorithm takes consecutive voltage and current samples, including the estimated current of the other terminal, into the analysis. This mathematical methodology results in a better accuracy than other single-terminal approaches found in the literature. The robustness of the proposed strategy against different fault resistances and locations is demonstrated using MATLAB simulations.
The Bayesian Separation Principle for Data-driven Control
Authors: Riccardo Alessandro Grimaldi, Giacomo Baggio, Ruggero Carli, Gianluigi Pillonetto
Date: 2024-09-25 00:00:00
Tasks: Model Predictive Control

This paper investigates the existence of a separation principle between model identification and control design in the context of model predictive control. First, we elucidate that the separation principle holds asymptotically in the number of data in a Fisherian setting, and universally in a Bayesian setting. Then, by formulating model predictive control within a Gaussian regression framework, we describe how the Bayesian separation principle can be used to derive explicit, uncertainty-aware expressions for the control cost and optimal input sequence, thereby bridging direct and indirect data-driven approaches.
A Fast Dynamic Internal Predictive Power Scheduling Approach for Power Management in Microgrids
Authors: Neethu Maya, Bala Kameshwar Poolla, Seshadhri Srinivasan, Narasimman Sundararajan, Suresh Sundaram
Date: 2024-09-25 00:00:00
Tasks: Management, Scheduling

This paper presents a Dynamic Internal Predictive Power Scheduling (DIPPS) approach for optimizing power management in microgrids, particularly focusingon external power exchanges among diverse prosumers. DIPPS utilizes a dynamic objective function with a time-varying binary parameter to control the timing of power transfers to the external grid, facilitated by efficient usage of energy storage for surplus renewable power. The microgrid power scheduling problem is modeled as a mixed-integer nonlinear programmig (MINLP-PS) and subsequently transformed into a mixed-integer linear programming (MILP-PS) optimization through McCormick's relaxation to reduce the computational complexity. A predictive window with 6 data points is solved at an average of 0.92s, a 97.6% improvement over the 38.27s required for the MINLP-PS formulation, implying the numerical feasibility of the DIPPS approach for real-time implementation. Finally, the approach is validated against a static objective using real-world load data across three case studies with different time-varying parameters, demonstrationg the ability of DIPPS to optimize power exchanges and efficiently utilize distributed resources whie shifting the eexternal power transfers to specified time durations.
Device for detection of activity-dependent changes in neural spheroids at MHz and GHz frequencies
Authors: Saeed Omidi, Gianluca Fabi, Xiaopeng Wang, James C. M. Hwang, Yevgeny Berdichevsky
Date: 2024-09-25 00:00:00

Intracellular processes triggered by neural activity include changes in ionic concentrations, protein release, and synaptic vesicle cycling. These processes play significant roles in neurological disorders. The beneficial effects of brain stimulation may also be mediated through intracellular changes. There is a lack of label-free techniques for monitoring activity-dependent intracellular changes. Electromagnetic (EM) waves at frequencies larger than 1x10^6 Hz (1 MHz) were previously used to probe intracellular contents of cells, as cell membrane becomes transparent at this frequency range. EM waves interact with membranes of intracellular organelles, proteins, and water in the MHz-GHz range. In this work, we developed a device for probing the interaction between intracellular contents of active neurons and EM waves. The device used an array of grounded coplanar waveguides (GCPWs) to deliver EM waves to a three-dimensional (3D) spheroid of rat cortical neurons. Neural activity was evoked using optogenetics, with synchronous detection of propagation of EM waves. Broadband measurements were conducted in the MHz-GHz range to track changes in transmission coefficients. Neuronal activity was found to reversibly alter EM wave transmission. Pharmacological suppression of neuronal activity abolished changes in transmission. Time constants of changes in transmission were in the range of seconds to tens of seconds, suggesting the presence of relatively slow, activity-dependent intracellular processes. This study provides the first evidence that EM transmission through neuronal tissue is activity-dependent in MHz-GHz range. Device developed in this work may find future applications in studies of the mechanisms of neurological disorders and the development of new therapies.
Active Perception with Initial-State Uncertainty: A Policy Gradient Method
Authors: Chongyang Shi, Shuo Han, Michael Dorothy, Jie Fu
Date: 2024-09-24 00:00:00

This paper studies the synthesis of an active perception policy that maximizes the information leakage of the initial state in a stochastic system modeled as a hidden Markov model (HMM). Specifically, the emission function of the HMM is controllable with a set of perception or sensor query actions. Given the goal is to infer the initial state from partial observations in the HMM, we use Shannon conditional entropy as the planning objective and develop a novel policy gradient method with convergence guarantees. By leveraging a variant of observable operators in HMMs, we prove several important properties of the gradient of the conditional entropy with respect to the policy parameters, which allow efficient computation of the policy gradient and stable and fast convergence. We demonstrate the effectiveness of our solution by applying it to an inference problem in a stochastic grid world environment.
System-Level Performance Metrics Sensitivity of an Electrified Heavy-Duty Mobile Manipulator
Authors: Mohammad Bahari, Alvaro Paz, Jouni Mattila
Date: 2024-09-24 00:00:00
Tasks: Autonomous Vehicles, Sensitivity

The shift to electric and hybrid powertrains in vehicular systems has propelled advancements in mobile robotics and autonomous vehicles. This paper examines the sensitivity of key performance metrics in a electrified heavy-duty mobile manipulator (HDMM) driven by electromechanical linear actuators (EMLAs) powered by permanent magnet synchronous motors (PMSMs). The study evaluates power delivery, force dynamics, energy consumption, and overall efficiency of the actuation mechanisms. By computing partial derivatives (PD) with respect to the payload mass at the tool center point (TCP), it provides insights into these factors under various loading conditions. This research aids in the appropriate choice or design of EMLAs for HDMM electrification, addressing actuation mechanism selection challenge in vehicular system with mounted manipulator and determines the necessary battery capacity requirements.
Mean Age of Information in Partial Offloading Mobile Edge Computing Networks
Authors: Ying Dong, Hang Xiao, Haonan Hu, Jiliang Zhang, Qianbin Chen, Jie Zhang
Date: 2024-09-24 00:00:00
Tasks: Edge-computing

The age of information (AoI) performance analysis is essential for evaluating the information freshness in the large-scale mobile edge computing (MEC) networks. This work proposes the earliest analysis of the mean AoI (MAoI) performance of large-scale partial offloading MEC networks. Firstly, we derive and validate the closed-form expressions of MAoI by using queueing theory and stochastic geometry. Based on these expressions, we analyse the effects of computing offloading ratio (COR) and task generation rate (TGR) on the MAoI performance and compare the MAoI performance under the local computing, remote computing, and partial offloading schemes. The results show that by jointly optimising the COR and TGR, the partial offloading scheme outperforms the local and remote computing schemes in terms of the MAoI, which can be improved by up to 51% and 61%, respectively. This encourages the MEC networks to adopt the partial offloading scheme to improve the MAoI performance.
Wind lulls and slews; consequences for the stability of future UK electricity systems
Authors: Anthony D Stephens, David R Walwyn
Date: 2024-09-24 00:00:00

As the United Kingdom wind fleet increases in size, wind lulls and slews will increasingly challenge the stability of its electricity system. The paper describes the use of models based on real time records and including solar slews, to investigate the most extreme wind variations likely to be encountered in future, enabling strategies to be devised to mitigate them. Wind lulls are surprisingly frequent, occasionally lasting a week or more, and are always likely to be beyond the capabilities of stored or imported electrical energy to mitigate them. The models indicate that there will be a continuing need for gas powered generation to mitigate wind lulls. Currently, Combined Cycle Gas Turbines (CCGTs) provide most of the dispatchable generation. However, CCGTs are not sufficiently fast acting to cope with the wind and solar slews anticipated in future. The paper suggests that a range of already proven fast-acting sources of dispatchable generation, including Open Cycle Gas Turbines (OCGTs), Internal Combustion Gas-Fired Reciprocating engines (ICGRs) and stored electrical energy systems, should be capable of coping with the largest wind and solar slews likely to be encountered up to the year 2035. Examples are given of the recent introduction of these fast-acting sources of generation which, it is suggested, will progressively replace CCGTs as the wind and solar fleets increase in size. Moreover, we see the pattern of recent investments, summarised in the paper, as a good indication of likely future investments, with OCGT investments mainly serving the 440 kV grid, and ICGRs and stored electrical energy more local networks.
Assessing strategies to manage distributed photovoltaics in Swiss low-voltage networks: An analysis of curtailment, export tariffs, and resource sharing
Authors: Alejandro Pena-Bello, Gerard Marias Gonzalez, Nicolas Wyrsch, Christophe Ballif
Date: 2024-09-24 00:00:00

The integration of photovoltaic systems poses several challenges for the distribution grid, mainly due to the infrastructure not being designed to handle the upstream flow and being dimensioned for consumption only, potentially leading to reliability and stability issues. This study investigates the use of capacity-based tariffs, export tariffs, and curtailment policies to reduce negative grid impacts without hampering PV deployment. We analyze the effect of such export tariffs on three typical Swiss low-voltage networks (rural, semi-urban, and urban), using power flow analysis to evaluate the power exchanges at the transformer station, as well as line overloading and voltage violations. Finally, a simple case of mutualization of resources is analyzed to assess its potential contribution to relieving network constraints and the economic costs of managing LV networks. We found that the tariff with capacity-based components on the export (CT export daily) severely penalizes PV penetration. This applies to other tariffs as well (e.g. IRR monthly, Curtailment 30, and DT variable) but to a lesser extent. However, the inclusion of curtailment at 50\% and 70\%, as well as mixed tariffs with capacity-based components at import and curtailment, allow for a high degree of PV installations in the three zones studied and help to mitigate the impact of PV on the distributed network.
Safe Output Feedback Improvement with Baselines
Authors: Ruoqi Zhang, Per Mattsson, Dave Zachariah
Date: 2024-09-24 00:00:00

In data-driven control design, an important problem is to deal with uncertainty due to limited and noisy data. One way to do this is to use a min-max approach, which aims to minimize some design criteria for the worst-case scenario. However, a strategy based on this approach can lead to overly conservative controllers. To overcome this issue, we apply the idea of baseline regret, and it is seen that minimizing the baseline regret under model uncertainty can guarantee safe controller improvement with less conservatism and variance in the resulting controllers. To exemplify the use of baseline controllers, we focus on the output feedback setting and propose a two-step control design method; first, an uncertainty set is constructed by a data-driven system identification approach based on finite impulse response models; then a control design criterion based on model reference control is used. To solve the baseline regret optimization problem efficiently, we use a convex approximation of the criterion and apply the scenario approach in optimization. The numerical examples show that the inclusion of baseline regret indeed improves the performance and reduces the variance of the resulting controller.
First Field Trial of LLM-Powered AI Agent for Lifecycle Management of Autonomous Driving Optical Networks
Authors: Xiaomin Liu, Qizhi Qiu, Yihao Zhang, Yuming Cheng, Lilin Yi, Weisheng Hu, Qunbi Zhuge
Date: 2024-09-22 00:00:00
Tasks: AI Agent, Autonomous Driving, Management

We design and demonstrate the first field trial of LLM-powered AI Agent for ADON. Three operation modes of the Agent are proposed for network lifecycle management. The Agent efficiently processes wavelength add/drop and soft/hard failures, and achieves comparable performance to human-designed algorithms for power optimization.
Regional stability conditions for recurrent neural network-based control systems
Authors: Alessio La Bella, Marcello Farina, William D'Amico, Luca Zaccarian
Date: 2024-09-24 00:00:00

In this paper we propose novel global and regional stability analysis conditions based on linear matrix inequalities for a general class of recurrent neural networks. These conditions can be also used for state-feedback control design and a suitable optimization problem enforcing H2 norm minimization properties is defined. The theoretical results are corroborated by numerical simulations, showing the advantages and limitations of the methods presented herein.
Reinforcement Leaning for Infinite-Dimensional Systems
Authors: Wei zhang, Jr-Shin Li
Date: 2024-09-24 00:00:00
Tasks: Reinforcement Learning (RL)

Interest in reinforcement learning (RL) for massive-scale systems consisting of large populations of intelligent agents interacting with heterogeneous environments has witnessed a significant surge in recent years across diverse scientific domains. However, due to the large-scale nature of the system, the majority of state-of-the-art RL techniques either encounter high computational cost or exhibit compromised performance. To mitigate these challenges, we propose a novel RL architecture along with the derivation of effective algorithms to learn optimal policies for any arbitrarily large system of agents. Specifically, we model such a system as a parameterized control system defined on an infinite-dimensional function space. We then develop a moment kernel transform to map the parameterized system and the value function of an RL problem into a reproducing kernel Hilbert space. This transformation subsequently generates a finite-dimensional moment representation for this RL problem. Leveraging this representation, we develop a hierarchical algorithm for learning optimal policies for the infinite-dimensional parameterized system. We further enhance efficiency of the algorithm by exploiting early stopping at each hierarchy, by which we show the fast convergence property of the algorithm through constructing a convergent spectral sequence. The performance and efficiency of the proposed algorithm are validated using practical examples.
Open-/Closed-loop Active Learning for Data-driven Predictive Control
Authors: Shilun Feng, Dawei Shi, Yang Shi, Kaikai Zheng
Date: 2024-09-24 00:00:00
Tasks: Active Learning

An important question in data-driven control is how to obtain an informative dataset. In this work, we consider the problem of effective data acquisition of an unknown linear system with bounded disturbance for both open-loop and closed-loop stages. The learning objective is to minimize the volume of the set of admissible systems. First, a performance measure based on historical data and the input sequence is introduced to characterize the upper bound of the volume of the set of admissible systems. On the basis of this performance measure, an open-loop active learning strategy is proposed to minimize the volume by actively designing inputs during the open-loop stage. For the closed-loop stage, an closed-loop active learning strategy is designed to select and learn from informative closed-loop data. The efficiency of the proposed closed-loop active learning strategy is proved by showing that the unselected data cannot benefit the learning performance. Furthermore, an adaptive predictive controller is designed in accordance with the proposed data acquisition approach. The recursive feasibility and the stability of the controller are proved by analyzing the effect of the closed-loop active learning strategy. Finally, numerical examples and comparisons illustrate the effectiveness of the proposed data acquisition strategy.
LDPC Codes in Cooperative Communication
Authors: Ali Mehrban
Date: 2024-09-23 00:00:00
Tasks: Decoder, Diversity

In fact, the broadcast nature of every transmitter makes it possible for other transceivers in the channel to overhear the broadcasted signal. The proposed idea in cooperative communication is to use these intermediate transceivers as relay for the transmitted signal, therefore we will have spatial diversity which can improve throughput and received data reliability in the system. In this dissertation we consider some important aspects of cooperative communication in a network composed of three nodes. First, we verify the increase of reliability for the received signal by comparing the reliability of the received bits in a cooperative network with a non-cooperative one. Then we step forward and use LDPC error correction technique to improve the reliability of the received bits even more and compare it with a network without LDPC codes (encoder and decoder) to measure the level of improvement for different SNRs. The overall aim of this dissertation is to deploy cooperative communication idea to consider and test its claimed aspects and also enhance its performance by using LDPC error correction technique.
A 3.5 GS/s 1-1 MASH VCO ADC With Second-Order Noise Shaping
Authors: Brendan Saux, Jonas Borgmans, Johan Raman, Pieter Rombouts
Date: 2024-09-23 00:00:00

In this work, a 3.5 GS/s voltage-controlled oscillator (VCO) analog-to-digital converter (ADC) using multi-stage noise shaping (MASH) is presented. This 28nm CMOS ADC achieves second-order noise shaping in an easily-scalable, open-loop configuration. A key enabler of the high-bandwidth MASH VCO ADC is the use of a multi-bit estimated error signal. With an OSR of 16, an SNDR of 67 dB and DR of 68 dB are achieved in 109.375 MHz bandwidth. The full-custom pseudo-analog circuits consume 9 mW, while the automatically generated digital circuits consume another 24 mW. A $\mathbf{FoM_{DR} = 163}$ dB and core area of $\mathbf{0.017\,\mathbf{mm}^2}$ are obtained.
Cloud Deployment of Large-Scale Electromagnetic Transient Simulation -- Discovery and Experiences
Authors: Xiaochuan Luo, Jason Ploof, Xinghao Fang, Qiang Zhang, Song Zhang
Date: 2024-09-23 00:00:00

Electromagnetic Transient (EMT) simulation starts to play a critical role in modern power system planning and operations due to large penetration of inverter based resources (IBRs). The EMT studies are computationally intensive due to very small simulation time step and complex modeling of the protection and control of IBRs. It has been challenging for the traditional on-premises computing infrastructure to meet the ever-increasing computing needs of large-scale EMT studies. This paper shares experience of ISO New England (ISO-NE) on a pilot deployment of EMT simulation in a public cloud using Amazon Web Services. The platform can successfully meet the large-scale EMT simulation computation needs in a cost-effective way while meeting cyber security and data privacy requirements.