pwc-archive/papers-with-abstracts huggingface dataset:
A Miniature Batteryless Bioelectronic Implant Using One Magnetoelectric Transducer for Wireless Powering and PWM Backscatter Communication
Authors: Zhanghao Yu, Yiwei Zou, Huan-Cheng Liao, Fatima Alrashdan, Ziyuan Wen, Joshua E Woods, Wei Wang, Jacob T Robinson, Kaiyuan Yang
Date: 2024-12-03 00:00:00

Wireless minimally invasive bioelectronic implants enable a wide range of applications in healthcare, medicine, and scientific research. Magnetoelectric (ME) wireless power transfer (WPT) has emerged as a promising approach for powering miniature bio-implants because of its remarkable efficiency, safety limit, and misalignment tolerance. However, achieving low-power and high-quality uplink communication using ME remains a challenge. This paper presents a pulse-width modulated (PWM) ME backscatter uplink communication enabled by a switched-capacitor energy extraction (SCEE) technique. The SCEE rapidly extracts and dissipates the kinetic energy within the ME transducer during its ringdown period, enabling time-domain PWM in ME backscatter. Various circuit techniques are presented to realize SCEE with low power consumption. This paper also describes the high-order modeling of ME transducers to facilitate the design and analysis, which shows good matching with measurement. Our prototyping system includes a millimeter-scale ME implant with a fully integrated system-on-chip (SoC) and a portable transceiver for power transfer and bidirectional communication. SCEE is proven to induce >50% amplitude reduction within 2 ME cycles, leading to a PWM ME backscatter uplink with 17.73 kbps data rate and 0.9 pJ/bit efficiency. It also achieves 8.5 x 10 -5 bit-error-rate (BER) at a 5 cm distance, using a lightweight multi-layer-perception (MLP) decoding algorithm. Finally, the system demonstrates continuous wireless neural local-field potential (LFP) recording in an in vitro setup.
Topology Reconstruction of a Resistor Network with Limited Boundary Measurements: An Optimization Approach
Authors: Shivanagouda Biradar, Deepak U Patil
Date: 2024-12-03 00:00:00

A problem of reconstruction of the topology and the respective edge resistance values of an unknown circular planar passive resistive network using limitedly available resistance distance measurements is considered. We develop a multistage topology reconstruction method, assuming that the number of boundary and interior nodes, the maximum and minimum edge conductance, and the Kirchhoff index are known apriori. First, a maximal circular planar electrical network consisting of edges with resistors and switches is constructed; no interior nodes are considered. A sparse difference in convex program $\mathbf{\Pi}_1$ accompanied by round down algorithm is posed to determine the switch positions. The solution gives us a topology that is then utilized to develop a heuristic method to place the interior nodes. The heuristic method consists of reformulating $\mathbf{\Pi}_1$ as a difference of convex program $\mathbf{\Pi}_2$ with relaxed edge weight constraints and the quadratic cost. The interior node placement thus obtained may lead to a non-planar topology. We then use the modified Auslander, Parter, and Goldstein algorithm to obtain a set of planar network topologies and re-optimize the edge weights by solving $\mathbf{\Pi}_3$ for each topology. Optimization problems posed are difference of convex programming problem, as a consequence of constraints triangle inequality and the Kalmansons inequality. A numerical example is used to demonstrate the proposed method.
Bidding in Ancillary Service Markets: An Analytical Approach Using Extreme Value Theory
Authors: Torine Reed Herstad, Jalal Kazempour, Lesia Mitridati, Bert Zwart
Date: 2024-12-03 00:00:00

To encourage the participation of stochastic distributed energy resources in Nordic ancillary service markets, the Danish transmission system operator, Energinet, has introduced grid codes requiring a minimum 90% reliability for the full availability of reserve capacity bids. This paper addresses the bidding strategy of flexibility aggregators under Energinet's reliability requirement by proposing a chance-constrained optimization model. An analytical solution is developed using ideas from extreme value theory, focusing on the "tail" of the empirical data used for flexibility estimation, capturing extreme events where failures are more likely to occur. The proposed model is applied to an electric vehicle aggregator participating in the Nordic market for frequency containment reserve for disturbances (FCR-D). Our results from a realistic case study show that the proposed analytical solution outperforms a commonly used sample-based approach in terms of out-of-sample constraint violation rate.
Development and Performance of a Static Pluviometer System
Authors: Parth Saxena, Pratham Saxena, Adarsh Sowcar, Sreeharsha Angara, Ashutosh Pandey, Thomas Basikolo
Date: 2024-12-03 00:00:00

As the frequency and severity of climate-related events such as droughts, floods, and water scarcity continue to escalate, accurate rainfall monitoring becomes increasingly critical. This paper covers various industry methods of measuring rainfall as well as our own ground pluviometer system. Our system consists of an inexpensive static rain gauge that can operate for approximately six to twelve months without maintenance. It utilizes resistive sensing technology accompanied by a microcontroller to measure the water level depth from the device vessel, recording rainfall at an hourly rate. This study also provides a side-by-side comparison of our pluviometer system with an industry rain gauge, the MeteoRain 200 Compact, from Barani Systems, with the differences in data being statistically insignificant. By prioritizing cost, sustainability, simplicity, ease of maintenance, and assembly, this research contributes to essential rainfall monitoring solutions, specifically for developing countries.
3D Modular Microrobots: Micro-Origami Cubes with Integrated Si Chips Dive, Communicate, Flash Programs, and Form Collectives
Authors: Yeji Lee, Vineeth K. Bandari, John S. McCaskill, Pranathi Adluri, Daniil Karnaushenko, Dmitriy D. Karnaushenko, Oliver G. Schmidt
Date: 2024-12-03 00:00:00
Tasks: Form

Modular microrobotics can potentially address many information-intensive microtasks in medicine, manufacturing and the environment. However, surface area has limited the natural powering, communication, functional integration, and self-assembly of smart mass-fabricated modular robotic devices at small scales. We demonstrate the integrated self-folding and self-rolling of functionalized patterned interior and exterior membrane surfaces resulting in programmable, self-assembling, inter-communicating and self-locomoting micromodules (smartlets ess-than 1 mm3) with interior chambers for on-board buoyancy control. The microbotic divers, with 360 deg solar harvesting rolls, function with sufficient ambient power for communication and programmed locomotion in water via electrolysis. The folding faces carry rigid microcomponents including silicon chiplets as microprocessors and micro-LEDs for communication. This remodels modular microrobotics closer to the surface-rich modular autonomy of biological cells and provides an economical platform for microscopic applications.
Estimation of the Plant Controller Communication Time-Delay Considering PMSG-Based Wind Turbines
Authors: Pablo Marchi, Pablo Gill Estevez, Alejandro Otero, Cecilia Galarza
Date: 2024-12-02 00:00:00
Tasks: State Estimation

The communication control delay between the inverters and the power plant controller can be caused by several factors related to the communication link between them. Under undesirable conditions, high delay values can produce oscillations in the wind power plant that can affect the rest of the power system. In this work, we present a new robust methodology for wind turbines to estimate the value of the communication control delay using PMU data. Several scenarios are considered where external faults are simulated and the performance of the algorithm is evaluated based on dynamic state estimation of the mathematical model of the wind turbine. In this paper, we have shown that the characterization of the delay can be performed offering the transmission system operator an online tool to identify the most suited communication delay for the plant controller models used in dynamic studies.
Energy Profiling and Analysis of 5G Private Networks: Evaluating Energy Consumption Patterns
Authors: Johirul Islam, Ijaz Ahmad, Shakthi Gimhana, Juho Markkula, Erkki Harjula
Date: 2024-12-02 00:00:00

Private 5G networks provide enhanced security, a wide range of optimized services through network slicing, reduced latency, and support for many IoT devices in a specific area, all under the owner's full control. Higher security and privacy to protect sensitive data is the most significant advantage of private networks, in e.g., smart hospitals. For long-term sustainability and cost-effectiveness of private 5G networks, analyzing and understanding the energy consumption variation holds a greater significance in reaching toward green private network architecture for 6G. This paper addresses this research gap by providing energy profiling of network components using an experimental laboratory setup that mimics real private 5G networks under various network conditions, which is a missing aspect in the existing literature.
Amplitude response and square wave describing functions
Authors: Thomas Chaffey, Fulvio Forni
Date: 2024-12-02 00:00:00

An analog of the describing function method is developed using square waves rather than sinusoids. Static nonlinearities map square waves to square waves, and their behavior is characterized by their response to square waves of varying amplitude - their amplitude response. The output of an LTI system to a square wave input is approximated by a square wave, to give an analog of the describing function. The classical describing function method for predicting oscillations in feedback interconnections is generalized to this square wave setting, and gives accurate predictions when oscillations are approximately square.
Data-Driven and Stealthy Deactivation of Safety Filters
Authors: Daniel Arnström, André M. H. Teixeira
Date: 2024-12-02 00:00:00

Safety filters ensure that control actions that are executed are always safe, no matter the controller in question. Previous work has proposed a simple and stealthy false-data injection attack for deactivating such safety filters. This attack injects false sensor measurements to bias state estimates toward the interior of a safety region, making the safety filter accept unsafe control actions. The attack does, however, require the adversary to know the dynamics of the system, the safety region used in the safety filter, and the observer gain. In this work we relax these requirements and show how a similar data-injection attack can be performed when the adversary only observes the input and output of the observer that is used by the safety filter, without any a priori knowledge about the system dynamics, safety region, or observer gain. In particular, the adversary uses the observed data to identify a state-space model that describes the observer dynamics, and then approximates a safety region in the identified embedding. We exemplify the data-driven attack on an inverted pendulum, where we show how the attack can make the system leave a safe set, even when a safety filter is supposed to stop this from happening.
Real-time Traffic Simulation and Management for Large-scale Urban Air Mobility: Integrating Route Guidance and Collision Avoidance
Authors: Canqiang Weng, Can Chen, Jingjun Tan, Tianlu Pan, Renxin Zhong
Date: 2024-12-02 00:00:00
Tasks: Collision Avoidance, Computational Efficiency, Management

Given the spatial heterogeneity of land use patterns in most cities, large-scale UAM will likely be deployed in specific areas, e.g., inter-transfer traffic between suburbs and city centers. However, large-scale UAM operations connecting multiple origin-destination pairs raise concerns about air traffic safety and efficiency with respect to conflict movements, particularly at large conflict points similar to roadway junctions. In this work, we propose an operational framework that integrates route guidance and collision avoidance to achieve an elegant trade-off between air traffic safety and efficiency. The route guidance mechanism aims to optimize aircraft distribution across both spatial and temporal dimensions by regulating their paths (composed of waypoints). Given the optimized paths, the collision avoidance module aims to generate collision-free aircraft trajectories between waypoints in 3D space. To enable large-scale operations, we develop a fast approximation method to solve the optimal path planning problem and employ the velocity obstacle model for collision avoidance. The proposed route guidance strategy significantly reduces the computational requirements for collision avoidance. As far as we know, this work is one of the first to combine route guidance and collision avoidance for UAM. The results indicate that the framework can enable efficient and flexible UAM operations, such as air traffic assignment, congestion prevention, and dynamic airspace clearance. Compared to the management scheme based on air corridors, the proposed framework has considerable improvements in computational efficiency (433%), average travel speed (70.2%), and trip completion rate (130%). The proposed framework has demonstrated great potential for real-time traffic simulation and management in large-scale UAM systems.
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain Disturbances
Authors: Takahito Fujimori
Date: 2024-12-02 00:00:00
Tasks: Gaussian Processes

Linear Quadratic Regulator (LQR) is often combined with feedback linearization (FBL) for nonlinear systems that have the nonlinearity additive to the input. Conventional approaches estimate and cancel the nonlinearity based on the first principle or data-driven methods such as Gaussian Processes (GPs). However, the former needs an elaborate modeling process, and the latter provides a fixed learned model, which may be suffering when the model dynamics are changing. In this letter, we take a Deep Neural Network (DNN) using a real-time-updated dataset to approximate the unknown nonlinearity while the controller is running. Spectrally normalizing the weights in each time-step, we stably incorporate the DNN prediction to an LQR controller and compensate for the nonlinear term. Leveraging the property of the bounded Lipschitz constant of the DNN, we provide theoretical analysis and locally exponential stability of the proposed controller. Simulation results show that our controller significantly outperforms Baseline controllers in trajectory tracking cases.
Data-driven optimal control of unknown nonlinear dynamical systems using the Koopman operator
Authors: Zhexuan Zeng, Ruikun Zhou, Yiming Meng, Jun Liu
Date: 2024-12-02 00:00:00
Tasks: Data Driven Optimal Control, Model-based Reinforcement Learning

Nonlinear optimal control is vital for numerous applications but remains challenging for unknown systems due to the difficulties in accurately modelling dynamics and handling computational demands, particularly in high-dimensional settings. This work develops a theoretically certifiable framework that integrates a modified Koopman operator approach with model-based reinforcement learning to address these challenges. By relaxing the requirements on observable functions, our method incorporates nonlinear terms involving both states and control inputs, significantly enhancing system identification accuracy. Moreover, by leveraging the power of neural networks to solve partial differential equations (PDEs), our approach is able to achieving stabilizing control for high-dimensional dynamical systems, up to 9-dimensional. The learned value function and control laws are proven to converge to those of the true system at each iteration. Additionally, the accumulated cost of the learned control closely approximates that of the true system, with errors ranging from $10^{-5}$ to $10^{-3}$.
Edge Computing for Microgrid via MATLAB Embedded Coder and Low-Cost Smart Meters
Authors: Linna Xu, Jian Huang, Shan Yang, Yongli Zhu
Date: 2024-12-02 00:00:00
Tasks: Edge-computing

In this paper, an edge computing-based machine-learning study is conducted for solar inverter power forecasting and droop control in a remote microgrid. The machine learning models and control algorithms are directly deployed on an edge-computing device (a smart meter-concentrator) in the microgrid rather than on a cloud server at the far-end control center, reducing the communication time the inverters need to wait. Experimental results on an ARM-based smart meter board demonstrate the feasibility and correctness of the proposed approach by comparing against the results on the desktop PC.
Resilience-oriented Planning and Cost Allocation of Energy Storage Integrated with Soft Open Point Based on Resilience Insurance
Authors: Bingkai Huang, Yuxiong Huang, Qianwen Hu, Gengfeng Li, Zhaohong Bie
Date: 2024-12-02 00:00:00
Tasks: Stochastic Optimization

In recent years, frequent extreme events have put forward higher requirements for improving the resilience of distribution networks (DNs). Introducing energy storage integrated with soft open point (E-SOP) is one of the effective ways to improve resilience. However, the widespread application of E-SOP is limited by its high investment cost. Based on this, we propose a cost allocation framework and optimal planning method of E-SOP in resilient DN. Firstly, a cost allocation mechanism for E-SOP based on resilience insurance service is designed; the probability of power users purchasing resilience insurance service is determined based on the expected utility theory. Then, a four-layer stochastic distributionally robust optimization (SDRO) model is developed for E-SOP planning and insurance pricing strategy, where the uncertainty in the intensity of contingent extreme events is addressed by a stochastic optimization approach, while the uncertainty in the occurrence of outages and resilience insurance purchases resulting from a specific extreme event is addressed via a distributionally robust optimization approach. Finally, The effectiveness of the proposed model is verified on the modified IEEE 33-bus DN.
Characterizing and Modeling AI-Driven Animal Ecology Studies at the Edge
Authors: Jenna Kline, Austin O'Quinn, Tanya Berger-Wolf, Christopher Stewart
Date: 2024-12-01 00:00:00
Tasks: Edge-computing

Platforms that run artificial intelligence (AI) pipelines on edge computing resources are transforming the fields of animal ecology and biodiversity, enabling novel wildlife studies in animals' natural habitats. With emerging remote sensing hardware, e.g., camera traps and drones, and sophisticated AI models in situ, edge computing will be more significant in future AI-driven animal ecology (ADAE) studies. However, the study's objectives, the species of interest, its behaviors, range, habitat, and camera placement affect the demand for edge resources at runtime. If edge resources are under-provisioned, studies can miss opportunities to adapt the settings of camera traps and drones to improve the quality and relevance of captured data. This paper presents salient features of ADAE studies that can be used to model latency, throughput objectives, and provision edge resources. Drawing from studies that span over fifty animal species, four geographic locations, and multiple remote sensing methods, we characterized common patterns in ADAE studies, revealing increasingly complex workflows involving various computer vision tasks with strict service level objectives (SLO). ADAE workflow demands will soon exceed individual edge devices' compute and memory resources, requiring multiple networked edge devices to meet performance demands. We developed a framework to scale traces from prior studies and replay them offline on representative edge platforms, allowing us to capture throughput and latency data across edge configurations. We used the data to calibrate queuing and machine learning models that predict performance on unseen edge configurations, achieving errors as low as 19%.
A Compact Hybrid Battery Thermal Management System for Enhanced Cooling
Authors: Zhipeng Lyu, Jinrong Su, Zhe Li, Xiang Li, Hanghang Yan, Lei Chen
Date: 2024-12-01 00:00:00
Tasks: Management

Hybrid battery thermal management systems (HBTMS) combining active liquid cooling and passive phase change materials (PCM) cooling have shown a potential for the thermal management of lithium-ion batteries. However, the fill volume of coolant and PCM in hybrid cooling systems is limited by the size and weight of the HBTMS at high charge/discharge rates. These limitations result in reduced convective heat transfer from the coolant during discharge. The liquefaction rate of PCM is accelerated and the passive cooling effect is reduced. In this paper, we propose a compact hybrid cooling system with multi-inlet U-shaped microchannels for which the gap between channels is embedded by PCM/aluminum foam for compactness. Nanofluid cooling (NC) technology with better thermal conductivity is used. A pulsed flow function is further developed for enhanced cooling (EC) with reduced power consumption. An experimentally validated thermal-fluid dynamics model is developed to optimize operating conditions including coolant type, cooling direction, channel height, inlet flow rate, and cooling scheme. The results show that the hybrid cooling solution of NC+PCM+EC adopted by HBTMS further reduces the maximum temperature of the Li-ion battery by 3.44{\deg}C under a discharge rate of 1C at room temperature of 25{\deg}C with only a 5% increase in power consumption, compared to the conventional liquid cooling method for electric vehicles (EV). The average number of battery charges has increased by about 6 to 15 percent. The results of this study can help improve the range as well as driving safety of new energy EV.
Online convex optimization for constrained control of nonlinear systems
Authors: Marko Nonhoff, Johannes Köhler, Matthias A. Müller
Date: 2024-12-01 00:00:00

This paper investigates the problem of controlling nonlinear dynamical systems subject to state and input constraints while minimizing time-varying and a priori unknown cost functions. We propose a modular approach that combines the online convex optimization framework and reference governors to solve this problem. Our method is general in the sense that we do not limit our analysis to a specific choice of online convex optimization algorithm or reference governor. We show that the dynamic regret of the proposed framework is bounded linearly in both the dynamic regret and the path length of the chosen online convex optimization algorithm, even though the online convex optimization algorithm does not account for the underlying dynamics. We prove that a linear bound with respect to the online convex optimization algorithm's dynamic regret is optimal, i.e., cannot be improved upon. Furthermore, for a standard class of online convex optimization algorithms, our proposed framework attains a bound on its dynamic regret that is linear only in the variation of the cost functions, which is known to be an optimal bound. Finally, we demonstrate implementation and flexibility of the proposed framework by comparing different combinations of online convex optimization algorithms and reference governors to control a nonlinear chemical reactor in a numerical experiment.