pwc-archive/papers-with-abstracts huggingface dataset:
Online SOC Estimation of Lithium-ion Battery Based on Improved Adaptive H Infinity Extended Kalman Filter
Authors: Jierui Wang, Wentao Yu, Guoyang Cheng, Lin Chen
Date: 2023-04-16 00:00:00
Tasks: Management

For the battery management system of electric vehicle, accurate estimation of the State of Charge of Lithium-ion battery can effectively avoid structural damage caused by overcharge or over discharge inside the battery. Considering that the lithium-ion battery is a time-varying nonlinear system, which needs real-time State of Charge estimation, a joint algorithm of forgetting factor recursive least squares and improved adaptive H Infinity Extended Kalman Filter is proposed for online estimation of model parameters and state of charge. Firstly, Thevenin equivalent circuit model is built in Simulink of MATLAB R2021b, and the model parameters are estimated by forgetting factor recursive least square in real time. Secondly, the improved adaptive H Infinity Extended Kalman Filter is used to estimate State of Charge in true time. Finally, the feasibility of the algorithm is verified by two different lithium-ion battery conditions. The experimental results show that improved adaptive H Infinity Extended Kalman Filter has the highest and most stable State of Charge estimation accuracy than the other three comparison methods. The Root Mean Square Error and Mean Absolute Error are 0.6008 % and 0.3578 % under the Dynamic Stress Test condition, and 1.0068 % and 0.8721 % under the Federal Urban Driving Schedule condition respectively
Sensor Data Validation for Garbage Collection Using Machine Learning
Authors: Kabeer Gulati, Zuhaib Ahmad, Abhishek Raj
Date: 2023-04-16 00:00:00

Any complex dynamic system's ability to function successfully depends in significant part on the accuracy of the sensor data; hence sensor data validation is crucial. Because sensor data is utilized for monitoring and oversight, erroneous sensor data would result in overall poor process output. In this study, the data confidence of the sensor data is ascertained using a Mamdani fuzzy inference system. Erroneous data can be corrected with this method. If the sensor outputs faulty value for a prolonged period of time, the system will be reported and a report will be generated. This can be used as a generic module for any system. This fuzzy system is then used on the readings from an ultrasonic sensor and is used as a part of a bigger and more complex IoT system.
Guaranteed Stability Margins for Decentralized Linear Quadratic Regulators
Authors: Mruganka Kashyap, Laurent Lessard
Date: 2023-04-15 00:00:00

It is well-known that linear quadratic regulators (LQR) enjoy guaranteed stability margins, whereas linear quadratic Gaussian regulators (LQG) do not. In this letter, we consider systems and compensators defined over directed acyclic graphs. In particular, there are multiple decision-makers, each with access to a different part of the global state. In this setting, the optimal LQR compensator is dynamic, similar to classical LQG. We show that when sub-controller input costs are decoupled (but there is possible coupling between sub-controller state costs), the decentralized LQR compensator enjoys similar guaranteed stability margins to classical LQR. However, these guarantees disappear when cost coupling is introduced.
A Machine Learning-Enhanced Benders Decomposition Approach to Solve the Transmission Expansion Planning Problem under Uncertainty
Authors: Stefan Borozan, Spyros Giannelos, Paola Falugi, Alexandre Moreira, Goran Strbac
Date: 2023-04-15 00:00:00
Tasks: Computational Efficiency

The necessary decarbonization efforts in energy sectors entail the integration of flexibility assets, as well as increased levels of uncertainty for the planning and operation of power systems. To cope with this in a cost-effective manner, transmission expansion planning (TEP) models need to incorporate progressively more details to represent potential long-term system developments and the operation of power grids with intermittent renewable generation. However, the increased modeling complexities of TEP exercises can easily lead to computationally intractable optimization problems. Currently, most techniques that address computational intractability alter the original problem, thus neglecting critical modeling aspects or affecting the structure of the optimal solution. In this paper, we propose an alternative approach to significantly alleviate the computational burden of large-scale TEP problems. Our approach integrates machine learning (ML) with the well-established Benders decomposition to manage the problem size while preserving solution quality. The proposed ML-enhanced Multicut Benders Decomposition algorithm improves computational efficiency by identifying effective and ineffective optimality cuts via supervised learning techniques. We illustrate the benefits of the proposed methodology by solving a number of multi-stage TEP problems of different sizes, based on the IEEE24 and IEEE118 test systems, while also considering energy storage investment options.
Globally Composite-Learning-Based Intelligent Fast Finite-Time Control for Uncertain Strict-Feedback Systems with Nonlinearly Periodic Disturbances
Authors: Xidong Wang, Zhan Li, Zhen He
Date: 2023-04-15 00:00:00
Tasks: valid

This brief aims at the issue of globally composite-learning-based neural fast finite-time (F-FnT) tracking control for a class of uncertain systems in strict-feedback form subject to nonlinearly periodic disturbances. First, uncertain dynamics with periodic parameters are identified by incorporating Fourier series expansion (FSE) into an intelligent estimator, which leverages the feedback of newly designed prediction errors in updating weights to boost learning performance. Then, a novel switching mechanism is constructed to fulfill smooth switching from the composite FSE-based neural controller to robust control law when the inputs of the intelligent estimator transcend the valid approximation domain. By fusing the switching mechanism with an improved F-FnT backstepping algorithm, the globally F-FnT boundedness of all variables in the closed-loop system is guaranteed. Finally, a simulation study is conducted to evince the availability of the theoretical result.
Singular Perturbation-based Large-Signal Order Reduction of Microgrids for Stability and Accuracy Synthesis with Control
Authors: Zixiao Ma, Zhaoyu Wang, Yuxuan Yuan, Tianqi Hong
Date: 2019-10-08 00:00:00

With the increasing penetration of distributed energy resources (DERs), it is of vital importance to study the dynamic stability of microgrids (MGs) with external control inputs in the electromagnetic transient (EMT) time scale. This requires detailed models of the underlying control structure of MGs and results in a high-order nonlinear MG control system. Higher-level controller design and stability analysis of such high-order systems are usually intractable and computation-costly. To overcome these challenges, this paper proposes a large-signal order reduction (LSOR) method for MGs with considerations of external control inputs and the detailed dynamics of underlying control levels based on singular perturbation theory (SPT). Specially, we innovatively proposed and strictly proved a general stability and accuracy assessment theorem that allows us to analyze the dynamic stability of a full-order nonlinear system by only leveraging its corresponding reduced-order model (ROM) and boundary layer model (BLM). Moreover, this theorem also theoretically provides a set of conditions under which the developed ROM is accurate. Finally, by embedding such a theorem into the SPT, we propose a novel LSOR approach with guaranteed accuracy and stability analysis equivalence. The proposed LSOR method is generic and can be applied to arbitrary dynamic systems. Multiple case studies are conducted on MG systems to show the effectiveness of the proposed approach.
FOCUS : A framework for energy system optimization from prosumer to district and city scale
Authors: Jingyu Gong, Yi Nie, Jonas van Ouwerkerk, Felix Wege, Mauricio Celi Cortés, Christoph von Oy, Jonas Brucksch, Christian Bußar, Thomas Schreiber, Dirk Uwe Sauer, Dirk Müller, Antonello Monti
Date: 2023-04-14 00:00:00

Decarbonizing the energy sector is one of the main challenges to combat the climate crisis. Cities play an important role to reach climate neutrality as more than 70% of global CO2 emissions originate from urban areas. Decarbonization of energy supply systems can be achieved through various means, including the use of renewable energy sources, improving the efficiency of technologies, the coupling of different energy sectors, and the use of flexibility considering individual prosumer behaviour. This leads to an increasingly decentralized energy system, which is challenging to operate in a robust and cost-effective way. The evaluation of technologies and subsystems can only be done from the perspective of the system in which it is embedded and it is highly dependent on their networking and application scenarios. Therefore, the design and operation of energy systems require adequate computation and evaluation tools, which offer a holistic view of all interconnected components. The currently available optimization tools have limitations, such as limited scope of technologies and sectors, high requirements on data, high computational cost, and difficulty in handling multi-objective optimization. To overcome these limitations a software framework called FOCUS for the flexible and dynamic modeling of any urban sector-coupled energy system is developed. The framework includes a library containing models for different technologies and offers a variety of parameter sets for each technology. FOCUS can handle multi-objective problems by returning Pareto-optimal fronts, which helps users to discover the trade-off between criteria and objectives. The developed tool can identify new flexibility potentials in the energy system, actively support companies in the respective field to optimize urban energy system planning solutions, and determine possible threads to the stable operation of such systems.
Stochastic Model Predictive Control using Initial State and Variance Interpolation
Authors: Henning Schlüter, Frank Allgöwer
Date: 2023-04-14 00:00:00
Tasks: Model Predictive Control

We present a Stochastic Model Predictive Control (SMPC) framework for linear systems subject to Gaussian disturbances. In order to avoid feasibility issues, we employ a recent initialization strategy, optimizing over an interpolation of the initial state between the current measurement and previous prediction. By also considering the variance in the interpolation, we can employ variable-size tubes, to ensure constraint satisfaction in closed-loop. We show that this novel method improves control performance and enables following the constraint closer, then previous methods. Using a DC-DC converter as numerical example we illustrated the improvement over previous methods.
Nonlinear feedback stabilisation and stochastic disturbance suppression of actively Q-switched lasers
Authors: Lukas Tarra, Andreas Deutschmann-Olek, Andreas Kugi
Date: 2023-04-14 00:00:00

Actively Q-switched lasers are widely used tools which are required to produce stable output pulse energies for many applications. In this paper, a model-based control concept for actively Q-switched lasers is presented which stabilises their nonlinear pulse-to-pulse dynamics and rejects stochastic disturbances arising from amplified spontaneous emission. The feasibility of the control task is demonstrated to strongly depend on the design of the semi-active prelasing approach. In contrast to state-of-the-art hardware-based controllers, the proposed concept is flexible and cost-effective as it is not tailored to specific operation parameters.
A Low-cost Through-metal Communication System for Sensors in Metallic Pipes
Authors: Hongzhi Guo, Marlin Prince, Javionn Ramsey, Jarvis Turner, Marcus Allen, Chevel Samuels, Jordan Atta Nuako
Date: 2023-04-14 00:00:00

Metallic pipes and other containers are widely used to store and transport toxic gases and liquids. Various sensors have been designed to monitor the environment inside metallic pipes and containers, such as pressure, liquid-level, and chemical sensors. Moreover, sensors are also used to inspect and detect pipe leakages. However, sensors are usually placed outside of metallic pipes and containers and use ultrasound to monitor the internal unseen environment. This is mainly due to the fact that internal sensors cannot communicate with external data sinks without cables, but using cables can dramatically affect the metal-sealed structure. Wireless communication is desirable to communicate with internal sensors, but it experiences high attenuation losses since metal can block wireless signals due to its high conductivity. This paper develops a low-cost through-metal communication system prototype using off-the-shelf electronic components. The system is fully reconfigurable, and arbitrary modulation and coding schemes can be implemented. We design the transmit module which includes a signal processing microcontroller, an amplifier, and a transmit coil, and the receive module which includes a receive coil, an amplifier, and a microcontroller with demodulation algorithms and bit-error-rate (BER) calculations. The performance of the prototype is evaluated using various symbol rates, distances, and transmission power. The results show that the communication system can achieve a 500 bps data rate with 0.01 BER and 3.4 cm communication range when penetrating an Aluminum pipe with 7 mm thickness.
Reachability Analysis Using Hybrid Zonotopes and Functional Decomposition
Authors: Jacob A. Siefert, Trevor J. Bird, Andrew F. Thompson, Jonah J. Glunt, Justin P. Koeln, Neera Jain, Herschel C. Pangborn
Date: 2023-04-13 00:00:00
Tasks: Model Predictive Control

This paper proposes methods for reachability analysis of nonlinear systems in both open loop and closed loop with advanced controllers. The methods combine hybrid zonotopes, a construct called a state-update set, functional decomposition, and special ordered set approximations to enable linear growth in reachable set memory complexity with time and linear scaling in computational complexity with the system dimension. Facilitating this combination are new identities for constructing nonconvex sets that contain nonlinear functions and for efficiently converting a collection of polytopes from vertex representation to hybrid zonotope representation. Benchmark numerical examples from the literature demonstrate the proposed methods and provide comparison to state-of-the-art techniques.
A Multi-Battery Model for the Aggregate Flexibility of Heterogeneous Electric Vehicles
Authors: Feras Al Taha, Tyrone Vincent, Eilyan Bitar
Date: 2023-04-13 00:00:00

The increasing prevalence of electric vehicles (EVs) in the transportation sector will introduce a large number of highly flexible electric loads that EV aggregators can pool and control to provide energy and ancillary services to the wholesale electricity market. To integrate large populations of EVs into electricity market operations, aggregators must express the aggregate flexibility of the EVs under their control in the form of a small number of energy storage (battery) resources that accurately capture the supply/demand capabilities of the individual EVs as a collective. To this end, we propose a novel multi-battery flexibility model defined as a linear combination of a small number of base sets (termed batteries) that reflect the differing geometric shapes of the individual EV flexibility sets, and suggest a clustering approach to identify these base sets. We study the problem of computing a multi-battery flexibility set that has minimum Hausdorff distance to the aggregate flexibility set, subject to the constraint that the multi-battery flexibility set be a subset of the aggregate flexibility set. We show how to conservatively approximate this problem with a tractable convex program, and illustrate the performance achievable by our method with several numerical experiments.
How the Moon Impacts Subsea Communication Cables
Authors: Lothar Moeller
Date: 2023-04-14 00:00:00

We report tidal-induced latency variations on a transpacific subsea cable. Week-long recordings with a precision phase meter suggest length changes in the sub-meter range caused by the Poisson effect. The described method adds to the toolbox for the new field >>optical oceanic seismology<<.
Graph-informed simulation-based inference for models of active matter
Authors: Namid R. Stillman, Silke Henkes, Roberto Mayor, Gilles Louppe
Date: 2023-04-05 00:00:00

Many collective systems exist in nature far from equilibrium, ranging from cellular sheets up to flocks of birds. These systems reflect a form of active matter, whereby individual material components have internal energy. Under specific parameter regimes, these active systems undergo phase transitions whereby small fluctuations of single components can lead to global changes to the rheology of the system. Simulations and methods from statistical physics are typically used to understand and predict these phase transitions for real-world observations. In this work, we demonstrate that simulation-based inference can be used to robustly infer active matter parameters from system observations. Moreover, we demonstrate that a small number (from one to three) snapshots of the system can be used for parameter inference and that this graph-informed approach outperforms typical metrics such as the average velocity or mean square displacement of the system. Our work highlights that high-level system information is contained within the relational structure of a collective system and that this can be exploited to better couple models to data.
Exclusion model of mRNA translation with collision-induced ribosome drop-off
Authors: Johannes Keisers, Joachim Krug
Date: 2023-04-14 00:00:00
Tasks: Translation

The translation of messenger RNA transcripts to proteins is commonly modeled as a one-dimensional totally asymmetric exclusion process with extended particles. Here we focus on the effects of premature termination of translation through the irreversible detachment of ribosomes. We consider a model where the detachment is induced by the unsuccessful attempt to move to an occupied site. The model is exactly solvable in a simplified geometry consisting of the translation initiation region followed by a single slow site representing a translation bottleneck. In agreement with recent experimental and computational studies we find a non-monotonic dependence of the ribosome current on the initiation rate, but only if the leading particle in a colliding pair detaches. Simulations show that the effect persists for larger lattices and extended bottlenecks. In the homogeneous system the ribosome density decays asymptotically as the inverse square root of the distance to the initiation site.
Synchronization in STDP-driven memristive neural networks with time-varying topology
Authors: Marius E. Yamakou, Mathieu Desroches, Serafim Rodrigues
Date: 2023-04-17 00:00:00

Synchronization is a widespread phenomenon in the brain. Despite numerous studies, the specific parameter configurations of the synaptic network structure and learning rules needed to achieve robust and enduring synchronization in neurons driven by spike-timing-dependent plasticity (STDP) and temporal networks subject to homeostatic structural plasticity (HSP) rules remain unclear. Here, we bridge this gap by determining the configurations required to achieve high and stable degrees of complete synchronization (CS) and phase synchronization (PS) in time-varying small-world and random neural networks driven by STDP and HSP. In particular, we found that decreasing $P$ (which enhances the strengthening effect of STDP on the average synaptic weight) and increasing $F$ (which speeds up the swapping rate of synapses between neurons) always lead to higher and more stable degrees of CS and PS in small-world and random networks, provided that the network parameters such as the synaptic time delay $\tau_c$, the average degree $\langle k \rangle$, and the rewiring probability $\beta$ have some appropriate values. When $\tau_c$, $\langle k \rangle$, and $\beta$ are not fixed at these appropriate values, the degree and stability of CS and PS may increase or decrease when $F$ increases, depending on the network topology. It is also found that the time delay $\tau_c$ can induce intermittent CS and PS whose occurrence is independent $F$. Our results could have applications in designing neuromorphic circuits for optimal information processing and transmission via synchronization phenomena.
Pattern Forming Mechanisms of Color Vision
Authors: Zily Burstein, David D. Reid, Peter J. Thomas, Jack D. Cowan
Date: 2023-04-15 00:00:00

While our understanding of the way single neurons process chromatic stimuli in the early visual pathway has advanced significantly in recent years, we do not yet know how these cells interact to form stable representations of hue. Drawing on physiological studies, we offer a dynamical model of how the primary visual cortex tunes for color, hinged on intracortical interactions and emergent network effects. After detailing the evolution of network activity through analytical and numerical approaches, we discuss the effects of the model's cortical parameters on the selectivity of the tuning curves. In particular, we explore the role of the model's thresholding nonlinearity in enhancing hue selectivity by expanding the region of stability, allowing for the precise encoding of chromatic stimuli in early vision. Finally, in the absence of a stimulus, the model is capable of explaining hallucinatory color perception via a Turing-like mechanism of biological pattern formation.
Locality sensitive hashing via mechanical behavior
Authors: Emma Lejeune, Peerasait Prachaseree
Date: 2023-03-09 00:00:00

From healing wounds to maintaining homeostasis in cyclically loaded tissue, living systems have a phenomenal ability to sense, store, and respond to mechanical stimuli. Broadly speaking, there is significant interest in designing engineered systems to recapitulate this incredible functionality. In engineered systems, we have seen significant recent computationally driven advances in sensing and control. And, there has been a growing interest - inspired in part by the incredible distributed and emergent functionality observed in the natural world - in exploring the ability of engineered systems to perform computation through mechanisms that are fundamentally driven by physical laws. In this work, we focus on a small segment of this broad and evolving field: locality sensitive hashing via mechanical behavior. Specifically, we will address the question: can mechanical information (i.e., loads) be transformed by mechanical systems (i.e., converted into sensor readouts) such that the mechanical system meets the requirements for a locality sensitive hash function? Overall, we not only find that mechanical systems are able to perform this function, but also that different mechanical systems vary widely in their efficacy at this task. Looking forward, we view this work as a starting point for significant future investigation into the design and optimization of mechanical systems for conveying mechanical information for downstream computing.