pwc-archive/papers-with-abstracts huggingface dataset:
OpenCL-based FPGA Accelerator for Semi-Global Approximate String Matching Using Diagonal Bit-Vectors
Authors: David Castells-Rufas, Santiago Marco-Sola, Quim Aguado-Puig, Antonio Espinosa-Morales, Juan Carlos Moure, Lluc Alvarez, Miquel Moretó
Date: 2021-09-03 00:00:00
Proceeding: International Conference on Field-Programmable Logic and Applications (FPL) 2021 9

An FPGA accelerator for the computation of the semi-global Levenshtein distance between a pattern and a reference text is presented. The accelerator provides an important benefit to reduce the execution time of read-mappers used in short-read genomic sequencing. Previous attempts to solve the same problem in FPGA use the Myers algorithm following a column approach to compute the dynamic programming table. We use an approach based on diagonals that allows for some resource savings while maintaining a very high throughput of 1 alignment per clock cycle. The design is implemented in OpenCL and tested on two FPGA accelerators. The maximum
performance obtained is 91.5 MPairs/s for 100 × 120 sequences and 47 MPairs/s for 300 × 360 sequences, the highest ever reported for this problem.
Modal equilibrium of a tradable credit scheme with a trip-based MFD and logit-based decision-making
Authors: Louis Balzer, Ludovic Leclercq
Date: 2021-12-14 00:00:00
Tasks: Decision Making, Management

The literature about tradable credit schemes (TCS) as a demand management system alleviating congestion flourished in the past decade. Most proposed formulations are based on static models and thus do not account for the congestion dynamics. This paper considers elastic demand and implements a TCS to foster modal shift by restricting the number of cars allowed in the network over the day. A trip-based Macroscopic Fundamental Diagram (MFD) model represents the traffic dynamics at the whole urban scale. We assume the users have different OD pairs and choose between driving their car or riding the transit following a logit model. We aim to compute the modal shares and credit price at equilibrium under TCS. The travel times are linearized with respect to the modal shares to improve the convergence. We then present a method to find the credit charge minimizing the total travel time alone or combined with the carbon emission. The proposed methodology is illustrated with a typical demand profile from 7:00 to 10:00 for Lyon Metropolis. We show that traffic dynamics and trip heterogeneity matter when deriving the modal equilibrium under a TCS. A method is described to compute the linearization of the travel times and compared against a classical descend method (MSA). The proposed linearization is a promising tool to circumvent the complexity of the implicit formulation of the trip-based MFD. Under an optimized TCS, the total travel time decreases by 17% and the carbon emission by 45% by increasing the PT share by 24 points.
Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios
Authors: Kaiwen Liu, Nan Li, H. Eric Tseng, Ilya Kolmanovsky, Anouck Girard
Date: 2021-12-14 00:00:00
Tasks: Autonomous Vehicles, Model Predictive Control, Trajectory Prediction

Merging is, in general, a challenging task for both human drivers and autonomous vehicles, especially in dense traffic, because the merging vehicle typically needs to interact with other vehicles to identify or create a gap and safely merge into. In this paper, we consider the problem of autonomous vehicle control for forced merge scenarios. We propose a novel game-theoretic controller, called the Leader-Follower Game Controller (LFGC), in which the interactions between the autonomous ego vehicle and other vehicles with a priori uncertain driving intentions is modeled as a partially observable leader-follower game. The LFGC estimates the other vehicles' intentions online based on observed trajectories, and then predicts their future trajectories and plans the ego vehicle's own trajectory using Model Predictive Control (MPC) to simultaneously achieve probabilistically guaranteed safety and merging objectives. To verify the performance of LFGC, we test it in simulations and with the NGSIM data, where the LFGC demonstrates a high success rate of 97.5% in merging.
Spatiogram: A phase based directional angular measure and perceptual weighting for ensemble source width
Authors: Arthi S, Sreenivas T V
Date: 2021-12-14 00:00:00

In concert hall studies, inter-aural cross-correlation (IACC), which is signal dependent, is used as a measure of perceptual source width. The same measure is used for perceptual source width in the case of distributed sources also. In this work, we examine the validity of IACC for both the cases and develop an improved measure for ensemble-like distributed sources. We decompose the new objective measure for perceptual ensemble source width (ESW) into two components (i) phase based directional angular measure, which is timbre independent (spatial measure) and (ii) mean time-bandwidth energy (MTBE), a perceptual weight, (timbre measure). This combination of spatial and timbral measures can be extended as an alternate measure for determining auditory source width (ASW) and listener envelopment (LEV) of arbitrary signals in concert-hall and room acoustics.
Towards Organic Distribution Systems -- The Vision of Self-Configuring, Self-Organising, Self-Healing, and Self-Optimising Power Distribution Management
Authors: Inga Loeser, Martin Braun, Christian Gruhl, Jan-Hendrik Menke, Bernhard Sick, Sven Tomforde
Date: 2021-12-14 00:00:00
Tasks: energy management, Management

Due to the decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the basic critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system's operational limits are not violated. On the grid level, distribution management systems (DMS) try to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by using batteries, photovoltaic generators, or flexible loads optimally. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing "organic" HEMS and DMS which possess multiple self-* capabilities. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this article, the vision of ODiS is presented in detail based on a thorough review of the state of the art.
On the Necessity and Sufficiency of Discrete-Time O'Shea-Zames-Falb Multipliers
Authors: Lanlan Su, Peter Seiler, Joaquin Carrasco, Sei Zhen Khong
Date: 2021-12-14 00:00:00

This paper considers the robust stability of a discrete-time Lurye system consisting of the feedback interconnection between a linear system and a bounded and monotone nonlinearity. It has been conjectured that the existence of a suitable linear time-invariant (LTI) O'Shea-Zames-Falb multiplier is not only sufficient but also necessary. Roughly speaking, a successful proof of the conjecture would require: (a) a conic parameterization of a set of multipliers that describes exactly the set of nonlinearities, (b) a lossless S-procedure to show that the non-existence of a multiplier implies that the Lurye system is not uniformly robustly stable over the set of nonlinearities, and (c) the existence of a multiplier in the set of multipliers used in (a) implies the existence of an LTI multiplier. We investigate these three steps, showing the current bottlenecks for proving this conjecture. In addition, we provide an extension of the class of multipliers which may be used to disprove the conjecture.
Towards a Network Control Theory of Electroconvulsive Therapy Response
Authors: Tim Hahn, Hamidreza Jamalabadi, Erfan Nozari, Nils R. Winter, Jan Ernsting, Marius Gruber, Marco J. Mauritz, Pascal Grumbach, Lukas Fisch, Ramona Leenings, Kelvin Sarink, Julian Blanke, Leon Kleine Vennekate, Daniel Emden, Nils Opel, Dominik Grotegerd, Verena Enneking, Susanne Meinert, Tiana Borgers, Melissa Klug, Elisabeth J. Leehr, Katharina Dohm, Walter Heindel, Joachim Gross, Udo Dannlowski, Ronny Redlich, Jonathan Repple
Date: 2021-12-14 00:00:00

Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of predicting individual response to ECT remains elusive. To address this, we posit a quantitative, mechanistic framework of ECT response based on Network Control Theory (NCT). Then, we empirically test our approach and employ it to predict ECT treatment response. To this end, we derive a formal association between Postictal Suppression Index (PSI) - an ECT seizure quality index - and whole-brain modal and average controllability, NCT metrics based on white matter brain network architecture, respectively. Exploiting the known association of ECT response and PSI, we then hypothesized an association between our controllability metrics and ECT response mediated by PSI. We formally tested this conjecture in N=50 depressive patients undergoing ECT. We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses. In addition, we show the expected mediation effects via PSI. Importantly, our theoretically motivated metrics are at least on par with extensive machine learning models based on pre-ECT connectome data. In summary, we derived and tested a control-theoretic framework capable of predicting ECT response based on individual brain network architecture. It makes testable, quantitative predictions regarding individual therapeutic response, which are corroborated by strong empirical evidence. Our work might constitute a starting point for a comprehensive, quantitative theory of personalized ECT interventions rooted in control theory.
Non-iterative Calculation of Quasi-Dynamic Energy Flow in the Heat and Electricity Integrated Energy Systems
Authors: Ruizhi Yu, Wei Gu
Date: 2021-12-14 00:00:00
Tasks: CPU, Time Series Analysis

Quasi-dynamic energy flow calculation is an indispensable tool for the heat and electricity integrated energy system (HE-IES) analysis. One solves the nonlinear partial differential algebraic equations to obtain thermal, hydraulic and electric variations. However, mainstream iteration solvers face the challenges of inefficiency and bad robustness. For one thing, the frequent update and factorization of Jacobian matrices utilize high CPU time. For another, the per-step iteration numbers grow exponentially as the system loading level creeps up. This paper presents a novel non-iterative algorithm for the quasi-dynamic energy flow calculation. The kernel of the proposed algorithm is to transform these nonlinear equations into linear recursive ones, by solving which, we obtain explicit closed-form solutions of unknown variables. In each step, the proposed algorithm requires only one matrix factorization and fixed times of arithmetic operations regardless of the loading levels, so that it achieves small and consistent per-step time costs. A semi-discrete scheme is used in PDE solution to avoid dissipative and dispersive errors that are often overlooked in previous literature. To ensure convergence, we also propose to control the temporal step sizes adaptively by estimating the simulation errors. Case studies showed that the proposed method manifested efficient and robust time performance compared with the iterative algorithms, and meanwhile preserved high accuracy.
Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use
Authors: Emilio T. Maddalena, Silvio A. Muller, Rafael M. dos Santos, Christophe Salzmann, Colin N. Jones
Date: 2021-12-14 00:00:00
Tasks: Gaussian Processes, Model Predictive Control

Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case study of an industrial plant responsible for cooling a hospital surgery center. The adopted approach to guaranteeing thermal comfort and reducing electrical energy consumption is based on a statistical non-parametric, non-linear regression technique named Gaussian processes. Our study aimed at assessing the suitability of the aforementioned technique to learning the building dynamics and yielding models for our model predictive control (MPC) scheme. Experimental results gathered while the building was under regular use showcase the final controller performance while subject to a number of measured and unmeasured disturbances. Finally, we provide readers with practical details and recommendations on how to manage the computational complexity of the on-line optimization problem and obtain high-quality solutions from solvers.
Composing MPC with LQR and Neural Network for Amortized Efficiency and Stable Control
Authors: Fangyu Wu, Guanhua Wang, Siyuan Zhuang, Kehan Wang, Alexander Keimer, Ion Stoica, Alexandre Bayen
Date: 2021-12-14 00:00:00
Tasks: Computational Efficiency, Model Predictive Control

Model predictive control (MPC) is a powerful control method that handles dynamical systems with constraints. However, solving MPC iteratively in real time, i.e., implicit MPC, remains a computational challenge. To address this, common solutions include explicit MPC and function approximation. Both methods, whenever applicable, may improve the computational efficiency of the implicit MPC by several orders of magnitude. Nevertheless, explicit MPC often requires expensive pre-computation and does not easily apply to higher-dimensional problems. Meanwhile, function approximation, although scales better with dimension, still requires pre-training on a large dataset and generally cannot guarantee to find an accurate surrogate policy, the failure of which often leads to closed-loop instability. To address these issues, we propose a triple-mode hybrid control scheme, named Memory-Augmented MPC, by combining a linear quadratic regulator, a neural network, and an MPC. From its standard form, we further derive two variants of such hybrid control scheme: one customized for chaotic systems and the other for slow systems. The proposed scheme does not require pre-computation and can improve the amortized running time of the composed MPC with a well-trained neural network. In addition, the scheme maintains closed-loop stability with any neural networks of proper input and output dimensions, alleviating the need for certifying optimality of the neural network in safety-critical applications.
Formal Estimation of Collision Risks for Autonomous Vehicles: A Compositional Data-Driven Approach
Authors: Abolfazl Lavaei, Luigi Di Lillo, Andrea Censi, Emilio Frazzoli
Date: 2021-12-14 00:00:00
Tasks: Autonomous Vehicles

In this work, we propose a compositional data-driven approach for the formal estimation of collision risks for autonomous vehicles (AVs) while acting in a stochastic multi-agent framework. The proposed approach is based on the construction of sub-barrier certificates for each stochastic agent via a set of data collected from its trajectories while providing an a-priori guaranteed confidence on the data-driven estimation. In our proposed setting, we first cast the original collision risk problem for each agent as a robust optimization program (ROP). Solving the acquired ROP is not tractable due to an unknown model that appears in one of its constraints. To tackle this difficulty, we collect finite numbers of data from trajectories of each agent and provide a scenario optimization program (SOP) corresponding to the original ROP. We then establish a probabilistic bridge between the optimal value of SOP and that of ROP, and accordingly, we formally construct the sub-barrier certificate for each unknown agent based on the number of data and a required level of confidence. We then propose a compositional technique based on small-gain reasoning to quantify the collision risk for multi-agent AVs with some desirable confidence based on sub-barrier certificates of individual agents constructed from data. For the case that the proposed compositionality conditions are not satisfied, we provide a relaxed version of compositional results without requiring any compositionality conditions but at the cost of providing a potentially conservative collision risk. Eventually, we also present our approaches for non-stochastic multi-agent AVs. We demonstrate the effectiveness of our proposed results by applying them to a vehicle platooning consisting of 100 vehicles with 1 leader and 99 followers. We formally estimate the collision risk by collecting data from trajectories of each agent.
Output fusion of MPC and PID and its application in intelligent layered water injection of oilfield
Authors: Yuan-Long Yue, Hao-Yang Wen, Xin Zuo, Mao Sheng, Fu-Chao Sun
Date: 2021-12-14 00:00:00

To improve the dynamic response performance of wave code communication in intelligent layered water injection of oilfield, this paper proposes an output optimal fusion control method based on MPC-PID. Firstly, depending on the well structure and the flow-pressure characteristics of the layer, the steady-state model between the differential pressure and flow of the whole well and different layer sections is established for layered water injection, and the corresponding wave code amplitude at the steady-state operating point of different layer sections is solved, the numerical calculation verifies that the increase of the nozzle opening in a single layer section will drive the pressure and flow curve of the whole well downward. Secondly, combining the dynamic response characteristics and steady-state model of the whole-well water distribution equipment, a dynamic model of layered intelligent water injection is established, and the generation process of the wave code is defined; Finally, the MPC-PID optimal fusion control algorithm structure is designed to derive the fusion control law that minimizes the cost function under fixed weights, , and the optimal weights are calculated by combining the internal model structure of controller, so the optimization performance of each algorithm in the optimal fusion control is balanced. By analyzing the control simulation results, the fast response characteristics of the fusion control method are verified. Meanwhile, the simulation comparison experiments of fast wave code communication under different methods are conducted with the actual working conditions, the results show that the fusion control method has both fast tracking control capability and strong robustness, which effectively enhances the efficiency of wave code communication and shortens the wave code operation time.
Optimal Memory Scheme for Accelerated Consensus Over Multi-Agent Networks
Authors: Jiahao Dai, Jing-Wen Yi, Li Chai
Date: 2021-12-14 00:00:00

The consensus over multi-agent networks can be accelerated by introducing agent's memory to the control protocol. In this paper, a more general protocol with the node memory and the state deviation memory is designed. We aim to provide the optimal memory scheme to accelerate consensus. The contributions of this paper are three: (i) For the one-tap memory scheme, we demonstrate that the state deviation memory is useless for the optimal convergence. (ii) In the worst case, we prove that it is a vain to add any tap of the state deviation memory, and the one-tap node memory is sufficient to achieve the optimal convergence. (iii) We show that the two-tap state deviation memory is effective on some special networks, such as star networks. Numerical examples are listed to illustrate the validity and correctness of the obtained results.
Hierarchical Stochastic Scheduling of Multi-Community Integrated Energy Systems in Uncertain Environments via Stackelberg Game
Authors: Yang Li, Bin Wang, Zhen Yang, Jiazheng Li, Chen Chen
Date: 2021-12-14 00:00:00
Tasks: energy management, Generative Adversarial Network, Management, Scheduling

An operating entity utilizing community-integrated energy systems with a large number of small-scale distributed energy sources can easily trade with existing distribution markets. To solve the energy management and pricing problem of multi-community integrated energy systems (MCIESs) with multi-energy interaction, this study investigated a hierarchical stochastic optimal scheduling method for uncertain environments. To handle multiple uncertainties, a Wasserstein generative adversarial network with a gradient penalty was used to generate renewable scenarios, and the Kmeans++ clustering algorithm was employed to generate typical scenarios. A Stackelberg-based hierarchical stochastic schedule with an integrated demand response was constructed, where the MCIES operator acted as the leader pursuing the maximum net profit by setting energy prices, while the building users were followers who adjusted their energy consumption plans to minimize their total costs. Finally, a distributed iterative solution method based on a metaheuristic was designed. The effectiveness of the proposed method was verified using practical examples.
On Control Schemes of Voltage Source Converters
Authors: Fahmid Sadeque, Tareq Hossen
Date: 2021-12-13 00:00:00

This paper discusses some aspects of control schemes for voltage source converters under abnormal conditions. The control schemes are developed specifically for the situations when one or more system parameters vary significantly to the extent that the system becomes unstable with a conventional controller. The paper will present some recent works on control of grid-interactive converters for parameter variations in weak grid. The paper will also discuss some methods under abnormal dc-bus variation. Finally, the paper focuses on the control schemes suitable for ac-dc converters that addresses input frequency, voltage and load variation. All the discussed control schemes have shown robust performance under abnormal conditions.
Metabolic cascades of energy
Authors: Marc-Antoine Fardin
Date: 2021-12-13 00:00:00

Life has a special status, it even has its own science: biology. In many ways, the logic of life seems to differ from that of atoms, molecules, planets, or any other `inanimate object'. However, life is increasingly measured using quantities shared by all sciences, like mass, force, energy or power. An analysis of the dimensions of these quantities provides powerful ways to infer the relationships they might have with one another. Here we show that a dimensional analysis of the metabolic laws connecting the characteristic powers and masses of living organisms offers new ways to understand the deep connections between the chemistry of microscopic molecules and the physics of macroscopic objects bound by gravity. This analysis reveals a link between metabolism and the cascades of energy observed in turbulent flows, opening new perspectives for both fields.