pwc-archive/papers-with-abstracts huggingface dataset:
Energy Management Strategy for Unmanned Tracked Vehicles Based on Local Speed Planning
Authors: Tianxing Sun, Shaohang Xu, Zirui Li, Yingqi Tan, Huiyan Chen
Date: 2021-07-05 00:00:00
Tasks: energy management, Management, Model Predictive Control, Prediction

The hybrid electric system has good potential for unmanned tracked vehicles due to its excellent power and economy. Due to unmanned tracked vehicles have no traditional driving devices, and the driving cycle is uncertain, it brings new challenges to conventional energy management strategies. This paper proposes a novel energy management strategy for unmanned tracked vehicles based on local speed planning. The contributions are threefold. Firstly, a local speed planning algorithm is adopted for the input of driving cycle prediction to avoid the dependence of traditional vehicles on driver's operation. Secondly, a prediction model based on Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) is proposed, which is used to process both the planned and the historical velocity series to improve the prediction accuracy. Finally, based on the prediction results, the model predictive control algorithm is used to realize the real-time optimization of energy management. The validity of the method is verified by simulation using collected data from actual field experiments of our unmanned tracked vehicle. Compared with multi-step neural networks, the prediction model based on CNN-LSTM improves the prediction accuracy by 20%. Compared with the traditional regular energy management strategy, the energy management strategy based on model predictive control reduces fuel consumption by 7%.
Supporting decisions by unleashing multiple mindsets using pairwise comparisons method
Authors: Salvatore Greco, Sajid Siraj, Michele Lundy
Date: 2021-07-04 00:00:00

Inconsistency in pairwise comparison judgements is often perceived as an unwanted phenomenon and researchers have proposed a number of techniques to either reduce it or to correct it. We take a viewpoint that this inconsistency unleashes different mindsets of the decision maker(s) that should be taken into account when generating recommendations as decision support. With this aim we consider the spanning trees analysis which is a recently emerging idea for use with the pairwise comparison approach that represents the plurality of mindsets (in terms of a plurality of vectors corresponding to different spanning trees). Until now, the multiplicity of the vectors supplied by the spanning trees approach have been amalgamated into a single preference vector, losing the information about the plurality of mindsets. To preserve this information, we propose a novel methodology taking an approach similar to Stochastic Multi-criteria Acceptability Analysis. Considering all the rankings of alternatives corresponding to the different mindsets, our methodology gives the probability that an alternative attains a given ranking position as well as the probability that an alternative is preferred to another one. Since the exponential number of spanning trees makes their enumeration prohibitive, we propose computing approximate probabilities using statistical sampling of the spanning trees. Our approach is also appealing because it can be applied also to incomplete sets of pairwise comparisons. We demonstrate its usefulness with a didactic example as well as with an application to a real-life case of selecting a Telecom backbone infrastructure for rural areas.
Scalable Zonotopic Under-approximation of Backward Reachable Sets for Uncertain Linear Systems
Authors: Liren Yang, Necmiye Ozay
Date: 2021-07-04 00:00:00

Zonotopes are widely used for over-approximating forward reachable sets of uncertain linear systems for verification purposes. In this paper, we use zonotopes to achieve more scalable algorithms that under-approximate backward reachable sets of uncertain linear systems for control design. The main difference is that the backward reachability analysis is a two-player game and involves Minkowski difference operations, but zonotopes are not closed under such operations. We under-approximate this Minkowski difference with a zonotope, which can be obtained by solving a linear optimization problem. We further develop an efficient zonotope order reduction technique to bound the complexity of the obtained zonotopic under-approximations. The proposed approach is evaluated against existing approaches using randomly generated instances and illustrated with several examples.
Model Predictive Control for Electron Beam Stabilization in a Synchrotron
Authors: Idris Kempf, Paul J. Goulart, Stephen R. Duncan
Date: 2021-07-04 00:00:00
Tasks: Model Predictive Control

Electron beam stabilization in a synchrotron is a disturbance rejection problem, with hundreds of inputs and outputs, that is sampled at frequencies higher than $10$ kHz. In this feasibility study, we focus on the practical issues of an efficient implementation of model predictive control (MPC) for the heavily ill-conditioned plant of the electron beam stabilization problem. To obtain a tractable control problem that can be solved using only a few iterations of the fast gradient method, we investigate different methods for preconditioning the resulting optimization problem and relate our findings to standard regularization techniques from cross-directional control. We summarize the single- and multi-core implementations of our control algorithm on a digital signal processor (DSP), and show that MPC can be executed at the rate required for synchrotron control. MPC overcomes various problems of standard electron beam stabilization techniques, and the successful implementation can increase the stability of photon beams in synchrotron light sources.
Synchronization Strategies for Multi-agent Networked Control Systems
Authors: Pratik K. Bajaria
Date: 2021-07-04 00:00:00

With the advent of 21st century and increasing advancements in the field of technology and connectivity, inter-networking in real-time has achieved great importance. Distributed control and multi-agent paradigm has groped rapidly with history of big time failures of centralized systems in the past. The concepts of synchronization and network control systems have been used extensively in the near past to map, analyze and solve defined set of objectives. In this thesis, a diverse set of applications from power flow point of view are taken into consideration and modelled/analyzed using synchronization as the central theme. These systems are proposed (or assumed) to be network connected and its control has been devised accordingly. It has been shown how some examples from nature can help recreate similar dynamics synthetically and help achieve system objectives. Few of the applications of the smart world have been ascribed in the thesis and distributed control of these seen from a multi-agent perspective have been devised in order to better design and operate such systems in real-time. Synchronization happens to be the heart of all the networks, as all the agents work in tandem in order to provide to a common objective as well abide by the defined constraints. As an inflection to the work, I set up a platform for development of new distributed and fast acting control strategies for conventional as well as futuristic control systems existing/non-existing in the literature. As a major contribution of this dissertation, by combining the ideas from physics and power systems, I open up various interesting phenomena already existing in either fields to be explored for one another.
Graphical State Space Model
Authors: Shaolin Lü
Date: 2021-07-04 00:00:00
Tasks: model

In this paper, a new framework, named as graphical state space model, is proposed for the real time optimal estimation of a class of nonlinear state space model. By discretizing this kind of system model as an equation which can not be solved by Extended Kalman filter, factor graph optimization can outperform Extended Kalman filter in some cases. A simple nonlinear example is given to demonstrate the efficiency of this framework.
Virtual synchronous generator of PV generation without energy storage for frequency support in autonomous microgrid
Authors: Cheng Zhong, Huayi Li, Yang Zhou, Yueming Lv, Jikai Chen, Yang Li
Date: 2021-07-04 00:00:00

In autonomous microgrids frequency regulation (FR) is a critical issue, especially with a high level of penetration of the photovoltaic (PV) generation. In this study, a novel virtual synchronous generator (VSG) control for PV generation was introduced to provide frequency support without energy storage. PV generation reserve a part of the active power in accordance with the pre-defined power versus voltage curve. Based on the similarities of the synchronous generator power-angle characteristic curve and the PV array characteristic curve, PV voltage Vpv can be analogized to the power angle {\delta}. An emulated governor (droop control) and the swing equation control is designed and applied to the DC-DC converter. PV voltage deviation is subsequently generated and the pre-defined power versus voltage curve is modified to provide the primary frequency and inertia support. A simulation model of an autonomous microgrid with PV, storage, and diesel generator was built. The feasibility and effectiveness of the proposed VSG strategy are examined under different operating conditions.
Controlled Interacting Particle Algorithms for Simulation-based Reinforcement Learning
Authors: Anant Joshi, Amirhossein Taghvaei, Prashant G. Mehta, Sean P. Meyn
Date: 2021-07-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper is concerned with optimal control problems for control systems in continuous time, and interacting particle system methods designed to construct approximate control solutions. Particular attention is given to the linear quadratic (LQ) control problem. There is a growing interest in re-visiting this classical problem, in part due to the successes of reinforcement learning (RL). The main question of this body of research (and also of our paper) is to approximate the optimal control law {\em without} explicitly solving the Riccati equation. A novel simulation-based algorithm, namely a dual ensemble Kalman filter (EnKF), is introduced. The algorithm is used to obtain formulae for optimal control, expressed entirely in terms of the EnKF particles. An extension to the nonlinear case is also presented. The theoretical results and algorithms are illustrated with numerical experiments.
A Study of UK Household Wealth through Empirical Analysis and a Non-linear Kesten Process
Authors: Samuel Forbes, Stefan Grosskinsky
Date: 2021-07-05 00:00:00

We study the wealth distribution of UK households through a detailed analysis of data from wealth surveys and rich lists, and propose a non-linear Kesten process to model the dynamics of household wealth. The main features of our model are that we focus on wealth growth and disregard exchange, and that the rate of return on wealth is increasing with wealth. The linear case with wealth-independent return rate has been well studied, leading to a log-normal wealth distribution in the long time limit which is essentially independent of initial conditions. We find through theoretical analysis and simulations that the non-linearity in our model leads to more realistic power-law tails, and can explain an apparent two-tailed structure in the empirical wealth distribution of the UK and other countries. Other realistic features of our model include an increase in inequality over time, and a stronger dependence on initial conditions compared to linear models.
Risk aversion and uniqueness of equilibrium: a polynomial approach
Authors: Andrea Loi, Stefano Matta
Date: 2021-07-05 00:00:00

We study the connection between risk aversion, number of consumers and uniqueness of equilibrium. We consider an economy with two goods and $c$ impatience types, where each type has additive separable preferences with HARA Bernoulli utility function, $u_H(x):=\frac{\gamma}{1-\gamma}\left(b+\frac{a}{\gamma}x\right)^{1-\gamma}$. We show that if $\gamma\in \left(1, \frac{c}{c-1}\right]$, the equilibrium is unique. Moreover, the methods used, involving Newton's symmetric polynomials and Descartes' rule of signs, enable us to offer new sufficient conditions for uniqueness in a closed-form expression highlighting the role played by endowments, patience and specific HARA parameters. Finally, new necessary and sufficient conditions in ensuring uniqueness are derived for the particular case of CRRA Bernoulli utility functions with $\gamma =3$.
Mobility decisions, economic dynamics and epidemic
Authors: Giorgio Fabbri, Salvatore Federico, Davide Fiaschi, Fausto Gozzi
Date: 2021-07-04 00:00:00

We propose a model, which nests a susceptible-infected-recovered-deceased (SIRD) epidemic model into a dynamic macroeconomic equilibrium framework with agents' mobility. The latter affect both their income and their probability of infecting and being infected. Strategic complementarities among individual mobility choices drive the evolution of aggregate economic activity, while infection externalities caused by individual mobility affect disease diffusion. The continuum of rational forward-looking agents coordinates on the Nash equilibrium of a discrete time, finite-state, infinite-horizon Mean Field Game. We prove the existence of an equilibrium and provide a recursive construction method for the search of an equilibrium(a), which also guides our numerical investigations. We calibrate the model by using Italian experience on COVID-19 epidemic and we discuss policy implications.
Trading patterns within and between regions: an analysis of Gould-Fernandez brokerage roles
Authors: Matthew Smith, Yasaman Sarabi
Date: 2021-07-04 00:00:00
Tasks: Clustering

This study examines patterns of regionalisation in the International Trade Network (ITN). The study makes use of Gould Fernandez brokerage to examine the roles countries play in the ITN linking different regional partitions. An examination of three ITNs is provided for three networks with varying levels of technological content, representing trade in high tech, medium tech, and low-tech goods. Simulated network data, based on multiple approaches including an advanced network model controlling for degree centralisation and clustering patterns, is compared to the observed data to examine whether the roles countries play within and between regions are a result of centralisation and clustering patterns. The findings indicate that the roles countries play between and within regions are indeed a result of centralisation patterns and chiefly clustering patterns; indicating a need to examine the presence of hubs when investigating regionalisation and globalisation patterns in the modern global economy.
Decentralizing Centralized Matching Markets: Implications from Early Offers in University Admissions
Authors: Julien Grenet, YingHua He, Dorothea Kübler
Date: 2021-07-04 00:00:00

The matching literature often recommends market centralization under the assumption that agents know their own preferences and that their preferences are fixed. We find counterevidence to this assumption in a quasi-experiment. In Germany's university admissions, a clearinghouse implements the early stages of the Gale-Shapley algorithm in real time. We show that early offers made in this decentralized phase, although not more desirable, are accepted more often than later ones. These results, together with survey evidence and a theoretical model, are consistent with students' costly learning about universities. We propose a hybrid mechanism to combine the advantages of decentralization and centralization. Published at The Journal of Political Economy under a new title, ``Preference Discovery in University Admissions: The Case for Dynamic Multioffer Mechanisms,'' available at https://doi.org/10.1086/718983 (Open Access).
Characterizing nonatomic admissions markets
Authors: Max Kapur
Date: 2021-07-03 00:00:00

This article proposes a characterization of admissions markets that can predict the distribution of students at each school or college under both centralized and decentralized admissions paradigms. The characterization builds on recent research in stable assignment, which models students as a probability distribution over the set of ordinal preferences and scores. Although stable assignment mechanisms presuppose a centralized admissions process, I show that stable assignments coincide with equilibria of a decentralized, iterative market in which schools adjust their admissions standards in pursuit of a target class size. Moreover, deferred acceptance algorithms for stable assignment are a special case of a well-understood price dynamic called t\^{a}tonnement. The second half of the article turns to a parametric distribution of student types that enables explicit computation of the equilibrium and is invertible in the schools' preferability parameters. Applying this model to a public dataset produces an intuitive ranking of the popularity of American universities and a realistic estimate of each school's demand curve, and does so without imposing an equilibrium assumption or requiring the granular student information used in conventional logistic regressions.
Low Rank Quaternion Matrix Recovery via Logarithmic Approximation
Authors: Liqiao Yang, Jifei Miao, Kit Ian Kou
Date: 2021-07-03 00:00:00
Tasks: Image Restoration, Matrix Completion

In color image processing, image completion aims to restore missing entries from the incomplete observation image. Recently, great progress has been made in achieving completion by approximately solving the rank minimization problem. In this paper, we utilize a novel quaternion matrix logarithmic norm to approximate rank under the quaternion matrix framework. From one side, unlike the traditional matrix completion method that handles RGB channels separately, the quaternion-based method is able to avoid destroying the structure of images via putting the color image in a pure quaternion matrix. From the other side, the logarithmic norm induces a more accurate rank surrogate. Based on the logarithmic norm, we take advantage of not only truncated technique but also factorization strategy to achieve image restoration. Both strategies are optimized based on the alternating minimization framework. The experimental results demonstrate that the use of logarithmic surrogates in the quaternion domain is more superior in solving the problem of color images completion.
A mmWave Oscillator Design Utilizing High-Q Active-Mode On-Chip MEMS Resonators for Improved Fundamental Limits of Phase Noise
Authors: Abhishek Srivastava, Baibhab Chatterjee, Udit Rawat, Yanbo He, Dana Weinstein, Shreyas Sen
Date: 2021-07-05 00:00:00

(RFT) allows very high-Q active mode resonators, promising crystal-less monolithic clock generation for mmWave systems. However, there is a strong need for design of mmWave oscillators that utilize the high-Q of active-mode RFT (AM-RFT) optimally, while handling unique challenges such as resonator's low electromechanical transduction. In this brief, we develop a theory and through design and post-layout simulations in 14 nm Global Foundry process, we show the first active oscillator with AM-RFT at 30 GHz, which improves the fundamental limits of phase noise and figure-of-merit as compared to the oscillators with conventional LC resonators. For AM-RFT with Q factor of 10K, post layout simulation results show that the proposed oscillator exhibits phase noise less than -140 dBc per Hz and figure-of-merit greater than 228 dBc per Hz at 1 MHz offset for 30 GHz center frequency, which are more than 25 dB better than the existing monolithic LC oscillators.
Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosis
Authors: Qin Wang, Cees Taal, Olga Fink
Date: 2021-07-05 00:00:00
Tasks: Domain Adaptation, Fault Diagnosis

Data-driven fault diagnosis methods often require abundant labeled examples for each fault type. On the contrary, real-world data is often unlabeled and consists of mostly healthy observations and only few samples of faulty conditions. The lack of labels and fault samples imposes a significant challenge for existing data-driven fault diagnosis methods. In this paper, we aim to overcome this limitation by integrating expert knowledge with domain adaptation in a synthetic-to-real framework for unsupervised fault diagnosis. Motivated by the fact that domain experts often have a relatively good understanding on how different fault types affect healthy signals, in the first step of the proposed framework, a synthetic fault dataset is generated by augmenting real vibration samples of healthy bearings. This synthetic dataset integrates expert knowledge and encodes class information about the fault types. However, models trained solely based on the synthetic data often do not perform well because of the distinct distribution difference between the synthetically generated and real faults. To overcome this domain gap between the synthetic and real data, in the second step of the proposed framework, an imbalance-robust domain adaptation~(DA) approach is proposed to adapt the model from synthetic faults~(source) to the unlabeled real faults~(target) which suffer from severe class imbalance. The framework is evaluated on two unsupervised fault diagnosis cases for bearings, the CWRU laboratory dataset and a real-world wind-turbine dataset. Experimental results demonstrate that the generated faults are effective for encoding fault type information and the domain adaptation is robust against the different levels of class imbalance between faults.
Optimal Signal Selection for Sensors
Authors: Abdulaziz M. Alqarni, Thomas G. Robertazzi
Date: 2021-07-04 00:00:00

The focus of this research is sensor applications including radar and sonar. Optimal sensing means achieving the best signal quality with the least time and energy cost, which allows processing more data. This paper presents novel work by using an integer linear programming "algorithm" to achieve optimal sensing by selecting the best possible number of signals of a type or a combination of multiple types of signals to ensure the best sensing quality considering all given constraints. A solution based on a heuristic algorithm is implemented to improve the computing time performance. What is novel in this solution is synthesis of an optimized signal mix using information such as but not limited to signal quality, energy and computing time.
Wireless Indoor Simultaneous Localization and Mapping Using Reconfigurable Intelligent Surface
Authors: Ziang Yang, Haobo Zhang, Boya Di, Hongliang Zhang, Kaigui Bian, Lingyang Song
Date: 2021-07-04 00:00:00
Tasks: Simultaneous Localization and Mapping

Indoor wireless simultaneous localization and mapping (SLAM) is considered as a promising technique to provide positioning services in future 6G systems. However, the accuracy of traditional wireless SLAM system heavily relies on the quality of propagation paths, which is limited by the uncontrollable wireless environment. In this paper, we propose a novel SLAM system assisted by a reconfigurable intelligent surface (RIS) to address this issue. By configuring the phase shifts of the RIS, the strength of received signals can be enhanced to resist the disturbance of noise. However, the selection of phase shifts heavily influences the localization and mapping phase, which makes the design very challenging. To tackle this challenge, we formulate the RIS-assisted indoor SLAM optimization problem and design an error minimization algorithm for it. Simulations show that the RIS assisted SLAM system can decrease the positioning error by at least 31% compared with benchmark schemes.