pwc-archive/papers-with-abstracts huggingface dataset:
Deep domain adaptation for polyphonic melody extraction
Authors: Kavya Ranjan Saxena, Vipul Arora
Date: 2022-10-22 00:00:00
Tasks: Domain Adaptation, Information Retrieval, Melody Extraction, Meta-Learning, Music Information Retrieval, Retrieval

Extraction of the predominant pitch from polyphonic audio is one of the fundamental tasks in the field of music information retrieval and computational musicology. To accomplish this task using machine learning, a large amount of labeled audio data is required to train the model that predicts the pitch contour. But a classical model pre-trained on data from one domain (source), e.g, songs of a particular singer or genre, may not perform comparatively well in extracting melody from other domains (target). The performance of such models can be boosted by adapting the model using some annotated data in the target domain. In this work, we study various adaptation techniques applied to machine learning models for polyphonic melody extraction. Experimental results show that meta-learning-based adaptation performs better than simple fine-tuning. In addition to this, we find that this method outperforms the existing state-of-the-art non-adaptive polyphonic melody extraction algorithms.
Control and Design Optimization of an Electric Vehicle Transmission Using Analytical Modeling Methods
Authors: Olaf Borsboom, Thijs de Mooy, Mauro Salazar, Theo Hofman
Date: 2022-10-24 00:00:00

This paper introduces a framework to systematically optimize the control and design of an electric vehicle transmission, connecting powertrain sizing studies to detailed gearbox design methods. To this end, we first create analytical models of individual components: gears, shafts, bearings, clutches, and synchronizers. Second, we construct a transmission by systematically configuring a topology with these components. Third, we place the composed transmission within a powertrain and vehicle model, and compute the minimum-energy control and design, employing solving algorithms that provide global optimality guarantees. Finally, we carry out the control and design optimization of a fixed- and two-gear transmission for a compact family electric vehicle, whereby we observe that a two-gear transmission can improve the energy consumption by 0.8 %, while also achieving requirements on gradeability and performance. We validate our framework by recreating the transmission that is mounted in the benchmark test case vehicle and recomputing the energy consumption over the New European Driving Cycle, where we notice an error in energy consumption of 0.2 %, affirming that our methods are suitable for gear ratio selection in powertrain design optimization.
Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow
Authors: Jiawei Wang, Yingzhao Lian, Yuning Jiang, Qing Xu, Keqiang Li, Colin N. Jones
Date: 2022-10-24 00:00:00
Tasks: LEMMA

Cooperative control of connected and automated vehicles (CAVs) promises smoother traffic flow. In mixed traffic, where human-driven vehicles with unknown dynamics coexist, data-driven predictive control techniques allow for CAV safe and optimal control with measurable traffic data. However, the centralized control setting in most existing strategies limits their scalability for large-scale mixed traffic flow. To address this problem, this paper proposes a cooperative DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control) formulation and its distributed implementation algorithm. In cooperative DeeP-LCC, the traffic system is naturally partitioned into multiple subsystems with one single CAV, which collects local trajectory data for subsystem behavior predictions based on the Willems' fundamental lemma. Meanwhile, the cross-subsystem interaction is formulated as a coupling constraint. Then, we employ the Alternating Direction Method of Multipliers (ADMM) to design the distributed DeeP-LCC algorithm. This algorithm achieves both computation and communication efficiency, as well as trajectory data privacy, through parallel calculation. Our simulations on different traffic scales verify the real-time wave-dampening potential of distributed DeeP-LCC, which can reduce fuel consumption by over 31.84% in a large-scale traffic system of 100 vehicles with only 5%-20% CAVs.
On DoS Resiliency Analysis of Networked Control Systems: Trade-Off Between Jamming Actions and Network Delays
Authors: Roberto Merco, Francesco Ferrante, Pierluigi Pisu
Date: 2022-10-24 00:00:00

This letter deals with the problem of quantifying resiliency of Networked Control Systems (NCSs) to Denial-of-Service (DoS) attacks and variable network delays. Internal exponential stability and $\mathcal{L}_2$ external stability are studied. The closed-loop system is augmented with an auxiliary timer variable and analyzed in a hybrid system framework. Lyapunov-like conditions are given to ensure $0$-input global exponential stability and $\mathcal{L}_2$ external stability. A computationally affordable algorithm based on linear matrix inequalities is devised to provide trade-off curves between maximum length of DoS attacks and largest network delays. Finally, the effectiveness of the proposed approach is shown in a numerical example.
Optimal Design of Volt/VAR Control Rules for Inverter-Interfaced Distributed Energy Resources
Authors: Ilgiz Murzakhanov, Sarthak Gupta, Spyros Chatzivasileiadis, Vassilis Kekatos
Date: 2022-10-23 00:00:00

The IEEE 1547 Standard for the interconnection of distributed energy resources (DERs) to distribution grids provisions that smart inverters could be implementing Volt/VAR control rules among other options. Such rules enable DERs to respond autonomously in response to time-varying grid loading conditions. The rules comprise affine droop control augmented with a deadband and saturation regions. Nonetheless, selecting the shape of these rules is not an obvious task, and the default options may not be optimal or dynamically stable. To this end, this work develops a novel methodology for customizing Volt/VAR rules on a per-bus basis for a single-phase feeder. The rules are adjusted by the utility every few hours depending on anticipated demand and solar scenarios. Using a projected gradient descent-based algorithm, rules are designed to improve the feeder's voltage profile, comply with IEEE 1547 constraints, and guarantee stability of the underlying nonlinear grid dynamics. The stability region is inner approximated by a polytope and the rules are judiciously parameterized so their feasible set is convex. Numerical tests using real-world data on the IEEE 141-bus feeder corroborate the scalability of the methodology and its explore the trade-offs of Volt/VAR control with alternatives.
Granger Causality for Predictability in Dynamic Mode Decomposition
Authors: G. Revati, Syed Shadab, K. Sonam, S. R. Wagh, N. M. Singh
Date: 2022-10-23 00:00:00

The dynamic mode decomposition (DMD) technique extracts the dominant modes characterizing the innate dynamical behavior of the system within the measurement data. For appropriate identification of dominant modes from the measurement data, the DMD algorithm necessitates ensuring the quality of the input measurement data sequences. On that account, for validating the usability of the dataset for the DMD algorithm, the paper proposed two conditions: Persistence of excitation (PE) and the Granger Causality Test (GCT). The virtual data sequences are designed with the hankel matrix representation such that the dimensions of the subspace spanning the essential system modes are increased with the addition of new state variables. The PE condition provides the lower bound for the trajectory length, and the GCT provides the order of the model. Satisfying the PE condition enables estimating an approximate linear model, but the predictability with the identified model is only assured with the temporal causation among data searched with GCT. The proposed methodology is validated with the application for coherency identification (CI) in a multi-machine power system (MMPS), an essential phenomenon in transient stability analysis. The significance of PE condition and GCT is demonstrated through various case studies implemented on 22 bus six generator system.
Adaptive Control with Global Exponential Stability for Parameter-Varying Nonlinear Systems under Unknown Control Gains
Authors: Hefu Ye, Haijia Wu, Kai Zhao, Yongduan Song
Date: 2022-10-23 00:00:00

It is nontrivial to achieve exponential stability even for time-invariant nonlinear systems with matched uncertainties and persistent excitation (PE) condition. In this paper, without the need for PE condition, we address the problem of global exponential stabilization of strict-feedback systems with mismatched uncertainties and unknown yet time-varying control gains. The resultant control, embedded with time-varying feedback gains, is capable of ensuring global exponential stability of parametric-strict-feedback systems in the absence of persistence of excitation. By using the enhanced Nussbaum function, the previous results are extended to more general nonlinear systems where the sign and magnitude of the time-varying control gain are unknown. In particular, the argument of the Nussbaum function is guaranteed to be always positive with the aid of nonlinear damping design, which is critical to perform a straightforward technical analysis of the boundedness of the Nussbaum function. Finally, the global exponential stability of parameter-varying strict-feedback systems, the boundedness of the control input and the update rate, and the asymptotic constancy of the parameter estimate are established. Numerical simulations are carried out to verify the effectiveness and benefits of the proposed methods.
Prescribed-Time Control and Its Latest Developments
Authors: Hefu Ye, Yongduan Song, Frank L. Lewis
Date: 2022-10-23 00:00:00

Prescribed-time (PT) control, originated from \textit{Song et al.}, has gained increasing attention among control community. The salient feature of PT control lies in its ability to achieve system stability within a finite settling time user-assignable in advance irrespective of initial conditions. It is such a unique feature that has enticed many follow-up studies on this technically important area, motivating numerous research advancements. In this article, we provide a comprehensive survey on the recent developments in PT control. Through a concise introduction to the concept of PT control, and a unique taxonomy covering: 1) from robust PT control to adaptive PT control; 2) from PT control for single-input-single-output (SISO) systems to multi-input-multi-output (MIMO) systems; and 3) from PT control for single systems to multi-agent systems, we present an accessible review of this interesting topic. We highlight key techniques, fundamental assumptions adopted in various developments as well as some new design ideas. We also discuss several possibles future research directions towards PT control.
Robust Adaptive Prescribed-Time Control for Parameter-Varying Nonlinear Systems
Authors: Hefu Ye, Yongduan Song
Date: 2022-10-23 00:00:00
Tasks: parameter estimation

It is an interesting open problem to achieve adaptive prescribed-time control for strict-feedback systems with unknown and fast or even abrupt time-varying parameters. In this paper we present a solution with the aid of several design and analysis innovations. First, by using a spatiotemporal transformation, we convert the original system operational over finite time interval into one operational over infinite time interval, allowing for Lyapunov asymptotic design and recasting prescribed-time stabilization on finite time domain into asymptotic stabilization on infinite time domain. Second, to deal with time-varying parameters with unknown variation boundaries, we use congelation of variables method and establish three separate adaptive laws for parameter estimation (two for the unknown parameters in the feedback path and one for the unknown parameter in the input path), in doing so we utilize two tuning functions to eliminate over-parametrization. Third, to achieve asymptotic convergence for the transformed system, we make use of nonlinear damping design and non-regressor-based design to cope with time-varying perturbations, and finally, we derive the prescribed-time control scheme from the asymptotic controller via inverse temporal-scale transformation. The boundedness of all closed-loop signals and control input is proved rigorously through Lyapunov analysis, squeeze theorem, and two novel lemmas built upon the method of variation of constants. Numerical simulation verifies the effectiveness of the proposed method.
Development of a Hybrid Simulation and Experiment Test Platform for Dynamic Positioning Vessels
Authors: Changjun Hu, Quan Shi, Xin Li, Xiaoxian Guo
Date: 2022-10-23 00:00:00

The harsh ocean environment and complex operating condition require high dynamic positioning (DP) capability of offshore vessel. The design, development and performance evaluation of DP system are generally carried out by numerical simulations or scale model experiments. Compared with the time-consuming and laborious experiment, the simulation is convenient and low cost, but its results lack practical reference due to oversimplification of the model. Therefore, this paper presents a hybrid simulation and experiment test platform for DP vessels. Its characteristics are: the realistic calculation of environmental loads and motion response, the consistency of algorithms and parameters for simulation and experiment greatly shortening the time of experiment adjusting, switchable and online renewable controller facilitating algorithm testing. The test platform can test the performance of DP system and determine the operational time window. In the hydrodynamic simulation, the six degree-of-freedom model is used to describe the dynamic response of the DP vessel, considering the fluid memory effect and frequency-dependent hydrodynamic parameters. In the experiment, the similarity theory based on the same Froude number is used to ensure the consistency of control parameters with simulation. Finally, a case study of DP shuttle tanker is used to verify the credibility of the test platform.
Design and Performance Analysis of a Chemically-Etched Flexible NFC Tag Antenna
Authors: Muhammad Enayetur Rahman, Marwan Abdelatti, Manbir Singh Sodhi, Kunal Mankodiya
Date: 2022-10-22 00:00:00
Tasks: TAG

Near Field Communication (NFC) is a perfect example of ubiquitous computing that is secured, short-ranged, low-powered contactless communication. High demand is predicted for NFC, especially with wearables, and the variety of applications may require that this technology be fabricated onto different materials. In this research, we first designed flexible NFC antennas based on mathematical models. A simulation of the NFC coil inductance was performed and verified using the predictive models. Later, two flexible NFC antennas, including 160x80 mm2 (rectangular) and 80x80 mm2 (square) were fabricated through a chemical etching process and verified at 13.56 MHz frequency. Two experiments 1) Read range detection and 2) User experience study on 22 participants validate longer read range and shorter connection time of our fabricated flexible NFC tags.
Attitude Control of Highly Maneuverable Aircraft Using an Improved Q-learning
Authors: Mohsen Zahmatkesh, Seyyed Ali Emami, Afshin Banazadeh, Paolo Castaldi
Date: 2022-10-22 00:00:00
Tasks: continuous-control, Continuous Control, Q-Learning, Reinforcement Learning (RL)

Attitude control of a novel regional truss-braced wing aircraft with low stability characteristics is addressed in this paper using Reinforcement Learning (RL). In recent years, RL has been increasingly employed in challenging applications, particularly, autonomous flight control. However, a significant predicament confronting discrete RL algorithms is the dimension limitation of the state-action table and difficulties in defining the elements of the RL environment. To address these issues, in this paper, a detailed mathematical model of the mentioned aircraft is first developed to shape an RL environment. Subsequently, Q-learning, the most prevalent discrete RL algorithm will be implemented in both the Markov Decision Process (MDP), and Partially Observable Markov Decision Process (POMDP) frameworks to control the longitudinal mode of the air vehicle. In order to eliminate residual fluctuations that are a consequence of discrete action selection, and simultaneously track variable pitch angles, a Fuzzy Action Assignment (FAA) method is proposed to generate continuous control commands using the trained Q-table. Accordingly, it will be proved that by defining an accurate reward function, along with observing all crucial states, the performance of the introduced control system surpasses a well-tuned PID controller.
Choosing The Best Incentives for Belief Elicitation with an Application to Political Protests
Authors: Nathan Canen, Anujit Chakraborty
Date: 2022-10-22 00:00:00

Many experiments elicit subjects' prior and posterior beliefs about a random variable to assess how information affects one's own actions. However, beliefs are multi-dimensional objects, and experimenters often only elicit a single response from subjects. In this paper, we discuss how the incentives offered by experimenters map subjects' true belief distributions to what profit-maximizing subjects respond in the elicitation task. In particular, we show how slightly different incentives may induce subjects to report the mean, mode, or median of their belief distribution. If beliefs are not symmetric and unimodal, then using an elicitation scheme that is mismatched with the research question may affect both the magnitude and the sign of identified effects, or may even make identification impossible. As an example, we revisit Cantoni et al.'s (2019) study of whether political protests are strategic complements or substitutes. We show that they elicit modal beliefs, while modal and mean beliefs may be updated in opposite directions following their experiment. Hence, the sign of their effects may change, allowing an alternative interpretation of their results.
BIM can help decarbonize the construction sector: life cycle evidence from Pavement Management Systems
Authors: Anne de Bortoli, Yacine Baouch, Mustapha Masdan
Date: 2022-10-22 00:00:00
Tasks: Management

Transforming the construction sector is key to reaching net-zero, and many stakeholders expect its decarbonization through digitalization. But no quantified evidence has been brought to date. We propose the first environmental quantification of the impact of Building Information Modeling (BIM) in the construction sector. Specifically, the direct and indirect greenhouse gas (GHG) emissions generated by a monofunctional BIM to plan road maintenance, a Pavement Management System (PMS), are evaluated using field data from France. The related carbon footprints are calculated following a life cycle approach, using different sources of data, including ecoinvent v3.6, and the IPCC 2013 GWP 100a characterization factors. Three design-build-maintain pavement alternatives are compared: scenario 1 relates to a massive design and surface maintenance, scenario 2 to a progressive design and pre-planned structural maintenance, and scenario 3 to a progressive design and tailored structural maintenance supported by the PMS. First, results show negligible direct emissions due to the PMS existence: 0.02% of the life cycle emissions of scenario 3. Second, complementary sensitivity analyses show that using a PMS is climate-positive over the life cycle when pavement subgrade bearing capacity improves over time, and climate-neutral otherwise. The GHG emissions savings using BIM can reach up to 30% of the life cycle emissions compared to other scenarios, and 65% when restraining the scope to maintenance and rehabilitation and excluding original pavement construction. Third, the neutral effect of BIM in case of a deterioration of the bearing capacity of the subgrade may be explained by design practices and safety margins, that could be enhanced using BIM. Fourth, the decarbonization potential of a multifunctional BIM is discussed, and research perspectives are presented.
A Theory of Stable Market Segmentations
Authors: Nima Haghpanah, Ron Siegel
Date: 2022-10-24 00:00:00
Tasks: Segmentation

We consider a monopolistic seller in a market that may be segmented. The surplus of each consumer in a segment depends on the price that the seller optimally charges, which depends on the set of consumers in the segment. We study which segmentations may result from the interaction among consumers and the seller. Instead of studying the interaction as a non-cooperative game, we take a reduced-form approach and introduce a notion of stability that any resulting segmentation must satisfy. A stable segmentation is one that, for any alternative segmentation, contains a segment of consumers that prefers the original segmentation to the alternative one. Our main result characterizes stable segmentations as efficient and saturated. A segmentation is saturated if no consumers can be shifted from a segment with a high price to a segment with a low price without the seller optimally increasing the low price. We use this characterization to constructively show that stable segmentations always exist. Even though stable segmentations are efficient, they need not maximize average consumer surplus, and segmentations that maximize average consumer surplus need not be stable. Finally, we relate our notion of stability to solution concepts from cooperative game theory and show that stable segmentations satisfy many of them.
Variable population manipulations of reallocation rules in economies with single-peaked preferences
Authors: Agustin G. Bonifacio
Date: 2022-10-23 00:00:00

In a one-commodity economy with single-peaked preferences and individual endowments, we study different ways in which reallocation rules can be strategically distorted by affecting the set of active agents. We introduce and characterize the family of monotonic reallocation rules and show that each rule in this class is withdrawal-proof and endowments-merging-proof, at least one is endowments-splitting-proof and that no such rule is pre-delivery-proof.
chowdsp_wdf: An Advanced C++ Library for Wave Digital Circuit Modelling
Authors: Jatin Chowdhury
Date: 2022-10-22 00:00:00

chowdsp_wdf is a C++ library for implementing real-time wave digital models of analog circuits. chowdsp_wdf differs from existing wave digital modelling libraries by providing a template meta-programming interface for modelling circuits with a fixed topology, and providing support for explicit SIMD acceleration. The motivation and design of the library are described, as well as real-world use-cases, and performance comparisons with other wave digital modelling libraries.
A geometry method for LED mapping
Authors: Junlin Huang, ShangSheng Wen, Weipeng Guan
Date: 2022-10-24 00:00:00
Tasks: Visual Odometry

With inputs from RGB-D camera, industrial camera and wheel odometer, in this letter, we propose a geometry-based detecting method, by which the 3-D modulated LED map can be acquired with the aid of visual odometry algorithm from ORB-SLAM2 system when the decoding result of LED-ID is inaccurate. Subsequently, an enhanced cost function is proposed to optimize the mapping result of LEDs. The average 3-D mapping error (8.5cm) is evaluated with a real-world experiment. This work can be viewed as a preliminary work of visible light positioning systems, offering a way to prevent the labor-intensive manual site surveys of LEDs.