pwc-archive/papers-with-abstracts huggingface dataset:
Forced Oscillation Identification and Filtering from Multi-Channel Time-Frequency Representation
Authors: Pablo Gill Estevez, Pablo Marchi, Francisco Messina, Cecilia Galarza
Date: 2021-08-19 00:00:00

Location of non-stationary forced oscillation (FO) sources can be a challenging task, especially in cases under resonance condition with natural system modes, where the magnitudes of the oscillations could be greater in places far from the source. Therefore, it is of interest to construct a global time-frequency (TF) representation (TFR) of the system, which can capture the oscillatory components present in the system. In this paper we develop a systematic methodology for frequency identification and component filtering of non-stationary power system forced oscillations (FO) based on multi-channel TFR. The frequencies of the oscillatory components are identified on the TF plane by applying a modified ridge estimation algorithm. Then, filtering of the components is carried out on the TF plane applying the anti-transform functions over the individual TFRs around the identified ridges. This step constitutes an initial stage for the application of the Dissipating Energy Flow (DEF) method used to locate FO sources. Besides, we compare three TF approaches: short-time Fourier transform (STFT), STFT-based synchrosqueezing transform (FSST) and second order FSST (FSST2). Simulated signals and signals from real operation are used to show that the proposed method provides a systematic framework for identification and filtering of power systems non-stationary forced oscillations.
Sparse Packetized Predictive Control of Disturbed Plants Over Channels with Data Loss
Authors: Mohsen Barforooshan, Masaaki Nagahara, Jan Ostergaard
Date: 2021-08-19 00:00:00

This paper investigates closed-loop stability of a linear discrete-time plant subject to bounded disturbances when controlled according to packetized predictive control (PPC) policies. In the considered feedback loop, the controller is connected to the actuator via a digital communication channel imposing bounded dropouts. Two PPC strategies are taken into account. In both cases, the control packets are generated by solving sparsity-promoting optimization problems. One is based upon an l2-constrained l0 optimization problem. Such problem is relaxed by an l1-l2 optimization problem in the other sparse PPC setting. We utilize effective solving methods for the latter optimization problems. Moreover, we show that in the presence of plant disturbances, the l2-constrained l0 sparse PPC and unconstrained l1-l2 sparse PPC guarantee practical stability for the system if certain conditions are met. More precisely, in each case, we can derive an upper bound on system state if the design parameters satisfy certain conditions. The bounds we derive are increasing with respect to the disturbance magnitude. We show via simulation that in both cases of proposed sparse PPC strategies, larger disturbances bring about performance degradation with no effect on system practical stability.
The Pareto-frontier-based Stiffness of A Controller: Trade-off between Trajectory Plan and Controller Design
Authors: Zhe Shen, Takeshi Tsuchiya
Date: 2021-08-19 00:00:00

Approaching a set goal for a UAV comprises a trajectory plan and a controller design (control after plan problems). The optimal trajectory (reference) is calculated before being tracked with a proper controller. It is believed that the quadrotor will follow the designed trajectory totally in the trajectory plan process. However, the dynamic state error usually, for a mismatched feed-forward, spoils this assumption, making the unwanted sacrifice in the objective function defined in the trajectory plan process. We base the target problem in this research on a second-order system model which widely exists in the dynamics of vehicles. Specially, the unavoidable dynamic state error is considered in the trajectory plan process, assuming the LQR without the feed-forward is applied in the subsequent control after plan problems. The Copenhagen Limit provides the possibility of estimating the dynamic state error in an analytical solution. The trade-off results are provided in multiobjective Pareto front solutions and the mapped pseudo Pareto fronts. We explore the relationship between the controller and the corresponding pseudo Pareto fronts.
Discrete-time Flatness-based Control of a Gantry Crane
Authors: Johannes Diwold, Bernd Kolar, Markus Schöberl
Date: 2021-08-19 00:00:00

This article addresses the design of a discrete-time flatness-based tracking control for a gantry crane and demonstrates the practical applicability of the approach by measurement results. The required sampled-data model is derived by an Euler-discretization with a prior state transformation in such a way that the flatness of the continuous-time system is preserved. Like in the continuous-time case, the flatness-based controller design is performed in two steps. First, the sampled-data system is exactly linearized by a discrete-time quasi-static state feedback. Subsequently, a further feedback enforces a stable linear tracking error dynamics. To underline its practical relevance, the performance of the novel discrete-time tracking control is compared to the classical continuous-time approach by measurement results from a laboratory setup. In particular, it turns out that the discrete-time controller is significantly more robust with respect to large sampling times. Moreover, it is shown how the discrete-time approach facilitates the design of optimal reference trajectories, and further measurement results are presented.
Large-scale Offshore Wind Farm Electrical Collector System Planning: A Mixed-Integer Linear Programming Approach
Authors: Xinwei Shen, Sunwei Li, Hongke Li
Date: 2021-08-19 00:00:00

In this paper, we propose a planning method for large-scale offshore wind farm (OWF) electrical collector system (ECS) based on mixed integer linear programming, in which the sizing and siting of offshore substations and the lines between wind turbines (WTs) are optimized. We found out that the problem is similar to power distribution system planning, where the topological constraints for distribution network expansion planning (DNEP) are applied to guarantee the radiality of ECS topology and accelerate the solving process. Case studies based on an OWF with 63 fixed-locations WTs demonstrate the effectiveness of proposed method, in which the cost of ECS's investment on cables is reduced by 23%, power loss reduced by 44% compared with a conventional design, and the calculation time reduced with the help of the radiality constraint.
Analyzing Novel Grant-Based and Grant-Free Access Schemes for Small Data Transmission
Authors: Hui Zhou, Yansha Deng, Luca Feltrin, Andreas Höglund
Date: 2021-02-20 00:00:00

Fifth Generation (5G) New Radio (NR) does not support data transmission during random access (RA) procedures, which results in unnecessary control signalling overhead and power consumption, especially for small data transmission (SDT). Motivated by this, 3GPP has proposed 4/2-step SDT RA schemes based on the existing grant-based (4-step) and grant-free (2-step) RA schemes, with the aim to enable data transmission during RA procedures in Radio Resource Control (RRC) Inactive state. To compare the 4/2-step SDT RA schemes with the benchmark 4/2-step RA schemes, we provide a spatio-temporal analytical framework to evaluate the RA schemes, which jointly models the preamble detection, Physical Uplink Shared Channel (PUSCH) decoding, and data transmission procedures. Based on this analytical model, we derive the analytical expressions for the overall packet transmission success probability and average throughput in each RACH attempt. We also derive the average energy consumption in each RACH attempt. Our results show that 2-step SDT RA scheme provides the highest overall packet transmission success probability, and the lowest average energy consumption, but the performance gain decreases with the increase of device intensity.
Packetized Energy Management Controller for Residential Consumers
Authors: Hafiz Majid Hussain, Ashfaq Ahmad, Arun Narayanan, Pedro H. J. Nardelli, Yongheng Yang
Date: 2021-08-19 00:00:00
Tasks: energy management, Management, Scheduling

In this paper, we investigate the management of energy storage control and load scheduling in scenarios considering a grid-connected photovoltaic (PV) system using packetized energy management. The aim is to reduce an average aggregated system cost through the proposed \textit{packetized energy management controller} considering household energy consumption, procurement price, load scheduling delays, PV self-sufficiency via generated renewable energy and battery degradation. The proposed approach solves the joint optimization problem using established heuristics, namely genetic algorithm (GA), binary particle swarm optimization (BPSO), and differential evolution (DE). Additionally, the performances of heuristic algorithms are also compared in terms of the effectiveness of load scheduling with delay constraints, packetized energy transactions, and battery degradation cost. Case studies have been provided to demonstrate and extensively evaluate the algorithms. The numerical results show that the proposed packetized energy management controller can considerably reduce the aggregated average system cost up to 4.7\%, 5.14\%, and 1.35\% by GA, BPSO, and DE, respectively while meeting the packetized energy demand and scheduling delays requirements.
$\mathcal{L}_1$ Adaptive Control with Switched Reference Models: Application to Learn-to-Fly
Authors: Steven Snyder, Pan Zhao, Naira Hovakimyan
Date: 2021-08-19 00:00:00

Learn-to-Fly (L2F) is a new framework that aims to replace the traditional iterative development paradigm for aerial vehicles with a combination of real-time aerodynamic modeling, guidance, and learning control. To ensure safe learning of the vehicle dynamics on the fly, this paper presents an $\mathcal{L}_1$ adaptive control ($\mathcal{L}_1$AC) based scheme, which actively estimates and compensates for the discrepancy between the intermediately learned dynamics and the actual dynamics. First, to incorporate the periodic update of the learned model within the L2F framework, this paper extends the $\mathcal{L}_1$AC architecture to handle a switched reference system subject to unknown time-varying parameters and disturbances. The paper also includes an analysis of both transient and steady-state performance of the $\mathcal{L}_1$AC architecture in the presence of non-zero initialization error for the state predictor. Second, the paper presents how the proposed $\mathcal{L}_1$AC scheme is integrated into the L2F framework, including its interaction with the baseline controller and the real-time modeling module. Finally, flight tests on an unmanned aerial vehicle (UAV) validate the efficacy of the proposed control and learning scheme.
Identifiability Implies Robust, Globally Exponentially Convergent On-line Parameter Estimation: Application to Model Reference Adaptive Control
Authors: Lei Wang, Romeo Ortega, Alexey Bobtsov, Jose Guadalupe Romero, Bowen Yi
Date: 2021-08-19 00:00:00
Tasks: parameter estimation, regression

In this paper we propose a new parameter estimator that ensures global exponential convergence of linear regression models requiring only the necessary assumption of identifiability of the regression equation,which we show is equivalent to interval excitation of the regressor vector. Continuous and discrete-time versions of the estimators are given. An extension to--separable and monotonic--non-linear parameterizations is also given. The estimators are shown to be robust to additive measurement noise and--not necessarily slow--parameter variations. Moreover, a version of the continuous-time estimator that rejects sinusoidal disturbances with unknown internal model is given. The estimator is shown to be applicable to the classical model reference adaptive control problem relaxing the conspicuous assumption of known sign of the high-frequency gain. Simulation results that illustrate the performance of the estimator are given.
Safe Learning MPC with Limited Model Knowledge and Data
Authors: Aaron Kandel, Scott J. Moura
Date: 2020-04-02 00:00:00
Tasks: Management, Video Compression, Zero-Shot Learning

This paper presents an end-to-end framework for safe learning-based control (LbC) using nonlinear stochastic MPC and distributionally robust optimization (DRO). This work is motivated by several open challenges in LbC literature. In particular, many control-theoretic LbC methods require subject matter expertise in order to translate their own safety guarantees, often manifested as preexisting data of safe trajectories or structural model knowledge. In this paper, we focus on LbC where the controller is applied directly to a system of which it has no or extremely limited direct experience, towards safety during \textit{tabula-rasa} or ``\textit{blank slate''} model-based learning and control as a challenging case for validation. This explores the boundary of the status-quo in control theory relating to requirements for subject matter expertise. We show under basic and limited assumptions on the underlying problem, we can translate probabilistic guarantees on feasibility to nonlinear systems using results in stochastic MPC and DRO literature whose relevance we formally extend in a mathematical analysis. We also present a coupled and intuitive formulation for persistence of excitation (PoE), and illustrate the connection between PoE and applicability of the proposed method. Our case studies of vehicle obstacle avoidance and safe extreme fast charging of lithium-ion batteries reveal powerful empirical results supporting the underlying DRO theory. Our method is widely applicable within the LbC domain to, for example, airborne wind energy systems, vehicle obstacle avoidance, and energy storage systems management. It is also applicable to quantifying uncertainty beyond the LbC case.
Learning explicit predictive controllers: theory and applications
Authors: Andrea Sassella, Valentina Breschi, Simone Formentin
Date: 2021-08-18 00:00:00
Tasks: LEMMA

In this paper, we deal with data-driven predictive control of linear time-invariant (LTI) systems. Specifically, we show for the first time how explicit predictive laws can be learnt directly from data, without needing to identify the system to control. To this aim, we resort to the Willems' fundamental lemma and we derive the explicit formulas by suitably elaborating the constrained optimization problem under investigation. The resulting optimal controller turns out to be a piecewise affine system coinciding with the solution of the original model-based problem in case of noiseless data. Such an equivalence is proven to hold asymptotically also in presence of measurement noise, thus making the proposed method a computationally efficient (but model-free) alternative to the state of the art predictive controls. The above statements are further supported by numerical simulations on three benchmark examples.
Permanent Magnet Linear Generator Design for Surface Riding Wave Energy Converters
Authors: Farid Naghavi, Shrikesh Sheshaprasad, Matthew Gardner, Aghamarshana Meduri, HeonYong Kang, Hamid Toliyat
Date: 2021-08-18 00:00:00

This paper describes the detailed analysis for the design of a linear generator developed for a Surface Riding Wave Energy Converter (SR-WEC), which was designed to improve energy capture over a wider range of sea states. The study starts with an analysis of the power take-off (PTO) control strategy to harness the maximum output power from given sea states. Passive, reactive, and discrete PTO control are explored. For the random wave excitation and limited sliding distance of the generator, the discrete strategy provides the highest average power output. The paper discusses the sizing requirement for the linear generator. Based on the force and power rating of the system and the application requirements, a slotless permanent magnet tubular generator is designed for the wave energy converter.
Drift in a Popular Metal Oxide Sensor Dataset Reveals Limitations for Gas Classification Benchmarks
Authors: Nik Dennler, Shavika Rastogi, Jordi Fonollosa, André van Schaik, Michael Schmuker
Date: 2021-08-19 00:00:00
Tasks: Benchmarking, Classification

Metal oxide (MOx) electro-chemical gas sensors are a sensible choice for many applications, due to their tunable sensitivity, their space-efficiency and their low price. Publicly available sensor datasets streamline the development and evaluation of novel algorithm and circuit designs, making them particularly valuable for the Artificial Olfaction / Mobile Robot Olfaction community. In 2013, Vergara et al. published a dataset comprising 16 months of recordings from a large MOx gas sensor array in a wind tunnel, which has since become a standard benchmark in the field. Here we report a previously undetected property of the dataset that limits its suitability for gas classification studies. The analysis of individual measurement timestamps reveals that gases were recorded in temporally clustered batches. The consequential correlation between the sensor response before gas exposure and the time of recording is often sufficient to predict the gas used in a given trial. Even if compensated by zero-offset-subtraction, residual short-term drift contains enough information for gas classification. We have identified a minimally drift-affected subset of the data, which is suitable for gas classification benchmarking after zero-offset-subtraction, although gas classification performance was substantially lower than for the full dataset. We conclude that previous studies conducted with this dataset very likely overestimate the accuracy of gas classification results. For the 17 potentially affected publications, we urge the authors to re-evaluate the results in light of our findings. Our observations emphasize the need to thoroughly document gas sensing datasets, and proper validation before using them for the development of algorithms.
Regional disparities in Social Mobility of India
Authors: Anuradha Singh
Date: 2021-08-19 00:00:00

Rapid rise in income inequality in India is a serious concern. While the emphasis is on inclusive growth, it seems difficult to tackle the problem without looking at the intricacies of the problem. The Social Mobility Index is an important tool that focuses on bringing long-term equality by identifying priority policy areas in the country. The PCA technique is employed in computation of the index. Overall, the Union Territory of Delhi ranks first, with the highest social mobility and the least social mobility is in Chhattisgarh. In addition, health and education access, quality and equity are key priority areas that can help improve social mobility in India. Thus, we conclude that human capital is of great importance in promoting social mobility and development in the present times.
The Impact of Research Funding on Knowledge Creation and Dissemination: A study of SNSF Research Grants
Authors: Rachel Heyard, Hanna Hottenrott
Date: 2020-11-23 00:00:00
Tasks: Articles

This study investigates the impact of competitive project-funding on researchers' publication outputs. Using detailed information on applicants at the Swiss National Science Foundation (SNSF) and their proposals' evaluation, we employ a case-control design that accounts for individual heterogeneity of researchers and selection into treatment (e.g. funding). We estimate the impact of grant award on a set of output indicators measuring the creation of new research results (the number of peer-reviewed articles), its relevance (number of citations and relative citation ratios), as well as its accessibility and dissemination as measured by the publication of preprints and by altmetrics. The results show that the funding program facilitates the publication and dissemination of additional research amounting to about one additional article in each of the three years following the grant. The higher citation metrics and altmetrics of publications by funded researchers suggest that impact goes beyond quantity, but that funding fosters quality and impact.
Risk Preferences in Time Lotteries
Authors: Yonatan Berman, Mark Kirstein
Date: 2021-08-18 00:00:00

An important but understudied question in economics is how people choose when facing uncertainty in the timing of events. Here we study preferences over time lotteries, in which the payment amount is certain but the payment time is uncertain. Expected discounted utility theory (EDUT) predicts decision makers to be risk-seeking over time lotteries. We explore a normative model of growth-optimality, in which decision makers maximise the long-term growth rate of their wealth. Revisiting experimental evidence on time lotteries, we find that growth-optimality accords better with the evidence than EDUT. We outline future experiments to scrutinise further the plausibility of growth-optimality.
A Partial Order on Preference Profiles
Authors: Wayne Yuan Gao
Date: 2021-08-19 00:00:00

We propose a theoretical framework under which preference profiles can be meaningfully compared. Specifically, given a finite set of feasible allocations and a preference profile, we first define a ranking vector of an allocation as the vector of all individuals' rankings of this allocation. We then define a partial order on preference profiles and write "$P \geq P^{'}$", if there exists an onto mapping $\psi$ from the Pareto frontier of $P^{'}$ onto the Pareto frontier of $P$, such that the ranking vector of any Pareto efficient allocation $x$ under $P^{'}$ is weakly dominated by the ranking vector of the image allocation $\psi(x)$ under $P$. We provide a characterization of the maximal and minimal elements under the partial order. In particular, we illustrate how an individualistic form of social preferences can be maximal in a specific setting. We also discuss how the framework can be further generalized to incorporate additional economic ingredients.
Unsupervised Cross-Lingual Speech Emotion Recognition Using Pseudo Multilabel
Authors: Jin Li, Nan Yan, Lan Wang
Date: 2021-08-19 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

Speech Emotion Recognition (SER) in a single language has achieved remarkable results through deep learning approaches in the last decade. However, cross-lingual SER remains a challenge in real-world applications due to a great difference between the source and target domain distributions. To address this issue, we propose an unsupervised cross-lingual Neural Network with Pseudo Multilabel (NNPM) that is trained to learn the emotion similarities between source domain features inside an external memory adjusted to identify emotion in cross-lingual databases. NNPM introduces a novel approach that leverages external memory to store source domain features and generates pseudo multilabel for each target domain data by computing the similarities between the external memory and the target domain features. We evaluate our approach on multiple different languages of speech emotion databases. Experimental results show our proposed approach significantly improves the weighted accuracy (WA) across multiple low-resource languages on Urdu, Skropus, ShEMO, and EMO-DB corpus. To facilitate further research, code is available at https://github.com/happyjin/NNPM