pwc-archive/papers-with-abstracts huggingface dataset:
A Distributed control framework for the optimal operation of DC microgrids
Authors: Zao Fu, Michele Cucuzzella, Carlo Cenedese, Wenwu Yu, Jacquelien M. A. Scherpen
Date: 2022-04-18 00:00:00
Tasks: energy trading

In this paper we propose an original distributed control framework for DC mcirogrids. We first formulate the (optimal) control objectives as an aggregative game suitable for the energy trading market. Then, based on the dual theory, we analyze the equivalent distributed optimal condition for the proposed aggregative game and design a distributed control scheme to solve it. By interconnecting the DC mcirogrid and the designed distributed control system in a power preserving way, we steer the DC microgrid's state to the desired optimal equilibrium, satisfying a predefined set of local and coupling constraints. Finally, based on the singular perturbation system theory, we analyze the convergence of the closed-loop system. The simulation results show excellent performance of the proposed control framework.
The Existence and Uniqueness of Solutions for Kernel-Based System Identification
Authors: Mohammad Khosravi, Roy S. Smith
Date: 2022-04-17 00:00:00

The notion of reproducing kernel Hilbert space (RKHS) has emerged in system identification during the past decade. In the resulting framework, the impulse response estimation problem is formulated as a regularized optimization defined on an infinite-dimensional RKHS consisting of stable impulse responses. The consequent estimation problem is well-defined under the central assumption that the convolution operators restricted to the RKHS are continuous linear functionals. Moreover, according to this assumption, the representer theorem hold, and therefore, the impulse response can be estimated by solving a finite-dimensional program. Thus, the continuity feature plays a significant role in kernel-based system identification. This paper shows that this central assumption is guaranteed to be satisfied in considerably general situations, namely when the kernel is an integrable function and the input signal is bounded. Furthermore, the strong convexity of the optimization problem and the continuity property of the convolution operators imply that the kernel-based system identification admits a unique solution. Consequently, it follows that kernel-based system identification is a well-defined approach.
A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches
Authors: Attai Ibrahim Abubakar, Iftikhar Ahmad, Kenechi G. Omeke, Metin Ozturk, Cihat Ozturk, Ali Makine Abdel-Salam, Michael S. Mollel, Qammer H. Abbasi, Sajjad Hussain, Muhammad Ali Imran
Date: 2022-04-17 00:00:00
Tasks: Novel Concepts

Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature.
Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach
Authors: Yuanzheng Li, Shangyang He, Yang Li, Leijiao Ge, Suhua Lou, Zhigang Zeng
Date: 2022-04-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Time Series, Time Series Analysis

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion Laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework.
Station Reallocation and Rebalancing Strategy for Bike-Sharing Systems: A Case Study of Washington DC
Authors: Pedram Beigi, Michel Khoueiry, Mohammad Sadra Rajabi, Samer Hamdar
Date: 2022-04-16 00:00:00

Bike-sharing is becoming increasingly popular as an urban traffic mode while increasing the affordability, flexibility, and reliability of interconnected public transportation systems (i.e., interconnected light rail, buses, micro-mobility, and ride-sharing modes of transportation). From the consumers perspective, 1) finding a bike station in convenient locations where demand usually occurs and 2) the availability of bikes at rush hours with a lesser probability of encountering empty docks (for fixed-station bike-share systems) are two key concerns. Some stations are more likely to be empty or full, reflecting an imbalance in bike supply and demand. Accordingly, it is essential to understand a bike-share system's demand pattern to select the optimal locations and reallocate bikes to the right stations to increase the utilization rate and reduce the number of unserved customers (i.e., potential demand). The Capital Bikeshare in the Washington DC Metropolitan Area is one of the prominent bike-share systems in the USA - with more than 4,300 bikes available at 654 stations across seven jurisdictions. This study provides a systematic analysis of a bike-sharing system's Capital Bikeshare system usage pattern. Our study intends to create an optimization strategy formulated as a deterministic integer programming for reallocating bike stations daily and rebalancing the bike supply system. From an operational perspective, such a strategy will allow overnight preparations to answer the rush-hour morning demand and during special events in Washington D.C.
Technical Report: Hybrid Autonomous Intersection Management
Authors: Aaron Parks-Young, Guni Sharon
Date: 2022-04-16 00:00:00
Tasks: Management

This document provides technical details regarding the Hybrid-AIM simulator that was used in Sharon and Stone (2017) and Parks-Young and Sharon (2022).
Circuit-theoretic Line Outage Distribution Factor
Authors: Shimiao Li, Amritanshu Pandey, Larry Pileggi
Date: 2022-04-16 00:00:00

This work presents the design of AC line outage distribution factor created from the circuit-theoretic power flow models. Experiment results are shown to demonstrate its efficacy in quantifying the impact of line outages on the grid, and its resulting potential for fast contingency screening.
Navigation between initial and desired community states using shortcuts
Authors: Benjamin W. Blonder, Michael H. Lim, Zachary Sunberg, Claire Tomlin
Date: 2022-04-15 00:00:00
Tasks: Management

Ecological management problems often involve navigating from an initial to a desired community state. We ask whether navigation is possible without brute-force additions and deletions of species, using actions of varying costs: adding/deleting a small number of individuals of a species, changing the environment, and waiting. Navigation can yield direct paths (single sequence of actions) or shortcut paths (multiple sequences of actions with lower cost than a direct path). We ask (1) when is non-brute-force navigation possible?; (2) do shortcuts exist and what are their properties?; and (3) what heuristics predict shortcut existence? Using several empirical datasets, we show that (1) non-brute-force navigation is only possible between some state pairs, (2) shortcuts exist between many state pairs; and (3) changes in abundance and richness are the strongest predictors of shortcut existence, independent of dataset and algorithm choices. State diagrams thus unveil hidden strategies for efficiently shifting between states.
On annular short-time stability conditions for generalized Persidskii systems
Authors: Wenjie Mei, Denis Efimov, Rosane Ushirobira
Date: 2022-04-15 00:00:00

This paper studies the trajectory behavior evaluation for generalized Persidskii systems with an essentially bounded input on a finite time interval. Also, the notions of annular settling and output annular settling for general nonlinear systems are introduced. We propose conditions for annular short-time stability, short-time boundedness with a nonzero initial state, annular settling, and output annular settling for a class of Persidskii systems. These conditions are based on the verification of linear matrix inequalities. An application to recurrent neural networks illustrates the usefulness of the proposed notions and conditions.
Structured Covariance Matrix Estimation for Noise-Type Radars
Authors: David Luong, Bhashyam Balaji, Sreeraman Rajan
Date: 2022-04-16 00:00:00
Tasks: parameter estimation, Vocal Bursts Type Prediction

Standard noise radars, as well as noise-type radars such as quantum two-mode squeezing radar, are characterized by a covariance matrix with a very specific structure. This matrix has four independent parameters: the amplitude of the received signal, the amplitude of the internal signal used for matched filtering, the correlation between the two signals, and the relative phase between them. In this paper, we derive estimators for these four parameters using two techniques. The first is based on minimizing the Frobenius norm between the structured covariance matrix and the sample covariance matrix; the second is maximum likelihood parameter estimation. The two techniques yield the same estimators. We then give probability density functions (PDFs) for all four estimators. Because some of these PDFs are quite complicated, we also provide approximate PDFs. Finally, we apply our results to the problem of target detection and derive expressions for the receiver operating characteristic curves of two different noise radar detectors.
A Family of Neyman-Pearson-Based Detectors for Noise-Type Radars
Authors: David Luong, Bhashyam Balaji, Sreeraman Rajan
Date: 2022-04-16 00:00:00
Tasks: LEMMA, Vocal Bursts Type Prediction

We derive a detector that optimizes the target detection performance of any single-input single-output noise radar satisfying the following properties: it transmits Gaussian noise, it retains an internal reference signal for matched filtering, all external noise is additive white Gaussian noise, and all signals are measured using heterodyne receivers. This class of radars, which we call noise-type radars, includes not only many types of standard noise radars, but also a type of quantum radar known as quantum two-mode squeezing radar. The detector, which we derive using the Neyman-Pearson lemma, is not practical because it requires foreknowledge of a target-dependent correlation coefficient that cannot be known beforehand. (It is, however, a natural standard of comparison for other detectors.) This motivates us to study the family of Neyman-Pearson-based detectors that result when the correlation coefficient is treated as a parameter. We derive the probability distribution of the Neyman-Pearson-based detectors when there is a mismatch between the pre-chosen parameter value and the true correlation coefficient. We then use this result to generate receiver operating characteristic curves. Finally, we apply our results to the case where the correlation coefficient is small. It turns out that the resulting detector is not only a good one, but that it has appeared previously in the quantum radar literature.
Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes
Authors: Federico Bugni, Ivan Canay, Azeem Shaikh, Max Tabord-Meehan
Date: 2022-04-18 00:00:00

This paper considers the problem of inference in cluster randomized experiments when cluster sizes are non-ignorable. Here, by a cluster randomized experiment, we mean one in which treatment is assigned at the cluster level. By non-ignorable cluster sizes, we refer to the possibility that the treatment effects may depend non-trivially on the cluster sizes. We frame our analysis in a super-population framework in which cluster sizes are random. In this way, our analysis departs from earlier analyses of cluster randomized experiments in which cluster sizes are treated as non-random. We distinguish between two different parameters of interest: the equally-weighted cluster-level average treatment effect, and the size-weighted cluster-level average treatment effect. For each parameter, we provide methods for inference in an asymptotic framework where the number of clusters tends to infinity and treatment is assigned using a covariate-adaptive stratified randomization procedure. We additionally permit the experimenter to sample only a subset of the units within each cluster rather than the entire cluster and demonstrate the implications of such sampling for some commonly used estimators. A small simulation study and empirical demonstration show the practical relevance of our theoretical results.
Nonlinear and Nonseparable Structural Functions in Fuzzy Regression Discontinuity Designs
Authors: Haitian Xie
Date: 2022-04-18 00:00:00
Tasks: regression

Many empirical examples of regression discontinuity (RD) designs concern a continuous treatment variable, but the theoretical aspects of such models are less studied. This study examines the identification and estimation of the structural function in fuzzy RD designs with a continuous treatment variable. The structural function fully describes the causal impact of the treatment on the outcome. We show that the nonlinear and nonseparable structural function can be nonparametrically identified at the RD cutoff under shape restrictions, including monotonicity and smoothness conditions. Based on the nonparametric identification equation, we propose a three-step semiparametric estimation procedure and establish the asymptotic normality of the estimator. The semiparametric estimator achieves the same convergence rate as in the case of a binary treatment variable. As an application of the method, we estimate the causal effect of sleep time on health status by using the discontinuity in natural light timing at time zone boundaries.
Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect
Authors: Tymon Słoczyński, S. Derya Uysal, Jeffrey M. Wooldridge
Date: 2022-04-15 00:00:00
Tasks: Sensitivity, valid

Recent research has demonstrated the importance of flexibly controlling for covariates in instrumental variables estimation. In this paper we study the finite sample and asymptotic properties of various weighting estimators of the local average treatment effect (LATE), motivated by Abadie's (2003) kappa theorem and offering the requisite flexibility relative to standard practice. We argue that two of the estimators under consideration, which are weight normalized, are generally preferable. Several other estimators, which are unnormalized, do not satisfy the properties of scale invariance with respect to the natural logarithm and translation invariance, thereby exhibiting sensitivity to the units of measurement when estimating the LATE in logs and the centering of the outcome variable more generally. We also demonstrate that, when noncompliance is one sided, certain weighting estimators have the advantage of being based on a denominator that is strictly greater than zero by construction. This is the case for only one of the two normalized estimators, and we recommend this estimator for wider use. We illustrate our findings with a simulation study and three empirical applications, which clearly document the sensitivity of unnormalized estimators to how the outcome variable is coded. We implement the proposed estimators in the Stata package kappalate.
Bad Weather, Social Network, and Internal Migration; Case of Japanese Sumo Wrestlers 1946-1985
Authors: Eiji Yamamura
Date: 2022-04-17 00:00:00

Post-World War II , there was massive internal migration from rural to urban areas in Japan. The location of Sumo stables was concentrated in Tokyo. Hence, supply of Sumo wrestlers from rural areas to Tokyo was considered as migration. Using a panel dataset covering forty years, specifically 1946-1985, this study investigates how weather conditions and social networks influenced the labor supply of Sumo wrestlers. Major findings are; (1) inclemency of the weather in local areas increased supply of Sumo wrestlers in the period 1946-1965, (2) the effect of the bad weather conditions is greater in the locality where large number of Sumo wrestlers were supplied in the pre-war period, (3) neither the occurrence of bad weather conditions nor their interactions with sumo-wrestlers influenced the supply of Sumo wrestlers in the period 1966-1985. These findings imply that the negative shock of bad weather conditions on agriculture in the rural areas incentivized young individuals to be apprenticed in Sumo stables in Tokyo. Additionally, in such situations, the social networks within Sumo wrestler communities from the same locality are important. However, once the share of workers in agricultural sectors became very low, this mechanism did not work.
AI, Ageing and Brain-Work Productivity: Technological Change in Professional Japanese Chess
Authors: Eiji Yamamura, Ryohei Hayashi
Date: 2022-04-17 00:00:00

Using Japanese professional chess (Shogi) players records in the novel setting, this paper examines how and the extent to which the emergence of technological changes influences the ageing and innate ability of players winning probability. We gathered games of professional Shogi players from 1968 to 2019. The major findings are: (1) diffusion of artificial intelligence (AI) reduces innate ability, which reduces the performance gap among same-age players; (2) players winning rates declined consistently from 20 years and as they get older; (3) AI accelerated the ageing declination of the probability of winning, which increased the performance gap among different aged players; (4) the effects of AI on the ageing declination and the probability of winning are observed for high innate skill players but not for low innate skill ones. This implies that the diffusion of AI hastens players retirement from active play, especially for those with high innate abilities. Thus, AI is a substitute for innate ability in brain-work productivity.
Research on the accumulation effect model of technological innovation in textile industry based on chaos theory
Authors: Xiangtai Zuo
Date: 2022-04-13 00:00:00

Technological innovation is one of the most important variables in the evolution of the textile industry system. As the innovation process changes, so does the degree of technological diffusion and the state of competitive equilibrium in the textile industry system, and this leads to fluctuations in the economic growth of the industry system. The fluctuations resulting from the role of innovation are complex, irregular and imperfectly cyclical. The study of the chaos model of the accumulation of innovation in the evolution of the textile industry can help to provide theoretical guidance for technological innovation in the textile industry, and can help to provide suggestions for the interaction between the government and the textile enterprises themselves. It is found that reasonable government regulation parameters contribute to the accelerated accumulation of innovation in the textile industry.
Eigen mode selection in human subject game experiment
Authors: Zhijian Wang, Qinmei Yao, Yijia Wang
Date: 2022-04-17 00:00:00

Eigen mode selection ought to be a practical issue in some real game systems, as it is a practical issue in the dynamics behaviour of a building, bridge, or molecular, because of the mathematical similarity in theory. However, its reality and accuracy have not been known in real games. We design a 5-strategy game which, in the replicator dynamics theory, is predicted to exist two eigen modes. Further, in behaviour game theory, the game is predicted that the mode selection should depends on the game parameter. We conduct human subject game experiments by controlling the parameter. The data confirm that, the predictions on the mode existence as well as the mode selection are significantly supported. This finding suggests that, like the equilibrium selection concept in classical game theory, eigen mode selection is an issue in game dynamics theory.
Wireless Semantic Communications for Video Conferencing
Authors: Peiwen Jiang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li
Date: 2022-04-16 00:00:00
Tasks: Semantic Communication, Video Compression

Video conferencing has become a popular mode of meeting even if it consumes considerable communication resources. Conventional video compression causes resolution reduction under limited bandwidth. Semantic video conferencing maintains high resolution by transmitting some keypoints to represent motions because the background is almost static, and the speakers do not change often. However, the study on the impact of the transmission errors on keypoints is limited. In this paper, we initially establish a basal semantic video conferencing (SVC) network, which dramatically reduces transmission resources while only losing detailed expressions. The transmission errors in SVC only lead to a changed expression, whereas those in the conventional methods destroy pixels directly. However, the conventional error detector, such as the cyclic redundancy check, cannot reflect the degree of expression changes. To overcome this issue, we develop an incremental redundancy hybrid automatic repeat-request (IR-HARQ) framework for the varying channels (SVC-HARQ) incorporating a novel semantic error detector. The SVC-HARQ has flexibility in bit consumption and achieves good performance. In addition, SVC-CSI is designed for channel state information (CSI) feedback to allocate the keypoint transmission and enhance the performance dramatically. Simulation shows that the proposed wireless semantic communication system can significantly improve the transmission efficiency.This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible