pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Operation of a Hydrogen-based Building Multi-Energy System Based on Deep Reinforcement Learning
Authors: Liang Yu, Shuqi Qin, Zhanbo Xu, Xiaohong Guan, Chao Shen, Dong Yue
Date: 2021-09-22 00:00:00
Tasks: Deep Reinforcement Learning, energy management, Management, Parameter Prediction

Since hydrogen has many advantages (e.g., free pollution, extensive sources, convenient storage and transportation), hydrogen-based multi-energy systems (HMESs) have received wide attention. However, existing works on the optimal operation of HMESs neglect building thermal dynamics, which means that the flexibility of building thermal loads can not be utilized for reducing system operation cost. In this paper, we investigate an optimal operation problem of an HMES with the consideration of building thermal dynamics. Specifically, we first formulate an expected operational cost minimization problem related to an HMES. Due to the existence of uncertain parameters, inexplicit building thermal dynamics models, temporally coupled operational constraints related to three kinds of energy storage systems and indoor temperatures, as well as the coupling between electric energy subsystems and thermal energy subsystems, it is challenging to solve the formulated problem. To overcome the challenge, we reformulate the problem as a Markov game and propose an energy management algorithm to solve it based on multi-agent discrete actor-critic with rules (MADACR). Note that the proposed algorithm does not require any prior knowledge of uncertain parameters, parameter prediction, and explicit building thermal dynamics model. Simulation results based on real-world traces show the effectiveness of the proposed algorithm.
A generic fixed-point iteration-based hierarchical control design: Application to a cryogenic process
Authors: Xuan-Huy Pham, Mazen Alamir, François Bonne, Patrick Bonnay
Date: 2021-09-22 00:00:00

This paper presents an extension of a recently proposed hierarchical control framework applied to a cryogenic system. While in the previous work, each sub-system in the decomposition needed to show at least one component of the control input, in the present contribution, this condition is removed enabling a higher flexibility in the definition of the decomposition graph. The impact of this extended flexibility on the computation time is shown using the same cryogenic station where a decomposition in four sub-system is made possible (instead of two in the previous setting).
Model Reference Adaptive Control with Linear-like Closed-loop Behavior
Authors: Mohamad T. Shahab, Daniel E. Miller
Date: 2021-09-22 00:00:00

It is typically proven in adaptive control that asymptotic stabilization and tracking holds, and that at best a bounded-noise bounded-state property is proven. Recently, it has been shown in both the pole-placement control and the $d$-step ahead control settings that if, as part of the adaptive controller, a parameter estimator based on the original projection algorithm is used and the parameter estimates are restricted to a convex set, then the closed-loop system experiences linear-like behavior: exponential stability, a bounded gain on the noise in every $p$-norm, and a convolution bound on the exogenous inputs; this can be leveraged to provide tolerance to unmodelled dynamics and plant parameter time-variation. In this paper, we extend the approach to the more general Model Reference Adaptive Control (MRAC) problem and demonstrate that we achieve the same desirable linear-like closed-loop properties.
Event-triggered observer design for linear systems
Authors: E. Petri, R. Postoyan, D. Astolfi, D. Nešić, W. P. M. H. Heemels
Date: 2021-09-22 00:00:00

We present an event-triggered observer design for linear time-invariant systems, where the measured output is sent to the observer only when a triggering condition is satisfied. We proceed by emulation and we first construct a continuous-time Luenberger observer. We then propose a dynamic rule to trigger transmissions, which only depends on the plant output and an auxiliary scalar state variable. The overall system is modeled as a hybrid system, for which a jump corresponds to an output transmission. We show that the proposed event-triggered observer guarantees global practical asymptotic stability for the estimation error dynamics. Moreover, under mild boundedness conditions on the plant state and its input, we prove that there exists a uniform strictly positive minimum inter-event time between any two consecutive transmissions, guaranteeing that the system does not exhibit Zeno solutions. Finally, the proposed approach is applied to a numerical case study of a lithium-ion battery.
Scheduling of Separable Mobile Energy Storage Systems with Mobile Generators and Fuel Tankers to Boost Distribution System Resilience
Authors: Wei Wang, Xiaofu Xiong, Yufei He, Jian Hu, Hongzhou Chen
Date: 2020-12-06 00:00:00
Tasks: Scheduling

Mobile energy resources (MERs) have been shown to boost DS resilience effectively in recent years. In this paper, we propose a novel idea, the separable mobile energy storage system (SMESS), as an attempt to further extend the flexibility of MER applications. "Separable" denotes that the carrier and the energy storage modules are treated as independent parts, which allows the carrier to carry multiple modules and scatter them independently throughout the DS. The constraints for scheduling SMESSs involving carriers and modules are derived based upon the interactive behavior among them and the DS. In addition, the fuel delivery issue of feeding mobile emergency generators (MEGs), which was usually bypassed in previous studies involving the scheduling of MEGs, is also considered and modeled. SMESSs, MEGs, and fuel tankers (FTs) are then jointly routed and scheduled, along with the dynamic DS reconfiguration, for DS service restoration by integrating them in a mixed-integer linear programming (MILP) model. Finally, the test is conducted on a modified IEEE 33-node test system, and results verify the effectiveness of the model in boosting DS resilience.
Optimal excitation and measurement pattern for cascade networks
Authors: Eduardo Mapurunga, Alexandre Sanfelice Bazanella
Date: 2021-09-21 00:00:00
Tasks: valid

This work deals with accuracy analysis of dynamical systems interconnected in a cascade structure. For a cascade network there are a number of experimental settings for which the dynamic systems within the network can be identified. We study the problem of choosing which excitation and measurement pattern delivers the most accurate parameter estimates for the whole network. The optimal experiment is based on the accuracy assessed through the asymptotic covariance matrix of the prediction error method, while the cost criterion is the number of excitations and measurements. We develop theoretical results under the assumptions that all dynamic systems are equal and with equal signal-to-noise ratio throughout the network. We show that there are experimental settings which result in equal overall precision and that there is an excitation and measurement pattern that yields more accurate results than others. From these results a guideline based on the topology of the network emerges for the choice of the experimental setting. We provide numerical results which attest that the principles behind this guideline are also valid for more general situations.
ICEV dismantling or recycling on a challenging environment
Authors: Manuel Alberto M. Ferreira, José António Filipe, José Moleiro Martins, Mário Nuno Mata, Pedro Neves Mata
Date: 2021-09-21 00:00:00
Tasks: Rhythm

Nowadays Sustainability is a huge issue. Sustainability deals with the need for the protection of the natural environment and ecosystems health and requires innovation and commitment with the future. This manuscript uses the infinite servers with Poisson arrivals queue system, modelling Internal Combustion Engine Vehicles (ICEV), normally cars but not only, which turn idle when conventional energy becomes scarce, or a new status quo is required. In such a case, they are recycled, becoming either EV-Electric Vehicles or HEV-Hybrid Electric Vehicles or FCEV-Fuel Cell Electric Vehicles, or are dismantled (DV-Dismantled Vehicles). Our model shows that when the rhythm ICEV become EV, HEV, FCEV and DV is greater than the rate at which they get idle the system tends to balance. In a cost-benefit analysis perspective, there are minimum benefits above which, both dismantling and recycling, are interesting. Additionally, the most interesting is the one for which the minimum benefit is the least.
Non-intrusive Balancing Transformation of Highly Stiff Systems with Lightly-damped Impulse Response
Authors: Elnaz Rezaian, Cheng Huang, Karthik Duraisamy
Date: 2021-09-21 00:00:00

Balanced truncation (BT) is a model reduction method that utilizes a coordinate transformation to retain eigen-directions that are highly observable and reachable. To address realizability and scalability of BT applied to highly stiff and lightly-damped systems, a non-intrusive data-driven method is developed for balancing discrete-time systems via the eigensystem realization algorithm (ERA). The advantage of ERA for balancing transformation makes full-state outputs tractable. Further, ERA enables balancing despite stiffness, by eliminating computation of balancing modes and adjoint simulations. As a demonstrative example, we create balanced ROMs for a one-dimensional reactive flow with pressure forcing, where the stiffness introduced by the chemical source term is extreme (condition number $10^{13}$), preventing analytical implementation of BT. We investigate the performance of ROMs in prediction of dynamics with unseen forcing inputs and demonstrate stability and accuracy of balanced ROMs in truly predictive scenarios whereas without ERA, POD-Galerkin and Least-squares Petrov-Galerkin projections fail to represent the true dynamics. We show that after the initial transients under unit impulse forcing, the system undergoes lightly-damped oscillations, which magnifies the influence of sampling properties on predictive performance of the balanced ROMs. We propose an output domain decomposition approach and couple it with tangential interpolation to resolve sharp gradients at reduced computational costs.
The supremum principle selects simple, transferable models
Authors: Cody Petrie, Christian Anderson, Casie Maekawa, Travis Maekawa, Mark K. Transtrum
Date: 2021-09-21 00:00:00
Tasks: Model Selection

We consider how mathematical models enable predictions for conditions that are qualitatively different from the training data. We propose techniques based on information topology to find models that can apply their learning in regimes for which there is no data. The first step is to use the Manifold Boundary Approximation Method to construct simple, reduced models of target phenomena in a data-driven way. We consider the set of all such reduced models and use the topological relationships among them to reason about model selection for new, unobserved phenomena. Given minimal models for several target behaviors, we introduce the supremum principle as a criterion for selecting a new, transferable model. The supremal model, i.e., the least upper bound, is the simplest model that reduces to each of the target behaviors. We illustrate how to discover supremal models with several examples; in each case, the supremal model unifies causal mechanisms to transfer successfully to new target domains. We use these examples to motivate a general algorithm that has formal connections to theories of analogical reasoning in cognitive psychology.
Application of Video-to-Video Translation Networks to Computational Fluid Dynamics
Authors: Hiromitsu Kigure
Date: 2021-09-12 00:00:00
Tasks: Image-to-Image Translation, Super-Resolution, Translation

In recent years, the evolution of artificial intelligence, especially deep learning, has been remarkable, and its application to various fields has been growing rapidly. In this paper, I report the results of the application of generative adversarial networks (GANs), specifically video-to-video translation networks, to computational fluid dynamics (CFD) simulations. The purpose of this research is to reduce the computational cost of CFD simulations with GANs. The architecture of GANs in this research is a combination of the image-to-image translation networks (the so-called "pix2pix") and Long Short-Term Memory (LSTM). It is shown that the results of high-cost and high-accuracy simulations (with high-resolution computational grids) can be estimated from those of low-cost and low-accuracy simulations (with low-resolution grids). In particular, the time evolution of density distributions in the cases of a high-resolution grid is reproduced from that in the cases of a low-resolution grid through GANs, and the density inhomogeneity estimated from the image generated by GANs recovers the ground truth with good accuracy. Qualitative and quantitative comparisons of the results of the proposed method with those of several super-resolution algorithms are also presented.
Automatic Plane Adjustment of Orthopedic Intra-operative Flat Panel Detector CT-Volumes
Authors: Celia Martin Vicario, Florian Kordon, Felix Denzinger, Jan Siad El Barbari, Maxim Privalov, Jochen Franke, Sarina Thomas, Lisa Kausch, Andreas Maier, Holger Kunze
Date: 2021-09-15 00:00:00
Tasks: Multi-Task Learning, regression

Purpose 3D acquisitions are often acquired to assess the result in orthopedic trauma surgery. With a mobile C-Arm system, these acquisitions can be performed intra-operatively. That reduces the number of required revision surgeries. However, due to the operation room setup, the acquisitions typically cannot be performed such that the acquired volumes are aligned to the anatomical regions. Thus, the multiplanar reconstructed (MPR) planes need to be adjusted manually during the review of the volume. In this paper, we present a detailed study of multi-task learning (MTL) regression networks to estimate the parameters of the MPR planes. Approach First, various mathematical descriptions for rotation, including Euler angle, quaternion, and matrix representation, are revised. Then, three different MTL network architectures based on the PoseNet are compared with a single task learning network. Results Using a matrix description rather than the Euler angle description, the accuracy of the regressed normals improves from $7.7^{\circ}$ to $7.3^{\circ}$ in the mean value for single anatomies. The multi-head approach improves the regression of the plane position from $7.4mm$ to $6.1mm$, while the orientation does not benefit from this approach. Conclusions The results show that a multi-head approach can lead to slightly better results than the individual tasks networks. The most important benefit of the MTL approach is that it is a single network for standard plane regression for all body regions with a reduced number of stored parameters.
Enabling variable high spatial resolution retrieval from a long pulse BOTDA sensor
Authors: Zhao Ge, Li Shen, Can Zhao, Hao Wu, Zhiyong Zhao, Ming Tang
Date: 2021-09-09 00:00:00
Tasks: Retrieval

In the field of Internet of Things, there is an urgent need for sensors with large-scale sensing capability for scenarios such as intelligent monitoring of production lines and urban infrastructure. Brillouin optical time domain analysis (BOTDA) sensors, which can monitor thousands of continuous points simultaneously, show great advantages in these applications. We propose a convolutional neural network (CNN) to process the data of conventional Brillouin optical time domain analysis (BOTDA) sensors, which achieves unprecedented performance improvement that allows to directly retrieve higher spatial resolution (SR) from the sensing system that use long pump pulses. By using the simulated Brillouin gain spectrums (BGSs) as the CNN input and the corresponding high SR BFS as the output target, the trained CNN is able to obtain a SR higher than the theoretical value determined by the pump pulse width. In the experiment, the CNN accurately retrieves 0.5-m hotspots from the measured BGS with pump pulses from 20 to 50 ns, and the acquired BFS is in great agreement with 45/40 ns differential pulse-width pair (DPP) measurement results. Compared with the DPP technique, the proposed CNN demonstrates a 2-fold improvement in BFS uncertainty with only half the measurement time. In addition, by changing the training datasets, the proposed CNN can obtain tunable high SR retrieval based on conventional BOTDA sensors that use long pulses without any requirement of hardware modifications. The proposed data post-processing approach paves the way to enable novel high spatial resolution BOTDA sensors, which brings substantial improvement over the state-of-the-art techniques in terms of system complexity, measurement time and reliability, etc.
Algorithms for Inference in SVARs Identified with Sign and Zero Restrictions
Authors: Matthew Read
Date: 2021-09-22 00:00:00
Tasks: Bayesian Inference

I develop algorithms to facilitate Bayesian inference in structural vector autoregressions that are set-identified with sign and zero restrictions by showing that the system of restrictions is equivalent to a system of sign restrictions in a lower-dimensional space. Consequently, algorithms applicable under sign restrictions can be extended to allow for zero restrictions. Specifically, I extend algorithms proposed in Amir-Ahmadi and Drautzburg (2021) to check whether the identified set is nonempty and to sample from the identified set without rejection sampling. I compare the new algorithms to alternatives by applying them to variations of the model considered by Arias et al. (2019), who estimate the effects of US monetary policy using sign and zero restrictions on the monetary policy reaction function. The new algorithms are particularly useful when a rich set of sign restrictions substantially truncates the identified set given the zero restrictions.
A new look at the anthropogenic global warming consensus: an econometric forecast based on the ARIMA model of paleoclimate series
Authors: Gilmar V. F. Santos, Lucas G. Cordeiro, Claudio A. Rojo, Edison L. Leismann
Date: 2021-09-21 00:00:00
Tasks: Time Series, Time Series Analysis

This paper aims to project a climate change scenario using a stochastic paleotemperature time series model and compare it with the prevailing consensus. The ARIMA - Autoregressive Integrated Moving Average Process model was used for this purpose. The results show that the parameter estimates of the model were below what is established by the anthropogenic current and governmental organs, such as the IPCC (UN), considering a 100-year scenario, which suggests a period of temperature reduction and a probable cooling. Thus, we hope with this study to contribute to the discussion by adding a statistical element of paleoclimate in counterpoint to the current scientific consensus and place the debate in a long-term historical dimension, in line with other existing research on the topic.
Circular City Index: An Open Data analysis to assess the urban circularity preparedness of cities to address the green transition -- A study on the Italian municipalities
Authors: Alessio Muscillo, Simona Re, Sergio Gambacorta, Giuseppe Ferrara, Nicola Tagliafierro, Emiliano Borello, Alessandro Rubino, Angelo Facchini
Date: 2021-09-22 00:00:00

We present a circularity transition index based on open data principles and circularity of energy, material, and information. The aim of the Circular City Index is to provide data and a succinct measurement of the attributes related to municipalities performances that can support the definition of green policies at national and local level. We have identified a set of key performance indicators, defined at municipality level, measuring factors that, directly and indirectly, could influence circularity and green transition, with a focus on the green new deal vision embraced by the European Union. The CCI is tested on a open dataset that collects data covering 100% of the Italian municipalities (7,904). Our results show that the computation of the CCI on a large sample leads to a normal distribution of the index, suggesting disparities both under the territorial point of view and under the point of view of city size. Results provide useful information to practitioner, policy maker and experts from academia alike, to define effective tools able to underpin a careful planning of investments supported by the national recovery and resilience plan recently issued by the Italian government. This may be particularly useful to enhance enabling factors of the green transition that may differ across territories, helping policymakers to promote a smooth and fair transition by fostering the preparedness of municipalities in addressing the challenge.
Does Foreign Debt Contribute to Economic Growth?
Authors: Tomoo Kikuchi, Satoshi Tobe
Date: 2021-09-22 00:00:00

We study the relationship between foreign debt and GDP growth using a panel dataset of 50 countries from 1997 to 2015. We find that economic growth correlates positively with foreign debt and that the relationship is causal in nature by using the sovereign credit default swap spread as an instrumental variable. Furthermore, we find that foreign debt increases investment and then GDP growth in subsequent years. Our findings suggest that lower sovereign default risks lead to higher foreign debt contributing to GDP growth more in OECD than non-OECD countries.
Deep Learning Model for Demodulation Reference Signal based Channel Estimation
Authors: Yu Tian, Chengguang Li, Sen yang
Date: 2021-09-22 00:00:00
Tasks: Deep Learning

In this paper, we propose a deep learning model for Demodulation Reference Signal (DMRS) based channel estimation task. Specifically, a novel Denoise, Linear interpolation and Refine (DLR) pipeline is proposed to mitigate the noise propagation problem during channel information interpolation and to restore the nonlinear variation of wireless channel over time. At the same time, the Small-norm Sample Cost-sensitive (SSC) learning method is proposed to equalize the qualities of channel estimation under different kinds of wireless environments and improve the channel estimation reliability. The effectiveness of the propose DLR-SSC model is verified on WAIC Dataset. Compared with the well know ChannelNet channel estimation model, our DLR-SSC model reduced normalized mean square error (NMSE) by 27.2dB, 22.4dB and 16.8dB respectively at 0dB, 10dB, and 20dB SNR. The proposed model has won the second place in the 2nd Wireless Communication Artificial Intelligence Competition (WAIC). The code is about to open source.
Polarimetric Room Electromagnetics
Authors: Troels Pedersen, Ramoni Adeogun
Date: 2021-09-22 00:00:00

A polarimetric model for the power delay spectrum for inroom communication is proposed. The proposed model describes the gradual depolarization of the signal with delay. The model is based on the theory of room electromagnetics, specifically the mirror source approach, which is straightforwardly generalized to the polarimetric case. Compared to the previously known unipolarized room electromagnetic models, which are contained as a special case, the new model holds one additional parameter describing the polarization leakage per wall bounce. \tprev{The proposed model is found to fit well to two sets of polarimetric data one mm-wave and one cm-wave measurements