pwc-archive/papers-with-abstracts huggingface dataset:
Finite-time stability properties of Lur'e systems with piecewise continuous nonlinearities
Authors: Simone Mariano, Romain Postoyan, Luca Zaccarian
Date: 2023-02-02 00:00:00
Tasks: Friction

We analyze the stability properties of Lur'e systems with piecewise continuous nonlinearities by exploiting the notion of set-valued Lie derivative for Lur'e-Postnikov Lyapunov functions. We first extend an existing result of the literature to establish the global asymptotic stability of the origin under a more general sector condition. We then present the main results of this work, namely additional conditions under which output and state finite-time stability properties also hold for the considered class of systems. We highlight the relevance of these results by certifying the stability properties of two engineering systems of known interest: mechanical systems affected by friction and cellular neural networks.
Grid Services by Behind-the-Meter Distributed Energy Resources: NY State Grid Case Study
Authors: Hossein Hooshyar, Rahul Kadavil, Victor Paduani, Aboutaleb Haddadi, AHM Jakaria, Aminul Huque
Date: 2023-02-02 00:00:00
Tasks: Management

This paper presents a case study for utilizing behind-the-meter (BTM) distributed energy resources (DERs) to provide grid services when controlled by a DER Management System (DERMS). The testbed consists of a 5,000 buses transient-stability (TS) real-time (RT) model, two 9,500 buses distribution feeders from local utilities modeled in a distribution system simulator (DSS), and hundreds of DERs. MQTT communication protocol is utilized to interface the models in RT. Two main studies are carried. In the first, it is found that DERs with frequency-watt droop response can help maintaining stability in the future NYS grid in which thermal synchronous generators have been substituted by renewable energy resources. In the second, results demonstrate that BTM DERs can provide similar level of frequency regulation services expected from large utility-scale generation.
Real-Time T&D Co-Simulation for Testing Grid Impact of High DER Participation
Authors: Victor Paduani, Rahul Kadavil, Hossein Hooshyar, Aboutaleb Haddadi, AHM Jakaria, Aminul Huque
Date: 2023-02-02 00:00:00
Tasks: Management

This paper presents the development of a real-time T&D co-simulation testbed for simulating large grids under high DER penetration. By integrating bulk power system, distribution feeders, and distributed energy resources (DER) models into one simulation environment, the testbed enables the performance analysis and validation of DER management systems (DERMS) algorithms. This work proposes a co-simulation timestep sequence for the cross-platform data exchange and time synchronization, with a communication framework based on MQTT communication protocol. The proposed strategy is tested with a 5,000 buses model of part of the North American bulk power system (BPS) and a 9,500 nodes distribution feeder model obtained from a local utility. Simulations are carried out to demonstrate the capability of the proposed framework to propagate events between the transmission and distribution models. Results are used to quantify how the co-simulation timestep size can affect the propagation of dynamics between the models.
A Near-Field Focused Phased-Array Antenna Design Using the Time-Reversal Concept for Weed Control Purpose
Authors: Adel Omrani, Guido Link, John Jelonnek
Date: 2023-02-02 00:00:00

Near-field focus (NFF) antennas have been recently used in several applications for different purposes. In this work, the time-reversal (TR) concept is used to shape the phase distribution of the phased array elements for the NFF of the electromagnetic field strength. It is shown that the TR concept is equivalent to the known ray optic method for the NFF. A slotted waveguide phased array antenna operating at 5.8 GHz is designed to provide the maximum electric field strength at the near-field region of the phased array. It is shown that the application of the full-wave simulation allows for an antenna design that provide high strength of the electromagnetic field and sufficient steerability even at near-field conditions close to the phased array antenna.
Event-triggered Hybrid Energy-aware Scheduling in Manufacturing Systems
Authors: Zhean Shao, Wen Li, Ying Tan
Date: 2023-02-02 00:00:00
Tasks: Manufacturing simulation, Scheduling

Incorporating renewable energy sources (RESs) into manufacturing systems has been an active research area in order to address many challenges originating from the unpredictable nature of RESs such as photovoltaics.In the energy-aware scheduling for manufacturing systems, the traditional off-line scheduling techniques cannot always work well due to their lack of robustness with respect to uncertainties coming from imprecise models or unexpected situations. On the other hand, on-line scheduling or rescheduling, which can improve the robustness by using the model and the latest measurements simultaneously, suffer from a high computational cost. This work proposes a hybrid scheduling framework, which combines the advantages of both off-line scheduling and on-line scheduling, to provide a balanced solution between robustness and computational cost. A novel concept of partially-dispatchable state is introduced. It can be treated as a constant in scheduling when the model works well. When the model does not work well, it is triggered as the variable to tune to improve the performance. Such an event-triggered structure can reduce the number of rescheduling and computational costs while achieving a reasonable performance and enhancing system robustness. Moreover, the choice of partially-dispatchable state also provides an extra design freedom in achieving green manufacturing. Simulation examples on a manufacturing system, of which consists a 100-kW solar photovoltaic system, a 10-machine flow shop production line, a 50-kWh energy storage system, a 100-kW gas turbine, and the grid for power supply, demonstrating the validity and applicability of this event-triggered hybrid scheduling (ETHS) framework.
Multi-zone HVAC Control with Model-Based Deep Reinforcement Learning
Authors: Xianzhong Ding, Alberto Cerpa, Wan Du
Date: 2023-02-01 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we conduct a set of experiments to analyze the limitations of current MBRL-based HVAC control methods, in terms of model uncertainty and controller effectiveness. Using the lessons learned, we develop MB2C, a novel MBRL-based HVAC control system that can achieve high control performance with excellent sample efficiency. MB2C learns the building dynamics by employing an ensemble of environment-conditioned neural networks. It then applies a new control method, Model Predictive Path Integral (MPPI), for HVAC control. It produces candidate action sequences by using an importance sampling weighted algorithm that scales better to high state and action dimensions of multi-zone buildings. We evaluate MB2C using EnergyPlus simulations in a five-zone office building. The results show that MB2C can achieve 8.23% more energy savings compared to the state-of-the-art MBRL solution while maintaining similar thermal comfort. MB2C can reduce the training data set by an order of magnitude (10.52x) while achieving comparable performance to MFRL approaches.
Reinforcement learning-based estimation for partial differential equations
Authors: Saviz Mowlavi, Mouhacine Benosman
Date: 2023-01-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In systems governed by nonlinear partial differential equations such as fluid flows, the design of state estimators such as Kalman filters relies on a reduced-order model (ROM) that projects the original high-dimensional dynamics onto a computationally tractable low-dimensional space. However, ROMs are prone to large errors, which negatively affects the performance of the estimator. Here, we introduce the reinforcement learning reduced-order estimator (RL-ROE), a ROM-based estimator in which the correction term that takes in the measurements is given by a nonlinear policy trained through reinforcement learning. The nonlinearity of the policy enables the RL-ROE to compensate efficiently for errors of the ROM, while still taking advantage of the imperfect knowledge of the dynamics. Using examples involving the Burgers and Navier-Stokes equations, we show that in the limit of very few sensors, the trained RL-ROE outperforms a Kalman filter designed using the same ROM. Moreover, it yields accurate high-dimensional state estimates for trajectories corresponding to various physical parameter values, without direct knowledge of the latter.
Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs
Authors: Beichen Li, Bolei Deng, Wan Shou, Tae-Hyun Oh, Yuanming Hu, Yiyue Luo, Liang Shi, Wojciech Matusik
Date: 2023-02-01 00:00:00
Tasks: Efficient Exploration

The conflict between stiffness and toughness is a fundamental problem in engineering materials design. However, the systematic discovery of microstructured composites with optimal stiffness-toughness trade-offs has never been demonstrated, hindered by the discrepancies between simulation and reality and the lack of data-efficient exploration of the entire Pareto front. We introduce a generalizable pipeline that integrates physical experiments, numerical simulations, and artificial neural networks to address both challenges. Without any prescribed expert knowledge of material design, our approach implements a nested-loop proposal-validation workflow to bridge the simulation-to-reality gap and discover microstructured composites that are stiff and tough with high sample efficiency. Further analysis of Pareto-optimal designs allows us to automatically identify existing toughness enhancement mechanisms, which were previously discovered through trial-and-error or biomimicry. On a broader scale, our method provides a blueprint for computational design in various research areas beyond solid mechanics, such as polymer chemistry, fluid dynamics, meteorology, and robotics.
Robust Perfect Adaptation of Reaction Fluxes Ensured by Network Topology
Authors: Yuji Hirono, Hyukpyo Hong, Jae Kyoung Kim
Date: 2023-02-02 00:00:00

Maintaining stability in an uncertain environment is essential for proper functioning of living systems. Robust perfect adaptation (RPA) is a property of a system that generates an output at a fixed level even after fluctuations in input stimulus without fine-tuning parameters, and it is important to understand how this feature is implemented through biochemical networks. The existing literature has mainly focused on RPA of the concentration of a chosen chemical species, and no generic analysis has been made on RPA of reaction fluxes, that play an equally important role. Here, we identify structural conditions on reaction networks under which all the reaction fluxes exhibit RPA against the perturbation of the parameters inside a subnetwork. Based on this understanding, we give a recipe for obtaining a simpler reaction network, from which we can fully recover the steady-state reaction fluxes of the original system. This helps us identify key parameters that determine the fluxes and study the properties of complex reaction networks using a smaller one without losing any information about steady-state reaction fluxes.
Biophysical aspects of neurocognitive modeling with long-term sustained temperature variations
Authors: Serge Kernbach, Olga Kernbach, Andreas Kernbach
Date: 2023-02-02 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Long-term focused attention with visualization and breathing exercises is at the core of various Eastern traditions. Neurocognitive and psychosomatic phenomena demonstrated during such exercises were instrumentally explored with EEG and other sensors. Neurocognitive modeling in the form of meditative visualization produced persistent temperature effects in the body long after the exercise finished; this raises the question about their psychosomatic or biophysical origin. The work explores this question by comparing experiments with focusing attention inside and outside the body. EEG, temperature, heart and breathing sensors monitor internal body conditions, high resolution differential calorimetric sensors are used to detect thermal effects outside the body. Experiments with 159 attempts (2427 operator-sensor sessions) were carried over five months, control measurements run in the same conditions in parallel to experimental series. Increase of body temperature up to moderate fever zone 38.5 C and intentional control of up and down trend of core temperature by 1.6 C are demonstrated. Persistent temperature variations last >60 min. Experiments also demonstrated induced thermal fluctuations at 10^-3 C level in external calorimetric systems with 15 ml of water for 60-90 min. Repeatability of these attempts is over 90%, statistical Chi-square and Mann-Whitney tests reject the null hypotheses about random character of outcomes. Thus, the obtained data confirm the persistent thermal effects reported in previous publications and indicate their biophysical dimension. To explain these results we refer to a new model in neuroscience that involves spin phenomena in biochemical and physical systems. These experiments demonstrate complex biophysical mechanisms of altered states of consciousness; their function in the body's neurohumoral regulation and non-classical brain functions is discussed.
Sparse High-Dimensional Vector Autoregressive Bootstrap
Authors: Robert Adamek, Stephan Smeekes, Ines Wilms
Date: 2023-02-02 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

We introduce a high-dimensional multiplier bootstrap for time series data based on capturing dependence through a sparsely estimated vector autoregressive model. We prove its consistency for inference on high-dimensional means under two different moment assumptions on the errors, namely sub-gaussian moments and a finite number of absolute moments. In establishing these results, we derive a Gaussian approximation for the maximum mean of a linear process, which may be of independent interest.
Zero-Leverage Puzzle
Authors: Mykola Pinchuk
Date: 2023-02-01 00:00:00

In this paper, I examine why some firms have zero leverage. I fail to find evidence that firms are unlevered because of managerial entrenchment since these firms do not have weaker corporate governance. I reject the hypothesis that firms become zero-leverage after prolonged periods of high market valuation, since before levering these firms do not suffer from declining valuations and continue to issue large amounts of equity. I find strong evidence in favor of the financial constraints explanation of the zero-leverage puzzle. Zero-leverage firms appear to be financially constrained using three different measures of financial constraints. I obtain mixed evidence on the financial flexibility hypothesis since all-equity firms increase investments and acquisitions after levering, but the probability of their levering decreased during the financial crisis. My results suggest that financial constraints are the first-order the driver of zero-leverage behavior and are more important than less obvious explanations such as managerial entrenchment.
Identification of consideration sets from choice data
Authors: Davide Carpentiere, Angelo Petralia
Date: 2023-02-02 00:00:00

We show that many bounded rationality patterns of choice can be alternatively represented as testable models of limited consideration, and we elicit the features of the associated unobserved consideration sets from the observed choice. Moreover, we characterize some testable choice procedures in which the DM considers as few alternatives as possible. These properties, compatible with the empirical evidence, allow the experimenter to uniquely infer the DM's unobserved consideration sets from irrational features of the observed behavior.
An Operator Theory for Analyzing the Resolution of Multi-illumination Imaging Modalities
Authors: Ping Liu, Habib Ammari
Date: 2023-02-02 00:00:00
Tasks: Super-Resolution

By introducing a new operator theory, we provide a unified mathematical theory for general source resolution in the multi-illumination imaging problem. Our main idea is to transform multi-illumination imaging into single-snapshot imaging with a new imaging kernel that depends on both the illumination patterns and the point spread function of the imaging system. We thus prove that the resolution of multi-illumination imaging is approximately determined by the essential cutoff frequency of the new imaging kernel, which is roughly limited by the sum of the cutoff frequency of the point spread function and the maximum essential frequency in the illumination patterns. Our theory provides a unified way to estimate the resolution of various existing super-resolution modalities and results in the same estimates as those obtained in experiments. In addition, based on the reformulation of the multi-illumination imaging problem, we also estimate the resolution limits for resolving both complex and positive sources by sparsity-based approaches. We show that the resolution of multi-illumination imaging is approximately determined by the new imaging kernel from our operator theory and better resolution can be realized by sparsity-promoting techniques in practice but only for resolving very sparse sources. This explains experimentally observed phenomena in some sparsity-based super-resolution modalities.
Local Implicit Neural Representations for Multi-Sequence MRI Translation
Authors: Yunjie Chen, Marius Staring, Jelmer M. Wolterink, Qian Tao
Date: 2023-02-02 00:00:00
Tasks: Anatomy, SSIM, Translation

In radiological practice, multi-sequence MRI is routinely acquired to characterize anatomy and tissue. However, due to the heterogeneity of imaging protocols and contra-indications to contrast agents, some MRI sequences, e.g. contrast-enhanced T1-weighted image (T1ce), may not be acquired. This creates difficulties for large-scale clinical studies for which heterogeneous datasets are aggregated. Modern deep learning techniques have demonstrated the capability of synthesizing missing sequences from existing sequences, through learning from an extensive multi-sequence MRI dataset. In this paper, we propose a novel MR image translation solution based on local implicit neural representations. We split the available MRI sequences into local patches and assign to each patch a local multi-layer perceptron (MLP) that represents a patch in the T1ce. The parameters of these local MLPs are generated by a hypernetwork based on image features. Experimental results and ablation studies on the BraTS challenge dataset showed that the local MLPs are critical for recovering fine image and tumor details, as they allow for local specialization that is highly important for accurate image translation. Compared to a classical pix2pix model, the proposed method demonstrated visual improvement and significantly improved quantitative scores (MSE 0.86 x 10^-3 vs. 1.02 x 10^-3 and SSIM 94.9 vs 94.3)
Compression of Dynamic Medical CT Data Using Motion Compensated Wavelet Lifting with Denoised Update
Authors: Daniela Lanz, Jürgen Seiler, Karina Jaskolka, André Kaup
Date: 2023-02-02 00:00:00
Tasks: Denoising, Motion Compensation

For the lossless compression of dynamic 3-D+t volumes as produced by medical devices like Computed Tomography, various coding schemes can be applied. This paper shows that 3-D subband coding outperforms lossless HEVC coding and additionally provides a scalable representation, which is often required in telemedicine applications. However, the resulting lowpass subband, which shall be used as a downscaled representative of the whole original sequence, contains a lot of ghosting artifacts. This can be alleviated by incorporating motion compensation methods into the subband coder. This results in a high quality lowpass subband but also leads to a lower compression ratio. In order to cope with this, we introduce a new approach for improving the compression efficiency of compensated 3-D wavelet lifting by performing denoising in the update step. We are able to reduce the file size of the lowpass subband by up to 1.64\%, while the lowpass subband is still applicable for being used as a downscaled representative of the whole original sequence.
Optimizing Rate-Distortion Performance of Motion Compensated Wavelet Lifting with Denoised Prediction and Update
Authors: Daniela Lanz, André Kaup
Date: 2023-02-02 00:00:00
Tasks: Denoising

Efficient lossless coding of medical volume data with temporal axis can be achieved by motion compensated wavelet lifting. As side benefit, a scalable bit stream is generated, which allows for displaying the data at different resolution layers, highly demanded for telemedicine applications. Additionally, the similarity of the temporal base layer to the input sequence is preserved by the use of motion compensated temporal filtering. However, for medical sequences the overall rate is increased due to the specific noise characteristics of the data. The use of denoising filters inside the lifting structure can improve the compression efficiency significantly without endangering the property of perfect reconstruction. However, the design of an optimum filter is a crucial task. In this paper, we present a new method for selecting the optimal filter strength for a certain denoising filter in a rate-distortion sense. This allows to minimize the required rate based on a single input parameter for the encoder to control the requested distortion of the temporal base layer.
Content Adaptive Wavelet Lifting for Scalable Lossless Video Coding
Authors: Daniela Lanz, Christian Herbert, André Kaup
Date: 2023-02-02 00:00:00

Scalable lossless video coding is an important aspect for many professional applications. Wavelet-based video coding decomposes an input sequence into a lowpass and a highpass subband by filtering along the temporal axis. The lowpass subband can be used for previewing purposes, while the highpass subband provides the residual content for lossless reconstruction of the original sequence. The recursive application of the wavelet transform to the lowpass subband of the previous stage yields coarser temporal resolutions of the input sequence. This allows for lower bit rates, but also affects the visual quality of the lowpass subband. So far, the number of total decomposition levels is determined for the entire input sequence in advance. However, if the motion in the video sequence is strong or if abrupt scene changes occur, a further decomposition leads to a low-quality lowpass subband. Therefore, we propose a content adaptive wavelet transform, which locally adapts the depth of the decomposition to the content of the input sequence. Thereby, the visual quality of the low-pass subband is increased by up to 10.28 dB compared to a uniform wavelet transform with the same number of total decomposition levels, while the required rate is reduced by 1.06% additionally.