pwc-archive/papers-with-abstracts huggingface dataset:
Prediction and validation of the strongly modulated forced response of two beams undergoing frictional impacts
Authors: Carlo Monjaraz-Tec, Lukas Kohlmann, Stefan Schwarz, Andreas Hartung, Johann Gross, Malte Krack
Date: 2022-07-02 00:00:00
Tasks: Friction

We consider two cantilevered beams undergoing frictional impacts at the free end. The beams are designed to be of similar geometry so that they have distinct but close natural frequencies. Under harmonic base excitation near the primary resonance with the higher-frequency fundamental bending mode, the system shows a strongly modulated non-periodic response. The purpose of this work is to analyze to what extent the non-periodic vibro-impact dynamics can be predicted. To this end, we use a recently developed modeling and simulation approach. The approach relies on component mode synthesis, the massless boundary concept and an appropriate time stepping scheme. Unilateral contact and dry friction are modeled as set-valued laws and imposed locally within the spatially resolved contact area. A linear model updating is carried out based on the natural frequencies and damping ratios identified in the regime without impacts. The nonlinear simulation of the steady-state response to forward and backward stepped sine excitation is compared against measurements. The results are in very good agreement, especially in the light of the uncertainty associated with the observed material loss in the contact region and the nonlinear behavior of the clamping.
A Distributionally Robust Resilience Enhancement Strategy for Distribution Networks Considering Decision-Dependent Contingencies
Authors: Yujia Li, Shunbo Lei, Wei Sun, Chenxi Hu, Yunhe Hou
Date: 2022-07-02 00:00:00

When performing the resilience enhancement for distribution networks, there are two obstacles to reliably model the uncertain contingencies: 1) decision-dependent uncertainty (DDU) due to various line hardening decisions, and 2) distributional ambiguity due to limited outage information during extreme weather events (EWEs). To address these two challenges, this paper develops scenario-wise decision-dependent ambiguity sets (SWDD-ASs), where the DDU and distributional ambiguity inherent in EWE-induced contingencies are simultaneously captured for each possible EWE scenario. Then, a two-stage trilevel decision-dependent distributionally robust resilient enhancement (DD-DRRE) model is formulated, whose outputs include the optimal line hardening, distributed generation (DG) allocation, and proactive network reconfiguration strategy under the worst-case distributions in SWDD-ASs. Subsequently, the DD-DRRE model is equivalently recast to a mixed-integer linear programming (MILP)-based master problem and multiple scenario-wise subproblems, facilitating the adoption of a customized column-and-constraint generation (C&CG) algorithm. Finally, case studies demonstrate a remarkable improvement in the out-of-sample performance of our model, compared to its prevailing stochastic and robust counterparts. Moreover, the potential values of incorporating the ambiguity and distributional information are quantitatively estimated, providing a useful reference for planners with different budgets and risk-aversion levels.
Optimal Placement of PV Smart Inverters with Volt-VAr Control in Electric Distribution Systems
Authors: Mengxi Chen, Shanshan Ma, Zahra Soltani, Raja Ayyanar, Vijay Vittal, Mojdeh Khorsand
Date: 2022-07-01 00:00:00
Tasks: Stochastic Optimization

The high R/X ratio of typical distribution systems makes the system voltage vulnerable to the active power injection from distributed energy resources (DERs). Moreover, the intermittent and uncertain nature of the DER generation brings new challenges to the voltage control. This paper proposes a two-stage stochastic optimization strategy to optimally place the PV smart inverters with Volt-VAr capability for distribution systems with high photovoltaic (PV) penetration to mitigate voltage violation issues. The proposed optimization strategy enables a planningstage guide for upgrading the existing PV inverters to smart inverters with Volt-VAr capability while considering the operationstage characteristics of the Volt-VAr control. One advantage of this planning strategy is that it utilizes the local control capability of the smart inverter that requires no communication, thus avoiding issues related to communication delays and failures. Another advantage is that the Volt-VAr control characteristic is internally integrated into the optimization model as a set of constraints, making placement decisions more accurate. The objective of the optimization is to minimize the upgrading cost and the number of the smart inverters required while maintaining the voltage profile within the acceptable range. Case studies on an actual 12.47kV, 9km long Arizona utility feeder have been conducted using OpenDSS to validate the effectiveness of the proposed placement strategy in both static and dynamic simulations. Index Terms
Distributed Frequency Control in Power Grids with Low and Time-Varying Inertia
Authors: Manasa Muralidharan, Jan Kleissl, Patricia Hidalgo-Gonzalez
Date: 2022-07-01 00:00:00

This paper presents a distributed frequency control method for power grids with high penetration of inverter-connected resources under low and time-varying inertia due to renewable energy (RE). We provide a distributed virtual inertia (VI) allocation method using the distributed subgradient algorithm. We implement our distributed control strategy under full and sparse communication architectures. The distributed full and sparse communication controllers achieve comparable performance to a centralized controller and stabilize the test system within 6~s. We study the sensitivity of the controller performance to varying objective function weights on phase angle versus frequency deviation, gradient step sizes, and allowed rate of change of inertia (RoCoI) coefficients. We observe that the settling time of the states and the controller performance and effort are susceptible to changes in the gradient step size and objective weights on the frequency and angle deviation. While a higher objective weight on angle versus frequency deviations positively affects their settling times, it negatively impacts controller performance and control effort. The impact of this work is to propose distributed control schemes as new mechanisms that act before or in alignment with primary and secondary control to safely regulate the frequency in future power grids.
An Alternative Method for Solving Security-Constrained Unit Commitment with Neural Network Based Battery Degradation Model
Authors: Cunzhi Zhao, Xingpeng Li
Date: 2022-07-01 00:00:00
Tasks: Scheduling

Battery energy storage system (BESS) can effectively mitigate the uncertainty of variable renewable generation and provide flexible ancillary services. However, degradation is a key concern for rechargeable batteries such as the most widely used Lithium-ion battery. A neural network based battery degradation (NNBD) model can accurately quantify the battery degradation. When incorporating the NNBD model into security-constrained unit commitment (SCUC), we can establish a battery degradation based SCUC (BD-SCUC) model that can consider the equivalent battery degradation cost precisely. However, the BD-SCUC may not be solved directly due to high non-linearity of the NNBD model. To address this issue, the NNBD model is linearized by converting the nonlinear activation function at each neuron into linear constraints, which enables BD-SCUC to become a linearized BD-SCUC (L-BD-SCUC) model. Case studies demonstrate the proposed L-BD-SCUC model can be efficiently solved for multiple BESS buses power system day-ahead scheduling problems with the lowest total cost including the equivalent degradation cost and normal operation cost.
Synthesis of General Decoupling Networks Using Transmission Lines
Authors: BinBin Yang
Date: 2022-07-01 00:00:00

In this paper, we introduce a synthesis technique for transmission line based decoupling networks, which find application in coupled systems such as multiple-antenna systems and compact antenna arrays. Employing the generalized $\pi$-network and the transmission line analysis technique, we reduce the decoupling network design into simple matrix calculations. The synthesized decoupling network is essentially a generalized $\pi$-network with transmission lines at all branches. A standard electrical length of $3\lambda/8$ and $5\lambda/8$ are chosen to simplify the physical implementation, leaving the characteristic impedances of the transmission line branches the main design parameters. The advantage of this proposed decoupling network is that it can be implemented using transmission lines, ensuring better control on loss, performance consistency and higher power handling capability when compared with lumped components, and can be easily scaled for operation at different frequencies. A two-port microstrip antenna system at 1.2 GHz and a three-port monopole antenna system at 1 GHz are investigated respectively to demonstrate the validity of the proposed synthesis method, and perfect decoupling ($S_{21}<-50$dB) are achieved at both design frequencies.
Solving Bilevel AC OPF Problems by Smoothing the Complementary Conditions -- Part II: Solution Techniques and Case Study
Authors: Karlo Sepetanc, Hrvoje Pandzic, Tomislav Capuder
Date: 2022-06-30 00:00:00

This is a second part of the research on AC optimal power flow being used in the lower level of the bilevel strategic bidding or investment models. As an example of a suitable upper-level problem, we observe a strategic bidding of energy storage and propose a novel formulation based on the smoothing technique. After presenting the idea and scope of our work, as well as the model itself and the solution algorithm in the companion paper (Part I), this paper presents a number of existing solution techniques and the proposed one based on smoothing the complementary conditions. The superiority of the proposed algorithm and smoothing techniques is demonstrated in terms of accuracy and computational tractability over multiple transmission networks of different sizes and different OPF models. The results indicate that the proposed approach outperforms all other options in both metrics by a significant margin. This is especially noticeable in the metric of accuracy where out of total 422 optimizations over 9 meshed networks the greatest AC OPF error is 0.023% that is further reduced to 3.3e-4% in the second iteration of our algorithm.
Multimodal 4DVarNets for the reconstruction of sea surface dynamics from SST-SSH synergies
Authors: Ronan Fablet, Quentin Febvre, Bertrand Chapron
Date: 2022-07-04 00:00:00

Due to the irregular space-time sampling of sea surface observations, the reconstruction of sea surface dynamics is a challenging inverse problem. While satellite altimetry provides a direct observation of the sea surface height (SSH), which relates to the divergence-free component of sea surface currents, the associated sampling pattern prevents from retrieving fine-scale sea surface dynamics, typically below a 10-day time scale. By contrast, other satellite sensors provide higher-resolution observations of sea surface tracers such as sea surface temperature (SST). Multimodal inversion schemes then arise as an appealing strategy. Though theoretical evidence supports the existence of an explicit relationship between sea surface temperature and sea surface dynamics under specific dynamical regimes, the generalization to the variety of upper ocean dynamical regimes is complex. Here, we investigate this issue from a physics-informed learning perspective. We introduce a trainable multimodal inversion scheme for the reconstruction of sea surface dynamics from multi-source satellite-derived observations. The proposed 4DVarNet schemes combine a variational formulation involving trainable observation and a priori terms with a trainable gradient-based solver. We report an application to the reconstruction of the divergence-free component of sea surface dynamics from satellite-derived SSH and SST data. An observing system simulation experiment for a Gulf Stream region supports the relevance of our approach compared with state-of-the-art schemes. We report relative improvement greater than 50% compared with the operational altimetry product in terms of root mean square error and resolved space-time scales. We discuss further the application and extension of the proposed approach for the reconstruction and forecasting of geophysical dynamics from irregularly-sampled satellite observations.
Perspectives and constraints on neural network models of neurobiological processes
Authors: Arsenii Onuchin
Date: 2022-07-02 00:00:00
Tasks: Anatomy, Community Detection, Stochastic Block Model

Artificial and natural neural network models are a new toolkit which could be potentially have been used for clarifying of complex brain functions. To attend this goal, such models need to be neurobiologically realistic. However, although neural networks have advanced keenly in recent decades their strict similarity in aspects of brain anatomy and physiology is imperfect. In this work we discuss different types of neural models, including localist, attractor and deep network models, and also identify aspects under which their biological credibility can be improved. These conditions range from the choice of neuron models and of mechanisms of synaptic plasticity and learning to implementation of inhibition and control, along with network architectures (modularity, connectivity). We highlight recent advances in biologically inspired neural network models and their constraints.
csa2sls: A complete subset approach for many instruments using Stata
Authors: Seojeong Lee, Siha Lee, Julius Owusu, Youngki Shin
Date: 2022-07-04 00:00:00

We develop a Stata command $\texttt{csa2sls}$ that implements the complete subset averaging two-stage least squares (CSA2SLS) estimator in Lee and Shin (2021). The CSA2SLS estimator is an alternative to the two-stage least squares estimator that remedies the bias issue caused by many correlated instruments. We conduct Monte Carlo simulations and confirm that the CSA2SLS estimator reduces both the mean squared error and the estimation bias substantially when instruments are correlated. We illustrate the usage of $\texttt{csa2sls}$ in Stata by an empirical application.
Capital Market Performance and Macroeconomic Dynamics in Nigeria
Authors: Oladapo Fapetu, Segun Michael Ojo, Adekunle Alexander Balogun, Adeoba Adepoju Asaolu
Date: 2022-07-02 00:00:00

The study examined the relationship between capital market performance and the macroeconomic dynamics in Nigeria, and it utilized secondary data spanning 1993 to 2020. The data was analyzed using vector error correction model (VECM) technology. The result revealed a significant long run relationship between capital market performance and macroeconomic dynamics in Nigeria. We observed long run causality running from the exchange rate, inflation, money supply, and unemployment rate to capital market performance indicator in Nigeria. The result supports the Arbitrage Pricing Theory (APT) proposition in the Nigerian context. The theory stipulates that the linear relationship between an asset expected returns and the macroeconomic factors whose dynamics affect the asset risk can forecast an asset's returns. In other words, the result of this study supports the proposition that the dynamics in the exchange rate, inflation, money supply, and unemployment rate influence the capital market performance. The study validates the recommendations of Arbitrage Pricing Theory (APT) in Nigeria.
Economic Consequences of the COVID-19 Pandemic on Sub-Saharan Africa: A historical perspective
Authors: Anthony Enisan Akinlo, Segun Michael Ojo
Date: 2022-07-01 00:00:00

This paper examined the economic consequences of the COVID-19 pandemic on sub-Saharan Africa (SSA) using the historical approach and analysing the policy responses of the region to past crises and their economic consequences. The study employed the manufacturing-value-added share of GDP as a performance indicator. The analysis shows that the wrong policy interventions to past crises led the sub-Saharan African sub-region into its deplorable economic situation. The study observed that the region leapfrogged prematurely to import substitution, export promotion, and global value chains. Based on these experiences, the region should adopt a gradual approach in responding to the COVID-19 economic consequences. The sub-region should first address relevant areas of sustainability, including proactive investment in research and development to develop homegrown technology, upgrade essential infrastructural facilities, develop security infrastructure, and strengthen the financial sector.
Inside the West Wing: Lobbying as a contest
Authors: Alastair Langtry
Date: 2022-07-02 00:00:00
Tasks: Position

When a government makes many different policy decisions, lobbying can be viewed as a contest between the government and many different special interest groups. The government fights lobbying by interest groups with its own political capital. In this world, we find that a government wants to `sell protection' -- give favourable treatment in exchange for contributions -- to certain interest groups. It does this in order to build its own `war chest' of political capital, which improves its position in fights with other interest groups. And it does so until it wins all remaining contests with certainty. This stands in contrast to existing models that often view lobbying as driven by information or agency problems.
Engagement Maximization
Authors: Benjamin Hébert, Weijie Zhong
Date: 2022-07-01 00:00:00
Tasks: Management

We investigate the management of information provision to maximize user engagement. A principal sequentially reveals signals to an agent who has a limited amount of information processing capacity and can choose to exit at any time. We identify a ``dilution'' strategy -- sending rare but highly informative signals -- that maximizes user engagement. The platform's engagement metric shapes the direction and magnitude of biases in provided information relative to a user-optimal benchmark. Even without intertemporal commitment, the platform replicates full-commitment revenue by inducing the user's belief to remain ``as uncertain as'' the prior until the rare, decisive signal arrives and induces stopping. We apply our results to two contexts: an ad-supported internet media platform and a teacher attempting to engage test-motivated students.
Multiple Selection Approximation for Improved Spatio-Temporal Prediction in Video Coding
Authors: Jürgen Seiler, André Kaup
Date: 2022-07-04 00:00:00
Tasks: Prediction

In this contribution, a novel spatio-temporal prediction algorithm for video coding is introduced. This algorithm exploits temporal as well as spatial redundancies for effectively predicting the signal to be encoded. To achieve this, the algorithm operates in two stages. Initially, motion compensated prediction is applied on the block being encoded. Afterwards this preliminary temporal prediction is refined by forming a joint model of the initial predictor and the spatially adjacent already transmitted blocks. The novel algorithm is able to outperform earlier refinement algorithms in speed and prediction quality. Compared to pure motion compensated prediction, the mean data rate can be reduced by up to 15% and up to 1.16 dB gain in PSNR can be achieved for the considered sequences.
Fast orthogonality deficiency compensation for improved frequency selective image extrapolation
Authors: Jürgen Seiler, André Kaup
Date: 2022-07-04 00:00:00

The purpose of this paper is to introduce a very efficient algorithm for signal extrapolation. It can widely be used in many applications in image and video communication, e. g. for concealment of block errors caused by transmission errors or for prediction in video coding. The signal extrapolation is performed by extending a signal from a limited number of known samples into areas beyond these samples. Therefore a finite set of orthogonal basis functions is used and the known part of the signal is projected onto them. Since the basis functions are not orthogonal regarding the area of the known samples, the projection does not lead to the real portion a basis function has of the signal. The proposed algorithm efficiently copes with this non-orthogonality resulting in very good objective and visual extrapolation results for edges, smooth areas, as well as structured areas. Compared to an existent implementation, this algorithm has a significantly lower computational complexity without any degradation in quality. The processing time can be reduced by a factor larger than 100.
Face Image Lighting Enhancement Using a 3D Model
Authors: Qiulin Chen, Jan P. Allebach
Date: 2022-07-02 00:00:00
Tasks: 3D geometry, Face Alignment, Image Enhancement, model

Image enhancement helps to generate balanced lighting distributions over faces. Our goal is to get an illuminance-balanced enhanced face image from a single view. Traditionally, image enhancement methods ignore the 3D geometry of the face or require a complicated multi-view geometry. Other methods cause color tone shifting or over saturation. Inspired by the new research achievements in face alignment and face 3D modeling, we propose an improved face image enhancement method by leveraging 3D face models. Given a face image as input, our method will first estimate its lighting distribution. Then we build an optimization process to refine the distribution. Finally, we generate an illuminance-balanced face image from a single view. Experiments on the FiveK dataset demonstrate that our method performs well and compares favorably with other methods.
The Importance of the Instantaneous Phase for classification using Convolutional Neural Networks
Authors: Luis Sanchez Tapia, Marios S. Pattichis, Sylvia Celedon-Pattichis, Carlos Lopez Leiva
Date: 2022-07-01 00:00:00
Tasks: Transfer Learning

Large-scale training of Convolutional Neural Networks (CNN) is extremely demanding in terms of computational resources. Also, for specific applications, the standard use of transfer learning also tends to require far more resources than what may be needed. This work examines the impact of using AM-FM representations as input images for CNN classification applications. A comparison was made between AM-FM components combinations and grayscale images as inputs for reduced and complete networks. The results showed that only the phase component produced significant predictions within a simple network. Neither IA or gray scale image were able to induce any learning in the system. Furthermore, the FM results were 7x faster during training and used 123x less parameters compared to state-of-the-art MobileNetV2 architecture, while maintaining comparable performance (AUC of 0.78 vs 0.79).