pwc-archive/papers-with-abstracts huggingface dataset:
Filtering Module on Satellite Tracking
Authors: Moh Kamalul Wafi
Date: 2023-04-08 00:00:00

The scope of satellite has increasingly attained as one of the most challenging topics due to the attraction of elaborating the outer space. The satellite, as a means of collecting data and communicating, needs a proper calculation so as to maintain the movement and its appearance. The concept of the proposed research lies in the mathematical model along with certain noises. The mathematical model is started by initial two variable states, constituting a radius and an angle, with no process noise on it. These two states then are formulated with certain assumption of noises in terms of the range and the scaled angle deviations from them in turn. Keep in mind that those two noises are mutually independent and their covariance are considered. the model is defined as Algebraic Riccati Equation (ARE) along with Kalman filter algorithm, from the estimation, the steady-state estimator, the computational of gain matrix to the stability of the predictor. The findings show that, as for the two pairs of states, the performance of the estimation can follow the state with just slight fluctuations in the first a fifth of a thousand iterations. With respect to the Mean Square Error (MSE), both noises are around 0.2 for the four states.
System Identification on the Families of Auto-Regressive with Least-Square-Batch Algorithm
Authors: Moh Kamalul Wafi
Date: 2023-04-08 00:00:00

The theories of system identification have been highly elaborated so as to achieve the true system. This paper much discuses regarding the stochastic processes along with the divergent of whether or not the system has zero-mean under scenario of either white and coloured noise. The mathematical foundations, including mean, variance, covariance, optimal parameters, along with some modified scenarios among them, are presented in detail from various system along with some basic idea behind them. The families of auto-regressive (1) and (2) are compared both mathematical and simulation in order to obtain the best design approaching the true system, Moreover, the least-square algorithm is used to examine the effectiveness of some number of iteration along with "batch" theorem
MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response
Authors: Jasper van Tilburg, Luciano C. Siebert, Jochen L. Cremer
Date: 2023-04-08 00:00:00
Tasks: Management, Multi-agent Reinforcement Learning, reinforcement-learning

This paper presents a decentralized Multi-Agent Reinforcement Learning (MARL) approach to an incentive-based Demand Response (DR) program, which aims to maintain the capacity limits of the electricity grid and prevent grid congestion by financially incentivizing residential consumers to reduce their energy consumption. The proposed approach addresses the key challenge of coordinating heterogeneous preferences and requirements from multiple participants while preserving their privacy and minimizing financial costs for the aggregator. The participant agents use a novel Disjunctively Constrained Knapsack Problem optimization to curtail or shift the requested household appliances based on the selected demand reduction. Through case studies with electricity data from $25$ households, the proposed approach effectively reduced energy consumption's Peak-to-Average ratio (PAR) by $14.48$% compared to the original PAR while fully preserving participant privacy. This approach has the potential to significantly improve the efficiency and reliability of the electricity grid, making it an important contribution to the management of renewable energy resources and the growing electricity demand.
On the Optimality of Procrastination Policy for EV charging under Net Energy Metering
Authors: Minjae Jeon, Lang Tong, Qing Zhao
Date: 2023-04-08 00:00:00

We consider the problem of behind-the-meter EV charging by a prosumer, co-optimized with rooftop solar, electric battery, and flexible consumptions such as water heaters and HVAC. Under the time-of-use net energy metering tariff with the stochastic solar production and random EV charging demand, a finite-horizon surplus-maximization problem is formulated. We show that a procrastination threshold policy that delays EV charging to the last possible moment is optimal when EV charging is co-optimized with flexible demand, and the policy thresholds can be computed easily offline. When battery storage is part of the co-optimization, it is shown that the net consumption of the prosumer is a two-threshold piecewise linear function of the behind-the-meter renewable generation under the optimal policy, and the procrastination threshold policy remains optimal, although the thresholds cannot be computed easily. We propose a simple myopic solution and demonstrate in simulations that the performance gap between the myopic policy and an oracle upper bound appears to be 0.5-7.5%.
Regularised Learning with Selected Physics for Power System Dynamics
Authors: Haiwei Xie, Federica Bellizio, Jochen L. Cremer, Goran Strbac
Date: 2023-04-08 00:00:00

Due to the increasing system stability issues caused by the technological revolutions of power system equipment, the assessment of the dynamic security of the systems for changing operating conditions (OCs) is nowadays crucial. To address the computational time problem of conventional dynamic security assessment tools, many machine learning (ML) approaches have been proposed and well-studied in this context. However, these learned models only rely on data, and thus miss resourceful information offered by the physical system. To this end, this paper focuses on combining the power system dynamical model together with the conventional ML. Going beyond the classic Physics Informed Neural Networks (PINNs), this paper proposes Selected Physics Informed Neural Networks (SPINNs) to predict the system dynamics for varying OCs. A two-level structure of feed-forward NNs is proposed, where the first NN predicts the generator bus rotor angles (system states) and the second NN learns to adapt to varying OCs. We show a case study on an IEEE-9 bus system that considering selected physics in model training reduces the amount of needed training data. Moreover, the trained model effectively predicted long-term dynamics that were beyond the time scale of the collected training dataset (extrapolation).
Optimal biasing and physical limits of DVS event noise
Authors: Rui Graca, Brian Mcreynolds, Tobi Delbruck
Date: 2023-04-08 00:00:00

Under dim lighting conditions, the output of Dynamic Vision Sensor (DVS) event cameras is strongly affected by noise. Photon and electron shot-noise cause a high rate of non-informative events that reduce Signal to Noise ratio. DVS noise performance depends not only on the scene illumination, but also on the user-controllable biasing of the camera. In this paper, we explore the physical limits of DVS noise, showing that the DVS photoreceptor is limited to a theoretical minimum of 2x photon shot noise, and we discuss how biasing the DVS with high photoreceptor bias and adequate source-follower bias approaches optimal noise performance. We support our conclusions with pixel-level measurements of a DAVIS346 and analysis of a theoretical pixel model.
A Smart Switch Configuration and Reliability Assessment Method for Large-Scale Offshore Wind Farm Electrical Collector System
Authors: Xiaochi Ding, Xinwei Shen, Qiuwei Wu, Liming Wang, Dechang Yang
Date: 2023-04-08 00:00:00

With the development of offshore wind farms (OWFs) in far-offshore and deep-sea areas, each OWF could contain more and more wind turbines and cables, making it imperative to study high-reliability electrical collector system (ECS) for OWF. Enlightened by active distribution network, for OWF, we propose an ECS switch configuration that enables post-fault network recovery, along with a reliability assessment (RA) method based on optimization models. It can also determine the optimal normal state and network reconfiguration strategies to maximize ECS reliability. Case studies on several OWFs demonstrate that the proposed RA method is more computationally efficient and accurate than the traditional sequential Monte-Carlo simulation method. Moreover, the proposed switch configuration, in conjunction with the network reconfiguration strategy and proper topology, provides significant benefits to ECS reliability.
Lipschitz Continuity of Signal Temporal Logic Robustness Measures: Synthesizing Control Barrier Functions from One Expert Demonstration
Authors: Prithvi Akella, Apurva Badithela, Richard M. Murray, Aaron D. Ames
Date: 2023-04-07 00:00:00
Tasks: Open-Ended Question Answering

Control Barrier Functions (CBFs) allow for efficient synthesis of controllers to maintain desired invariant properties of safety-critical systems. However, the problem of identifying a CBF remains an open question. As such, this paper provides a constructive method for control barrier function synthesis around one expert demonstration that realizes a desired system specification formalized in Signal Temporal Logic (STL). First, we prove that all STL specifications have Lipschitz-continuous robustness measures. Second, we leverage this Lipschitz continuity to synthesize a time-varying control barrier function. By filtering control inputs to maintain the positivity of this function, we ensure that the system trajectory satisfies the desired STL specification. Finally, we demonstrate the effectiveness of our approach on the Robotarium.
A Computational Efficient Pumped Storage Hydro Optimization in the Look-ahead Unit Commitment and Real-time Market Dispatch Under Uncertainty
Authors: Bing Huang, Arezou Ghesmati, Yonghong Chen, Ross Baldick
Date: 2023-04-07 00:00:00
Tasks: Stochastic Optimization

Pumped storage hydro units (PSHU) are great sources of flexibility in power systems. This is especially valuable in modern systems with increasing shares of intermittent renewable resources. However, the flexibility from PSHUs, particularly in the real-time market, has not been thoroughly studied. The storage optimization in a real-time market hasn't been well addressed. To enhance the use of PSH resources and leverage their flexibility, it is important to incorporate the uncertainties, properly address the risks and avoid increasing too much computational burdens in the real-time market operation. To provide a practical solution to the daily operation of a PSHU in a single day look-ahead commitment (LAC) and real-time market, this paper proposes two pumped storage hydro (PSH) models that only use probabilistic price forecast to incorporate uncertainties and manage risks in the LAC and real-time market operation. The price forecast scenarios are formulated only on PSHUs that minimizes the computational challenges to the Security Constrained Unit Commitment (SCUC) problem. Numerical studies in Mid-continent Independent System Operator (MISO) demonstrate that the proposed models improves market efficiency. Compared to traditional stochastic and robust unit commitment, the proposed methods only moderately increase the solving time from current practice of deterministic LAC. Probabilistic forecast for Real Time Locational Marginal Price (RT-LMP) on PSH locations is created and embedded into the proposed stochastic optimization model, an statistical robust approach is used to generate scenarios for reflecting the temporal inter-dependence of the LMP forecast uncertainties.
Policy Poisoning in Batch Learning for Linear Quadratic Control Systems via State Manipulation
Authors: Courtney M. King, Son Tung Do, Juntao Chen
Date: 2023-04-07 00:00:00

In this work, we study policy poisoning through state manipulation, also known as sensor spoofing, and focus specifically on the case of an agent forming a control policy through batch learning in a linear-quadratic (LQ) system. In this scenario, an attacker aims to trick the learner into implementing a targeted malicious policy by manipulating the batch data before the agent begins its learning process. An attack model is crafted to carry out the poisoning strategically, with the goal of modifying the batch data as little as possible to avoid detection by the learner. We establish an optimization framework to guide the design of such policy poisoning attacks. The presence of bi-linear constraints in the optimization problem requires the design of a computationally efficient algorithm to obtain a solution. Therefore, we develop an iterative scheme based on the Alternating Direction Method of Multipliers (ADMM) which is able to return solutions that are approximately optimal. Several case studies are used to demonstrate the effectiveness of the algorithm in carrying out the sensor-based attack on the batch-learning agent in LQ control systems.
N,N,N-Trimethyl chitosan as a permeation enhancer for inhalation drug delivery: interaction with a model pulmonary surfactant
Authors: Jana Szabová, Filip Mravec, Mostafa Mokhtari, Rémi Le Borgne, Michal Kalina, Jean-François Berret
Date: 2023-04-01 00:00:00

N,N,N-Trimethyl chitosan (TMC), a biocompatible and biodegradable derivative of chitosan, is currently used as a permeation enhancer to increase the translocation of drugs to the bloodstream in the lungs. This article discusses the effect of TMC on a mimetic pulmonary surfactant, Curosurf, a low-viscosity lipid formulation administered to preterm infants with acute respiratory distress syndrome. Curosurf exhibits a strong interaction with TMC, resulting in the formation of aggregates at electrostatic charge stoichiometry. At nanoscale, Curosurf undergoes a profound reorganization of its lipid vesicles in terms of size and lamellarity. The initial micron-sized vesicles (average size 4.8 microns) give way to a froth-like network of unilamellar vesicles about 300 nm in size. Under such conditions, neutralization of the cationic charges by pulmonary surfactant may inhibit TMC permeation enhancer capacity, especially as electrostatic charge complexation is found at low TMC content. The permeation properties of pulmonary surfactant-neutralized TMC should then be evaluated for its applicability as a permeation enhancer for inhalation in the alveolar region.
Universal Transitions between Growth and Dormancy via Intermediate Complex Formation
Authors: Jumpei F. Yamagishi, Kunihiko Kaneko
Date: 2023-04-09 00:00:00

A simple cell model consisting of a catalytic reaction network with intermediate complex formation is numerically studied. As nutrients are depleted, the transition from the exponential growth phase to the growth-arrested dormant phase occurs along with hysteresis and a lag time for growth recovery. This transition is caused by the accumulation of intermediate complexes, leading to the jamming of reactions and the diversification of components. These properties are generic in random reaction networks, as supported by dynamical systems analyses of corresponding mean-field models.
On the state-space model of unawareness
Authors: Alex A. T. Rathke
Date: 2023-04-10 00:00:00
Tasks: model

We show that the knowledge of an agent carrying non-trivial unawareness violates the standard property of 'necessitation', therefore necessitation cannot be used to refute the standard state-space model. A revised version of necessitation preserves non-trivial unawareness and solves the classical Dekel-Lipman-Rustichini result. We propose a generalised knowledge operator consistent with the standard state-space model of unawareness, including the model of infinite state-space.
Asymmetric networks, clientelism and their impacts: households' access to workfare employment in rural India
Authors: Anindya Bhattacharya, Anirban Kar, Alita Nandi
Date: 2023-04-09 00:00:00

In this paper we explore two intertwined issues. First, using primary data we examine the impact of asymmetric networks, built on rich relational information on several spheres of living, on access to workfare employment in rural India. We find that unidirectional relations, as opposed to reciprocal relations, and the concentration of such unidirectional relations increase access to workfare jobs. Further in-depth exploration provides evidence that patron-client relations are responsible for this differential access to such employment for rural households. Complementary to our empirical exercises, we construct and analyse a game-theoretical model supporting our findings.
Recursive Preferences, Correlation Aversion, and the Temporal Resolution of Uncertainty
Authors: Lorenzo Maria Stanca
Date: 2023-04-10 00:00:00

This paper investigates a novel behavioral feature of recursive preferences: aversion to risks that persist over time, or simply correlation aversion. Greater persistence provides information about future consumption but reduces opportunities to hedge consumption risk. I show that, for recursive preferences, correlation aversion is equivalent to increasing relative risk aversion. To quantify correlation aversion, I develop the concept of the persistence premium, which measures how much an individual is willing to pay to eliminate persistence in consumption. I provide an approximation of the persistence premium in the spirit of Arrow-Pratt, which provides a quantitative representation of the trade-off between information and hedging. I present several applications. The persistence premium helps create more realistic calibrations for macro-finance models. In an optimal taxation model, I show that recursive preferences unlike standard preferences-lead to more progressive taxation when human capital persistence is greater. Finally, I show that correlation-averse preferences have a variational representation, linking correlation aversion to concerns about model misspecification.
A micro-founded comparison of fiscal policies between indirect and direct job creation
Authors: Kensuke Ohtake
Date: 2023-04-10 00:00:00

The purpose of this paper is to provide a micro-economic foundation for an argument that the direct employment by the government is more desirable than the government purchase of private goods to eliminate unemployment. A general equilibrium model with monopolistic competition is devised, and the effects of policies (government purchase, tax rate operation, and government employment) on macroeconomic variables (consumption, price, and profit) are investigated. It is shown that 1) the government purchase is inflationary in the sense that additional effective demand by the government not only increases private employment but also raises prices; 2) the government employment can achieve full employment without causing a rise in prices.
Waiting for Fake News
Authors: Raphael Boleslavsky
Date: 2023-04-08 00:00:00

This paper studies a dynamic model of information acquisition, in which information might be secretly manipulated. A principal must choose between a safe action with known payoff and a risky action with uncertain payoff, favoring the safe action under the prior belief. She may delay her decision to acquire additional news that reveals the risky action's payoff, without knowing exactly when such news will arrive. An uninformed agent with a misaligned preference may have the capability to generate a false arrival of news, which is indistinguishable from a real one, distorting the information content of news and the principal's search. The analysis characterizes the positive and normative distortions in the search for news arising from such manipulation, and it considers three remedies that increase the principal's payoff: a commitment to naive search, transfer of authority to the agent, and delegation to an intermediary who is biased in the agent's favor.
On The Application Of Log Compression and Enhanced Denoising In Contrast Enhancement Of Digital Radiography Images
Authors: M. S. Asif, Mahesh Raveendranatha Panicker
Date: 2023-04-08 00:00:00
Tasks: Denoising

Digital radiography (DR) is becoming popular for the point of care imaging in the recent past. To reduce the radiation exposure, controlled radiation based on as low as reasonably achievable (ALARA) principle is employed and this results in low contrast images. To address this issue, post-processing algorithms such as the Multiscale Image Contrast Amplification (MUSICA) algorithm can be used to enhance the contrast of DR images even with a low radiation dose. In this study, a modification of the MUSICA algorithm is investigated to determine the potential for further contrast improvement specifically for DR images. The conclusion is that combining log compression and its inverse at the appropriate stage with a multi-stage MUSICA and denoising is very promising. The proposed method resulted in an average of 66.5 % increase in the mean contrast-to-noise ratio (CNR) for the test images considered.
Multiuser beam steering OWC system based on NOMA
Authors: Y. Zeng, Sanaa H. Mohamed, Ahmad Qidan, Taisir E. H. El-Gorashi, Jaafar M. H. Elmirghani
Date: 2023-04-10 00:00:00

In this paper, we propose applying Non-Orthogonal Multiple Access (NOMA) technology in a multiuser beam steering OWC system. We study the performance of the NOMA-based multiuser beam steering system in terms of the achievable rate and Bit Error Rate (BER). We investigate the impact of the power allocation factor of NOMA and the number of users in the room. The results show that the power allocation factor is a vital parameter in NOMA-based transmission that affects the performance of the network in terms of data rate and BER.
Relay Assisted Multiuser OWC Systems under Human Blockage
Authors: Y. Zeng, Sanaa H. Mohamed, Ahmad Qidan, Taisir E. H. El-Gorashi, Jaafar M. H. Elmirghani
Date: 2023-04-10 00:00:00

This paper proposes using cooperative communication based on optoelectronic (O-E-O) amplify-and-forward relay terminals to reduce the influence of the blockage and shadowing resulting from human movement in a beam steering Optical Wireless Communication (OWC) system. The simulation results indicate that on average, the outage probability of the cooperative communication mode with O-E-O relay terminals is two orders of magnitude lower than the outage probability of the system without relay terminals.
SPHR-SAR-Net: Superpixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation
Authors: Guoru Zhou, Zhongqiu Xu, Yizhe Fan, Zhe Zhang, Xiaolan Qiu, Bingchen Zhang, Kun fu, Yirong Wu
Date: 2023-04-10 00:00:00

High-resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. Firstly, these algorithms tend to focus on local information, neglecting non-local information between different pixel patches. Secondly, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Thirdly, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a Superpixel High-Resolution SAR Imaging Network (SPHR-SAR-Net) for rapid despeckling in high-resolution SAR mode. Based on the concept of superpixel techniques, we initially combine non-convex and non-local total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the Alternating Direction Method of Multipliers (ADMM) algorithm and unfold it into a Deep Unfolded Network (DUN). The network's parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the Deep Unfolded Network is compatible with high-resolution imaging modes such as spotlight, staring spotlight, and sliding spotlight. In this paper, we demonstrate the superiority of SPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that SPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results.