pwc-archive/papers-with-abstracts huggingface dataset:
Using EDA Technique to analyze sales of Electric Vehicles
Authors: . Santhosh Ainleni
Date: 2023-02-28 00:00:00
Proceeding: Published in Vol 8 Issue 2, February-2023 2023 2

Electric vehicles are dominating the market with respect to other vehicles. The demand for Electric Vehicles is rapidly Increasing across different countries, so many companies are investing money to manufacture Electric vehicles. By the end of the year 2040, all vehicles across the world will be electric. The main aim of this paper by using an Exploratory Data Analysis to analyze sales data of Electric vehicles in India and the data of other countries. By using this technique to study and analyze the data and make conclusions from the data.
Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN
Authors: Vitaliy Kinakh, Mariia Drozdova, Guillaume Quétant, Tobias Golling, Slava Voloshynovskiy
Date: 2021-12-17 00:00:00
Tasks: Attribute, Generative Adversarial Network, Image Generation

Conditional generation is a subclass of generative problems where the output of the generation is conditioned by the attribute information. In this paper, we present a stochastic contrastive conditional generative adversarial network (InfoSCC-GAN) with an explorable latent space. The InfoSCC-GAN architecture is based on an unsupervised contrastive encoder built on the InfoNCE paradigm, an attribute classifier and an EigenGAN generator. We propose a novel training method, based on generator regularization using external or internal attributes every $n$-th iteration, using a pre-trained contrastive encoder and a pre-trained classifier. The proposed InfoSCC-GAN is derived based on an information-theoretic formulation of mutual information maximization between input data and latent space representation as well as latent space and generated data. Thus, we demonstrate a link between the training objective functions and the above information-theoretic formulation. The experimental results show that InfoSCC-GAN outperforms the "vanilla" EigenGAN in the image generation on AFHQ and CelebA datasets. In addition, we investigate the impact of discriminator architectures and loss functions by performing ablation studies. Finally, we demonstrate that thanks to the EigenGAN generator, the proposed framework enjoys a stochastic generation in contrast to vanilla deterministic GANs yet with the independent training of encoder, classifier, and generator in contrast to existing frameworks. Code, experimental results, and demos are available online at https://github.com/vkinakh/InfoSCC-GAN.
Brightfield vs Fluorescent Staining Dataset–A Test Bed Image Set for Machine Learning based Virtual Staining
Authors: Elena Y. Trizna, Aleksandr M. Sinitca, Asya I. Lyanova, Diana R. Baidamshina, Pavel V. Zelenikhin, Dmitrii I. Kaplun, Airat R. Kayumov, Mikhail I. Bogachev
Date: 2023-03-22 00:00:00
Proceeding: Scientific Data 2023 3
Tasks: Segmentation, Virtual Staining

Differential fluorescent staining is an effective tool widely adopted for the visualization, segmentation and quantification of cells and cellular substructures as a part of standard microscopic imaging protocols. Incompatibility of staining agents with viable cells represents major and often inevitable limitations to its applicability in live experiments, requiring extraction of samples at different stages of experiment increasing laboratory costs. Accordingly, development of computerized image analysis methodology capable of segmentation and quantification of cells and cellular substructures from plain monochromatic images obtained by light microscopy without help of any physical markup techniques is of considerable interest. The enclosed set contains human colon adenocarcinoma Caco-2 cells microscopic images obtained under various imaging conditions with different viable vs non-viable cells fractions. Each field of view is provided in a three-fold representation, including phase-contrast microscopy and two differential fluorescent microscopy images with specific markup of viable and non-viable cells, respectively, produced using two different staining schemes, representing a prominent test bed for the validation of image analysis methods.
Higher-order Organization in the Human Brain from Matrix-Based Rényi's Entropy
Authors: Qiang Li, Shujian Yu, Kristoffer H Madsen, Vince D Calhoun, Armin Iraji
Date: 2023-03-21 00:00:00
Tasks: Time Series

Pairwise metrics are often employed to estimate statistical dependencies between brain regions, however they do not capture higher-order information interactions. It is critical to explore higher-order interactions that go beyond paired brain areas in order to better understand information processing in the human brain. To address this problem, we applied multivariate mutual information, specifically, Total Correlation and Dual Total Correlation to reveal higher-order information in the brain. In this paper, we estimate these metrics using matrix-based R\'enyi's entropy, which offers a direct and easily interpretable approach that is not limited by direct assumptions about probability distribution functions of multivariate time series. We applied these metrics to resting-state fMRI data in order to examine higher-order interactions in the brain. Our results showed that the higher-order information interactions captured increase gradually as the interaction order increases. Furthermore, we observed a gradual increase in the correlation between the Total Correlation and Dual Total Correlation as the interaction order increased. In addition, the significance of Dual Total Correlation values compared to Total Correlation values also indicate that the human brain exhibits synergy dominance during the resting state.
Capacity Maximization of the 6G Networks Deploying IRS
Authors: Mobasshir Mahbub, Raed M. Shubair
Date: 2023-03-21 00:00:00

The objective of the work is to improve the capacity of the micro cell, i.e., enabling the micro cell base station of a two-tier network to serve an increased number of devices. Therefore, the work deployed an IRS in a micro cell and achieved significant improvements in the performance with a reduced transmit power of the micro base station.
Learning Model-Free Robust Precoding for Cooperative Multibeam Satellite Communications
Authors: Steffen Gracla, Alea Schröder, Maik Röper, Carsten Bockelmann, Dirk Wübben, Armin Dekorsy
Date: 2023-03-13 00:00:00
Tasks: Deep Reinforcement Learning

Direct Low Earth Orbit satellite-to-handheld links are expected to be part of a new era in satellite communications. Space-Division Multiple Access precoding is a technique that reduces interference among satellite beams, therefore increasing spectral efficiency by allowing cooperating satellites to reuse frequency. Over the past decades, optimal precoding solutions with perfect channel state information have been proposed for several scenarios, whereas robust precoding with only imperfect channel state information has been mostly studied for simplified models. In particular, for Low Earth Orbit satellite applications such simplified models might not be accurate. In this paper, we use the function approximation capabilities of the Soft Actor-Critic deep Reinforcement Learning algorithm to learn robust precoding with no knowledge of the system imperfections.
RSSI-based Localization with Adaptive Noise Covariance Estimation for Resilient Multi-Agent Formations
Authors: Paul J Bonczek, Nicola Bezzo
Date: 2023-03-21 00:00:00
Tasks: Position

Typical cooperative multi-agent systems (MASs) exchange information to coordinate their motion in proximity-based control consensus schemes to complete a common objective. However, in the event of faults or cyber attacks to on-board positioning sensors of agents, global control performance may be compromised resulting in a hijacking of the entire MAS. For systems that operate in unknown or landmark-free environments (e.g., open terrain, sea, or air) and also beyond range/proximity sensing of nearby agents, compromised agents lose localization capabilities. To maintain resilience in these scenarios, we propose a method to recover compromised agents by utilizing Received Signal Strength Indication (RSSI) from nearby agents (i.e., mobile landmarks) to provide reliable position measurements for localization. To minimize estimation error: i) a multilateration scheme is proposed to leverage RSSI and position information received from neighboring agents as mobile landmarks and ii) a Kalman filtering method adaptively updates the unknown RSSI-based position measurement covariance matrix at runtime that is robust to unreliable state estimates. The proposed framework is demonstrated with simulations on MAS formations in the presence of faults and cyber attacks to on-board position sensors.
Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning
Authors: Hengxi Zhang, Zhendong Shi, Yuanquan Hu, Wenbo Ding, Ercan E. Kuruoglu, Xiao-Ping Zhang
Date: 2023-03-15 00:00:00
Tasks: Decision Making, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Quantitative markets are characterized by swift dynamics and abundant uncertainties, making the pursuit of profit-driven stock trading actions inherently challenging. Within this context, reinforcement learning (RL), which operates on a reward-centric mechanism for optimal control, has surfaced as a potentially effective solution to the intricate financial decision-making conundrums presented. This paper delves into the fusion of two established financial trading strategies, namely the constant proportion portfolio insurance (CPPI) and the time-invariant portfolio protection (TIPP), with the multi-agent deep deterministic policy gradient (MADDPG) framework. As a result, we introduce two novel multi-agent RL (MARL) methods, CPPI-MADDPG and TIPP-MADDPG, tailored for probing strategic trading within quantitative markets. To validate these innovations, we implemented them on a diverse selection of 100 real-market shares. Our empirical findings reveal that the CPPI-MADDPG and TIPP-MADDPG strategies consistently outpace their traditional counterparts, affirming their efficacy in the realm of quantitative trading.
Quantized Zero Dynamics Attacks against Sampled-data Control Systems
Authors: Kosuke Kimura, Hideaki Ishii
Date: 2023-03-21 00:00:00
Tasks: Quantization

For networked control systems, cyber-security issues have gained much attention in recent years. In this paper, we consider the so-called zero dynamics attacks, which form an important class of false data injection attacks, with a special focus on the effects of quantization in a sampled-data control setting. When the attack signals must be quantized, some error will be necessarily introduced, potentially increasing the chance of detection through the output of the system. In this paper, we show however that the attacker may reduce such errors by avoiding to directly quantize the attack signal. We look at two approaches for generating quantized attacks which can keep the error in the output smaller than a specified level by using the knowledge of the system dynamics. The methods are based on a dynamic quantization technique and a modified version of zero dynamics attacks. Numerical examples are provided to verify the effectiveness of the proposed methods.
Combining Robust Control and Machine Learning for Uncertain Nonlinear Systems Subject to Persistent Disturbances
Authors: A. Banderchuk, D. Coutinho, E. Camponogara
Date: 2023-03-21 00:00:00

This paper proposes a control strategy consisting of a robust controller and an Echo State Network (ESN) based control law for stabilizing a class of uncertain nonlinear discrete-time systems subject to persistent disturbances. Firstly, the robust controller is designed to ensure that the closed-loop system is Input-to-State Stable (ISS) with a guaranteed stability region regardless of the ESN control action and exogenous disturbances. Then, the ESN based controller is trained in order to mitigate the effects of disturbances on the system output. A numerical example demonstrates the potentials of the proposed control design method.
Risk-Aware Objective-Based Forecasting in Inertia Management
Authors: Haipeng Zhang, Ran Li, Yan Chen, Zhongda Chu, Mingyang Sun, Fei Teng
Date: 2023-03-21 00:00:00
Tasks: Decision Making, Management

The objective-based forecasting considers the asymmetric and non-linear impacts of forecasting errors on decision objectives, thus improving the effectiveness of its downstream decision-making process. However, existing objective-based forecasting methods are risk-neutral and not suitable for tasks like power system inertia management and unit commitment, of which decision-makers are usually biased toward risk aversion in practice. To tackle this problem, this paper proposes a generic risk-aware objective-based forecasting method. It enables decision-makers to customize their forecasting with different risk preferences. The equivalence between the proposed method and optimization under uncertainty (stochastic/robust optimization) is established for the first time. Case studies are carried out on a Great Britain 2030 power system with system operational data from National Grid. The results show that the proposed model with deterministic optimization can approximate the performance of stochastic programming or robust optimization at only a fraction of their computational cost.
Distributed Parameter Estimation under Gaussian Observation Noises
Authors: Jiaqi Yan, Hideaki Ishii
Date: 2023-03-21 00:00:00
Tasks: parameter estimation

In this paper, we consider the problem of distributed parameter estimation in sensor networks. Each sensor makes successive observations of an unknown $d$-dimensional parameter, which might be subject to Gaussian random noises. They aim to infer true value of the unknown parameter by cooperating with each other. To this end, we first generalize the so-called dynamic regressor extension and mixing (DREM) algorithm to stochastic systems, with which the problem of estimating a $d$-dimensional vector parameter is transformed to that of $d$ scalar ones: one for each of the unknown parameters. For each of the scalar problem, an estimation scheme is given, where each sensor fuses the regressors and measurements in its in-neighborhood and updates its local estimate by using least-mean squares. Particularly, a counter is also introduced for each sensor, which prevents any (noisy) measurement from being repeatedly used such that the estimation performance will not be greatly affected by certain extreme values. A novel excitation condition termed as \textit{local persistent excitation} (Local-PE) condition is also proposed, which relaxes the traditional persistent excitation (PE) condition and only requires that the collective signals in each sensor's in-neighborhood are sufficiently excited. With the Local-PE condition and proper step sizes, we show that the proposed estimator guarantee that each sensor infers the true parameter in mean square, even if any individual of them cannot. Numerical examples are finally provided to illustrate the established results.
Adaptive Super-Twisting Controller Design for Accurate Trajectory Tracking Performance of Unmanned Aerial Vehicles
Authors: D. M. K. K. Venkateswara Rao, Hamed Habibi, Jose Luis Sanchez-Lopez, Prathyush P. Menon, Christopher Edwards, Holger Voos
Date: 2023-03-21 00:00:00

In this paper, an adaptive super-twisting controller is designed for an agile maneuvering quadrotor unmanned aerial vehicle to achieve accurate trajectory tracking in the presence of external disturbances. A cascaded control architecture is designed to determine the desired accelerations using the proposed controller and subsequently used to compute the desired orientation and angular rates. The finite-time convergence of sliding functions and closed-loop system stability are analytically proven. Furthermore, the restrictive assumption on the maximum variation of the disturbance is relaxed by designing a gain adaptation law and low-pass filtering of the estimated equivalent control. The proper selection of design parameters is discussed in detail. Finally, the effectiveness of the proposed method is evaluated by high-fidelity software-in-the-loop simulations and validated by experimental studies.
Micro Power Supply Based on Piezoelectric Effect
Authors: M. Husak, A. Budkova, T. Pycha, A. Laposa, V. Povolny, V. Janicek, J. Novak, A. Boura, J. Foit
Date: 2023-03-21 00:00:00

The paper solves the model of the miniature Power supply based on the piezoelectric cantilever. The aim of the future is to further hybrid integration and use of nanotechnology. Contents of the article belongs to the category of renewable energy sources with environment energy conversion into electrical energy. The work is focused on the use in small temperature differences.
Vehicle Sequencing at Signal-Free Intersections: Analytical Performance Guarantees Based on PDMP Formulation
Authors: Xiangchen Cheng, Wei Tang, Ming Yang, Li Jin
Date: 2023-03-21 00:00:00
Tasks: Autonomous Driving, Trajectory Planning

Signal-free intersections are a representative application of smart and connected vehicle technologies. Although extensive results have been developed for trajectory planning and autonomous driving, the formulation and evaluation of vehicle sequencing have not been well understood.In this paper, we consider theoretical guarantees of macroscopic performance (i.e., capacity and delay) of typical sequencing policies at signal-free intersections. We model intersection traffic as a piecewise-deterministic Markov process (PDMP). We analytically characterize the intersection capacity regions and provide upper bounds on travel delay under three typical policies, viz. first-in-first-out, min-switchover, and longer-queue-first. We obtain these results by constructing policy-specific Lyapunov functions and computing mean drift of the PDMP. We also validate the results via a series of micro-simulation-based experiments.
An Observer-based Switching Algorithm for Safety under Sensor Denial-of-Service Attacks
Authors: Santiago Jimenez Leudo, Kunal Garg, Ricardo G. Sanfelice, Alvaro A. Cardenas
Date: 2023-03-21 00:00:00
Tasks: State Estimation

The design of safe-critical control algorithms for systems under Denial-of-Service (DoS) attacks on the system output is studied in this work. We aim to address scenarios where attack-mitigation approaches are not feasible, and the system needs to maintain safety under adversarial attacks. We propose an attack-recovery strategy by designing a switching observer and characterizing bounds in the error of a state estimation scheme by specifying tolerable limits on the time length of attacks. Then, we propose a switching control algorithm that renders forward invariant a set for the observer. Thus, by satisfying the error bounds of the state estimation, we guarantee that the safe set is rendered conditionally invariant with respect to a set of initial conditions. A numerical example illustrates the efficacy of the approach.
Koopman-Hopf Hamilton-Jacobi Reachability and Control
Authors: Will Sharpless, Nikhil Shinde, Matthew Kim, Yat Tin Chow, Sylvia Herbert
Date: 2023-03-21 00:00:00

The Hopf formula for Hamilton-Jacobi reachability (HJR) analysis has been proposed to solve high-dimensional differential games, producing the set of initial states and corresponding controller required to reach (or avoid) a target despite bounded disturbances. As a space-parallelizable method, the Hopf formula avoids the curse of dimensionality that afflicts standard dynamic-programming HJR, but is restricted to linear time-varying systems. To compute reachable sets for high-dimensional nonlinear systems, we pair the Hopf solution with Koopman theory for global linearization. By first lifting a nonlinear system to a linear space and then solving the Hopf formula, approximate reachable sets can be efficiently computed that are much more accurate than local linearizations. Furthermore, we construct a Koopman-Hopf disturbance-rejecting controller, and test its ability to drive a 10-dimensional nonlinear glycolysis model. We find that it significantly out-competes expectation-minimizing and game-theoretic model predictive controllers with the same Koopman linearization in the presence of bounded stochastic disturbance. In summary, we demonstrate a dimension-robust method to approximately solve HJR, allowing novel application to analyze and control high-dimensional, nonlinear systems with disturbance. An open-source toolbox in Julia is introduced for both Hopf and Koopman-Hopf reachability and control.
Reducing transmission expansion by co-optimizing sizing of wind, solar, storage and grid connection capacity
Authors: Aneesha Manocha, Neha Patankar, Jesse D. Jenkins
Date: 2023-03-21 00:00:00

Expanding transmission capacity is likely a bottleneck that will restrict variable renewable energy (VRE) deployment required to achieve ambitious emission reduction goals. Grid interconnection and inter-regional transmission capacity may be reduced by the optimal sizing of VREs to grid connection or co-location of VRE and battery resources behind the grid interconnection, but neither of these capabilities are commonly captured in macro-energy system models. We thus develop these two new functionalities to explore the substitutability of storage for transmission and VRE resource trade-offs through 2030 in the Western Interconnection of the United States. Our findings indicate that not modeling co-location fails to capture the full substitutability of storage and solar photovoltaic (PV) resources for transmission: co-location can reduce long-distance inter-regional transmission expansion by 12-31% and decrease grid connection capacity and shorter-distance transmission interconnection by 20-25%. We also demonstrate that not modeling colocated storage does not accurately reflect competition between wind and solar PV resources and underestimates the value of energy storage: co-location of VREs and storage favors solar PV (4-5% increase) and lithium-ion battery deployment (1.7-6 times increase), while decreasing wind buildout (0.9-1.6% decline).
On Buffer Centering for Bittide Synchronization
Authors: Sanjay Lall, Calin Cascaval, Martin Izzard, Tammo Spalink
Date: 2023-03-20 00:00:00

We discuss distributed reframing control of bittide systems. In a bittide system, multiple processors synchronize by monitoring communication over the network. The processors remain in logical synchrony by controlling the timing of frame transmissions. The protocol for doing this relies upon an underlying dynamic control system, where each node makes only local observations and performs no direct coordination with other nodes. In this paper we develop a control algorithm based on the idea of reset control, which allows all nodes to maintain small buffer offsets while also requiring very little state information at each node. We demonstrate that with reframing, we can achieve separate control of frequency and phase, allowing both the frequencies to be syntonized and the buffers to be moved the desired points, rather than combining their control via a proportional-integral controller. This offers the potential for simplified boot processes and failure handling.