pwc-archive/papers-with-abstracts huggingface dataset:
Implicit Incorporation of Heuristics in MPC-Based Control of a Hydrogen Plant
Authors: Thomas Schmitt, Jens Engel, Martin Kopp, Tobias Rodemann
Date: 2023-09-19 00:00:00
Tasks: energy management, Management, Model Predictive Control, valid

The replacement of fossil fuels in combination with an increasing share of renewable energy sources leads to an increased focus on decentralized microgrids. One option is the local production of green hydrogen in combination with fuel cell vehicles (FCVs). In this paper, we develop a control strategy based on Model Predictive Control (MPC) for an energy management system (EMS) of a hydrogen plant, which is currently under installation in Offenbach, Germany. The plant includes an electrolyzer, a compressor, a low pressure storage tank, and six medium pressure storage tanks with complex heuristic physical coupling during the filling and extraction of hydrogen. Since these heuristics are too complex to be incorporated into the optimal control problem (OCP) explicitly, we propose a novel approach to do so implicitly. First, the MPC is executed without considering them. Then, the so-called allocator uses a heuristic model (of arbitrary complexity) to verify whether the MPC's plan is valid. If not, it introduces additional constraints to the MPC's OCP to implicitly respect the tanks' pressure levels. The MPC is executed again and the new plan is applied to the plant. Simulation results with real-world measurement data of the facility's energy management and realistic fueling scenarios show its advantages over rule-based control.
Battery-Electric Powertrain System Design for the HorizonUAM Multirotor Air Taxi Concept
Authors: Florian Jäger, Oliver Bertram, Sascha M. Lübbe, Alexander H. Bismark, Jan Rosenberg, Lukas Bartscht
Date: 2023-09-19 00:00:00
Tasks: Management

The work presented herein has been conducted within the DLR internal research project HorizonUAM, which encompasses research within numerous areas related to urban air mobility. One of the project goals was to develop a safe and certifiable onboard system concept. This paper aims to present the conceptual propulsion system architecture design for an all-electric battery-powered multirotor electric Vertical Takeoff and Landing (eVTOL) vehicle. Therefore, a conceptual design method was developed that provides a structured approach for designing the safe multirotor propulsion architecture. Based on the concept of operation the powertrain system was initially predefined, iteratively refined based on the safety assessment and validated through component sizing and simulations. The analysis was conducted within three system groups that were developed in parallel: the drivetrain, the energy supply and the thermal management system. The design process indicated that a pure quadcopter propulsion system can merely be designed reasonably for meeting the European Union Aviation Safety Agency (EASA) reliability specifications. By adding two push propellers and implementing numerous safety as well as passivation measures the reliability specifications defined by EASA could finally be fulfilled. The subsequent system simulations also verified that the system architecture is capable of meeting the requirements of the vehicle concept of operations. However, further work is required to extend the safety analysis to additional system components as the thermal management system or the battery management system and to reduce propulsion system weight.
Gateway Station Geographical Planning for Emerging Non-Geostationary Satellites Constellations
Authors: Victor Monzon Baeza, Flor Ortiz, Eva Lagunas, Tedros Salih Abdu, Symeon Chatzinotas
Date: 2023-09-19 00:00:00

Among the recent advances and innovations in satellite communications, Non-Geostationary Orbit (NGSO) satellite constellations are gaining popularity as a viable option for providing widespread broadband internet access and backhauling services. However, a more complex ground segment with multiple ground stations is necessary due to these satellites' high speeds and low altitudes. The complete dimensioning of the ground segment, including gateway optimal placement and the number of ground access points, remains a relevant open challenge. In this article, we provide an overview of the key factors that shall be considered for NGSO gateway station geographical planning. Subsequently, we propose a ground segment dimensioning approach that combines several criteria, such as rain attenuation, elevation angle, visibility, geographical constraints, and user traffic demands. The operational concept is first discussed, followed by a methodology that combines all these constraints into a single map-grid to select the best position for each gateway. Furthermore, a case study is presented, which demonstrates the performance of the proposed methodology, for one example constellation. Finally, we highlight relevant open challenges and key research directions in this area.
Computation of Ultra-Short-Term Prediction Intervals of the Power Prosumption in Active Distribution Networks
Authors: Plouton Grammatikos, Fabrizio Sossan, Jean-Yves Le Boudec, Mario Paolone
Date: 2023-09-19 00:00:00
Tasks: Prediction Intervals, Time Series

Microgrids and, in general, active distribution networks require ultra-short-term prediction, i.e., for sub-second time scales, for specific control decisions. Conventional forecasting methodologies are not effective at such time scales. To address this issue, we propose a non-parametric method for computing ultra short-term prediction intervals (PIs) of the power prosumption of generic electrical-distribution networks. The method groups historical observations into clusters according to the values of influential variables. It is applied either to the original or to the differentiated power-prosumption time series. The clusters are considered statistically representative pools of future realizations of power prosumption (or its derivative). They are used to determine empirical PDFs and, by extracting the quantiles, to deliver PIs for respective arbitrary confidence levels. The models are validated a posteriori by carrying out a performance analysis that uses experimentally observed power-prosumption for different building types, thus allowing the identification of the dominant model.
Learning-Based Detection of Malicious Volt-VAr Control Parameters in Smart Inverters
Authors: Ahmad Mohammad Saber, Amr Youssef, Davor Svetinovic, Hatem Zeineldin, Ehab El-Saadany
Date: 2023-09-19 00:00:00
Tasks: Intrusion Detection

Distributed Volt-Var Control (VVC) is a widely used control mode of smart inverters. However, necessary VVC curve parameters are remotely communicated to the smart inverter, which opens doors for cyberattacks. If the VVC curves of an inverter are maliciously manipulated, the attacked inverter's reactive power injection will oscillate, causing undesirable voltage oscillations to manifest in the distribution system, which, in turn, threatens the system's stability. In contrast with previous works that proposed methods to mitigate the oscillations after they are already present in the system, this paper presents an intrusion detection method to detect malicious VVC curves once they are communicated to the inverter. The proposed method utilizes a Multi-Layer Perceptron (MLP) that is trained on features extracted from only the local measurements of the inverter. After a smart inverter is equipped with the proposed method, any communicated VVC curve will be verified by the MLP once received. If the curve is found to be malicious, it will be rejected, thus preventing unwanted oscillations beforehand. Otherwise, legitimate curves will be permitted. The performance of the proposed scheme is verified using the 9-bus Canadian urban benchmark distribution system simulated in PSCAD/EMTDC environment. Our results show that the proposed solution can accurately detect malicious VVC curves.
Safe Control Design through Risk-Tunable Control Barrier Functions
Authors: Vipul K. Sharma, S. Sivaranjani
Date: 2023-09-19 00:00:00

We consider the problem of designing controllers to guarantee safety in a class of nonlinear systems under uncertainties in the system dynamics and/or the environment. We define a class of uncertain control barrier functions (CBFs), and formulate the safe control design problem as a chance-constrained optimization problem with uncertain CBF constraints. We leverage the scenario approach for chance constrained optimization to develop a risk-tunable control design that provably guarantees the satisfaction of CBF safety constraints up to a user-defined probabilistic risk bound, and provides a trade-off between the sample complexity and risk tolerance. We demonstrate the performance of this approach through simulations on a quadcopter navigation problem with obstacle avoidance constraints.
Harnessing Kernel Regression for Stochastic State Estimation in Solar-Integrated Power Grids
Authors: Mohammad Ensaf, Masoud Barati
Date: 2023-09-18 00:00:00
Tasks: regression, State Estimation

The paper presents a Gaussian/kernel process regression method for real-time state estimation and forecasting of phase angle and angular speed in systems with a high penetration of solar generation units, operating under a sparse measurements regime on both sunny and cloudy days. The method treats unknown terms in the swing equations, such as solar power, as random processes, thereby transforming these equations into stochastic differential equations. The proposed method accurately forecasts and estimates both observed and unobserved operating states, delivering forecasts comparable to those of the standard data-driven Gaussian/kernel process for observed system states. Additionally, the method demonstrates improved accuracy with increased observation frequency and reduced measurement errors in the IEEE 14-bus test system.
Unified Force and Motion Adaptive-Integral Control of Flexible Robot Manipulators
Authors: Carlos R. de Cos, José Ángel Acosta
Date: 2023-09-18 00:00:00
Tasks: Position

In this paper, an adaptive nonlinear strategy for the motion and force control of flexible manipulators is proposed. The approach provides robust motion control until contact is detected when force control is then available--without any control switch--, and vice versa. This self-tuning in mixed contact/non-contact scenarios is possible thanks to the unified formulation of force and motion control, including an integral transpose-based inverse kinematics and adaptive-update laws for the flexible manipulator link and contact stiffnesses. Global boundedness of all signals and asymptotic stability of force and position are guaranteed through Lyapunov analysis. The control strategy and its implementation has been validated using a low-cost basic microcontroller and a manipulator with 3 flexible joints and 4 actuators. Complete experimental results are provided in a realistic mixed contact scenario, demonstrating very-low computational demand with inexpensive force sensors.
Bearing and Distance Formation Control of Rigid Bodies in SE(3) with Bearing and Distance Constraints
Authors: Sara Mansourinasab, Mahdi Sojoodi, Seyed Reza Moghadasi
Date: 2023-09-18 00:00:00

Rigidity of the interaction graph is a fundamental condition for achieving the desired formation which can be defined in terms of distance or bearing constraints between agents. In this paper, for reaching a unique formation with the same scaling and orientation as the target formation, both distance and bearing constraints are considered for defining the desired formation. Besides, both distance and bearing measurements are also available. Each agent is able to gather the measurements with respect to other agents in its own body frame. So, the agents are coordinated-free concerning a global reference frame. On the other hand, the framework is embedded in SE(3). The control signal is designed based on a gradient descent method by introducing a cost function. Firstly, the formation problem is considered for bearing-only constraints in SE(3) configuration. Then, the formation control is expressed for the general case of both bearing and distance constraints. Furthermore, the essential conditions that guarantee reaching the desired formation are discussed. Finally, the validity of the proposed formation control is verified by numerical simulations.
A System-Level Energy-Efficient Digital Twin Framework for Runtime Control of Batch Manufacturing Processes
Authors: Hongliang Li, Herschel C. Pangborn, Ilya Kovalenko
Date: 2023-09-18 00:00:00
Tasks: Decision Making, Scheduling

The manufacturing sector has a substantial influence on worldwide energy consumption. Therefore, improving manufacturing system energy efficiency is becoming increasingly important as the world strives to move toward a more resilient and sustainable energy paradigm. Batch processes are a major contributor to energy consumption in manufacturing systems. In batch manufacturing, a number of parts are grouped together before starting a batch process. To improve the scheduling and control of batch manufacturing processes, we propose a system-level energy-efficient Digital Twin framework that considers Time-of-Use (TOU) energy pricing for runtime decision-making. As part of this framework, we develop a model that combines batch manufacturing process dynamics and TOU-based energy cost. We also provide an optimization-based decision-making algorithm that makes batch scheduling decisions during runtime. A simulated case study showcases the benefits of the proposed framework.
Vertical Power Delivery for Emerging Packaging and Integration Platforms -- Power Conversion and Distribution
Authors: Sriharini Krishnakumar, Dr. Inna Partin-Vaisband
Date: 2023-09-18 00:00:00

Efficient delivery of current from PCB to point-of-load (POL) is a primary concern in modern high-power high-density integrated systems. Traditionally, a 48 V power signal is converted to the low, POL voltage at the board and/or package level. As interconnect has become the dominant power loss component, minimizing voltage drop across the laterally routed portions of the board-to-die interconnect (referred to as horizontal interconnect) is a promising approach to enhance the efficiency of the power delivery system. Delivering lower current vertically, at a higher voltage should therefore be considered. High-power conversion near POL, however, results in higher switching and inductor losses, exhibiting an undesired power efficiency tradeoff. To address this problem, four vertical power delivery architectures are proposed in this paper, considering state-of-the-art power converter topologies, integration levels, and voltage conversion schemes. Embedding Silicon (Si) and Gallium Nitride (GaN) power devices and inductors on top of and/or within the interposer is investigated. Integrating GaN power devices on a dedicated power die is also discussed. Various multi-stage 48V-to-1V power conversion schemes are examined and state-of-the-art power conversion circuits are reviewed. Power delivery characteristics with these architectures are determined for a high power (1 kW) high-current density (2 A/mm$^2$) system.
Actively Learning Reinforcement Learning: A Stochastic Optimal Control Approach
Authors: Mohammad S. Ramadan, Mahmoud A. Hayajnh, Michael T. Tolley, Kyriakos G. Vamvoudakis
Date: 2023-09-18 00:00:00
Tasks: Model Predictive Control, reinforcement-learning, Reinforcement Learning

In this paper we propose a framework towards achieving two intertwined objectives: (i) equipping reinforcement learning with active exploration and deliberate information gathering, such that it regulates state and parameter uncertainties resulting from modeling mismatches and noisy sensory; and (ii) overcoming the computational intractability of stochastic optimal control. We approach both objectives by using reinforcement learning to compute the stochastic optimal control law. On one hand, we avoid the curse of dimensionality prohibiting the direct solution of the stochastic dynamic programming equation. On the other hand, the resulting stochastic optimal control reinforcement learning agent admits caution and probing, that is, optimal online exploration and exploitation. Unlike fixed exploration and exploitation balance, caution and probing are employed automatically by the controller in real-time, even after the learning process is terminated. We conclude the paper with a numerical simulation, illustrating how a Linear Quadratic Regulator with the certainty equivalence assumption may lead to poor performance and filter divergence, while our proposed approach is stabilizing, of an acceptable performance, and computationally convenient.
Comparative study of Deep Learning Models for Binary Classification on Combined Pulmonary Chest X-ray Dataset
Authors: Shabbir Ahmed Shuvo, Md Aminul Islam, Md. Mozammel Hoque, Rejwan Bin Sulaiman
Date: 2023-09-16 00:00:00
Tasks: Binary Classification, Classification, Deep Learning

CNN-based deep learning models for disease detection have become popular recently. We compared the binary classification performance of eight prominent deep learning models: DenseNet 121, DenseNet 169, DenseNet 201, EffecientNet b0, EffecientNet lite4, GoogleNet, MobileNet, and ResNet18 for their binary classification performance on combined Pulmonary Chest Xrays dataset. Despite the widespread application in different fields in medical images, there remains a knowledge gap in determining their relative performance when applied to the same dataset, a gap this study aimed to address. The dataset combined Shenzhen, China (CH) and Montgomery, USA (MC) data. We trained our model for binary classification, calculated different parameters of the mentioned models, and compared them. The models were trained to keep in mind all following the same training parameters to maintain a controlled comparison environment. End of the study, we found a distinct difference in performance among the other models when applied to the pulmonary chest Xray image dataset, where DenseNet169 performed with 89.38 percent and MobileNet with 92.2 percent precision. Keywords: Pulmonary, Deep Learning, Tuberculosis, Disease detection, Xray
Latent Disentanglement in Mesh Variational Autoencoders Improves the Diagnosis of Craniofacial Syndromes and Aids Surgical Planning
Authors: Simone Foti, Alexander J. Rickart, Bongjin Koo, Eimear O' Sullivan, Lara S. van de Lande, Athanasios Papaioannou, Roman Khonsari, Danail Stoyanov, N. u. Owase Jeelani, Silvia Schievano, David J. Dunaway, Matthew J. Clarkson
Date: 2023-09-05 00:00:00
Tasks: Disentanglement

The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accurate modelling, especially when operating on both a global and local level. In this work, we will discuss the application of the Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon, Apert and Muenke syndromes. Although syndrome classification is performed on the entire mesh, it is also possible, for the first time, to analyse the influence of each region of the head on the syndromic phenotype. By manipulating specific parameters of the generative model, and producing procedure-specific new shapes, it is also possible to simulate the outcome of a range of craniofacial surgical procedures. This opens new avenues to advance diagnosis, aids surgical planning and allows for the objective evaluation of surgical outcomes.
Generative Semi-supervised Learning for Multivariate Time Series Imputation
Authors: Xiaoye Miao, Yangyang Wu, Jun Wang, Yunjun Gao, Xudong Mao, and Jianwei Yin
Date: 2021-05-18 00:00:00
Proceeding: AAAI Conference on Artificial Intelligence 2021 5
Tasks: Generative Adversarial Network, Imputation, Missing Values, Multivariate Time Series Imputation, Time Series, Time Series Classification

The missing values, widely existed in multivariate time series data, hinder the effective data analysis. Existing time series imputation methods do not make full use of the label information in real-life time series data. In this paper, we propose a novel semi-supervised generative adversarial network model, named SSGAN, for missing value imputation in multivariate time series data. It consists of three players, i.e., a generator, a discriminator, and a classifier. The classifier predicts labels of time series data, and thus it drives the generator to estimate the missing values (or components), conditioned on observed components and data labels at the same time. We introduce a temporal reminder matrix to help the discriminator better distinguish the observed components from the imputed ones. Moreover, we theoretically prove that, SSGAN using the temporal reminder matrix and the classifier does learn to estimate missing values converging to the true data distribution when the Nash equilibrium is achieved. Extensive experiments on three public real-world datasets demonstrate that, SSGAN yields a more than 15% gain in performance, compared with the state-of-the-art methods.
Two-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-supervised Depth Estimation
Authors: Zhengming Zhou, Qiulei Dong
Date: 2023-09-02 00:00:00
Proceeding: ICCV 2023 1
Tasks: Depth Estimation, Monocular Depth Estimation

Monocular and binocular self-supervised depth estimations are two important and related tasks in computer vision, which aim to predict scene depths from single images and stereo image pairs respectively. In literature, the two tasks are usually tackled separately by two different kinds of models, and binocular models generally fail to predict depth from single images, while the prediction accuracy of monocular models is generally inferior to binocular models. In this paper, we propose a Two-in-One self-supervised depth estimation network, called TiO-Depth, which could not only compatibly handle the two tasks, but also improve the prediction accuracy. TiO-Depth employs a Siamese architecture and each sub-network of it could be used as a monocular depth estimation model. For binocular depth estimation, a Monocular Feature Matching module is proposed for incorporating the stereo knowledge between the two images, and the full TiO-Depth is used to predict depths. We also design a multi-stage joint-training strategy for improving the performances of TiO-Depth in both two tasks by combining the relative advantages of them. Experimental results on the KITTI, Cityscapes, and DDAD datasets demonstrate that TiO-Depth outperforms both the monocular and binocular state-of-the-art methods in most cases, and further verify the feasibility of a two-in-one network for monocular and binocular depth estimation. The code is available at https://github.com/ZM-Zhou/TiO-Depth_pytorch.