pwc-archive/papers-with-abstracts huggingface dataset:
State Derivative Normalization for Continuous-Time Deep Neural Networks
Authors: Jonas Weigand, Gerben I. Beintema, Jonas Ulmen, Daniel Görges, Roland Tóth, Maarten Schoukens, Martin Ruskowski
Date: 2024-01-05 00:00:00

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature.
On the Selection of Intermediate Length Representative Periods for Capacity Expansion
Authors: Osten Anderson, Nanpeng Yu, Konstantinos Oikonomou, Di wu
Date: 2024-01-05 00:00:00

As the decarbonization of power systems accelerates, there has been increasing interest in capacity expansion models for their role in guiding this transition. Representative period selection is an important component of capacity expansion modeling, enabling computational tractability of optimization while ensuring fidelity between the representative periods and the full year. However, little attention has been devoted to selecting representative periods longer than a single day. This prevents the capacity expansion model from directly simulating interday energy sharing, which is of key importance as energy generation becomes more variable and storage more important. To this end, we propose a novel method for selecting representative periods of any length. The method is validated using a capacity expansion model and production cost model based on California's decarbonization goals. We demonstrate that the representative period length has a substantial impact in the results of the capacity expansion investment plan.
Powerformer: A Section-adaptive Transformer for Power Flow Adjustment
Authors: KaiXuan Chen, Wei Luo, Shunyu Liu, Yaoquan Wei, Yihe Zhou, Yunpeng Qing, Quan Zhang, Jie Song, Mingli Song
Date: 2024-01-05 00:00:00
Tasks: Graph Neural Network

In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections. Specifically, our proposed approach, named Powerformer, develops a dedicated section-adaptive attention mechanism, separating itself from the self-attention used in conventional transformers. This mechanism effectively integrates power system states with transmission section information, which facilitates the development of robust state representations. Furthermore, by considering the graph topology of power system and the electrical attributes of bus nodes, we introduce two customized strategies to further enhance the expressiveness: graph neural network propagation and multi-factor attention mechanism. Extensive evaluations are conducted on three power system scenarios, including the IEEE 118-bus system, a realistic 300-bus system in China, and a large-scale European system with 9241 buses, where Powerformer demonstrates its superior performance over several baseline methods.
A full-time scale energy management and battery size optimization for off-grid renewable power to hydrogen systems: A battery energy storage-based grid-forming case in Inner Mongolian
Authors: Jie Zhu
Date: 2024-01-05 00:00:00
Tasks: energy management, Scheduling

Hydrogen plays an important role in the context of global carbon reduction. For an off-grid renewable power to hydrogen system (OReP2HS), a grid-forming (GFM) source is essential to provide frequency and voltage references. Here, we take battery works as a GFM source, and the OReP2HS we focus on is comprised of solar photovoltaic, wind turbines, and alkaline electrolyzers for hydrogen generation. An elaborative full-time scale energy management strategy (EMS) covers GFM control to system scheduling (from milliseconds to hours) and is proposed to support high-precision production simulations. Loads of electrolysers are designed to track the renewable power closely to partly replace the energy balancing requirement of the battery. A continuous operation strategy during an emergency like a unit drop is also included in the presented EMS. An off-line simulation-based iterative searching algorithm is developed to find the most suitable size of the battery with the minimum levelized cost of hydrogen (LCOH). A practical OReP2H project located in Inner Mongolian, China is taken as a case study. Simulation results demonstrate the feasibility of the proposed method, and the optimized capacity of the battery (3.4MWh) only accounts for 13.6% of the total installed capacity of the sources with the proposed EMS. Sensitivity analysis shows that LOCH rises from 28.829 CYN/kg to 37.814 CYN/kg, with the time-step for fast power regulation of electrolysers changing from 4s to 1 min with a ramp rate of 0.05MW/s.
A Deep Q-Learning based Smart Scheduling of EVs for Demand Response in Smart Grids
Authors: Viorica Rozina Chifu, Tudor Cioara, Cristina Bianca Pop, Horia Rusu, Ionut Anghel
Date: 2024-01-05 00:00:00
Tasks: Q-Learning, Scheduling

Economic and policy factors are driving the continuous increase in the adoption and usage of electrical vehicles (EVs). However, despite being a cleaner alternative to combustion engine vehicles, EVs have negative impacts on the lifespan of microgrid equipment and energy balance due to increased power demand and the timing of their usage. In our view grid management should leverage on EVs scheduling flexibility to support local network balancing through active participation in demand response programs. In this paper, we propose a model-free solution, leveraging Deep Q-Learning to schedule the charging and discharging activities of EVs within a microgrid to align with a target energy profile provided by the distribution system operator. We adapted the Bellman Equation to assess the value of a state based on specific rewards for EV scheduling actions and used a neural network to estimate Q-values for available actions and the epsilon-greedy algorithm to balance exploitation and exploration to meet the target energy profile. The results are promising showing that the proposed solution can effectively schedule the EVs charging and discharging actions to align with the target profile with a Person coefficient of 0.99, handling effective EVs scheduling situations that involve dynamicity given by the e-mobility features, relying only on data with no knowledge of EVs and microgrid dynamics.
Image-based Deep Learning for Smart Digital Twins: a Review
Authors: Md Ruman Islam, Mahadevan Subramaniam, Pei-Chi Huang
Date: 2024-01-04 00:00:00
Tasks: Anomaly Detection, Data Augmentation, Deep Learning, Edge-computing

Smart Digital twins (SDTs) are being increasingly used to virtually replicate and predict the behaviors of complex physical systems through continual data assimilation enabling the optimization of the performance of these systems by controlling the actions of systems. Recently, deep learning (DL) models have significantly enhanced the capabilities of SDTs, particularly for tasks such as predictive maintenance, anomaly detection, and optimization. In many domains, including medicine, engineering, and education, SDTs use image data (image-based SDTs) to observe and learn system behaviors and control their behaviors. This paper focuses on various approaches and associated challenges in developing image-based SDTs by continually assimilating image data from physical systems. The paper also discusses the challenges involved in designing and implementing DL models for SDTs, including data acquisition, processing, and interpretation. In addition, insights into the future directions and opportunities for developing new image-based DL approaches to develop robust SDTs are provided. This includes the potential for using generative models for data augmentation, developing multi-modal DL models, and exploring the integration of DL with other technologies, including 5G, edge computing, and IoT. In this paper, we describe the image-based SDTs, which enable broader adoption of the digital twin DT paradigms across a broad spectrum of areas and the development of new methods to improve the abilities of SDTs in replicating, predicting, and optimizing the behavior of complex systems.
Moving-Horizon Estimators for Hyperbolic and Parabolic PDEs in 1-D
Authors: Luke Bhan, Yuanyuan Shi, Iasson Karafyllis, Miroslav Krstic, James B. Rawlings
Date: 2024-01-04 00:00:00

Observers for PDEs are themselves PDEs. Therefore, producing real time estimates with such observers is computationally burdensome. For both finite-dimensional and ODE systems, moving-horizon estimators (MHE) are operators whose output is the state estimate, while their inputs are the initial state estimate at the beginning of the horizon as well as the measured output and input signals over the moving time horizon. In this paper we introduce MHEs for PDEs which remove the need for a numerical solution of an observer PDE in real time. We accomplish this using the PDE backstepping method which, for certain classes of both hyperbolic and parabolic PDEs, produces moving-horizon state estimates explicitly. Precisely, to explicitly produce the state estimates, we employ a backstepping transformation of a hard-to-solve observer PDE into a target observer PDE, which is explicitly solvable. The MHEs we propose are not new observer designs but simply the explicit MHE realizations, over a moving horizon of arbitrary length, of the existing backstepping observers. Our PDE MHEs lack the optimality of the MHEs that arose as duals of MPC, but they are given explicitly, even for PDEs. In the paper we provide explicit formulae for MHEs for both hyperbolic and parabolic PDEs, as well as simulation results that illustrate theoretically guaranteed convergence of the MHEs.
Gain Scheduling with a Neural Operator for a Transport PDE with Nonlinear Recirculation
Authors: Maxence Lamarque, Luke Bhan, Rafael Vazquez, Miroslav Krstic
Date: 2024-01-04 00:00:00
Tasks: Scheduling

To stabilize PDE models, control laws require space-dependent functional gains mapped by nonlinear operators from the PDE functional coefficients. When a PDE is nonlinear and its "pseudo-coefficient" functions are state-dependent, a gain-scheduling (GS) nonlinear design is the simplest approach to the design of nonlinear feedback. The GS version of PDE backstepping employs gains obtained by solving a PDE at each value of the state. Performing such PDE computations in real time may be prohibitive. The recently introduced neural operators (NO) can be trained to produce the gain functions, rapidly in real time, for each state value, without requiring a PDE solution. In this paper we introduce NOs for GS-PDE backstepping. GS controllers act on the premise that the state change is slow and, as a result, guarantee only local stability, even for ODEs. We establish local stabilization of hyperbolic PDEs with nonlinear recirculation using both a "full-kernel" approach and the "gain-only" approach to gain operator approximation. Numerical simulations illustrate stabilization and demonstrate speedup by three orders of magnitude over traditional PDE gain-scheduling. Code (Github) for the numerical implementation is published to enable exploration.
Social and Economic Impact Analysis of Solar Mini-Grids in Rural Africa: A Cohort Study from Kenya and Nigeria
Authors: Amy Town Carabajal, Akoua Orsot, Marie Pelagie Elimbi Moudio, Tracy Haggai, Chioma Joy Okonkwo, George Truett Jarrard III, Nicholas Stearns Selby
Date: 2024-01-01 00:00:00
Tasks: Decision Making

This study presents the first comprehensive analysis of the social and economic effects of solar mini-grids in rural African settings, specifically in Kenya and Nigeria. A group of 2,658 household heads and business owners connected to mini-grids over the last five years were interviewed both before and one year after their connection. These interviews focused on changes in gender equality, productivity, health, safety, and economic activity. The results show notable improvements in all areas. Economic activities and productivity increased significantly among the connected households and businesses. The median income of rural Kenyan community members quadrupled. Gender equality also improved, with women gaining more opportunities in decision making and business. Health and safety enhancements were linked to reduced use of hazardous energy sources like kerosene lamps. The introduction of solar mini-grids not only transformed the energy landscape but also led to broad socioeconomic benefits in these rural areas. The research highlights the substantial impact of decentralized renewable energy on the social and economic development of rural African communities. Its findings are crucial for policymakers, development agencies, and stakeholders focused on promoting sustainable energy and development in Africa.
Identification of 4FGL uncertain sources at Higher Resolutions with Inverse Discrete Wavelet Transform
Authors: Haitao Cao, Hubing Xiao, Zhijian Luo, Xiangtao Zeng, Junhui Fan
Date: 2024-01-05 00:00:00

In the forthcoming era of big astronomical data, it is a burden to find out target sources from ground-based and space-based telescopes. Although Machine Learning (ML) methods have been extensively utilized to address this issue, the incorporation of in-depth data analysis can significantly enhance the efficiency of identifying target sources when dealing with massive volumes of astronomical data. In this work, we focused on the task of finding AGN candidates and identifying BL Lac/FSRQ candidates from the 4FGL DR3 uncertain sources. We studied the correlations among the attributes of the 4FGL DR3 catalogue and proposed a novel method, named FDIDWT, to transform the original data. The transformed dataset is characterized as low-dimensional and feature-highlighted, with the estimation of correlation features by Fractal Dimension (FD) theory and the multi-resolution analysis by Inverse Discrete Wavelet Transform (IDWT). Combining the FDIDWT method with an improved lightweight MatchboxConv1D model, we accomplished two missions: (1) to distinguish the Active Galactic Nuclei (AGNs) from others (Non-AGNs) in the 4FGL DR3 uncertain sources with an accuracy of 96.65%, namely, Mission A; (2) to classify blazar candidates of uncertain type (BCUs) into BL Lacertae objects (BL Lacs) or Flat Spectrum Radio Quasars (FSRQs) with an accuracy of 92.03%, namely, Mission B. There are 1354 AGN candidates in Mission A, 482 BL Lacs candidates and 128 FSRQ candidates in Mission B were found. The results show a high consistency of greater than 98% with the results in previous works. In addition, our method has the advantage of finding less variable and relatively faint sources than ordinary methods.
DISO: A Domain Ontology for Modeling Dislocations in Crystalline Materials
Authors: Ahmad Zainul Ihsan, Said Fathalla, Stefan Sandfeld
Date: 2024-01-04 00:00:00

Crystalline materials, such as metals and semiconductors, nearly always contain a special defect type called dislocation. This defect decisively determines many important material properties, e.g., strength, fracture toughness, or ductility. Over the past years, significant effort has been put into understanding dislocation behavior across different length scales via experimental characterization techniques and simulations. This paper introduces the dislocation ontology (DISO), which defines the concepts and relationships related to linear defects in crystalline materials. We developed DISO using a top-down approach in which we start defining the most general concepts in the dislocation domain and subsequent specialization of them. DISO is published through a persistent URL following W3C best practices for publishing Linked Data. Two potential use cases for DISO are presented to illustrate its usefulness in the dislocation dynamics domain. The evaluation of the ontology is performed in two directions, evaluating the success of the ontology in modeling a real-world domain and the richness of the ontology.
Dynamical ensembles equivalence in fluid mechanics
Authors: Giovanni Gallavotti
Date: 1996-05-09 00:00:00

Dissipative Euler and Navier Stokes equations are discussed with the aim of proposing several experiments apt to test the equivalence of dynamical ensembles and the chaotic hypothesis.
Zero-shot Microclimate Prediction with Deep Learning
Authors: Iman Deznabi, Peeyush Kumar, Madalina Fiterau
Date: 2024-01-05 00:00:00
Tasks: Deep Learning, Prediction, Weather Forecasting, Zero-Shot Learning

Weather station data is a valuable resource for climate prediction, however, its reliability can be limited in remote locations. To compound the issue, making local predictions often relies on sensor data that may not be accessible for a new, previously unmonitored location. In response to these challenges, we propose a novel zero-shot learning approach designed to forecast various climate measurements at new and unmonitored locations. Our method surpasses conventional weather forecasting techniques in predicting microclimate variables by leveraging knowledge extracted from other geographic locations.
Correlation-enhanced viable core in metabolic networks
Authors: Mi Jin Lee, Sudo Yi, Deok-Sun Lee
Date: 2024-01-05 00:00:00

Cellular ingredient concentrations can be stabilized by adjusting generation and consumption rates through multiple pathways. To explore the portion of cellular metabolism equipped with multiple pathways, we categorize individual metabolic reactions and compounds as viable or inviable: A compound is viable if processed by two or more reactions, and a reaction is viable if all of its substrates and products are viable. Using this classification, we identify the maximal subnetwork of viable nodes, referred to as the {\it viable core}, in bipartite metabolic networks across thousands of species. The obtained viable cores are remarkably larger than those in degree-preserving randomized networks, while their broad degree distributions commonly enable the viable cores to shrink gradually as reaction nodes are deleted. We demonstrate that the positive degree-degree correlations of the empirical networks may underlie the enlarged viable cores compared to the randomized networks. By investigating the relation between degree and cross-species frequency of metabolic compounds and reactions, we elucidate the evolutionary origin of the correlations.
Complex systems approach to natural language
Authors: Tomasz Stanisz, Stanisław Drożdż, Jarosław Kwapień
Date: 2024-01-05 00:00:00
Tasks: Time Series, Time Series Analysis

The review summarizes the main methodological concepts used in studying natural language from the perspective of complexity science and documents their applicability in identifying both universal and system-specific features of language in its written representation. Three main complexity-related research trends in quantitative linguistics are covered. The first part addresses the issue of word frequencies in texts and demonstrates that taking punctuation into consideration restores scaling whose violation in the Zipf's law is often observed for the most frequent words. The second part introduces methods inspired by time series analysis, used in studying various kinds of correlations in written texts. The related time series are generated on the basis of text partition into sentences or into phrases between consecutive punctuation marks. It turns out that these series develop features often found in signals generated by complex systems, like long-range correlations or (multi)fractal structures. Moreover, it appears that the distances between punctuation marks comply with the discrete variant of the Weibull distribution. In the third part, the application of the network formalism to natural language is reviewed, particularly in the context of the so-called word-adjacency networks. Parameters characterizing topology of such networks can be used for classification of texts, for example, from a stylometric perspective. Network approach can also be applied to represent the organization of word associations. Structure of word-association networks turns out to be significantly different from that observed in random networks, revealing genuine properties of language. Finally, punctuation seems to have a significant impact not only on the language's information-carrying ability but also on its key statistical properties, hence it is recommended to consider punctuation marks on a par with words.
Digital-analog quantum learning on Rydberg atom arrays
Authors: Jonathan Z. Lu, Lucy Jiao, Kristina Wolinski, Milan Kornjača, Hong-Ye Hu, Sergio Cantu, Fangli Liu, Susanne F. Yelin, Sheng-Tao Wang
Date: 2024-01-05 00:00:00

We propose hybrid digital-analog learning algorithms on Rydberg atom arrays, combining the potentially practical utility and near-term realizability of quantum learning with the rapidly scaling architectures of neutral atoms. Our construction requires only single-qubit operations in the digital setting and global driving according to the Rydberg Hamiltonian in the analog setting. We perform a comprehensive numerical study of our algorithm on both classical and quantum data, given respectively by handwritten digit classification and unsupervised quantum phase boundary learning. We show in the two representative problems that digital-analog learning is not only feasible in the near term, but also requires shorter circuit depths and is more robust to realistic error models as compared to digital learning schemes. Our results suggest that digital-analog learning opens a promising path towards improved variational quantum learning experiments in the near term.
Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training
Authors: M. Emre Sahin, Benjamin C. B. Symons, Pushpak Pati, Fayyaz Minhas, Declan Millar, Maria Gabrani, Stefano Mensa, Jan Lukas Robertus
Date: 2024-01-05 00:00:00
Tasks: Quantum Machine Learning

Quantum machine learning with quantum kernels for classification problems is a growing area of research. Recently, quantum kernel alignment techniques that parameterise the kernel have been developed, allowing the kernel to be trained and therefore aligned with a specific dataset. While quantum kernel alignment is a promising technique, it has been hampered by considerable training costs because the full kernel matrix must be constructed at every training iteration. Addressing this challenge, we introduce a novel method that seeks to balance efficiency and performance. We present a sub-sampling training approach that uses a subset of the kernel matrix at each training step, thereby reducing the overall computational cost of the training. In this work, we apply the sub-sampling method to synthetic datasets and a real-world breast cancer dataset and demonstrate considerable reductions in the number of circuits required to train the quantum kernel while maintaining classification accuracy.