pwc-archive/papers-with-abstracts huggingface dataset:
Robust Backstepping Control of a Quadrotor Unmanned Aerial Vehicle Under Colored Noises
Authors: Mehmet Karahan
Date: 2024-08-09 00:00:00

Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles (UAVs). Nowadays, people actively use quadrotor UAVs in essential missions such as search and rescue, counter-terrorism, firefighting, surveillance, and cargo transportation. While performing these tasks, quadrotors must operate in noisy environments. Therefore, a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance. Many researchers have focused only on white Gaussian noise in their studies, whereas researchers need to consider the effects of all colored noises during the operation of the quadrotor. This study aims to design a robust controller that is resistant to all colored noises. Firstly, a nonlinear quadrotor model was created with MATLAB. Then, a backstepping controller resistant to colored noises was designed. The designed backstepping controller was tested under Gaussian white, pink, brown, blue, and purple noises. PID and Lyapunov-based controller designs were also carried out, and their time responses (rise time, overshoot, settling time) were compared with those of the backstepping controller. In the simulations, time was in seconds, altitude was in meters, and roll, pitch, and yaw references were in radians. Rise and settling time values were in seconds, and overshoot value was in percent. When the obtained values are examined, simulations prove that the proposed backstepping controller has the least overshoot and the shortest settling time under all noise types.
Synthetic Grid Generator: Synthesizing Large-Scale Power Distribution Grids using Open Street Map
Authors: Chandra Sekhar Charan Dande, Luca Mattorolo, Joel da Silva Andre, Lydia Lavecchia, Nikolaos Efkarpidis, Damiano Toffanin
Date: 2024-08-09 00:00:00

Nowadays, various stakeholders involved in the analysis of electric power distribution grids face difficulties in the data acquisition related to the grid topology and parameters of grid assets. To mitigate the problem and possibly accelerate the accomplishment of grid studies without access to real data, we propose a novel approach for generating synthetic distribution grids (Syngrids) of (almost) arbitrary size replicating the characteristics of real medium- and low-voltage distribution networks. The method enables large-scale testing without incurring the burden of retrieving and pre-processing real-world data. The proposed algorithm exploits the publicly available information of Open Street Map (OSM). By leveraging geospatial data of real buildings and road networks, the approach allows to construct a Syngrid of chosen size with realistic topology and electrical parameters. It is shown that typical power-flow and short-circuit calculations can be performed on Syngrids ensuring convergence. Within the context of validating the effectiveness of the algorithm and the meaningful similarity of the output to real grids, the topological and electrical characteristics of a Syngrid are compared to their real-world counterparts. Finally, an open-source web platform named as Synthetic Grid Generator (SGG) and based on the proposed algorithm can be used by various stakeholders for the creation of synthetic grids.
Optimal Power Flow in Renewable-Integrated Power Systems: A Comprehensive Review
Authors: Zigang Chen
Date: 2024-08-09 00:00:00
Tasks: Management

This paper explores the integration of renewable energy sources into power systems, highlighting the resulting complexities such as variability and intermittency that challenge traditional power flow dynamics. We delve into innovative Optimal Power Flow (OPF) strategies designed to manage the unpredictability of renewable sources while ensuring economically viable and stable grid operations. A thorough review of state-of-the-art OPF algorithms, particularly those that enhance systems with substantial renewable integration, is presented. The discussion spans fundamental OPF principles, adaptations to renewable energies, and categorization of the latest advancements in areas such as energy uncertainty management, energy storage integration, linearization techniques application, and data-driven strategy utilization. Each sector's application benefits and limitations are critically analyzed. The paper concludes by pinpointing ongoing challenges and suggesting future research trajectories to foster adaptable and robust power system operations in the renewable-dominant energy era.
On the Asymptotic Convergence of Subgraph Generated Models
Authors: Xinchen Xu, Francesca Parise
Date: 2024-08-08 00:00:00

We study a family of random graph models - termed subgraph generated models (SUGMs) - initially developed by Chandrasekhar and Jackson in which higher-order structures are explicitly included in the network formation process. We use matrix concentration inequalities to show convergence of the adjacency matrix of networks realized from such SUGMs to the expected adjacency matrix as a function of the network size. We apply this result to study concentration of centrality measures (such as degree, eigenvector, and Katz centrality) in sampled networks to the corresponding centralities in the expected network, thus proving that node importance can be predicted from knowledge of the random graph model without the need of exact network data.
Goal-Oriented UAV Communication Design and Optimization for Target Tracking: A MachineLearning Approach
Authors: Wenchao Wu, Yanning Wu, Yuanqing Yang, Yansha Deng
Date: 2024-08-08 00:00:00
Tasks: Deep Reinforcement Learning

To accomplish various tasks, safe and smooth control of unmanned aerial vehicles (UAVs) needs to be guaranteed, which cannot be met by existing ultra-reliable low latency communications (URLLC). This has attracted the attention of the communication field, where most existing work mainly focused on optimizing communication performance (i.e., delay) and ignored the performance of the task (i.e., tracking accuracy). To explore the effectiveness of communication in completing a task, in this letter, we propose a goal-oriented communication framework adopting a deep reinforcement learning (DRL) algorithm with a proactive repetition scheme (DeepP) to optimize C&C data selection and the maximum number of repetitions in a real-time target tracking task, where a base station (BS) controls a UAV to track a mobile target. The effectiveness of our proposed approach is validated by comparing it with the traditional proportional integral derivative (PID) algorithm.
Artificial Intelligence based Approach for Identification and Mitigation of Cyber-Attacks in Wide-Area Control of Power Systems
Authors: Jishnudeep Kar, Aranya Chakrabortty
Date: 2024-08-08 00:00:00
Tasks: Decoder, Generative Adversarial Network

We propose a generative adversarial network (GAN) based deep learning method that serves the dual role of both identification and mitigation of cyber-attacks in wide-area damping control loops of power systems. Two specific types of attacks considered are false data injection and denial-of-service (DoS). Unlike existing methods, which are either model-based or model-free and yet require two separate learning modules for detection and mitigation leading to longer response times before clearing an attack, our deep learner incorporate both goals within the same integrated framework. A Long Short-Term Memory (LSTM) encoder-decoder based GAN is proposed that captures the temporal dynamics of the power system significantly more accurately than fully-connected GANs, thereby providing better accuracy and faster response for both goals. The method is validated using the IEEE 68-bus power system model.
Semantic-Enabled 6G Communication: A Task-oriented and Privacy-preserving Perspective
Authors: Shuaishuai Guo, Anbang Zhang, Yanhu Wang, Chenyuan Feng, Tony Q. S. Quek
Date: 2024-08-08 00:00:00
Tasks: Privacy Preserving, Quantization, Semantic Communication

Task-oriented semantic communication (ToSC) emerges as an innovative approach in the 6G landscape, characterized by the transmission of only vital information that is directly pertinent to a specific task. While ToSC offers an efficient mode of communication, it concurrently raises concerns regarding privacy, as sophisticated adversaries might possess the capability to reconstruct the original data from the transmitted features. This paper provides an in-depth analysis of privacy-preserving strategies specifically designed for ToSC relying on deep neural network-based joint source and channel coding (DeepJSCC). Our study encompasses a detailed comparative assessment of trustworthy feature perturbation methods such as differential privacy (DP) and encryption, alongside intrinsic security incorporation approaches like adversarial learning to train the JSCC and learning-based vector quantization (LBVQ). Our comparative analysis underscores the integration of advanced explainable learning algorithms into communication systems, positing a new benchmark for privacy standards in the forthcoming 6G era.
A Robust Clustering Scheme for Vehicular Communication Networks
Authors: Maryam Hosseini, Gunes Karabulut Kurt
Date: 2024-08-07 00:00:00
Tasks: Clustering

Clustering, as a technique for grouping nodes in geographical proximity together, in vehicular communication networks, is a key technique to enhance network robustness and scalability despite challenges such as mobility and routing. This paper presents a robust clustering scheme based on cluster head backup list algorithm for unmanned aerial vehicles (UAVs)-assisted vehicular communication network, where multiple UAVs act as communication base stations for a vehicular network. To tackle the high mobility issues in vehicular communications, instead of allowing direct communication between all vehicles to the UAV, clustering methods will potentially be efficient in overcoming delay limitations, excessive power consumption and resource issues. Using the clustering technique, neighboring vehicles are grouped into clusters with a specific vehicle selected as the cluster head (CH) in each cluster. The selected CH connects directly to the UAV through an infrastructure-to-vehicle (I2V) link, subsequently establishing vehicle-to-vehicle (V2V) communications with vehicles in the same cluster. To increase cluster connectivity period, the proposed clustering scheme is developed based on considering the vehicle behavior for efficient selection of CHs and providing a CH backup list to maintain the stability of the cluster structure. Numerical evaluations show that the proposed system outperforms benchmark schemes in terms of clustering stability and reliability. It is also shown that the performance of the proposed scheme is not much affected by the increase in the number of vehicles. This indicates that the proposed scheme can be efficient in dense vehicular networks where resource constraints pose significant challenges.
Quantum Annealing based Power Grid Partitioning for Parallel Simulation
Authors: Carsten Hartmann, Junjie Zhang, Carlos D. Gonzalez Calaza, Thiemo Pesch, Kristel Michielsen, Andrea Benigni
Date: 2024-08-07 00:00:00
Tasks: CPU, GPU, graph partitioning, State Estimation

Graph partitioning has many applications in powersystems from decentralized state estimation to parallel simulation. Focusing on parallel simulation, optimal grid partitioning minimizes the idle time caused by different simulation times for the sub-networks and their components and reduces the overhead required to simulate the cuts. Partitioning a graph into two parts such that, for example, the cut is minimal and the subgraphs have equal size is an NP-hard problem. In this paper we show how optimal partitioning of a graph can be obtained using quantum annealing (QA). We show how to map the requirements for optimal splitting to a quadratic unconstrained binary optimization (QUBO) formulation and test the proposed formulation using a current D-Wave QPU. We show that the necessity to find an embedding of the QUBO on current D-Wave QPUs limits the problem size to under 200 buses and notably affects the time-to-solution. We finally discuss the implications on near-term implementation of QA in combination to traditional CPU or GPU based simulation.
Scheduling Battery-Electric Bus Charging under Stochasticity using a Receding-Horizon Approach
Authors: Justin Whitaker, Derek Redmond, Greg Droge, Jacob Gunther
Date: 2024-08-07 00:00:00
Tasks: Scheduling

A significant challenge of adopting battery electric buses into fleets lies in scheduling the charging, which in turn is complicated by considerations such as timing constraints imposed by routes, long charging times, limited numbers of chargers, and utility cost structures. This work builds on previous network-flow-based charge scheduling approaches and includes both consumption and demand time-of-use costs while accounting for uncontrolled loads on the same meter. Additionally, a variable-rate, non-linear partial charging model compatible with the mixed-integer linear program (MILP) is developed for increased charging fidelity. To respond to feedback in an uncertain environment, the resulting MILP is adapted to a hierarchical receding horizon planner that utilizes a static plan for the day as a reference to follow while reacting to stochasticity on a regular basis. This receding horizon planner is analyzed with Monte-Carlo techniques alongside two other possible planning methods. It is found to provide up to 52\% cost savings compared to a non-time-of-use aware method and significant robustness benefits compared to an optimal open-loop method.
From Black Box to Clarity: AI-Powered Smart Grid Optimization with Kolmogorov-Arnold Networks
Authors: Xiaoting Wang, Yuzhuo Li, Yunwei Li, Gregory Kish
Date: 2024-08-07 00:00:00
Tasks: Kolmogorov-Arnold Networks

This work is the first to adopt Kolmogorov-Arnold Networks (KAN), a recent breakthrough in artificial intelligence, for smart grid optimizations. To fully leverage KAN's interpretability, a general framework is proposed considering complex uncertainties. The stochastic optimal power flow problem in hybrid AC/DC systems is chosen as a particularly tough case study for demonstrating the effectiveness of this framework.
Minimal L2-Consistent Data-Transmission
Authors: Antoine Aspeel, Laurent Bako, Necmiye Ozay
Date: 2024-08-07 00:00:00

In this work, we consider non-collocated sensors and actuators, and we address the problem of minimizing the number of sensor-to-actuator transmissions while ensuring that the L2 gain of the system remains under a threshold. By using causal factorization and system level synthesis, we reformulate this problem as a rank minimization problem over a convex set. When heuristics like nuclear norm minimization are used for rank minimization, the resulting matrix is only numerically low rank and must be truncated, which can lead to an infeasible solution. To address this issue, we introduce approximate causal factorization to control the factorization error and provide a bound on the degradation of the L2 gain in terms of the factorization error. The effectiveness of our method is demonstrated using a benchmark.
Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow
Authors: Salvador Pineda, Juan Pérez-Ruiz, Juan Miguel Morales
Date: 2024-07-30 00:00:00

The AC optimal power flow (AC-OPF) problem is essential for power system operations, but its non-convex nature makes it challenging to solve. A widely used simplification is the linearized DC optimal power flow (DC-OPF) problem, which can be solved to global optimality, but whose optimal solution is always infeasible in the original AC-OPF problem. Recently, neural networks (NN) have been introduced for solving the AC-OPF problem at significantly faster computation times. However, these methods necessitate extensive datasets, are difficult to train, and are often viewed as black boxes, leading to resistance from operators who prefer more transparent and interpretable solutions. In this paper, we introduce a novel learning-based approach that merges simplicity and interpretability, providing a bridge between traditional approximation methods and black-box learning techniques. Our approach not only provides transparency for operators but also achieves competitive accuracy. Numerical results across various power networks demonstrate that our method provides accuracy comparable to, and often surpassing, that of neural networks, particularly when training datasets are limited.
Neural Networks as Spin Models: From Glass to Hidden Order Through Training
Authors: Richard Barney, Michael Winer, Victor Galitksi
Date: 2024-08-12 00:00:00

We explore a one-to-one correspondence between a neural network (NN) and a statistical mechanical spin model where neurons are mapped to Ising spins and weights to spin-spin couplings. The process of training an NN produces a family of spin Hamiltonians parameterized by training time. We study the magnetic phases and the melting transition temperature as training progresses. First, we prove analytically that the common initial state before training--an NN with independent random weights--maps to a layered version of the classical Sherrington-Kirkpatrick spin glass exhibiting a replica symmetry breaking. The spin-glass-to-paramagnet transition temperature is calculated. Further, we use the Thouless-Anderson-Palmer (TAP) equations--a theoretical technique to analyze the landscape of energy minima of random systems--to determine the evolution of the magnetic phases on two types of NNs (one with continuous and one with binarized activations) trained on the MNIST dataset. The two NN types give rise to similar results, showing a quick destruction of the spin glass and the appearance of a phase with a hidden order, whose melting transition temperature $T_c$ grows as a power law in training time. We also discuss the properties of the spectrum of the spin system's bond matrix in the context of rich vs. lazy learning. We suggest that this statistical mechanical view of NNs provides a useful unifying perspective on the training process, which can be viewed as selecting and strengthening a symmetry-broken state associated with the training task.
MaterioMiner -- An ontology-based text mining dataset for extraction of process-structure-property entities
Authors: Ali Riza Durmaz, Akhil Thomas, Lokesh Mishra, Rachana Niranjan Murthy, Thomas Straub
Date: 2024-08-05 00:00:00
Tasks: Benchmarking, Graph Generation, named-entity-recognition, Named Entity Recognition

While large language models learn sound statistical representations of the language and information therein, ontologies are symbolic knowledge representations that can complement the former ideally. Research at this critical intersection relies on datasets that intertwine ontologies and text corpora to enable training and comprehensive benchmarking of neurosymbolic models. We present the MaterioMiner dataset and the linked materials mechanics ontology where ontological concepts from the mechanics of materials domain are associated with textual entities within the literature corpus. Another distinctive feature of the dataset is its eminently fine-granular annotation. Specifically, 179 distinct classes are manually annotated by three raters within four publications, amounting to a total of 2191 entities that were annotated and curated. Conceptual work is presented for the symbolic representation of causal composition-process-microstructure-property relationships. We explore the annotation consistency between the three raters and perform fine-tuning of pre-trained models to showcase the feasibility of named-entity recognition model training. Reusing the dataset can foster training and benchmarking of materials language models, automated ontology construction, and knowledge graph generation from textual data.
MetMamba: Regional Weather Forecasting with Spatial-Temporal Mamba Model
Authors: Haoyu Qin, Yungang Chen, Qianchuan Jiang, Pengchao Sun, Xiancai Ye, Chao Lin
Date: 2024-08-12 00:00:00
Tasks: Deep Learning, Mamba, Weather Forecasting

Deep Learning based Weather Prediction (DLWP) models have been improving rapidly over the last few years, surpassing state of the art numerical weather forecasts by significant margins. While much of the optimization effort is focused on training curriculum to extend forecast range in the global context, two aspects remains less explored: limited area modeling and better backbones for weather forecasting. We show in this paper that MetMamba, a DLWP model built on a state-of-the-art state-space model, Mamba, offers notable performance gains and unique advantages over other popular backbones using traditional attention mechanisms and neural operators. We also demonstrate the feasibility of deep learning based limited area modeling via coupled training with a global host model.
On Networks and their Applications: Stability of Gene Regulatory Networks and Gene Function Prediction using Autoencoders
Authors: Hamza Coban
Date: 2024-08-13 00:00:00
Tasks: Sensitivity

We prove that nested canalizing functions are the minimum-sensitivity Boolean functions for any activity ratio and we determine the functional form of this boundary which has a nontrivial fractal structure. We further observe that the majority of the gene regulatory functions found in known biological networks (submitted to the Cell Collective database) lie on the line of minimum sensitivity which paradoxically remains largely in the unstable regime. Our results provide a quantitative basis for the argument that an evolutionary preference for nested canalizing functions in gene regulation (e.g., for higher robustness) and for elasticity of gene activity are sufficient for concentration of such systems near the "edge of chaos." The original structure of gene regulatory networks is unknown due to the undiscovered functions of some genes. Most gene function discovery approaches make use of unsupervised clustering or classification methods that discover and exploit patterns in gene expression profiles. However, existing knowledge in the field derives from multiple and diverse sources. Incorporating this know-how for novel gene function prediction can, therefore, be expected to improve such predictions. We here propose a function-specific novel gene discovery tool that uses a semi-supervised autoencoder. Our method is thus able to address the needs of a modern researcher whose expertise is typically confined to a specific functional domain. Lastly, the dynamics of unorthodox learning approaches like biologically plausible learning algorithms are investigated and found to exhibit a general form of Einstein relation.