pwc-archive/papers-with-abstracts huggingface dataset:
An IoT-based Smart Parking System
Authors: Ridhi Choudhary, Arnav Sanjay Sinha, Krishna Jaiswal, Anurag Chandra
Date: 2023-11-21 00:00:00

The number of vehicles on the road is growing every day, thus there's a growing need to develop effective and hassle-free parking systems. Finding a parking space may be a big challenge, especially in crowded cities or areas with scheduled sporting or cultural events. The project suggests an automated parking system that makes use of technology like sensor systems and microcontrollers. In order to make it easier for drivers to park in empty spots and cut down on the time and effort needed for manual searches, this system is made to identify empty parking spaces and display the available parking spots on an LCD screen.
Power System Capacity Planning Considering Seasonal Hydrogen Storage by Salt Caverns
Authors: Xueqian He, Tianguang Lu, Jing Li, Wanxing Sheng, Rui Li
Date: 2023-11-21 00:00:00

In China, air conditioning in summer and electric heating in winter lead to seasonal volatility in load power. Therefore, it is urgent to develop economic and efficient long-term energy storage systems to enhance peak regulation. Power-to-hydrogen technology is a perspective solution to balance seasonal power fluctuation. However, current hydrogen storage methods have shortcomings such as small storage capacity, high levelized cost and low operation safety, which the salt cavern hydrogen storage could overcome. This paper considers the use of hydrogen storage in salt caverns as a means of peak shaving. To minimize the overall operating cost, a comprehensive power system capacity planning model is proposed with the consideration of hydrogen storage in salt caverns, which is implemented by adopting an improved fast unit commitment method. Considering the seasonal characteristics of the load power in Jiangsu Province, the capacity of the power system in 2050 has been planned. According to the case study, after the optimal deployment of the salt cavern hydrogen storage system (SCHSS), the construction capacity of renewable units (especially wind power) will be significantly increased with environmental friendliness and lower costs. Compared with the current energy storage method, the overall cost of SCHSS-incorporated power system will be reduced by 22.2% with a carbon emission reduction of 24.4%, and the amount of curtailed wind and solar power will be reduced by 27.0% and 13.6%, respectively.
Voltage-Dependent Electromechanical Wave Propagation Modeling for Dynamic Stability Analysis in Power Systems
Authors: Somayeh Yarahmadi, Daniel Adrian Maldonado, Lamine Mili, Junbo Zhao, Mihai Anitescu
Date: 2023-11-21 00:00:00

Accurate dynamic modeling of power systems is essential to assess the stability of electrical power systems when faced with disturbances, which can trigger cascading failures leading to blackouts. A continuum model proves to be effective in capturing Electromechanical Wave (EMW) propagation characteristics, including its velocity, arrival time, and deviations. Analyzing these characteristics enables the assessment of the impacts of EMW on the performance of the protection system. Prior research has often modeled nonlinear EMW propagation through Partial Differential Equations (PDEs) within a homogeneous and uniform frame structure, assuming constant bus voltages across the entire power system. However, this assumption can produce inaccurate results. In this paper, we relax this assumption by introducing a second-order nonlinear hyperbolic EMW propagation equation model that accounts for voltage variations. Additionally, we present numerical solutions for the EMW propagation equation using the Lax-Wendroff integration method. To validate our approach, we conduct simulations on two test systems: a two-bus one-machine system and the New England 39-bus 10-machine system. The simulation results demonstrate the effectiveness of our proposed model and emphasize the importance of including the bus voltage equations in the analysis.
Micro Energy-Water-Hydrogen Nexus: Data-driven Real-time Optimal Operation
Authors: Mostafa Goodarzi, QiFeng Li
Date: 2023-11-21 00:00:00

This paper extends a new concept of energy-water-hydrogen (EWH) nexus, which was recently developed as a solution for reducing carbon emissions from the generation side of power systems, to the distribution side. Under the concept of distribution-level EWH (micro EWH) nexus, renewable energy sources (RES) are utilized to meet the energy needs of a small community. To avoid the uncertainty caused by RESs, this paper aims to investigate the real-time optimal operation of the micro EWH nexus which is however a challenging optimization problem. First, such a large-scale mixed-integer nonlinear programming problem is relaxed into a mixed-integer convex program (MICP) by leveraging the effective convex-hull relaxation technique. Second, a fast data-driven solution method based on active constraint and integer variable prediction is presented, which can solve the MICP problem very fast since it utilizes historical optimization data to quickly predict binary variable values and a limited set of active constraints.
Stochastic flexibility needs assessment: learnings from H2020 EUniversal's German demonstration
Authors: Md Umar Hashmi, Simon Nagels, Dirk Van Hertem
Date: 2023-11-20 00:00:00

Operational flexibility needs assessment (FNA) is crucial for system operators to plan/procure flexible resources in order to avoid probable network issues. We implemented an FNA tool in the framework of the H2020 EUniversal project for the German demonstration. In this work, we summarize our learnings from the demo implementation to cope with the limited availability of measurement data. Using a reduced network model and key performance indicators, we evaluate the digital-twin results with real-world implementations. The paper aims to motivate future research directions by duly considering real-world limitations in their modelling and developing innovative tailor-made solutions for an improved decision support framework for system operators.
Day-Ahead Programming of Energy Communities Participating in Pay-as-Bid Service Markets
Authors: F. Conte, S. Massucco, G. Natrella, M. Saviozzi, F. Silvestro
Date: 2023-11-20 00:00:00

This paper proposes an optimal strategy for a Renewable Energy Community participating in the Italian pay-as-bid ancillary service market. The community is composed by a group of residential customers sharing a common facility equipped with a PV power plant and a battery energy storage system. This battery is employed to maximize the community cash flow obtained by the participation in the service market. A scenario-based optimization problem is defined to size the bids to be submitted to the market and to define the corresponding optimal battery energy exchange profile for the day ahead. The proposed optimization scheme is able to take in to account the probability of acceptance of the service market bids and the uncertainties on the forecasts of PV generation and energy demands. Results prove the effectiveness of the approach and the economic advantages on participating in the service market.
Scaling up the formation of agents with heterogeneous sensing: mixed distance and bearing-only
Authors: Jin Chen, Bayu Jayawardhana, Hector Garcia de Marina
Date: 2023-11-20 00:00:00

Unlike the case with identical neighboring agents whose actions are mirrored, the problem of distributed formation control design with heterogeneous sensing is not straightforward. In this paper, we consider the problem of distributed formation control where each agent can only control distances or bearings with its neighbors. We firstly develop a rigidity theory with heterogeneous sensing to ensure that the desired shape is well-posed. Secondly, we propose an iterative method that allows us to scale up the number of agents with heterogeneous sensing. Finally, numerical simulations show the effectiveness of our method.
Equitable Coordination in Multi-agent Power Systems: Impacts of Computation Granularity
Authors: Yuhan Du, Javad Mohammadi
Date: 2023-11-20 00:00:00

The growing integration of distributed energy resources drives the centralized power system towards a decentralized multi-agent network. Operating multi-agent networks significantly relies on inter-agent communications. Computation granularity in this context refers to the number of nodes overseen by an agent. The impact of granularity on equitable power coordination, particularly among marginalized customers with limited communication bandwidth (e.g., intermittent internet connectivity) is not well studied. This work explores different levels of computation granularity for agent-based energy dispatch and studies their impact on equitable coordination. We will leverage and utilize the Consensus + Innovations approach to model the equitable coordination of a multi-agent power system.
Small-signal stability in inverter-dominated grids: exploring the role of gains, line dynamics, and operating conditions
Authors: Ruth Kravis, Gabriel Colon-Reyes, Duncan Callaway
Date: 2023-11-20 00:00:00

In a power grid with growing penetrations of renewable energy sources, inverters play a larger role in the dynamic interactions among network components. However, much is yet to be studied regarding inverter-dominated grid stability. This has initiated a re-examination of long-established assumptions made when conducting power systems studies. In this work we study the small-signal stability characteristics of a power system comprised of inverter and synchronous machine sources, and consider the effect of inverter control gains, transmission line dynamics, and network operating conditions. We utilize PowerSimulationsDynamics.jl to perform this study on the IEEE WSCC 9 Bus test system. We vary inverter parameters across reported ranges in the literature, we consider three transmission line models of varying fidelity, and compare results across different operating conditions. We find that line dynamics are generally important for correctly assessing small-signal stability in networks with large penetrations of grid-following converters, and that gain tuning in these networks should utilize high fidelity line models.
Gene expression in growing cells: A biophysical primer
Authors: Ido Golding, Ariel Amir
Date: 2023-11-20 00:00:00

Cell growth and gene expression, essential elements of all living systems, have long been the focus of biophysical interrogation. Advances in single-cell methods have invigorated theoretical studies into these processes. However, until recently, there was little dialog between the two areas of study. Most theoretical models for gene regulation assumed gene activity to be oblivious to the progression of the cell cycle between birth and division. But there are numerous ways in which the periodic character of all cellular observables can modulate gene expression. The molecular factors required for transcription and translation increase in number during the cell cycle, but are also diluted due to the continuous increase in cell volume. The replication of the genome changes the dosage of those same cellular players but also provides competing targets for regulatory binding. Finally, cell division reduces their number again, and so forth. Stochasticity is inherent to all these biological processes, manifested in fluctuations in the synthesis and degradation of new cellular components as well as the random partitioning of molecules at each cell division. The notion of gene expression as stationary is thus hard to justify. In this review, we survey the emerging paradigm of cell-cycle regulated gene expression, with an emphasis on the global expression patterns rather than gene-specific regulation. We discuss recent experimental reports where cell growth and gene expression were simultaneously measured in individual cells, providing first glimpses into the coupling between the two. While the experimental findings, not surprisingly, differ among genes and organisms, several theoretical models have emerged that attempt to reconcile these differences and form a unifying framework for understanding gene expression in growing cells.
Underreaction and dynamic inconsistency in communication games under noise
Authors: Gerrit Bauch
Date: 2023-11-21 00:00:00

Communication is rarely perfect, but rather prone to error of transmission and reception. Often the origin of these errors cannot be properly quantified and is thus imprecisely known. We analyze the impact of an ambiguous noise which may alter the received message on a communication game of common interest. The noise is ambiguous in the sense that the parameters of the error-generating process and thus the likelihood to receive a message by mistake are Knightianly unknown. Ex-ante and interim responses are characterized under maxmin preferences. While the sender can disregard ambiguity, the receiver reveals a dynamically inconsistent, but astonishing behavior under a quadratic loss. Their interim actions will be closer to the pooling action than their ex-ante ones, as if facing a higher likelihood of an occurring error.
Successive Incentives
Authors: Jens Gudmundsson, Jens Leth Hougaard, Juan D. Moreno-Ternero, Lars Peter Østerdal
Date: 2023-11-21 00:00:00

We study the design of optimal incentives in sequential processes. To do so, we consider a basic and fundamental model in which an agent initiates a value-creating sequential process through costly investment with random success. If unsuccessful, the process stops. If successful, a new agent thereafter faces a similar investment decision, and so forth. For any outcome of the process, the total value is distributed among the agents using a reward rule. Reward rules thus induce a game among the agents. By design, the reward rule may lead to an asymmetric game, yet we are able to show equilibrium existence with optimal symmetric equilibria. We characterize optimal reward rules that yield the highest possible welfare created by the process, and the highest possible expected payoff for the initiator of the process. Our findings show that simple reward rules invoking short-run incentives are sufficient to meet long-run objectives.
Uniformly Strict Equilibrium for Repeated Games with Private Monitoring and Communication
Authors: Richard McLean, Ichiro Obara, Andrew Postlewaite
Date: 2023-11-20 00:00:00

Cooperation through repetition is an important theme in game theory. In this regard, various celebrated ``folk theorems'' have been proposed for repeated games in increasingly more complex environments. There has, however, been insufficient attention paid to the robustness of a large set of equilibria that is needed for such folk theorems. Starting with perfect public equilibrium as our starting point, we study uniformly strict equilibria in repeated games with private monitoring and direct communication (cheap talk). We characterize the limit equilibrium payoff set and identify the conditions for the folk theorem to hold with uniformly strict equilibrium.
Learning-based Array Configuration-Independent Binaural Audio Telepresence with Scalable Signal Enhancement and Ambience Preservation
Authors: Yicheng Hsu, Mingsian R. Bai
Date: 2023-11-21 00:00:00

Audio Telepresence (AT) aims to create an immersive experience of the audio scene at the far end for the user(s) at the near end. The application of AT could encompass scenarios with varying degrees of emphasis on signal enhancement and ambience preservation. It is desirable for an AT system to be scalable between these two extremes. To this end, we propose an array-based Binaural AT (BAT) system using the DeepFilterNet as the backbone to convert the array microphone signals into the Head-Related Transfer Function (HRTF)-filtered signals, with a tunable weighting between signal enhancement and ambience preservation. An array configuration-independent Spatial COherence REpresentation (SCORE) feature is proposed for the model training so that the network remains robust to different array geometries and sensor counts. magnitude-weighted Interaural Phase Difference error (mw-IPDe), magnitude-weighted Interaural Level Difference error (mw-ILDe), and modified Scale-Invariant Signal-to-Distortion Ratio (mSI-SDR) are defined as performance metrics for objective evaluation. Subjective listening tests were also performed to validate the proposed BAT system. The results have shown that the proposed BAT system can achieve superior telepresence performance with the desired balance between signal enhancement and ambience preservation, even when the array configurations are unseen in the training phase.
A Distributed Algorithm for Personal Sound Zones Systems
Authors: Sipei Zhao, Guoqiang Zhang, Eva Cheng, Ian S. Burnett
Date: 2023-11-21 00:00:00

A Personal Sound Zones (PSZ) system aims to generate two or more independent listening zones that allow multiple users to listen to different music/audio content in a shared space without the need for wearing headphones. Most existing studies assume that the acoustic paths between loudspeakers and microphones are measured beforehand in a stationary environment. Recently, adaptive PSZ systems have been explored to adapt the system in a time-varying acoustic environment. However, because a PSZ system usually requires multiple loudspeakers, the multichannel adaptive algorithms impose a high computational load on the processor. To overcome that problem, this paper proposes an efficient distributed algorithm for PSZ systems, which not only spreads the computational burden over multiple nodes but also reduces the overall computational complexity, at the expense of a slight decrease in performance. Simulation results with true room impulse responses measured in a Hemi-Anechoic chamber are performed to verify the proposed distributed PSZ system.
AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive Learning
Authors: Jisheng Bai, Han Yin, Mou Wang, Dongyuan Shi, Woon-Seng Gan, Jianfeng Chen, Susanto Rahardja
Date: 2023-11-21 00:00:00
Tasks: Acoustic Scene Classification, Audio captioning, Contrastive Learning, Event Detection, Multi-Task Learning, Scene Classification, Sound Event Detection

Previous studies in automated audio captioning have faced difficulties in accurately capturing the complete temporal details of acoustic scenes and events within long audio sequences. This paper presents AudioLog, a large language models (LLMs)-powered audio logging system with hybrid token-semantic contrastive learning. Specifically, we propose to fine-tune the pre-trained hierarchical token-semantic audio Transformer by incorporating contrastive learning between hybrid acoustic representations. We then leverage LLMs to generate audio logs that summarize textual descriptions of the acoustic environment. Finally, we evaluate the AudioLog system on two datasets with both scene and event annotations. Experiments show that the proposed system achieves exceptional performance in acoustic scene classification and sound event detection, surpassing existing methods in the field. Further analysis of the prompts to LLMs demonstrates that AudioLog can effectively summarize long audio sequences. To the best of our knowledge, this approach is the first attempt to leverage LLMs for summarizing long audio sequences.
Eliminating Quantization Errors in Classification-Based Sound Source Localization
Authors: Linfeng Feng, Xiao-Lei Zhang, Xuelong Li
Date: 2023-11-21 00:00:00
Tasks: Classification, Quantization, regression, Sound Source Localization

Sound Source Localization (SSL) involves estimating the Direction of Arrival (DOA) of sound sources. Since the DOA estimation output space is continuous, regression might be more suitable for DOA, offering higher precision. However, in practice, classification often outperforms regression, exhibiting greater robustness to interference. Conversely, classification's drawback is inherent quantization error. Within the classification paradigm, the DOA output space is discretized into intervals, each treated as a class. These classes show strong inter-class correlations, being inherently ordered, with higher similarity as intervals grow closer. Nevertheless, this has not been fully exploited. To address this, we propose an Unbiased Label Distribution (ULD) to eliminate quantization error in training targets. Furthermore, we tailor two loss functions for the soft label family: Negative Log Absolute Error (NLAE) and Mean Squared Error without activation (MSE(wo)). Finally, we introduce Weighted Adjacent Decoding (WAD) to overcome quantization error during model prediction decoding. Experimental results demonstrate our approach surpasses classification quantization limits, achieving state-of-the-art performance. Our code and supplementary materials are available at https://github.com/linfeng-feng/ULD.
Satellite Swarms for Narrow Beamwidth Applications
Authors: Juan A. Vásquez-Peralvo, Juan Carlos Merlano Duncan, Geoffrey Eappen, Symeon Chatzinotas
Date: 2023-11-21 00:00:00

Satellite swarms have recently gained attention in the space industry due to their ability to provide extremely narrow beamwidths at a lower cost than single satellite systems. This paper proposes a concept for a satellite swarm using a distributed subarray configuration based on a 2D normal probability distribution. The swarm comprises multiple small satellites acting as subarrays of a big aperture array limited by a radius of 20000 wavelengths working at a central frequency of 19 GHz. The main advantage of this approach is that the distributed subarrays can provide extremely directive beams and beamforming capabilities that are not possible using a conventional antenna and satellite design. The proposed swarm concept is analyzed, and the simulation results show that the radiation pattern achieves a beamwidth as narrow as 0.0015-degrees with a maximum side lobe level of 18.8 dB and a grating lobe level of 14.8 dB. This concept can be used for high data rates applications or emergency systems.
A Unified Framework for Pulse-Shaping on Delay-Doppler Plane
Authors: Mohsen Bayat, Arman Farhang
Date: 2023-11-21 00:00:00

Delay-Doppler multiplexing has recently stirred a great deal of attention in research community. While multiple studies have investigated pulse-shaping aspects of this technology, it is challenging to identify the relationships between different pulse-shaping techniques and their properties. Hence, in this paper, we classify these techniques into two types, namely, circular and linear pulse-shaping. This paves the way towards the development of a unified framework that brings deep insights into the properties, similarities, and distinctions of different pulse-shaping techniques. This framework reveals that the recently emerged waveform orthogonal delay-Doppler multiplexing (ODDM) is a linear pulse-shaping technique with an interesting staircase spectral behaviour. Using this framework, we derive a generalized input-output relationship that captures the influence of pulse-shaping on the effective channel. We also introduce a unified modem for delay-Doppler plane pulse-shaping that leads to the proposal of fast convolution based low-complexity structures. Based on our complexity analysis, the proposed modem structures are substantially simpler than the existing ones in the literature. Furthermore, we propose effective techniques that not only reduce the out-of-band (OOB) emissions of circularly pulse-shaped signals but also improve the bit-error-rate (BER) performance of both circular and linear pulse-shaping techniques. Finally, we extensively compare different pulse-shaping techniques using various performance metrics.