pwc-archive/papers-with-abstracts huggingface dataset:
A Bibliographic Study on Artificial Intelligence Research: Global Panorama and Indian Appearance
Authors: Amit Tiwari, Susmita Bardhan, Vikas Kumar
Date: 2023-07-04 00:00:00
Tasks: Articles

The present study identifies and assesses the bibliographic trend in Artificial Intelligence (AI) research for the years 2015-2020 using the science mapping method of bibliometric study. The required data has been collected from the Scopus database. To make the collected data analysis-ready, essential data transformation was performed manually and with the help of a tool viz. OpenRefine. For determining the trend and performing the mapping techniques, top five open access and commercial journals of AI have been chosen based on their citescore driven ranking. The work includes 6880 articles published in the specified period for analysis. The trend is based on Country-wise publications, year-wise publications, topical terms in AI, top-cited articles, prominent authors, major institutions, involvement of industries in AI and Indian appearance. The results show that compared to open access journals; commercial journals have a higher citescore and number of articles published over the years. Additionally, IEEE is the prominent publisher which publishes 84% of the top-cited publications. Further, China and the United States are the major contributors to literature in the AI domain. The study reveals that neural networks and deep learning are the major topics included in top AI research publications. Recently, not only public institutions but also private bodies are investing their resources in AI research. The study also investigates the relative position of Indian researchers in terms of AI research. Present work helps in understanding the initial development, current stand and future direction of AI.
Caching-at-STARS: the Next Generation Edge Caching
Authors: Zhaoming Hu, Ruikang Zhong, Chao Fang, Yuanwei Liu
Date: 2023-08-01 00:00:00
Tasks: Deep Reinforcement Learning

A simultaneously transmitting and reflecting surface (STARS) enabled edge caching system is proposed for reducing backhaul traffic and ensuring the quality of service. A novel Caching-at-STARS structure, where a dedicated smart controller and cache memory are installed at the STARS, is proposed to satisfy user demands with fewer hops and desired channel conditions. Then, a joint caching replacement and information-centric hybrid beamforming optimization problem is formulated for minimizing the network power consumption. As long-term decision processes, the optimization problems based on independent and coupled phase-shift models of Caching-at-STARS contain both continuous and discrete decision variables, and are suitable for solving with deep reinforcement learning (DRL) algorithm. For the independent phase-shift Caching-at-STARS model, we develop a frequency-aware based twin delayed deep deterministic policy gradient (FA-TD3) algorithm that leverages user historical request information to serialize high-dimensional caching replacement decision variables. For the coupled phase-shift Caching-at-STARS model, we conceive a cooperative TD3 \& deep-Q network (TD3-DQN) algorithm comprised of FA-TD3 and DQN agents to decide on continuous and discrete variables respectively by observing the network external and internal environment. The numerical results demonstrate that: 1) The Caching-at-STARS-enabled edge caching system has advantages over traditional edge caching, especially in scenarios where Zipf skewness factors or cache capacity is large; 2) Caching-at-STARS outperforms the RIS-assisted edge caching systems; 3) The proposed FA-TD3 and cooperative TD3-DQN algorithms are superior in reducing network power consumption than conventional TD3.
De Re and De Dicto Knowledge in Egocentric Setting
Authors: Pavel Naumov, Anna Ovchinnikova
Date: 2023-07-18 00:00:00

Prior proposes the term "egocentric" for logical systems that study properties of agents rather than properties of possible worlds. In such a setting, the paper introduces two different modalities capturing de re and de dicto knowledge and proves that these two modalities are not definable through each other.
FREIDA: A Framework for developing quantitative agent based models based on qualitative expert knowledge
Authors: Frederike Oetker, Vittorio Nespeca, Rick Quax
Date: 2023-07-21 00:00:00
Tasks: Uncertainty Quantification

Agent Based Models (ABMs) often deal with systems where there is a lack of quantitative data or where quantitative data alone may be insufficient to fully capture the complexities of real-world systems. Expert knowledge and qualitative insights, such as those obtained through interviews, ethnographic research, historical accounts, or participatory workshops, are critical in constructing realistic behavioral rules, interactions, and decision-making processes within these models. However, there is a lack of systematic approaches that are able to incorporate both qualitative and quantitative data across the entire modeling cycle. To address this, we propose FREIDA (FRamework for Expert-Informed Data-driven Agent-based models), a systematic mixed-methods framework to develop, train, and validate ABMs, particularly in data-sparse contexts. Our main technical innovation is to extract what we call Expected System Behaviors (ESBs) from qualitative data, which are testable statements that can be evaluated on model simulations. Divided into Calibration Statements (CS) for model calibration and Validation Statements (VS) for model validation, they provide a quantitative scoring mechanism on the same footing as quantitative data. In this way, qualitative insights can inform not only model specification but also its parameterization and assessment of fitness for purpose, which is a long standing challenge. We illustrate the application of FREIDA through a case study of criminal cocaine networks in the Netherlands.
VeriGen: A Large Language Model for Verilog Code Generation
Authors: Shailja Thakur, Baleegh Ahmad, Hammond Pearce, Benjamin Tan, Brendan Dolan-Gavitt, Ramesh Karri, Siddharth Garg
Date: 2023-07-28 00:00:00
Tasks: Code Generation, Language Modeling, Language Modelling, Large Language Model

In this study, we explore the capability of Large Language Models (LLMs) to automate hardware design by generating high-quality Verilog code, a common language for designing and modeling digital systems. We fine-tune pre-existing LLMs on Verilog datasets compiled from GitHub and Verilog textbooks. We evaluate the functional correctness of the generated Verilog code using a specially designed test suite, featuring a custom problem set and testing benches. Here, our fine-tuned open-source CodeGen-16B model outperforms the commercial state-of-the-art GPT-3.5-turbo model with a 1.1% overall increase. Upon testing with a more diverse and complex problem set, we find that the fine-tuned model shows competitive performance against state-of-the-art gpt-3.5-turbo, excelling in certain scenarios. Notably, it demonstrates a 41% improvement in generating syntactically correct Verilog code across various problem categories compared to its pre-trained counterpart, highlighting the potential of smaller, in-house LLMs in hardware design automation.
Planar Friction Modelling with LuGre Dynamics and Limit Surfaces
Authors: Gabriel Arslan Waltersson, Yiannis Karayiannidis
Date: 2023-08-02 00:00:00
Tasks: Friction

During planar motion, contact surfaces exhibit a coupling between tangential and rotational friction forces. This paper proposes planar friction models grounded in the LuGre model and limit surface theory. First, distributed planar extended state models are proposed and the Elasto-Plastic model is extended for multi-dimensional friction. Subsequently, we derive a reduced planar friction model, coupled with a pre-calculated limit surface, that offers reduced computational cost. The limit surface approximation through an ellipsoid is discussed. The properties of the planar friction models are assessed in various simulations, demonstrating that the reduced planar friction model achieves comparable performance to the distributed model while exhibiting ~80 times lower computational cost.
Black-Box System Identification for Low-Cost Quadrotor Attitude at Hovering
Authors: Khaled Telli, Boumehraz Mohamed
Date: 2023-08-01 00:00:00

The accuracy of dynamic modelling of unmanned aerial vehicles, specifically quadrotors, is gaining importance since strict conditionalities are imposed on rotorcraft control. The system identification plays a crucial role as an effective approach for the problem of the fine-tuning dynamic models for applications such control system design and as handling quality evaluation. This paper focuses on black-box identification, describing the quadrotor dynamics based on experimental setup through sensor preparation for data collection, modelling, control design, and verification stages.
DeepTSF: Codeless machine learning operations for time series forecasting
Authors: Sotiris Pelekis, Evangelos Karakolis, Theodosios Pountridis, George Kormpakis, George Lampropoulos, Spiros Mouzakitis, Dimitris Askounis
Date: 2023-07-28 00:00:00
Tasks: Load Forecasting, Management, Time Series, Time Series Forecasting

This paper presents DeepTSF, a comprehensive machine learning operations (MLOps) framework aiming to innovate time series forecasting through workflow automation and codeless modeling. DeepTSF automates key aspects of the ML lifecycle, making it an ideal tool for data scientists and MLops engineers engaged in machine learning (ML) and deep learning (DL)-based forecasting. DeepTSF empowers users with a robust and user-friendly solution, while it is designed to seamlessly integrate with existing data analysis workflows, providing enhanced productivity and compatibility. The framework offers a front-end user interface (UI) suitable for data scientists, as well as other higher-level stakeholders, enabling comprehensive understanding through insightful visualizations and evaluation metrics. DeepTSF also prioritizes security through identity management and access authorization mechanisms. The application of DeepTSF in real-life use cases of the I-NERGY project has already proven DeepTSF's efficacy in DL-based load forecasting, showcasing its significant added value in the electrical power and energy systems domain.
A Real-Time Robust Ecological-Adaptive Cruise Control Strategy for Battery Electric Vehicles
Authors: Sheng Yu, Xiao Pan, Anastasis Georgiou, Boli Chen, Imad M. Jaimoukha, Simos A. Evangelou
Date: 2023-08-02 00:00:00
Tasks: Computational Efficiency, Model Predictive Control

This work addresses the ecological-adaptive cruise control problem for connected electric vehicles by a computationally efficient robust control strategy. The problem is formulated in the space-domain with a realistic description of the nonlinear electric powertrain model and motion dynamics to yield a convex optimal control problem (OCP). The OCP is approached by a novel robust model predictive control (RMPC) method handling various disturbances due to modelling mismatch and inaccurate leading vehicle information. The RMPC problem is solved by semi-definite programming relaxation and single linear matrix inequality (sLMI) techniques for further enhanced computational efficiency. The performance of the proposed real-time robust ecological-adaptive cruise control (REACC) method is evaluated using an experimentally collected driving cycle. Its robustness is verified by comparison with a nominal MPC which is shown to result in speed-limit constraint violations. The energy economy of the proposed method outperforms a state-of-the-art time-domain RMPC scheme, as a more precisely fitted convex powertrain model can be integrated into the space-domain scheme. The additional comparison with a traditional constant distance following strategy (CDFS) further verifies the effectiveness of the proposed REACC. Finally, it is verified that the REACC can be potentially implemented in real-time owing to the sLMI and resulting convex algorithm.
Integrating Optimal EV Charging in the Energy Management of Electric Railway Stations
Authors: Georgia Pierrou, Gabriela Hug
Date: 2023-08-02 00:00:00
Tasks: energy management, Management

In this paper, an electric railway Energy Management System (EMS) with integration of an Energy Storage System (ESS), Regenerative Braking Energy (RBE), and renewable generation is proposed to minimize the daily operating costs of the railway station while meeting railway and Electric Vehicle (EV) charging demand. Compared to other railway EMS methods, the proposed approach integrates an optimal EV charging policy at the railway station to avoid high power demand due to charging requirements. Specifically, receding horizon control is leveraged to minimize the daily peak power spent on EV charging. The numerical study on an actual railway station in Chur, Switzerland shows that the proposed method that integrates railway demand and optimal EV charging along with ESS, RBE, and renewable generation can significantly reduce the average daily operating cost of the railway station over a large number of different scenarios while ensuring that peak load capacity limits are respected.
Game-theoretical approach to decentralized multi-drone conflict resolution and emergent traffic flow operations
Authors: Serge Hoogendoorn, Victor Knoop, Hani Mahmassani, Sascha Hoogendoorn-Lanser
Date: 2023-08-02 00:00:00

This paper introduces decentralized control concepts for drones using differential game theory. The approach optimizes the behavior of an ego drone, assuming the anticipated behavior of the opponent drones using a receding horizon approach. For each control instant, the scheme computes the Nash equilibrium control signal which is applied for the control period. This results in a multi-drone conflict resolution scheme that is applied to all drones considered. The paper discusses the approach and presents the numerical algorithm, showing several examples that illustrate the performance of the model. We examine at the behavior of the ego drone, and the resulting collective drone flow operations. The latter shows that while the approach aims to optimize the operation cost of the ego drone, the experiments provide evidence that resulting flow operations are very efficient due to the self-organization of various flow patterns. The presented work contributes to the state of the art in providing a generic approach to multi-drone conflict resolution with good macroscopic flow performance characteristics. The approach enables relatively straightforward inclusion of error due to sensing and communication. The approach also allows for including different risk levels (e.g., for malfunctioning of sensor and communication technology), priority rules, regulations, and higher-level control signals (e.g., routing, dynamic speed limits).
Error Analysis of CORDIC Processor with FPGA Implementation
Authors: Young-Man Kim
Date: 2023-08-02 00:00:00
Tasks: Quantization

The coordinate rotation digital computer (CORDIC) is a shift-add based fast computing algorithm which has been found in many digital signal processing (DSP) applications. In this paper, a detailed error analysis based on mean square error criteria and its implementation on FPGA is presented. Two considered error sources are an angle approximation error and a quantization error due to finite word length in fixed-point number system. The error bound and variance are discussed in theory. The CORDIC algorithm is implemented on FPGA using the Xilinx Zynq-7000 development board called ZedBoard. Those results of theoretical error analysis are practically investigated by implementing it on actual FPGA board. In addition, Matlab is used to provide theoretical value as a baseline model by being set up in double-precision floating-point to compare it with the practical value of errors on FPGA implementation.
A Model Predictive Approach for Enhancing Transient Stability of Grid-Forming Converters
Authors: Ali Arjomandi-Nezhad, Yifei Guo, Bikash C. Pal, Damiano Varagnolo
Date: 2023-08-02 00:00:00
Tasks: Model Predictive Control

A model predictive control (MPC) method for enhancing post-fault transient stability of a grid-forming (GFM) inverter based resources (IBRs) is developed in this paper. This proposed controller is activated as soon as the converter enters into the post-fault current-saturation mode. It aims at mitigating the instability arising from insufficient deceleration due to current saturation and thus improving the transient stability of a GFM-IBR. The MPC approach optimises the post-fault trajectory of GFM IBRs by introducing appropriate corrective phase angle jumps and active power references where the post-fault dynamics of GFM IBRs are addressed. These two signals provide controllability over GFM IBR's post-fault trajectory. This paper addresses the mitigation of oscillations between current-saturation mode and normal mode by forced saturation if conditions for remaining in the normal mode do not hold. The performance of the proposal is tested via dynamic simulations under various grid conditions and compared with other existing strategies. The results demonstrate significant improvement in transient stability.
A Universal Attenuation Model of Terahertz Wave in Space-Air-Ground Channel Medium
Authors: Zhirong Yang, Weijun Gao, Chong Han
Date: 2023-08-02 00:00:00

Providing continuous bandwidth over several tens of GHz, the Terahertz (THz) band (0.1-10 THz) supports space-air-ground integrated network (SAGIN) in 6G and beyond wireless networks. However, it is still mystery how THz waves interact with the channel medium in SAGIN. In this paper, a universal space-air-ground attenuation model is proposed for THz waves, which incorporates the attenuation effects induced by particles including condensed particles, molecules, and free electrons. The proposed model is developed from the insight into the attenuation effects, namely, the physical picture that attenuation is the result of collision between photons that are the essence of THz waves and particles in the environment. Based on the attenuation model, the propagation loss of THz waves in the atmosphere and the outer space are numerically assessed. The results indicate that the attenuation effects except free space loss are all negligible at the altitude higher than 50 km while they need to be considered in the atmosphere lower than 50 km. Furthermore, the capacities of THz SAGIN are evaluated in space-ground, space-sea, ground-sea, and sea-sea scenarios, respectively.
Adaptive Sliding Mode Controller and Observer for Altitude and Attitude Control of a Quadrotor
Authors: Telli Khaled, M. Boumehraz
Date: 2023-08-01 00:00:00

In this paper an adaptive sliding mode control approach for a quadrotor stabilization and trajectory tracking is presented. The closed loop control consists of three parts; the first part is quadrotor altitude and attitude stabilization, and trajectory tracking. Second part is used for parameters estimation where we focus in mass estimation, while the third part is the full states observation. Disturbances, sensors noise and parameter uncertainties, are taken into consideration. Sliding mode control law and observer adaptation are developed based on Lyapunov stability principle. Numerical simulations show the effectiveness of the proposed control technique
Data quality challenges in existing distribution network datasets
Authors: Frederik Geth, Marta Vanin, Dirk Van Hertem
Date: 2023-08-01 00:00:00

Existing digital distribution network models, like those in the databases of network utilities, are known to contain erroneous or untrustworthy information. This can compromise the effectiveness of physics-based engineering simulations and technologies, in particular those that are needed to deliver the energy transition. The large-scale rollout of smart meters presents new opportunities for data-driven system identification in distribution networks, enabling the improvement of existing data sets. Despite the increasing academic attention to system identification for distribution networks, researchers often make troublesome assumptions on what data is available and/or trustworthy. In this paper, we highlight some differences between academic efforts and first-hand industrial experiences, in order to steer the former towards more applicable research solutions.
Exploiting Sparsity for Localization of Large-Scale Wireless Sensor Networks
Authors: Shiraz Khan, Inseok Hwang, James Goppert
Date: 2023-08-01 00:00:00

Wireless Sensor Network (WSN) localization refers to the problem of determining the position of each of the agents in a WSN using noisy measurement information. In many cases, such as in distance and bearing-based localization, the measurement model is a nonlinear function of the agents' positions, leading to pairwise interconnections between the agents. As the optimal solution for the WSN localization problem is known to be computationally expensive in these cases, an efficient approximation is desired. In this paper, we show that the inherent sparsity in this problem can be exploited to greatly reduce the computational effort of using an Extended Kalman Filter (EKF) for large-scale WSN localization. In the proposed method, which we call the Low-Bandwidth Extended Kalman Filter (LB-EKF), the measurement information matrix is converted into a banded matrix by relabeling (permuting the order of) the vertices of the graph. Using a combination of theoretical analysis and numerical simulations, it is shown that typical WSN configurations (which can be modeled as random geometric graphs) can be localized in a scalable manner using the proposed LB-EKF approach.
Screening Curve Method for Economic Analysis of Household Solar Energy Self-Consumption
Authors: Hikaru Hoshino, Yosuke Irie, Eiko Furutani
Date: 2023-08-01 00:00:00

The profitability of solar energy self-consumption in households, the so-called photovoltaic (PV) self-consumption, is expected to boost the deployment of PV and battery storage systems. This paper develops a novel method for economic analysis of PV self-consumption using battery storage based on an extension of the Screening Curve Method (SCM). The SCM enables quick and intuitive estimation of the least-cost generation mix for a target load curve and has been used for generation planning for bulk power systems. In this paper, we generalize the framework of existing SCM to take into account the intermittent nature of renewable energy sources and apply it to the problem of optimal sizing of PV and battery storage systems for a household. Numerical studies are provided to verify the estimation accuracy of the proposed SCM and to illustrate its effectiveness in a sensitivity analysis, owing to its ability to show intuitive plots of cost curves for researchers or policy-makers to understand the reasons behind the optimization results.