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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Ebrahim Balouji Karl Bäckstrüm Tomas McKelvey Dept. of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Dept. of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

This research work introduces Deep Deterministic Policy Gradient (DDPG), a type of Reinforcement Learning (RL), for grid modeling, estimating voltage and phase angle, and control method for grid-forming inverters. The aim is to develop a grid-forming inverter that sets the voltage level and frequency of the grid and mitigates voltage dips originating from faults and frequency deviation fluctuations. Unlike conventional methods for estimating setpoints for the controller loops, we do not need several chains of estimation tools such as Fast Fourier Transform (FFT), Synchronous Reference Frame (SRF), or lowpass filters. With the DDPG, we also optimize the phase lock-loop (PLL) and accurately deliver the angle for the actuation part of the inverter to generate the given reference signal. The developed method does not need exhaustive tuning of parameters such as coefficients of PID controllers and lowpass filters. We observe that the proposed method has a faster response time than the PID-based control unit (15ms compared to 50ms) for the grid-forming inverter in the case of compensating voltage dips. We also observed that the DDPG-based grid-forming inverter is more efficient in compensating continuous voltage variations and frequency deviations than a trivial PID-based version.

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A centrality measure for quantifying spread on weighted, directed networks

arXiv

引用

arXiv 2023年

作者： Fink, Christian G. Fullin, Kelly Gutierrez, Guillermo Omodt, Nathan Zinnecker, Sydney Sprint, Gina McCulloch, Sean Dept. of Physics Gonzaga University United States Dept. of Computer Science Ashland University United States Dept. of Industrial and Systems Engineering Georgia Institute of Technology United States Dept. of Mechanical Engineering Gonzaga University United States Dept. of Computer Science Gonzaga University United States Dept. of Computer Science Ohio Wesleyan University United States

While many centrality measures for complex networks have been proposed, relatively few have been developed specifically for weighted, directed (WD) networks. Here we propose a centrality measure for spread (of information, pathogens, etc.) through WD networks based on the independent cascade model (ICM). While deriving exact results for the ICM requires Monte Carlo simulations, we show that our centrality measure (Viral Centrality) provides excellent approximation to ICM results for networks in which the weighted strength of cycles is not too large. We show this can be quantified with the leading eigenvalue of the weighted adjacency matrix, and we show that Viral Centrality outperforms other common centrality measures in both simulated and empirical WD networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Complex networks

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Optimizing Concrete Compressive Strength Prediction: A Comparative Study of Machine Learning Algorithms

Optimizing Concrete Compressive Strength Prediction: A Compa...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： H. P. Khandagale Mahesh S. Salunkhe Vaibhav K. Kamble Amrita A. Manjarekar P. P. Halkarnikar Suraj S. Redekar Department of Technology Shivaji University Kolhapur India Department of Computer Engineering D Y Patil Institute of Engineering Pune India Dept. of Computer Science and Engg. Ashokrao Mane Group of Institutions Kolhapur India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This study investigates the predictive capabilities of LBGM, CATB, GBR, ADAB, and XGB models for concrete compressive strength prediction. Through an evaluation of default hyperparameters and comprehensive metrics, including R2, MSE, RMSE, MAE, RMSLE, and MAPE, nuanced insights into each algorithm’s performance are obtained. ADAB emerges as a standout performer, displaying lower error rates across multiple metrics, suggesting AdaBoost’s suitability for concrete compressive strength prediction. The analysis of a self-prepared dataset reveals significant variations across different mixture combinations and curing times. This research not only establishes a benchmark for current practices but also provides avenues for future research, including hyperparameter tuning to optimize model performance further. The practical implications for concrete engineering are significant, guiding material composition decisions and contributing to the development of more durable and resilient structures. As the intersection of machine learning and concrete engineering progresses, this study lays a foundation for tailored approaches to address the specific challenges of predicting concrete compressive strength.

关键词： Measurement Machine learning algorithms Error analysis Machine learning Predictive models Curing Benchmark testing Prediction algorithms Concrete Tuning

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Ultra-Wideband Pattern Reconfigurable Antenna for Base station Applications

Ultra-Wideband Pattern Reconfigurable Antenna for Base stati...

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India Council International Subsections Conference (INDISCON), IEEE

作者： Shalini Bhickta Naveen Jaglan Amit Kumar Dept. of Electronics and Communication Engineering Jaypee University of Information Technology Waknaghat Solan India Dept. of Computer Science & Engineering and Information Technology Jaypee University of Information Technology Waknaghat Solan India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

The proposed work aims at 5G base station antenna communication applications. The Reconfigurable Antenna is built with an idea of parasitic loaded elements in antenna. The proposed design has a zig-zag S-shape structure with a driven Monopole antenna located along the central axis and a parasitic structure enfolding around the monopole. Reconfiguration is accomplished by adjusting the PIN diodes bias voltages. The 2.44-9.46 GHz frequency range i.e., the Ultra-Wide-Band frequency spectrum is used at which this reconfigurable antenna is designed to operate. This paper has three radiation pattern which are obtained by two PIN diodes. Beam-steering capability in the elevation plane is achieved i.e., θ (0°-180°). The outcome shows the design’s potential and its wide range concurrent applications for base station antennas.

关键词： Base stations PIN photodiodes 5G mobile communication Ultra wideband antennas Loaded antennas Antenna radiation patterns

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Synchronous and Asynchronous Implementation of Radix-2 Booth Multiplication Algorithm 3

Synchronous and Asynchronous Implementation of Radix-2 Booth...

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3rd International Conference on Electrical and Electronic engineering, ICEEE 2021

作者： Ul Sadad, Nahin Afrin, Afsana Mondal, Md. Nazrul Islam Rajshahi University of Engineering and Technology Dept. of Computer Science and Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9781665482813

Multiplication is one of the most common operations used in any program. Program working on massively large data always requires high computation power. In the age of big data, conventional general-purpose CPU based on Von Neumann architecture is no longer enough to satisfy high computation demand. Field Programmable Gate Array (FPGA) can perform hardware acceleration of any program. Since multiplier is the slowest component in any hardware accelerator, thus faster and re-configurable multiplier which can handle integers of any size must be implemented on FPGA. In this paper, we implemented both synchronous and asynchronous radix-2 booth multiplier using Verilog HDL on a Xilinx FPGA. We found that simulation time of asynchronous radix-2 booth multiplier is faster than synchronous radix-2 booth multiplier but synchronous radix-2 booth multiplier consumes fewer resources than asynchronous radix-2 booth multiplier. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Classification of Rail Track Crack using CNN with Pre-Trained VGG16 Model

Classification of Rail Track Crack using CNN with Pre-Traine...

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2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Bhat, Shreetha Karegowda, Asha Gowda Leena Rani, A. Vidya, S. Devika, G. Reva University School of Computer Science and Applications Karnataka Bengaluru India Siddaganga Institute of Technology Department of Mca Karnataka Tumkur India Government Engineering College Kr Pet. Dept of Computer Science and Engineering Karnataka India

ISBN: (纸本)9781665483162

One of the vital components of railway infrastructure is rail tracks. Maintenance of rail track has been a major challenge in most of the countries and one such challenge is the detection of cracks on the rail surface. To maintain good health of the tracks requires regular inspection and prompt action, failure to which, may lead to accidents and loss of lives. The railway department is introducing many innovative methods to make the inspection process efficient. In the past, various methods have been explored to detect defects on rail surfaces such as computer Vision-Based method, but full automation is far from achievement. Few of the advanced countries are making use of Deep Learning techniques to monitor and maintain the condition of rail tracks. In, this paper, amalgamation of Convolutional Neural Network (CNN) and transfer learning is applied for classifying defective (with cracks) and non-defective rail surfaces. © 2022 IEEE.

关键词： Convolutional neural networks

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Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art

arXiv

引用

arXiv 2023年

作者： Mahmud, Tanjim Ptaszynski, Michal Eronen, Juuso Masui, Fumito Text Information Processing Lab Kitami Institute of Technology Kitami Japan Dept. of Computer Science and Engineering Rangamati Science and Technology University Bangladesh

The struggle of social media platforms to moderate content in a timely manner, encourages users to abuse such platforms to spread vulgar or abusive language, which, when performed repeatedly becomes cyberbullying – a social problem taking place in virtual environments, yet with real-world consequences, such as depression, withdrawal, or even suicide attempts of its victims. Systems for the automatic detection and mitigation of cyberbullying have been developed but, unfortunately, the vast majority of them are for the English language, with only a handful available for low-resource languages. To estimate the present state of research and recognize the needs for further development, in this paper we present a comprehensive systematic survey of studies done so far for automatic cyberbullying detection in low-resource languages. We analyzed all studies on this topic that were available. We investigated more than seventy published studies on automatic detection of cyberbullying or related language in low-resource languages and dialects that were published between around 2017 and January 2023. There are 23 low-resource languages and dialects covered by this paper, including Bangla, Hindi, Dravidian languages and others. In the survey, we identify some of the research gaps of previous studies, which include the lack of reliable definitions of cyberbullying and its relevant subcategories, biases in the acquisition, and annotation of data. Based on recognizing those research gaps, we provide some suggestions for improving the general research conduct in cyberbullying detection, with a primary focus on low-resource languages. Based on those proposed suggestions, we collect and release a cyberbullying dataset in the Chittagonian dialect of Bangla and propose a number of initial ML solutions trained on that dataset. In addition, pre-trained transformer-based the BanglaBERT model was also attempted. We conclude with additional discussions on ethical issues regarding such studies

关键词： Machine learning

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APC System: A system for the Blind and Deaf Who Face Visual and dept. Perception Challenges

APC System: A system for the Blind and Deaf Who Face Visual ...

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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： Suraya Mubeen G.V. Ashritha Sanjeev Bandru Marri Sireesha Nuthanakanti Bhaskar Voruganti Naresh Kumar Dept. of Electronics and Communication Engineering CMR Technical Campus Hyderabad Telangana India Dept. of Computer Science and Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India Dept. of Information Technology Sreenidhi Institute Of Science And Technology Hyderabad Telangana India Dept. of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India

In this paper, APC System (Assistive Path Control System) is proposed to provide blind and deaf individuals with the assistance they need to navigate their environment by using the "third eye" technology. As part of the technology, a hand glove and a stick are used to identify obstacles using ultrasonic sensors and to provide haptic feedback through vibrations in order to detect obstacles. Wearers of the glove are alerted through a buzzer and provided with feedback on distance and direction of obstacles in their path, while those of the stick are given feedback on the change in elevation and dept. over time. The use of vibrations as a form of feedback is crucial to the functioning of deaf people. Testing of the technology has shown promising results, and future work will focus on optimizing accuracy and accessibility by using machine learning algorithms in order to improve the system. As a primary goal of the APC System is to enhance the quality of life for blind and deaf individuals by providing them with a tool that will help them navigate their environment in the most effective manner.

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The Role of AI in Optimizing Healthcare Appointment Scheduling

The Role of AI in Optimizing Healthcare Appointment Scheduli...

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Disruptive Technologies (ICDT), International Conference on

作者： Bhupendra Kumar MD. IQBAL Reeta Parmer Puneet Garg Sunita Rani Atul Agrawal Department of Computer Science & Engineering ITS Engineering College Greater Noida India Department of Computer Science & Engineering Quantum University Roorkee India dept. name of organization St. Andrews Institute of Technology and Management Gurugram Delhi NCR India Computer Science and Engineering (AI) KIET Group of Institutions Delhi NCR Ghaziabad India Golgoti University Greater Noida India Computer Science and Engineering ITS Engineering College (AKTU) Greater Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

AI is the most interesting area of healthcare, which is currently making its way into the medical field. Artificial Intelligence has become a game changer, in the healthcare industry bringing forth solutions to problems. A particular area of focus for AI advancements in years has been appointment scheduling with the aim of improving efficiency and accessibility for patients. To overcome these challenges AI technologies have been utilized to develop scheduling systems that utilize data-driven insights and machine learning algorithms. This paper is to investigate appointment scheduling systems in the healthcare sector. The healthcare sector is a major part of the economy. Through surveys, the appointment scheduling system can improve its efficiency. Artificial Intelligence proposes methods for advancing appointment scheduling, which helps control the scheduling window. It can reduce patient no-shows, manage long waitlists, take rapid action over medical urgency, and enhance the department's use of resources for outpatients. Further, the paper examines the problem of appointment management in the healthcare sector considering the various outcoming problems. This paper overlooks the different components of Artificial Intelligence that are applied to the appointment system. We also observe the challenges in the system and the open issues. The objective is to improve the appointment system in the healthcare sector and regulate it with the advancement of Artificial Intelligence.

关键词： Surveys Privacy Job shop scheduling Machine learning algorithms Natural languages Medical treatment Medical services Planning Teamwork Artificial intelligence

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Metasurface-Based Low-Dispersive Leaky-Wave Antenna

Metasurface-Based Low-Dispersive Leaky-Wave Antenna

引用

2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2023

作者： Mahmoud, Adham Ettorre, Mauro Garcia, Jorge Ruiz Quevedo-Teruel, Oscar Grbic, Anthony Univ Rennes Institut d'Electronique et de Telecommunications de Rennes Cnrs Ietr Umr 6164 Rennes35000 France Michigan State University Electrical and Computer Engineering East LansingMI48824 United States University of Michigan Dept. of Electrical Engineering & Computer Science Ann ArborMI48109 United States Kth Royal Institute of Technology Electromagnetic Engineering and Fusion Science Stockholm10044 Sweden

ISBN: (纸本)9781665442282

In this communication, we present a low-dispersive leaky-wave antenna consisting of a parallel-plate-waveguide structure loaded by a modulated metasurface and a dense medium. The low-dispersive leaky-wave mode is engineered by a proper design of the metasurface and the chosen permittivity of the dense medium. The dispersion analysis is based on the transverse resonance technique and validated through full-wave simulations. A dense medium with a prism shape and a relative permittivity of 12 is used to radiate a beam pointing at 28.3° in elevation in the Ks-band (27-33 GHz) with only 0.5° variation over a 20% relative bandwidth. The leaky-wave mode supported by the structure is efficiently launched by using a compact quasi-optical system. The full antenna dimensions are 225×175×57 mm3 with an expected average gain of 27.5 dBi and total efficiency greater than 55%. © 2023 IEEE.

关键词： Permittivity

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