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23rd Wind and Solar Integration Workshop, WIW 2024

作者： Jiang, Zimin Zhang, Peng Liu, Xiaodong Department of Electrical and Computer Engineering Stony Brook University New York United States System Planning and Transmission Reliability Eversource Energy Boston United States

ISBN: (纸本)9781837242689

Faults in power electronic devices dominated offshore power grids may cause severe problems, e.g. blocking of inverters and instability issues, in consideration of inverters' limited overloading capability. An orchestrated active fault management (OAFM) method is devised to manage offshore wind HVDC grids' performance under grid faults. OAFM aims to 1) avoid blocking of HVDC converters by minimizing fault current contributions from offshore wind farms, 2) minimize the power fluctuation of offshore wind farm inverters and 3) provide a fast and orchestrated frequency response to reduce frequency deviations and suppress possible frequency instability issues. OAFM is formulated as a multi-objective optimization problem to incorporate customized fault management objectives and constraints in a flexible manner. Experimental studies are performed on an extended CIGRE HVDC grid system to validate the effectiveness and efficacy of OAFM. Testing results show that OAFM works effectively in real-time to reduce fault current contributions from offshore wind farms and enhance system performance. © Energynautics GmbH.

关键词： Offshore wind farms

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Sports Events Recognition Using Multi Features and Deep Belief Network

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computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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IoT-Enabled Smart Manhole Management system for Real-time Status, Water Level, and Gas Detection

IoT-Enabled Smart Manhole Management System for Real-time St...

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2024 International Conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Imran, Md Asif Masfequier Rahman Swapno, S.M. Chhabra, Gunjan Kaushik, Keshav Anzum Mahi, Md. Arif Islam, Md Babul Haque, Rezaul Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Graphic Era Hill University Department of Cse Uttarakhand Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Modeling Electronic and System Engineering Unical Dept. of Computer Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Modeling Electronic and System Engineering Unical Department of Computer Rende Italy

ISBN: (纸本)9798350375237

Regular monitoring of the maintenance manhole system is imperative to uphold the city's health and cleanliness standards. Our study offers an intelligent manhole that monitors the manhole's condition, water level, and gases. We tested the status, gas, and water levels of our revolutionary, innovative manhole in several places. Our system's Blink app position tracking of our smart manhole is critical. This capability improves monitoring efficiency and speeds up issue resolution. Our work revolutionizes municipal utility maintenance and safety by optimizing manhole performance. Integrating a Node MCU, GPS module, gas sensor, ultrasonic sensor, and Android Blynk software creates a complete real-time monitoring and data-collecting system. Combining these technologies, our Manhole Monitoring system improves safety and streamlines maintenance, making municipal infrastructure more robust and efficient. This paper highlights the promising features of IoT and shows how it may be used to improve the smart manhole. In the future, this creative method may result in a more durable and better intelligent city. © 2024 IEEE.

关键词： Maintenance

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Smart Material Selection: Leveraging Machine Learning for Sustainable and Cost-Effective Building Design 2

Smart Material Selection: Leveraging Machine Learning for Su...

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2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Hashim, Wassan Adnan Khoshaba, Lina Sami Kareem, Shahab Wahhab Khoshaba, Farah Sami Al-Qalam University College Medical Device Engineering Department Kirkuk Iraq Lebanese French University College of Engineering and Computer Science Architecture Engineering Department Erbil Iraq Polytechnic University Technical Engineering College Information System Engineering Department Erbil Iraq

ISBN: (纸本)9798331539542

In modern-day architecture, the choice of building material plays an important role in sustainability, cost-value engineering, and design integrity. Moreover, the traditional methods for selecting materials tended to make use of manual approaches, which at times led to drawbacks by not focusing on the basics of integration and proper data management. The paper is a very interesting approach of how a complex material selection process can be optimized with simple machine learning algorithms. In that sense, the current research developed a framework from environmental sustainability to economic cost, durability, and aesthetic appeal, with an intention for its use in guiding the design final decision process. The proposed machine learning model suggests ideal prediction for material combination from different material datasets concerning the properties and performance metrics applicable to architecture. The research showed that the approach had a hand in getting thoughtful, analytic-based decisions while assuring, at the same time, that an architect would be able to satisfy the project goals and budget constraints with environmentally safe execution. It elaborates on the cases, or rather case studies, that have applied the model and gives illustrations of great improvements not only in efficiency in design but also in the sustainability of the project coupled with better strategies for saving costs. The current research is opening an entirely new field of innovation in architecture that emphasizes the role of technology for the future form of building practice. © 2024 IEEE.

关键词： Budget control

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Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

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computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed Masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

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A Big Data, Bigger Impact: A Comprehensive Review of Machine Learning Advancements 4

A Big Data, Bigger Impact: A Comprehensive Review of Machine...

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4th International Conference on Electrical engineering and computer Science, ICECOS 2024

作者： Dlshad Hamad Ameen, Didar Wahhab Kareem, Shahab Baez Hasan, Sarhad Soran University Faculty of Science Computer Science Department Soran Kurdistan Iraq Engineering Erbil Polytechnic University Erbil Department of Information System Kurdistan Iraq

ISBN: (纸本)9798350368253

The exponential growth of data driven by the internet has necessitated effective extraction of insights, with big data and machine learning standing as pivotal tools. This paper aims to provide insights into the evolving landscape of machine learning and big data analytics through a comprehensive literature review. It seeks to underscore the key advancements and intersections between these domains and their transformative applications across industries. Specifically, the study will delve into the foundational theories that underpin machine learning algorithms, data collection and preprocessing techniques involved in big data analysis, and the prominent challenges associated with extracting insights from large and complex datasets. It will further explore major studies that exemplify the integration of machine learning methodologies and big data technologies for addressing real-world problems. The study will also examine visualization methods that facilitate comprehending insights gleaned from big data. Finally, it will discuss future research directions and highlight best practices for leveraging machine learning and big data analytics in decision-making processes. © 2024 IEEE.

关键词： Adversarial machine learning

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A Spatial Data Pipeline for Streaming Smart City Data 22

A Spatial Data Pipeline for Streaming Smart City Data

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22nd IEEE/ACIS International Conference on Software engineering Research, Management and Applications, SERA 2024

作者： Carthen, Chase Zaremehrjardi, Araam Le, Vinh Cardillo, Carlos Strachan, Scotty Tavakkoli, Alireza Dascalu, Sergiu M. Harris, Frederick C. University of Nevada Computer Science and Engineering Reno United States Nevada Center for Applied Research University of Nevada Reno United States Nevada System of Higher Education Reno System Computing Services RenoNV United States

ISBN: (纸本)9798350391343

Point cloud data in the form of LiDAR is often utilized for its spatial qualities, especially in smart city projects for tasks involving vehicles and pedestrians. However, the process in which LiDAR data is acquired can be cumbersome to setup and automate. In this paper, we introduce a streaming and an on-demand pipeline for capturing LiDAR data from Velodyne Ultra Pucks placed along northern Nevada intersections known as the Living Lab as part of a smart city project for the city of Reno. The data coming from these intersections consist of the following formats: ROS 2 bag file, PCD, LAZ, Google Draco, and PCAP. A streaming point cloud service with PCD, LAZ, and Draco was implemented to stream any of these formats, as well as to allow the user to capture the current monitored point cloud. Additionally, two on-demand web services were implemented for both the PCAP and ROS 2 bag file to enable a user to start and stop the acquisition of LiDAR data in these formats. Through our analysis, it was discovered that Draco provided the best processing time and had a wider range of options that affected the quality of the point cloud. To evaluate this pipeline, the features of existing software were compared and a discussion was provided with an analysis of the point cloud formats. © 2024 IEEE.

关键词： Data streams

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RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models 32

RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion...

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32nd International Joint Conference on Artificial Intelligence, IJCAI 2023

作者： Zhou, Xingchen He, Ying Richard Yu, F. Li, Jianqiang Li, You College of Computer Science and Software Engineering Shenzhen University China China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

ISBN: (纸本)9781956792034

The emergence of Neural Radiance Fields (NeRF) has promoted the development of synthesized high-fidelity views of the intricate real world. However, it is still a very demanding task to repaint the content in NeRF. In this paper, we propose a novel framework that can take RGB images as input and alter the 3D content in neural scenes. Our work leverages existing diffusion models to guide changes in the designated 3D content. Specifically, we semantically select the target object and a pre-trained diffusion model will guide the NeRF model to generate new 3D objects, which can improve the editability, diversity, and application range of NeRF. Experiment results show that our algorithm is effective for editing 3D objects in NeRF under different text prompts, including editing appearance, shape, and more. We validate our method on both real-world datasets and synthetic-world datasets for these editing tasks. Please visit https://*** for a better view of our results. © 2023 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： Semantics

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Cramer-Rao Bound Investigation of the Double-Nested Arc Array Virtual Extension and Butterfly Antenna Configuration 3rd

Cramer-Rao Bound Investigation of the Double-Nested Arc Arra...

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3rd World Conference on Internet of Things: Applications and Future, ITAF 2023

作者： El-Rahman, Tarek Abd Salama, Amgad A. Saad, Walid M. El-Shafey, Mohamed H. Faculty of Engineering Computer and System Engineering Department Ain Shams University Cairo11517 Egypt The Egyptian Technical Research and Development Centre Cairo11618 Egypt

ISBN: (纸本)9789819947638

This paper exploits a sparse array representation, a novel double-nested arc array to generate a multiple virtual nested arc array that forms the virtual non-uniform circular array which has been presented and investigated. The nested arc array has been developed using higher-order statistics, namely using the fourth-order cumulants (FOC), to increase the array aperture. The extended array contains a hybrid of physical and virtual sensors which is distributed in a butterfly shape. The enhanced degrees of freedom (DOFs) in the resultant virtual array make it appropriate for over-and under-determined direction-of-arrival (DOA) estimation. The Cramer-Rao bound (CRB), the number of nested arc arrays, the structure of the double-nested arc related to each other, and the overlap between sensors for such an array are developed and investigated for various arrays. The proposed array performance is superior to that of the uniform one. Further, the proposed array is appropriate for two-dimensional (2D) DOA estimation problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Direction of arrival

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Study on Improving Ship Punctuality to Port Based on Response Surface Methodology

Study on Improving Ship Punctuality to Port Based on Respons...

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2023 International Conference on computer, Internet of Things and Smart City, CIOTSC 2023

作者： Wei, Jinlong Li, Shunmin Zhao, Junhua Luo, Ruihan Wang, Zhili Feng, Xiaoning Harbin Engineering University College of Computer Science and Technology Harbin China China State Shipbuilding Corporation Limited System Engineering Research Institute Beijing China

ISBN: (纸本)9798350306569

The punctuality rate of vessels arriving at ports is a pivotal operational indicator in the maritime industry, exerting a direct influence on cargo efficiency and economic benefits. Therefore, the exploration of effective approaches to enhance ship punctuality to ports holds significant practical implications and economic value for the maritime industry. In this study, the response surface methodology is employed to establish a predictive model for ship punctuality to ports. Firstly, a substantial amount of simulated shipping data was collected and subjected to exploratory data analysis to identify the primary factors influencing ship punctuality to ports. Subsequently, leveraging the modeling technique based on response surface methodology, a relationship model between ship punctuality to ports and different factor conditions was established. By subjecting the response surface equation to response surface analysis and residual analysis, the accuracy and reliability of the predictive model were validated. Concurrently, a comprehensive analysis of the significance of various influencing factors on different levels of ship punctuality to ports was conducted. Finally, through residual analysis and validation of predictive outcomes, the enhancement of ship punctuality to ports was evaluated. © 2023 IEEE.

关键词： Marine industry

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