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Incorporating Lasso Regression to Physics-Informed Neural Network for Inverse PDE Problem

computer Modeling in engineering & Sciences 2024年第10期141卷 385-399页

作者： Meng Ma Liu Fu Xu Guo Zhi Zhai National Key Lab of Aerospace Power System and Plasma Technology Xi’an Jiaotong UniversityXi’an710049China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an710049China Department of Electrical and Computer Engineering University of Massachusetts LowellLowellMA 01854USA

Partial Differential Equation(PDE)is among the most fundamental tools employed to model dynamic *** PDE modeling methods are typically derived from established knowledge and known phenomena,which are time-consuming and ***,discovering governing PDEs from collected actual data via Physics Informed Neural Networks(PINNs)provides a more efficient way to analyze fresh dynamic systems and establish *** study proposes Sequentially Threshold Least Squares-Lasso(STLasso),a module constructed by incorporating Lasso regression into the Sequentially Threshold Least Squares(STLS)algorithm,which can complete sparse regression of PDE coefficients with the constraints of l0 *** further introduces PINN-STLasso,a physics informed neural network combined with Lasso sparse regression,able to find underlying PDEs from data with reduced data requirements and better *** addition,this research conducts experiments on canonical inverse PDE problems and compares the results to several recent *** results demonstrated that the proposed PINN-STLasso outperforms other methods,achieving lower error rates even with less data.

关键词： Physics-informed neural network inverse partial differential equation Lasso regression scientific machine learning

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Personalized Collaborative Search Engine for Decision Making

Personalized Collaborative Search Engine for Decision Making

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Murshleen, Mohd. Prabhu, A. Jain, Garima Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Computer Science & Business System Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

Web search engines are used for retrieving information but still there exists ambiguity in their retrieval process. It is so because traditional search engines laid emphasis on matching of keywords rather than maintaining semantic relations among terms lack of ability to produce precise results, presence of ambiguous keywords and verifying needs of users are few hindrances that are acting as scenario for web search engines. The paper focuses on improving search quality of users by collecting user experiences through creating hierarchical user profile it leads to collaborative search engine that helps in reaching at particular decision then the collected user's data are uploaded to recommendation server which are than displayed a search engines like Google/Yahoo. © 2024 IEEE.

关键词： Search engines

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Controller Design And Modelling of UAV Quadcopter 4

Controller Design And Modelling of UAV Quadcopter

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4th International Multidisciplinary Information Technology and engineering Conference, IMITEC 2024

作者： Maijama'a, Ladan Jiya, Jibril Danladi Anene, Ejike C. Miya, Hassan Sabo School of Engineering Technology Federal Polytechnic Department of Computer Engineering Bauchi Nigeria Abubakar Tafawa Balewa University Department of Electrical and Electronics Engineering Bauchi Nigeria Abubakar Tafawa Balewa University Department of Mechatronics and System Engineering Bauchi Nigeria

ISBN: (纸本)9798350387988

The dynamics of a quadcopter unmanned aerial vehicle (UAV) and proportional integral derivative (PID) control based on disturbance rejection are mathematically modelled in this work. The model presented is a nonlinear in nature and modelled with respect to the two fundamental frames of reference and physical mathematical laws. The objective is to create the most accurate replica of the vehicle. The created model is utilized to develop a PID controller method that will precisely and steadily operate the quadrotor system. The development of a controller is to obtain stability in the quadcopter system. First, the nonlinear and couple model quadcopter were established, and control described the principles of the ability of decoupling rejection control then the model was decoupled. The system contains four input forces, which are basically the thrust provided by each propeller coupled to each rotor with a fixed angle. Pitch angle must change in order to maintain forward (backward) motion, which is accomplished by simultaneously increasing (decreasing) front (rear) rotor speed and decreasing (increasing) rear (front) rotor speed. The same method is used to change the roll angle for left and right motion. The motors in the front and rear spin counter-clockwise, while the motors in other motor positions rotate clockwise. This causes the yaw command to be generated by either raising or reducing the speed of the counter-clockwise motors while reducing or increasing the speed of the clockwise motors. The achieved goal here is the derivation of a model that properly reflects the physical properties enabling realistic simulations and is simple enough to be applied to controller design in the future. The system and controller's mathematical models were converted to matching Simulink models in order to make system simulations and investigations easier. The results show the accuracy and significant disturbance rejection of the nonlinear model. © 2024 IEEE.

关键词： Three term control systems

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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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Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

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computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif Al Mudawi Mohammed Alonazi Samia Allaoua Chelloug Jeongmin Park Department of Creative Technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

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Attack-detection and multi-clock source cooperation-based accurate time synchronization for PLC-AIoT in smart parks

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Digital Communications and Networks 2024年第6期10卷 1732-1740页

作者： Zhigang Du Sunxuan Zhang Zijia Yao Zhenyu Zhou Muhammad Tariq State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Electrical Engineering Department National University of Computer and Emerging SciencesIslamabad 44000Pakistan

Power Line Communications-Artificial Intelligence of Things(PLC-AIo T)combines the low cost and high coverage of PLC with the learning ability of Artificial Intelligence(AI)to provide data collection and transmission capabilities for PLC-AIo T devices in smart *** the development of smart parks,their emerging services require secure and accurate time synchronization of PLC-AIo T ***,the impact of attackers on the accuracy of time synchronization cannot be *** solve the aforementioned problems,we propose a tampering attack-aware Deep Q-Network(DQN)-based time synchronization ***,we construct an abnormal clock source detection ***,the abnormal clock source is detected and excluded by comparing the time synchronization information between the device and the ***,the proposed algorithm realizes the joint guarantee of high accuracy and low delay for PLC-AIo T in smart parks by intelligently selecting the multi-clock source cooperation strategy and timing *** results show that the proposed algorithm has better time synchronization delay and accuracy performance.

关键词： Smart park Power line communications Artificial intelligence of things Tampering attack awareness Abnormal clock source detection Multi-clock source cooperation

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Arabic Abstractive Summarization Using the Multilingual T5 Model 6

Arabic Abstractive Summarization Using the Multilingual T5 M...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Karim, Abdelrhman A. Usama, Mohammed Ibrahim, Manar A. Hatem, Youssef Wael, Waleed Mazrua, Ahmad T. El-Monayer, Ghada K. Elbanhawy, Magy Foad, Khaled Moawad, Ibrahim F. Artificial Intelligence Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Artificial Intelligence Science Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer and System Department Faculty of Engineering Portsaid University Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9798350373875

Natural language processing (NLP) is a branch of artificial intelligence (Al) that enables computers to comprehend, generate, and manipulate human language. Natural language processing can interrogate the data with natural language text or voice. Abstractive Text Summarization is based on the Natural Language Processing technique that tries to provide new and more concise textual summaries for huge texts. Artificial intelligence and deep learning techniques are used in abstractive summarization to examine the text's essential information and create a new summary that conveys the content more concisely and accurately. This type of summary differs from normal extractive summarizing in that it can generate new summaries beyond simply extracting key lines from the source text. This study presents an abstractive Arabic summarization based on the Multilingual TS (MTS) model AASMTS to address these concerns. This technique is based on deep neural networks and models like transformers, which have changed the ability to summarize text. This research has explored the various types of summarizations and highlighted the significance of two prominent techniques: abstractive and extractive summarization. This research describes a complete process for developing an Arabic abstractive summarization model using the MTS architecture. Experiments on different datasets show that this model achieves state-of-the-art results across MTS. © 2024 IEEE.

关键词： Deep neural networks

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Operational Reliability Modeling and Assessment of Battery Energy Storage Based on Lithium-ion Battery Lifetime Degradation

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Journal of Modern Power systems and Clean Energy 2022年第6期10卷 1738-1749页

作者： Lin Cheng Yuxiang Wan Yanglin Zhou David Wenzhong Gao State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Electrical and Computer Engineering University of DenverDenverUSA IEEE

Battery energy storage(BES)systems can effectively meet the diversified needs of power system dispatching and assist in renewable energy *** reliability of energy storage is essential to ensure the operational safety of the power ***,BES systems are composed of battery *** suggests that BES performance depends not only on the configuration but also on the operating state over different lifetime *** lack of safety and reliability is the main bottleneck preventing widespread applications of BES ***,a reliability assessment algorithm and a weak-link analytical method for BES systems are proposed while considering battery lifetime ***,a novel lithium-ion battery model is proposed to identify the degradation rate of solid electrolyte interphase film formation and capacity *** impacts of different operating conditions are considered in stress factor ***,a reliability assessment algorithm for a BES system is introduced based on a universal generating *** innovative weak-link analytical method based on the reliability importance index is proposed that combines the evaluation results of state-oriented and state-change-oriented indexes through an entropy weight *** model,algorithm,indexes,and the usefulness are demonstrated in case studies based on aging test data and actual bus operating *** results demonstrate the effects of the battery status and working conditions on BES ***-link analysis is also used to assist BES systems in avoiding short-board batteries to achieve long lifetimes and efficient operation.

关键词： Battery energy storage lifetime degradation operational reliability capacity plummeting weak-link analysis

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

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computers, Materials & Continua 2025年第6期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoNSW 2007Australia School of Data Science and Engineering Indian Institute of Science Education and ResearchBhopal462066India Research Institute of Marine Systems Engineering Seoul National UniversitySeoul08826Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyDelhi NCR CampusGhaziabad201204India Battery System Modelling Department CLARIOS VARTA Hannover GmbHHanover30419Germany Science and Innovation Group Bureau of MeteorologyBrisbane4000Australia

Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric *** study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 *** indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s *** computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction *** GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational *** contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery *** of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep *** findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained *** work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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SN computer Science 2024年第5期5卷 564页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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