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arXiv 2024年

作者： Poe, Kyle Mallada, Enrique Vidal, René Applied Mathematics and Computational Science Group University of Pennsylvania PA19104 United States Department of Electrical and Computer Engineering Johns Hopkins University MD21218 United States Departments of Electrical and Systems Engineering Radiology Computer and Information Science Statistics and Data Science University of Pennsylvania PA19104 United States

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparse-input setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control. © 2024, CC BY.

关键词： Linear systems

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Forecasting of Fires Hotspot in Tropical Region Using LSTM Algorithm Based on Satellite Data

Forecasting of Fires Hotspot in Tropical Region Using LSTM A...

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2022 IEEE Region 10 Symposium, TENSYMP 2022

作者： Kadir, Evizal Abdul Kung, Hsiang Tsung Rosa, Sri Listia Sabot, Andrew Othman, Mahmod Ting, Mark Universitas Islam Riau Department of Informatics Engineering Pekanbaru28284 Indonesia Harvard University Department of Computer Science CambridgeMA02134 United States Universiti Teknologi Petronas Department of Applied Mathematics Perak86400 Malaysia

ISBN: (数字)9781665466585

ISBN: (纸本)9781665466585

Raising of temperature due to global warming impacted to the number of fires hotspot globally, in tropical region some of the places a high risk such as wildfire. Fire in Indonesia is one of the big disasters because of forestry region and peat land that highly risk to wildfire especially in dry season. This research aims to do a forecasting number of fires hotspot in future time based on training data collected in previous years. Long Short-Term Memory (LSTM) algorithm applied in this forecasting, the advantages of LSTM model to forecast a timeseries data make the high prediction accuracy. Fires hotspot data was collected since year 2010 to 2021 with total number of datasets more than 700,000 hotspot and forecast for the future year 2022. Collected datasets analyzed use LSTM model divided into two classification which are training and test data with 70% and 30% respectively. Results shows a similar pattern of forecasting fires hotspot in 2022 compared into previous last 2 years which 2021 and 2020. Furthermore, to proof proposed algorithm is working fine then a forecast number of fire hotspot for year 2021 have been done which compared actual and forecasting data and percentage of error 4.56%. LSTM Algorithm is one of the models suitable to use in data forecasting in high volume time series data. © 2022 IEEE.

关键词： Fires

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Joint Sensing and Communication with Graphene FETs Targeting Terahertz Band

Joint Sensing and Communication with Graphene FETs Targeting...

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International Symposium on Communication Systems, Networks and Digital Signal Processing, CNSDSP

作者： Monica La Mura Patrizia Lamberti Alessandro Stuart Savoia Roberto Alesii Dajana Cassioli Vincenzo Tucci Dept. of Industrial Electronic and Mechanical Engineering Roma Tre University Rome Italy Dept. of Information and Electrical Engineering and Applied Mathematics University of Salerno Fisciano Italy Dept. of Information Engineering Computer Science and Mathematics University of L'Aquila L'Aquila Italy

ISBN: (数字)9798350348743

ISBN: (纸本)9798350348750

The upcoming 6G paradigm for wireless communication networks promises enhanced data rates, reduced latency, and pervasive connectivity. In this scenario, intelligent and adaptive communication protocols and innovative smart sensing devices can be efficiently integrated to offer innovative systems for different applications. In the future, graphene technology might greatly contribute to the evolving landscape of sensing combined with high-frequency communication. This paper explores possible architectures for radiofrequency (RF) electronics for joint sensing and modulation based on graphene field-effect transistors (GFETs). Current challenges and limitations of graphene technology are also discussed.

关键词： Radio frequency Wireless communication 6G mobile communication Technological innovation Protocols Graphene Sensor phenomena and characterization

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Predicting Power Output of Solar Photovoltaic Panels Using Machine Learning Techniques

Predicting Power Output of Solar Photovoltaic Panels Using M...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Karimulla Syed Mohammad Ameer Hamza Yousuf Murali Krishna Boddu Sainadh Singh K Mahaboob Shareef Syed Afaq Ahmad Engineering Department University of Technology and Applied science-Shinas Shinas Sultanate of Oman Department of EEE BVRIT Narsapur India Vignan's Lara ITS India Department of Mathematics and Computer Science Modern College of Bussiness & Science Muscat Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The prediction of solar power generation is essential for effective integration of renewable energy into power grids, aiding in grid stability, energy planning, and efficient resource allocation. Due to the inherent variability of solar energy caused by factors like weather patterns, time of day, and seasonal changes, machine learning (ML) has appeared as a powerful tool to improve forecasting accuracy. Solar panels with various tilt angle combinations are set up to collect experimental data. This paper uses supervised regression learner in machine learning application for solar power prediction. In this paper, linear regression and step wise linear regression models are giving fruitful results out of 27 models that are analyzed. A detailed study of model selection is provided, alongside an examination of evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Means Square Error (RMSE), and regression coefficient R 2 scores. This study shows the effectiveness of Machine Learning (ML) in enhancing short, and medium-term solar power forecasting, supporting more efficient energy management and promoting the scalability of renewable energy systems. For the linear regression algorithm, we obtained regression coefficient (R 2 ) of 1, mean absolute percentage error (MAPE) of 0.7% and for the stepwise linear regression algorithm, we obtained a regression coefficient (R 2 ) of 1, mean absolute percentage error (MAPE) 0.45%.

关键词： Training Renewable energy sources Analytical models Machine learning algorithms Linear regression Machine learning Prediction algorithms Solar panels Forecasting Meteorology

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PARTICLE MULTI-AXIS TRANSFORMER FOR JET TAGGING

arXiv

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arXiv 2024年

作者： Usman, Muhammad Shahid, M. Husnain Ejaz, Maheen Hani, Ummay Fatima, Nayab Khan, Abdul Rehman Khan, Asifullah Mirza, Nasir Majid Pattern Recognition Lab Department of Computer & Information Science Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Center for Mathematical Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Department of Physics & Applied Mathematics Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan

Jet tagging is an essential categorization problem in high energy physics. In recent times, Deep Learning has not only risen to the challenge of jet tagging but also significantly improved its performance. In this article, we proposed an idea of a new architecture, Particle Multi-Axis transformer (ParMAT) which is a modified version of Particle transformer (ParT). ParMAT contains local and global spatial interactions within a single unit which improves its ability to handle various input lengths. We trained our model on JETCLASS, a publicly available large dataset that contains 100M jets of 10 different classes of particles. By integrating a parallel attention mechanism and pairwise interactions of particles in the attention mechanism, ParMAT achieves robustness and higher accuracy over the ParT and ParticleNet. The scalability of the model to huge datasets and its ability to automatically extract essential features demonstrate its potential for enhancing jet tagging. © 2024, CC BY.

关键词： Deep learning

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Prediction of diabetes progress based on machine learning approach

Prediction of diabetes progress based on machine learning ap...

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作者： Yiteng Han Qixuan Li Jinghui Lou Jingrui Zhang School of Engineering and Applied Sciences University at buffalo School of Computer Science and Technology Jilin University School of Business New York University Shanghai School of Mathematics and Information Science Nanjing Normal University of Special Education

Uropathy is a serious chronic disease whose prevalence is increasing at an alarming *** detection and prediction of diabetes in women is important because of the increased risk of diabetes-related complications during *** study introduces machine learning models to assess the likelihood of diabetes in *** importance of studying characteristics and improving prediction accuracy to understand the nuances of ***,for data preprocessing,experiments are conducted to solve the problem of missing values and outliers by replacing the zero values of certain features with the median values of the corresponding *** step reduces the impact of less reliable data on model *** recognition models,Gaussian Naive Bayes(GNB),Support Vector Machine(SVM),and Random Forest(RF) are *** analysis is performed along with a careful exploration of the hyperparameter *** for Receiver Operating Characteristic-Area Under the Curve(ROC-AUC) are used to compare various *** features affect the classification to different *** experimental findings indicate that the modified random forest model demonstrates superior prediction accuracy and *** findings can assist physicians in predicting a patient's risk of developing diabetes earlier.

关键词： machine learning diabetes gaussian naive bayes support vector machine random forest

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Isoperimetric Inequalities Made Simpler

arXiv

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arXiv 2022年

作者： Eldan, Ronen Kindler, Guy Lifshitz, Noam Minzer, Dor Department of Computer Science and Applied Mathematics Weizmann Institute School of Computer Science and Engineering Hebrew University School of Mathematics Hebrew University Department of Mathematics Massachusetts Institute of Technology United States

We give an alternative, simple method to prove isoperimetric inequalities over the hypercube. In particular, we show: 1. An elementary proof of classical isoperimetric inequalities of Talagrand, as well as a stronger isoperimetric result conjectured by Talagrand and recently proved by Eldan and Gross. 2. A strengthening of the Friedgut junta theorem, asserting that if the p-moment of the sensitivity of a function is constant for some 1/2 + Ε ≼ p ≼ 1, then the function is close to a junta. In this language, Friedgut's theorem is the special case that p = 1. Copyright © 2022, The Authors. All rights reserved.

关键词： Choquet integral

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Technology for Performing Emergency Dismantling Works at an Industrial Facility Destroyed Due to Military Actions 10th

Technology for Performing Emergency Dismantling Works at an ...

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10th International Conference on Civil engineering, ICCE2023

作者： Naumov, V. Bilokon, A. Sokolov, I. Plakhtii, Ye. Nesevrya, P. Department of Organization and Management in Construction Prydniprovska State Academy of Civil Engineering and Architecture 24-а Architect Oleg Petrov St. Dnipro Ukraine Department of Construction Technologies Prydniprovska State Academy of Civil Engineering and Architecture 24-а Architect Oleg Petrov St. Dnipro Ukraine Department of Computer Science Information Technologies and Applied Mathematics Prydniprovska State Academy of Civil Engineering and Architecture 24-а Architect Oleg Petrov St. Dnipro Ukraine

ISBN: (纸本)9789819743544

This investigation develops an innovative dismantling methodology for industrial facilities impacted by a 2022 missile strike, aiming to restore operations with minimal interruption. The purpose of the research is to establish a rapid and secure dismantling process that integrates seamlessly with ongoing industrial activities, ensuring safety and efficiency. It focuses on the damaged workshop facilities, analyzing the affected structures and utilities to inform emergency dismantling and recovery efforts. Advanced technologies, including specialized excavator attachments and carts, are introduced for precise dismantling, maintaining the integrity of adjacent structures. Our findings illustrate the efficacy of integrating advanced dismantling technologies within active industrial settings, significantly enhancing operational safety and efficiency. The successful application of these methodologies not only aids in the rapid recovery of damaged facilities but also sets a new benchmark for emergency industrial operations. Object of Research: The primary focus is on the damaged industrial workshop facilities, specifically examining the structures, utilities, and operational frameworks affected by the missile strike. This includes the physical site, the technological layout, and the existing industrial processes within the context of emergency dismantling and restoration efforts. © The Author(s) 2024.

关键词： Restoration

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Human-computer Interaction in IoT System for Water Tank Monitoring and Controlling

Human-Computer Interaction in IoT System for Water Tank Moni...

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International computer sciences and Information Technologies (CSIT)

作者： Viktor Zhukovskyy Damon Printz Nataliia Zhukovska Department of Computer Science and Applied Mathematics The National University of Water and Environmental Engineering Rivne Ukraine Meadowlark Solutions LLC Albin Wyoming United States

Building scalable infrastructure capable of processing large amounts of data from remote IoT devices is a significant challenge in industry today. This article examines one solution to this problem through a case study of Meadowlark Solution’s extensive IoT network. Meadowlark Solutions designs, produces and deploys the Tank Toad, a remote water management device for agriculture. Livestock producers need to check water storage levels and operate pumps to effectively care for livestock. The Tank Toad automates the task of data collection and remote control, reducing workload and drive time. Information flows in both directions, giving the customer real-time authority over the water supply. The system as a whole, including remote devices, endpoints, data storage, dashboard elements, information distribution and customer interaction, is described below.

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Artificial Intelligence Methods to Support People Management in Organizations

Artificial Intelligence Methods to Support People Management...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Sonawane, Prashant Ashok Mishra, Krishna Nand Raman, P. Kumar, Kishor Jawarkar, Nilesh G. Kandavalli, Sunanda Ratna. Gondwana University Department of Commerce Gadchiroli442605 India Radha Govind University Department of Computer Science Jharkhand Ramgarh829122 India College Poonamallee Department of Management Studies Panimalar Engineering Tamil Nadu Chennai600123 India Delhi Technological University Department of Applied Mathematics Delhi India JSPM' Rajarshi Shahu College of Engineering Department of Mechanical Engineering Pune India Sri Padmavati Mahila Visva Vidyalayam Department of Electronics & Communication Engineering Andhra Pradesh Tirupati517502 India

ISBN: (纸本)9798350309140

The research paper showcases in-depth research on AI technology and its direct impact on organizations and people. Along with this research paper also discussed how AI technology can support organizations in streamlining their business processes, improving their decision-making skills, enhancing staff engagement, and raising the rate of production with the help of machine learning and natural language processing. Besides this, it is examined in this paper that AI technology can also be applied to automate the entire screening method, shorten the timetable, and manage the available resources needed for new staff recruitment. © 2024 IEEE.

关键词： Adversarial machine learning

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