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Multi-Beam Beamforming-Based ml algorithm to Optimize the Routing of Drone Swarms

DRONES 2024年第2期8卷 57-57页

作者： Myers, Rodman J. Perera, Sirani M. Mclewee, Grace Huang, David Song, Houbing Florida Southern Coll Dept Comp Sci Lakeland FL 33801 USA Embry Riddle Aeronaut Univ Dept Math Daytona Beach FL 32114 USA Coastal Carolina Univ Dept Comp Sci Conway SC 29528 USA Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA Univ Maryland Baltimore Cty Dept Informat Syst Baltimore MD 21250 USA

The advancement of wireless networking has significantly enhanced beamforming capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a simple and efficient classical algorithm to route a collection of AUAS or drone swarms extending our previous work on AUAS. The algorithm is based on the sparse factorization of frequency Vandermonde matrices that correspond to each drone, and its entries are determined through spatiotemporal data of drones in the AUAS. The algorithm relies on multibeam beamforming, making it suitable for large-scale AUAS networking in wireless communications. We show a reduction in the arithmetic and time complexities of the algorithm through theoretical and numerical results. Finally, we also present an ml-based AUAS routing algorithm using the classical AUAS algorithm and feed-forward neural networks. We compare the beamformed signals of the ml-based AUAS routing algorithm with the ground truth signals to minimize the error between them. The numerical error results show that the ml-based AUAS routing algorithm enhances the accuracy of the routing. This error, along with the numerical and theoretical results for over 100 drones, provides the basis for the scalability of the proposed ml-based AUAS algorithms for large-scale deployments.

关键词： drone swarms UAS MIMO communication complexity and performance of algorithms wireless communications multi-beam beamforming ml algorithm ANN

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Dual port ring cylindrical dielectric resonator antenna optimization using ml algorithm

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WAVES IN RANDOM AND COMPLEX MEDIA 2022年

作者： Mishra, Shivam Maurya, Shubahm Das, Yashbardhan Kumar, Vinay Ranjan, Pinku Gupta, Harshit Pandey, Ashish Sharma, Anand MNNIT Allahabad Dept Elect & Commun Engn Prayagraj India Indian Inst Informat Technol & Management IIITM ABV Gwalior India NIT Meghalaya Shillong India

In the present scenario, Machine Learning techniques are used in much ongoing research as a powerful tool. This paper proposes the applications of machine learning in antenna design optimization by implementing different machine learning algorithms like KNN, ANN, Random Forest, XGBoost and Decision Tree. A Double ring Cylindrical Di-electric Resonator Antenna is designed using High-Frequency Structure Simulator (HFSS). For the proposed antenna design, the frequency range is 2-3.5 GHz, while the range of height and radius is 6.5-19.5 mm and 12-18 mm respectively. The data set is generated for the proposed antenna design and S-11 parameter is optimized using machine learning algorithms. Out of the five algorithms, the models for KNN, XGBoost and Artificial Neural Network perform almost similarly and Random Forest has the highest performance.

关键词： Antenna double ring CDRA HFSS ml algorithm KNN ANN

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Electromechanical vibrations of smart piezoelectric structures on auxetic foundations for roadway support: a machine learning-enhanced CUF approach

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2025年

作者： Yang, Yiran Du, Yunhang Li, Jianying Lu, Zidong Li, Hongwei Wu, Jiangjie Yuan, Kekuo Yunnan Vocat Inst Energy Technol Sch Resources & Environm Engn Qujing Yunnan Peoples R China Yunnan Vocat Inst Energy Technol Sch Mech & Elect Engn Qujing Yunnan Peoples R China Xijing Univ Sch Civil Engn Xian Shaanxi Peoples R China

This study presents a comprehensive analysis of electromechanical vibrations in smart piezoelectric structures resting on auxetic foundations, intended for roadway support applications. The mathematical simulation is developed using the Carrera unified formulation (CUF), which enables the implementation of refined, higher-order structural models within a unified theoretical framework. CUF allows for the accurate representation of coupled mechanical and electrical fields in piezoelectric materials, while also capturing the complex deformation behavior introduced by auxetic substrates. The simulation accounts for dynamic loading conditions relevant to transportation infrastructure and evaluates critical parameters such as displacement, stress distribution, and electric potential. To validate and complement the numerical results, a machine learning (ml) algorithm is designed and trained using the dataset generated from the CUF-based simulations. The trained ml algorithm demonstrates strong predictive accuracy and computational efficiency, successfully replicating the nonlinear electromechanical behavior of the system. This hybrid methodology not only verifies the mathematical model but also offers a powerful, data-driven alternative for real-time response prediction and structural health monitoring. The integration of CUF and machine learning provides a scalable and robust platform for designing intelligent roadway support systems. The findings underscore the potential of combining advanced mechanics formulations with artificial intelligence to enhance the performance, reliability, and adaptability of next-generation civil infrastructure.

关键词： CUF approach electromechanical vibrations ml algorithm smart piezoelectric structures auxetic foundation roadway support

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Determination of the surface roller length of hydraulic jumps in horizontal rectangular channels using the machine learning method

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2024年第7期38卷 2539-2562页

作者： Ho, Hung Viet Thuyloi Univ Fac Water Resources Engn 175 Tay Son Hanoi 116705 Vietnam

This study aims to develop and assess seven machine learning (ml) models, including an Artificial Neural Network (ANN), Gradient Boosting (GB), Extra Trees (ET), Lasso Regression (LASSO), Least Angle Regression (Lars), Random Forest (RF), and Adaptive Boosting (Adaboost), that are effective and simple in forecasting the roller length of a hydraulic jump in a rectangular horizontal canal. This study also proposes a novel approach for applying the Buckingham Pi theorem to ascertain four model inputs and one output, corresponding to five dimensionless Pi terms that have not previously been explored. The impact of channel surface roughness, fluid viscosity, channel geometry, and gravity on the hydraulic jump's roller length was considered to improve the model's performance. Furthermore, the importance of input features was determined, resulting in two model input scenarios. The scenario with four inputs containing the inflow Reynolds number Re1* surpasses the three-input scenario. Because of their high accuracy and efficiency, six ml models outperformed three empirical equations. The GB, ANN, and ET models showed the best performance in both validation and testing phases, whereas LASSO, Lars, RF, and Adaboost performed with lower precision, in descending order. Regarding the ANN model, the best has two hidden layers of sixteen neurons each. In the validation and testing phases, this model achieves the highest Nash-Sutcliffe efficiency of 0.992 and 0.933, respectively. As a result, this study recommends using the GB, ANN, and ET models to forecast the roller length of the hydraulic jump instead of empirical equations when the inflow Froude number, Fr1, varies between 2.4 and 15.9.

关键词： Hydraulic jump ml algorithm ANN Buckingham theorem Roller length

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Analysis of demand forecasting of agriculture using machine learning algorithm

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2024年第1期26卷 1731-1747页

作者： Chelliah, Balika J. Latchoumi, T. P. Senthilselvi, A. SRM Inst Sci & Technol Dept Comp Sci & Engn Ramapuram Campus Chennai 600089 India

The state of India was situated on fertile land and river deltas with appropriate agricultural land. In 2019, agricultural fields, primarily cropland, occupy more than 40% of the country's total surface area. Moreover, agricultural industry revenues less than 3% of the country's Gross Provincial Product (GPP). While manufacturing has become the country's major financial activity, accounting countries represent half of GPP's revenues. The objective of the study is to find a way to improve the financial profitability and efficiency of farming supply chain networks as follows: (1) Fixation of national-level targets for zonal-level groups information after assessment and forecasts that affect production and distribution of agriculture. (2) Producers risk can be reduced by directing people to multiple factory and industrialization options based on market assessment. (3) Insurance costs and reduction of bank borrowing by standardizing the connection between bankers and producers using the structure to centralise land information. (4), Stabilize the agricultural sector by looking at nearby potential destinations for production and regulation of the Public Distribution System (PDS) flow for the security stock. In this paper, the novel ml target prediction algorithm to inform the farmers about the market target product and improve the relationships between the farmer and bankers for centralizing the information about recent government plans. The crop prediction ml algorithm proposed to improve the revenue of agriculture field.

关键词： Crop rotation ml algorithm Farming sector Crop cultivation Tracking and Forecasting

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Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approaches

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BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2023年 14卷 939-950页

作者： Roy, Joyita Pore, Souvik Roy, Kunal Jadavpur Univ Dept Pharmaceut Technol Drug Theoret & Cheminformat Lab Kolkata 700032 India

Nanoparticles with their unique features have attracted researchers over the past decades. Heavy metals, upon release and emission, may interact with different environmental components, which may lead to co-exposure to living organisms. Nanoscale titanium dioxide (nano-TiO2) can adsorb heavy metals. The current idea is that nanoparticles (NPs) may act as carriers and facilitate the entry of heavy metals into organisms. Thus, the present study reports nanoscale quantitative structure-activity relationship (nano-QSAR) models, which are based on an ensemble learning approach, for predicting the cytotoxicity of heavy metals adsorbed on nano-TiO2 to human renal cortex proximal tubule epithelial (HK-2) cells. The ensemble learning approach implements gradient boosting and bagging algorithms;that is, random forest, AdaBoost, Gradient Boost, and Extreme Gradient Boost were constructed and utilized to establish statistically significant relationships between the structural properties of NPs and the cause of cytotoxicity. To demonstrate the predictive ability of the developed nano-QSAR models, simple periodic table descriptors requiring low compu-tational resources were utilized. The nano-QSAR models generated good R2 values (0.99-0.89), Q2 values (0.64-0.77), and Q2F1 values (0.99-0.71). Thus, the present work manifests that ml in conjunction with periodic table descriptors can be used to explore the features and predict unknown compounds with similar properties.

关键词： heavy metals HK-2 cell ml algorithm periodic table descriptors QSAR

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AI Driven Medical Prediction and Suggestive System to Establish Good Health and Well Being in Society - A Real Time Case Study

AI Driven Medical Prediction and Suggestive System to Establ...

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International Conference on Intelligent and Fuzzy Systems (INFUS)

作者： Verma, Sunita Jain, Anjana Shukla, Sangeeta Kothari, Vineeta Shri GS Inst Technol & Sci Indore India SAGE Univ Indore Indore India Cent Pathol Lab Indore India

ISBN: (纸本)9783031671913;9783031671920

To Achieve one of the goal of United Nation for 2030 to Establish good health & well being for sustainable development, various steps are being taken globally. To achieve the objectives of good health, monitoring and prediction based suggestive system is required specific to specific class. The emergence of Artificial Intelligent techniques and algorithms may play a vital role in fast, reliable and accurate data analysis and prediction. Further the echo system may be developed and policies may be reframed to achieve the UN goal 2030. In this paper a case study is presented, which is carried out in the host city Indore situated in central India. The city has uniform climate and living style across it's boundary & inner part. Hence the data taken can be categorized under one class. The sampling has been done in real time for the Medical Parameters like B Glucose, Cholesterol, Keratinize and Protein. Different subclasses were created for recognizing pattern of specific class. The classification is done on urban and Rural population, on Gender basis, on caste and age wise. Almost ten subclasses are described which are having unique and distinguishable characteristics in their food style. Present Data is compared with data set of interval of 10 years. With the use of AI algorithm the estimation on medical health has been predicted while considering different parameters. These algorithms generate functions that map inputs to desire outputs and consist of a dependent variable which is predicted from a given set of independent variables. The predicted values are compared with the original value to determine the accuracy of the model. The machine learning algorithm include Naive-Bayes, k-NN, Decision Tree, Support Vector Machines, Random Forest, bagging etc. The results are derived formentioned subclasses and specific groups. Based on the analysis and prediction a specific suggestive health care system can be developed for specific class or subclass. The relevant society may

关键词： UN 2030 Artificial Intelligent techniques Health care Lifestyle Predictive analysis ml algorithm

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Symbol Detection Based on Back Tracking Search algorithm in MIMO-NOMA Systems

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Computer Systems Science & Engineering 2022年第2期40卷 795-804页

作者： M.Nuri Seyman Bandirma Onyedi Eylul University Enginering and Natural Sciences FacultyBandirma10200Turkey

One of the most important methods used to cope with multipath fading effects,which cause the symbol to be received incorrectly in wireless communication systems,is the use of multiple transceiver antenna *** combining the multi-input multi-output(MIMO)antenna structure with non-orthogonal multiple access(NOMA),which is a new multiplexing method,the fading effects of the channels are not only reduced but also high data rate transmission is ***,when the maximum likelihood(ml)algorithm that has high performance on coherent detection,is used as a symbol detector in MIMO NOMA systems,the computational complexity of the system increases due to higher-order constellations and antenna *** a result,the implementation of this algorithm will be *** this study,the backtracking search algorithm(BSA)is proposed to reduce the computational complexity of the symbol detection and have a good bit error performance for MIMO-NOMA *** emphasize the efficiency of the proposed algorithm,simulations have been made for the system with various antenna *** can be seen from the obtained results,a considerable reduction in complexity has occurred using BSA compared to the ml algorithm,also the bit error performance of the system is increased compared to other algorithms.

关键词： MIMO-NOMA ml algorithm heuristic BSA algorithm

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STBC Recognition for OFDM Transmissions: Channel Decoder Aided algorithm

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IEEE COMMUNICATIONS LETTERS 2022年第7期26卷 1658-1662页

作者： Marey, Mohamed Mostafa, Hala Alshebeili, Saleh A. Dobre, Octavia A. Prince Sultan Univ Coll Engn Smart Syst Engn Lab Riyadh 11586 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 11671 Saudi Arabia King Saud Univ Dept Elect Engn Riyadh 11451 Saudi Arabia Memorial Univ Dept Elect & Comp Engn St John NL A1C 5S7 Canada

Recently, the process of space-time block coding (STBC) recognition has found applications in a wide range of wireless transmissions. Numerous investigations on this topic have been developed for single-carrier transmissions and a few studies have been introduced for orthogonal frequency division multiplexing (OFDM) transmissions. The previous research work in the literature has focused on exploiting the space-time redundancy to blindly estimate the type of STBC signal. In this work, we propose a new approach to design a STBC recognizer for OFDM transmissions by taking benefits from the a posteriori probabilities provided by a channel decoder. The mathematical treatments have shown that the exact maximum-likelihood (ml) solution to this problem is computationally demanding in the absence of a closed form. Therefore, we settle for the expectation-maximization (EM) algorithm as a low-complexity procedure to perform STBC recognition. In addition, we develop a channel estimation algorithm to be integrated into the proposed recognition algorithm. Simulation results are presented to confirm the theoretical analysis, with exhibiting superior recognition performance over the state-of-the-art algorithms.

关键词： Symbols OFDM Signal processing algorithms Codes Channel estimation Wireless communication Maximum likelihood estimation Signal recognition STBCs ml algorithm

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Analytic Solutions Help Predict Sucker-Rod-Pump Failure

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JPT, Journal of Petroleum Technology 2022年第3期74卷 62-64页

作者： Carpenter, Chris JPT Technology Editor

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200825, “Failure Prediction for SRP Using Analytic Solutions,” by Viorica Sirghii, SPE, Rudolf Konrad Fruhwirth, and Herbert Hofstaetter, University of Leoben, et al. The paper has not been peer reviewed. A method for prediction of sucker-rod-pump (SRP) failure based on improved, completely connected perceptron (ICCP) artificial neural networks (ANNs) is presented. Results are compared with historical wellbore data. The validity of the ANN model is proved to meet SRP failure prediction with a ±5% error, helping identify and prevent potential damage, reduce costs and risks, and optimize *** Diagnosis. To evaluate the performance of SRPs, dynamometer cards are used. The load on the top rod is measured and plotted in relation to the polished rod position as the pumping unit moves through each stroke cycle. The result is a surface card that illustrates the polished rod load vs. position (downhole diagnostics).To determine above-pump liquid accumulation, pump-filling efficiency, and displacement, the well’s producing and lifting efficiencies can be analyzed and quantified. Depending on the results, operational and equipment changes can be made to improve efficiency and production optimization. How the plunger picks up, holds, and releases the fluid load at each stroke can be seen in a dynamometer card’s shape. The most-common and most-harmful pump states are fluid pound, gas interference, and tagging. When the pump is not completely full, the rod string is damaged because of an excessive number of strokes.

关键词： beam pumping neural network accuracy descriptor machine learning dynamometer card ml algorithm artificial lift system upstream oil & gas skewness Artificial Lift Systems Beam and related pumping techniques Information Management and Systems

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