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Thermoacoustic stability prediction using classification algorithms

DATA-CENTRIC ENGINEERING 2022年第12期3卷 e17-e17页

作者： Gaudron, Renaud Morgans, Aimee S. Imperial Coll London Dept Mech Engn London England

Predicting the occurrence of thermoacoustic instabilities is of major interest in a variety of engineering applications such as aircraft propulsion, power generation, and industrial heating. Predictive methodologies based on a physical approach have been developed in the past decades, but have a moderate-to-high computational cost when exploring a large number of designs. In this study, the stability prediction capabilities and computational cost of four wellestablished classification algorithms-the K-Nearest Neighbors, Decision Tree (DT), Random Forest (RF), and Multilayer Perceptron (MLP) algorithms-are investigated. These algorithms are trained using an in-house physicsbased low-order network model tool called OSCILOS. All four algorithms are able to predict which configurations are thermoacoustically unstable with a very high accuracy and a very low runtime. Furthermore, the frequency intervals containing unstable modes for a given configuration are also accurately predicted using multilabel classification. The RF algorithm correctly predicts the overall stability and finds all frequency intervals containing unstable modes for 99.6 and 98.3% of all configurations, respectively, which makes it the most accurate algorithm when a large number of training examples is available. For smaller training sets, the MLP algorithm becomes the most accurate algorithm. The DTalgorithm is found to be slightly less accurate, but can be trained extremely quickly and runs about a million times faster than a traditional physics-based low-order network model tool. These findings could be used to devise a new generation of combustor optimization tools that would run much faster than existing codes while retaining a similar accuracy.

关键词： classification algorithm combustion instability machine learning thermoacoustics

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An examination of thematic research,development,and trends in remote sensing applied to conservation agriculture

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International Soil and Water Conservation Research 2024年第1期12卷 77-95页

作者： Zobaer Ahmed Aaron Shew Lawton Nalley Michael Popp V.Steven Green Kristofor Brye Center for Advanced Spatial Technologies University of ArkansasFayettevilleARUSA Environmental Dynamics Program University of ArkansasFayettevilleARUSA Department of Agricultural Economics and Agribusiness University of ArkansasFayettevilleARUSA College of Agriculture Arkansas State UniversityJonesboroARUSA University of Arkansas System Division of Agriculture Little RockARUSA Department of Crop Soiland Environmental SciencesUniversity of ArkansasFayettevilleARUSA

Conservation agriculture seeks to reduce environmental degradation through sustainable management of agricultural *** the 1990s,agricultural research has been conducted using remote sensing technologies;however,few previous reviews have been conducted focused on different conservation management *** of the previous literature has focused on the application of remote sensing in agriculture without focusing exclusively on conservation practices,with some only providing a narrative review,others using biophysical remote sensing for quantitative estimates of the bio-geo-chemical-physical properties of soils and crops,and few others focused on single agricultural management *** paper used the preferred reporting items for systematic review(PRISMA)methodology to examine the last 30 years of thematic research,development,and trends associated with remote sensing technologies and methods applied to conservation agriculture research at various spatial and temporal scales.A set of predefined key concepts and keywords were applied in three databases:Scopus,Web of Science,and Google Scholar.A total of 188 articles were compiled for initial examination,where 68 articles were selected for final analysis and grouped into cover crops,crop residue,crop rotation,mulching,and tillage *** on conservation agriculture research using remote sensing have been increasing since 1991 and peaked at 10 publications in *** the 68 articles,94%used a pixel-based,while only 6%used an object-based classification *** to 2005,tillage practices were abundantly studied,then crop residue was a focused theme between 2004 and *** 2012 to 2020,the focus shifted again to cover *** spectral indices were used in 76%of the 68 *** examination offered a summary of the new potential and identifies crucial future research needs and directions that could improve the contribution of remote sensing to the provision of long-term operat

关键词： Remote sensing Conservation agriculture classification algorithm Spatial resolution Satellite Spectral indices PRISMA

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Machine learning framework for selective and sensitive metal ion sensing with nitrogen-doped graphene quantum dots heterostructure

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CARBON 2025年 232卷

作者： Das, Ruma Paria, Abhirup Giri, P. K. Haldia Inst Technol Dept Appl Sci Phys Haldia 721657 India Haldia Inst Technol Dept Comp Sci & Engn Artificial Intelligence & Mac Haldia 721657 India Indian Inst Technol Guwahati Dept Phys Gauhati 781039 India Indian Inst Technol Guwahati Ctr Nanotechnol Gauhati 781039 India

This study introduces a machine learning (ML) framework to optimize photodetector performance for sensor applications. Using the data from the fabricated photodetector with the heterostructure of nitrogen-doped graphene quantum dot and gold nanoparticles (Au@N-GQDs), various supervised ML models (more than 20 models) are trained and tested for the selection and refinement of the most effective algorithm for our work. Depending on the best-performed ML model, the optimized working wavelength of the photodetector is found for the detection of metal ions. Remarkably, the ML-based sensor shows a high level of selectivity and sensitivity in nM level towards Fe3+ ions in Brahmaputra river water. A strong alignment between model predictions and experimental outcomes validates the efficacy of the proposed ML-based framework. Moreover, data visualization techniques such as heatmaps, classification algorithms, and confusion matrices are introduced to identify the trends in the database. The mechanistic insight of the sensor performance towards Fe3+ ion sensing is further explained with heatmap analysis and experimental verification, which emphasizes the role of photo-induced charge transfer and Fe-O bond formation between metal ions and Au@N-GQDs due to the high electron affinity of Fe3+ ions.

关键词： Supervised machine learning Heatmap matrix classification algorithm Confusion matrix Metal ion sensing in river water

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Application of Big Data Technology to Assessments of Female Ovarian Reserve Dysfunction

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IEEE ACCESS 2025年 13卷 19408-19419页

作者： Xia, Ji'An Ma, Yunfei Wu, Yiyun Zhao, Youlin Ni, Haorang Liu, Xinyan Nanjing Vocat Univ Ind Technol Sch Comp & Software Nanjing 210023 Jiangsu Peoples R China Jiangsu Prov Hosp Chinese Med Dept Ultrasound Nanjing 210029 Jiangsu Peoples R China

This paper evaluated the potential application of big data technology to assessments of diminished ovarian reserve (DOR). The study enrolled 162 patients who underwent ovarian reserve function assessment for the first time in the Department of Ultrasound, Jiangsu Province Hospital of Chinese Medicine from January 2023 to December 2023. Patients were divided into normal ovarian reserve function (n = 68), early-stage DOR (n = 66), mid-stage DOR (n = 12), and late-stage DOR (n = 16) groups. Hadoop and Spark frameworks were used to build a big data platform, and the MLlib parallel machine learning library was used to implement three multivariate classification models-multilayer perceptron, one-vs-rest, and random forest classifiers-to classify and analyse the ovarian reserve function dataset and evaluate the platform's performance. In the big data platform, the random forest algorithm achieved the highest classification accuracy (89.47%), followed by the neural network (81.06%) and support vector machine (72.91%) methods. The random forest algorithm had the least time overhead for datasets smaller than 50 MB;for datasets exceeding 50 MB, the support vector machine algorithm had the least time overhead, followed by the random forest and neural network algorithms. The neural network algorithm's speedup ratio was lower than that of the other two algorithms for small datasets, but with increasing dataset size, its speedup ratio significantly exceeded those of the other two algorithms. The random forest algorithm showed substantial growth for large datasets.

关键词： Medical diagnostic imaging Big Data Machine learning algorithms Sparks classification algorithms Indexes Data models Random forests Biochemistry Support vector machines Diminished ovarian reserve three-dimensional power Doppler ultrasound medical big data machine learning classification algorithm performance evaluation

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A Review of Machine Learning in Organic Solar Cells

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PROCESSES 2025年第2期13卷 393-393页

作者： Ahmed, Darya Rasul Muhammadsharif, Fahmi F. Koya Univ Fac Sci & Hlth Dept Phys Koya KOY45 Koy Sanjaq Iraq

Organic solar cells (OSCs) are a promising renewable energy technology due to their flexibility, lightweight nature, and cost-effectiveness. However, challenges such as inconsistent efficiency and low stability limit their widespread application. Addressing these issues requires extensive experimentation to optimize device performance, a process hindered by the complexity of OSC molecular structures and device architectures. Machine learning (ML) offers a solution by accelerating material discovery and optimizing performance through the analysis of large datasets and prediction of outcomes. This review explores the application of ML in advancing OSC technologies, focusing on predicting critical parameters such as power conversion efficiency (PCE), energy levels, and absorption spectra. It emphasizes the importance of supervised, unsupervised, and reinforcement learning techniques in analyzing molecular descriptors, processing data, and streamlining experimental workflows. Concludingly, integrating ML with quantum chemical simulations, alongside high-quality datasets and effective feature engineering, enables accurate predictions that expedite the discovery of efficient and stable OSC materials. By synthesizing advancements in ML-driven OSC research, the gap between theoretical potential and practical implementation can be bridged. ML can viably accelerate the transition of OSCs from laboratory research to commercial adoption, contributing to the global shift toward sustainable energy solutions.

关键词： machine learning organic solar cell feature selection photovoltaic parameter classification algorithm

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Development of a GUI for Automated classification of Scientific Journal Articles using clustering 5

Development of a GUI for Automated Classification of Scienti...

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5th International Conference on Innovative Trends in Information Technology (ICITIIT)

作者： Sateesh, Nayani Kaur, Kuljeet Lakshminarayana, M. Vekariya, Vipul Patil, Harshal Maranan, Ramya CVR Coll Engn Dept Comp Sci & Engn Hyderabad 501510 Telangana India Graph Era Deemed Be Univ Dept Management Dehra Dun 248002 Uttarakhand India MS Ramaiah Inst Technol Dept Med Elect Engn Bengaluru 560054 Karnataka India Parul Univ Parul Inst Engn & Technol Dept Comp Sci & Engn Waghodia 391760 Gujarat India Symbiosis Int Deemed Univ Symbiosis Inst Technol Dept Comp Sci & Engn Pune 412115 Maharashtra India SIMATS Saveetha Sch Engn Dept Res & Innovat Chennai 602105 Tamil Nadu India

ISBN: (纸本)9798350386813;9798350386820

The ever-expanding volume of scientific literature necessitates innovative solutions for efficient information organization and retrieval. This final qualifying work focuses on the development of a robust algorithm for the automated categorization of articles in a scientific journal. The primary objective is to streamline the process of classifying diverse research contributions, thereby enhancing accessibility and knowledge discovery within scholarly domains. The goal of the final qualifying work is to develop an algorithm for automated categorization of articles in a scientific journal. To achieve this goal, an application is developed for the administrator of a scientific journal, allowing for the preparation, classification and visualization of data.

关键词： Automated Categorization Scientific Journal Articles Natural Language Processing Machine Learning Textual Analysis Information Retrieval Knowledge Management classification algorithm

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An Approach to Recognize and Classify Arm Activities Using Wearable Ultra Wide Band Antennas

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WIRELESS PERSONAL COMMUNICATIONS 2024年第2期139卷 1303-1318页

作者： Tiwari, Bhawna Gupta, Sindhu Hak Balyan, Vipin Amity Univ Dept Elect & Commun Noida Uttar Pradesh India GL Bajaj Inst Technol & Management Greater Noida Uttar Pradesh India Cape Peninsula Univ Technol Dept Elect Elect & Comp Engn Cape Town South Africa

Continuous observation, recognition and classification of various body movements and activities is essential for the implementation of Wireless Body Area Networks (WBAN) to discern the status of body parts functionalities or abnormalities if any. WBAN can continuously observe various movements of human body parts and human body activities. Recognition and classification of arm activities plays an important role in fitness monitoring, assisted living, and sports tracking, etc. In the presented work, Ultra Wide Band antennas are designed and employed on human body to observe datasets of antenna performance parameters associated with various arm movements and activities. The classification of three arm activities i.e. boxing, rowing, and clapping are implemented using Support Vector Machine, K-Nearest Neighbor, Random Forest and Decision Tree machine learning algorithms. Performance of classification depends on accuracy of implemented algorithm. The highest classification accuracies are found to be 99% in case of Decision Tree algorithm.

关键词： Arm activity recognition and classification Health monitoring WBAN UWB antenna S-11 S-21 VSWR Machine learning classification algorithm

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Methodology and application of digital twin-driven diesel engine fault diagnosis and virtual fault model acquisition

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 131卷

作者： Bo, Yaqing Wu, Han Che, Weifan Zhang, Zeyu Li, Xiangrong Myagkov, Leonid Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Bauman Moscow State Tech Univ Dept Piston Engines Moscow 105005 Russia

Digital real-time fault diagnosis is an effective way to ensure the reliable long-term operation of the diesel engine, but there is still a lack of systematic methods with high integrity and practicability. Therefore, a digital twindriven diesel engine fault diagnosis method based on the combination of the classification algorithm and the optimization algorithm is proposed and a case study of fuel injection system fault diagnosis is used to illustrate and verify the proposed method. This method closely links the physical system, virtual model, database, and diagnosis system through data transmission and the diagnostic process consists of three parts: classification, diagnosis, and decision. The fault classification part can preliminarily lock the possible types and degrees of faults, and point out the key classification features for each fault type by using classification algorithms such as Random Forest. The fault diagnosis part can diagnose and reproduce the diesel engine faults by using an optimization-simulation joint calculation model, where the virtual model variables and optimization algorithm are determined according to the possible fault types, and the optimization target depends on the importance of classification features. Then the maintenance decision can be made according to the fault detailed information. The proposed method reduces the requirement of covering the fault degree of the database, and the obtained fault model provides the possibility for subsequent online optimization and also facilitates the development of intelligent engine management.

关键词： Diesel engine Fault diagnosis classification algorithm Optimization algorithm Digital twin

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Construction and enumeration of self-orthogonal and self-dual codes over Galois rings of even characteristic

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DESIGNS CODES AND CRYPTOGRAPHY 2024年第2期92卷 303-339页

作者： Yadav, Monika Sharma, Anuradha IIIT Delhi Dept Math Delhi 110020NEW India

Let e >= 2 and r >= 1 be integers, and let R-e,R-r denote the Galois ring of characteristic 2(e) and cardinality 2(er). The Teichmuller set T-r of the Galois ring R-e,R-r can be viewed as the finite field of order 2(r) under the addition operation. and the multiplication operation of R-e,R-r, where for a, b is an element of Tr, a circle plus b is the unique element in T-r satisfying a circle plus b = (a + b) (mod 2). Now a linear code C of length n over T-r is said to be k-doubly even if it has a k-dimensional linear subcode C-0 satisfying c center dot c = 0 (mod 4) for all c is an element of C-0, where each c is an element of C-0 is viewed as an element of R-e,r(n) and center dot denotes the Euclidean bilinear form on R-e,r(n). A k-doubly even code of length n and dimension k over T-r is simply called a doubly even code. In this paper, we count all doubly even codes over T-r and their two special classes, viz. the codes containing the all-one vector and the codes that do not contain the all-one vector by studying the geometry of a certain special quadratic space over T-r. We further provide a recursive method to construct self-orthogonal and self-dual codes of the type {k(1), k(2),..., k(e)} and length n over R-e,R-r from a (k(1)+ k(2)+ ...+ k(left) (perpendiculare/2right perpendicular))-doubly even self-orthogonal code of the same length n and dimension k(1) + k(2) + ... + k(inverted right perpendiculare/2inverted left perpendicular) over T-r, where n is a positive integer and k(1), k(2),..., k(e) are non-negative integers satisfying 2k(1) + 2k(2) + ... + 2k(e-i+1) + k(e-i+2) + k(e-i+3) + ... + k(i) <= n for inverted right perpendiculare+1/2inverted left perpendicular <= i <= e, (here left perpendicuar center dot right perpendicuar denotes the floor function and inverted right perpendicular center dot inverted left perpendicular denotes the ceiling function). With the help of this recursive construction method and the enumeration formulae for doubly even codes ove

关键词： classification algorithm Galois rings Doubly even codes Witt decomposition

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Hybridizing graph-based Gaussian mixture model with machine learning for classification of fraudulent transactions

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COMPUTATIONAL INTELLIGENCE 2022年第6期38卷 2134-2160页

作者： Prusti, Debachudamani Behera, Ranjan Kumar Rath, Santanu Kumar Natl Inst Technol Rourkela Dept Comp Sci & Engn Rourkela India Birla Inst Technol Dept Comp Sci & Engn Ranchi India Birla Inst Technol Dept Comp Sci & Engn Ranchi Jharkhand India

It has been observed that a good number of financial organizations often face a number of threats due to credit card fraud that affects consistently to the card holder as well as the organizations. This is one of the fastest-growing frauds of its kind and the most emerging problems for the institutions to prevent. A number of researchers and analysts have shown interest to work on this area in order to identify such issues in an effective manner by applying various supervised as well as unsupervised learning approaches. In this assessment, three classification techniques such as support vector machine (SVM), k-nearest neighbor (k-NN), and extreme learning machine (ELM) that come under supervised learning category are applied to the BankSim data to categorize the normal and fraudulent class transactions in credit card. These algorithms are incorporated with the graph features extracted from the dataset by using a database tool Neo4j. The nodes of the graph represent the transactional data samples and the edges create relationships among the nodes to find the patterns of data using connected data analysis. k-fold cross validation approach in Gaussian mixture model (GMM) has been applied for classification of the credit card transaction data in a single distribution. Further, a combined graph-based Gaussian mixture model (CGB-GMM) has been proposed to effectively detect the fraudulent instances in credit card transactions with the application of graph algorithms such as degree centrality, LPA, page rank, and so forth. Each of the learning algorithms are implemented with and without the application of graph algorithms and their performances are assessed empirically for analysis.

关键词： classification algorithm credit card fraud Gaussian mixture model graph feature Neo4j$$ Neo4j $$ database

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