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Integrated dominating and hit set-inspired unequal clusteringbased data aggregation in wireless sensor networks

International Journal of Intelligent Computing and Cybernetics 2022年第4期15卷 642-655页

作者： G.Vennira Selvi V.Muthukumaran A.C.Kaladevi S.Satheesh Kumar B.Swapna Department of Computer Science and Engineering Global Institute of Engineering and TechnologyVelloreIndia Department of Mathematics School of Applied SciencesREVA UniversityBangaloreIndia Department of Computer Science and Engineering Sona College of TechnologySalemIndia Department of Computer Science School of Applied SciencesREVA UniversityBangaloreIndia Department of Electronics and Communication Engineering Dr MGR Educational and Research InstituteChennaiIndia

Purpose-In wireless sensor networks,improving the network lifetime is considered as the prime objective that needs to be significantly addressed during data *** the traditional data aggregation techniques,cluster-based dominating set algorithms are identified as more effective in aggregating data through cluster ***,the existing cluster-based dominating set algorithms suffer from a major drawback of energy deficiency when a large number of communicating nodes need to collaborate for transferring the aggregated ***,due to this reason,the energy of each communicating node is gradually decreased and the network lifetime is also *** increase the lifetime of the network,the proposed algorithm uses two sets:Dominating set and hit ***/methodology/approach-The proposed algorithm uses two sets:Dominating set and hit *** dominating set constructs an unequal clustering,and the hit set minimizes the number of communicating nodes by selecting the optimized cluster head for transferring the aggregated data to the base *** simulation results also infer that the proposed optimized unequal clustering algorithm(OUCA)is greater in improving the network lifetime to a maximum amount of 22%than the existing cluster head selection approach considered for ***-In this paper,lifetime of the network is prolonged by constructing an unequal cluster using the dominating set and electing an optimized cluster head using hit *** dominator set chooses the dominator based on the remaining energy and its node degree of each *** optimized cluster head is chosen by the hit set to minimize the number of communicating nodes in the *** proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit *** simulation result confirms that the proposed algorithm prolonging the lifetime of the network efficiently when compared with the existing algorithms.

关键词： Unequal clustering Hit set Dominating set Network lifetime

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High-precision chaotic radial basis function neural network model:Data forecasting for the Earth electromagnetic signal before a strong earthquake

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Geoscience Frontiers 2022年第1期13卷 364-373页

作者： Guocheng Hao Juan Guo Wei Zhang Yunliang Chen David AYuen School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China School of Computer Science China University of GeosciencesWuhan 430074China Department of Mathematics Duke UniversityDurhamNC 27708USA Department of Applied Physics and Applied Mathematics Columbia UniversityNew YorkNY 10027USA

The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and *** change tendency may be related to the occurrence of earthquake *** of the underlying intensity trend plays an important role in the analysis of data and disaster *** chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field *** main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field *** verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data *** to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function ***,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.

关键词： Earth’s natural pulse electromagnetic field Chaos theory Radial Basis Function neural network Forecasting model

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PCA Lab's solution for RePSS 2024 by vision-based self-supervised remote heart rate sensing 3

PCA Lab's solution for RePSS 2024 by vision-based self-super...

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3rd Vision-Based Remote Physiological Signal Sensing Challenge and Workshop, RePSS 2024

作者： Shao, Hang Luo, Lei Qian, Jianjun Zhuo, Wei Hu, Chuanfei Yang, Jian PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Automation Southeast University Nanjing211189 China Nanjing Center for Applied Mathematics Nanjing211135 China

Vision-based remote non-contact physiological measurements are crucial for detecting indicators (such as heart rate and blood pressure) that reflect important vital signs. This paper introduces the approach proposed by PCA Lab in the third challenge on Vision-based Remote Physiological Signal Sensing (RePSS) organized within IJCAI 2024. Specifically, we design an end-to-end self-supervised contrastive learning network for remote heart rate detection, which can generalize light reflection changes caused by cardiac activity in subcutaneous capillaries based on unlabeled facial videos. At the same time, we optimize the facial skin region of interest extraction method and remove most of the irrelevant content and redundant information. In addition, we construct the hybrid pipeline temporal attention module and spatiotemporal reconstruction pretraining paradigm to improve the network's ability to model long-distance sequence features. Our network is tested on 1000 samples from 200 participants provided by the Track 1 of this challenge, and it achieve the root mean squared error of 8.96941. The codes and model are publicly available at https://***/Sachiel0916/repss-track1-top3/. © 2024 Copyright for this paper by its authors.

关键词： contrastive learning remote heart rate sensing RePSS self-supervision framework spatiotemporal reconstruction

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Optimization of Quality Process Control and Preventive Maintenance Strategy: A Case Study

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Operations Research Forum 2024年第4期5卷 84页

作者： Sadraoui, Youssef Er-ratby, Mohamed Kadiri, Moulay Saddik Kobi, Abdessamad Laboratory of Process Engineering Computer Science and Mathematics LIPIM National School of Applied Sciences of Khouribga Sultan Moulay Slimane University Beni Mellal Morocco Laboratory of Science and Engineering Techniques LaSTI National School of Applied Sciences of Khouribga Sultan Moulay Slimane University Beni Mellal Morocco LARIS Angevin Laboratory for Systems Engineering Research Polytech Angers University of Angers Angers France

The studied process concerns a wet grinding and cycloning unit, set up to ensure that the particle size it processes complies with the requirements in terms of pipe transport mode. Frequent shutdowns, low reliability, the risk of delivering a product with inappropriate particle size, and the lack of operational parameters’ control are all obstacles for the unit, having enormous repercussions on its relevance and performance. To improve this situation, the implementation of an innovative 5-month quality and process control study has proven crucial to establish a culture of quality among operators, aiming to achieve the targeted grain size. However, the innovative aspects of our approach lie in its thoroughness and inclusiveness since it addresses the process both qualitatively and quantitatively, focusing not only on theoretically modeling and simulating the grinding circuit but also providing an integrated method to address every component of the unit. By going through the seven stages of this study, we were able to enhance the overall performance of the unit controlling the particle size at the output. After establishing a set of quality control tools, we observed a remarkable development in mastering various parameters influencing the targeted particle size. The benefits generated by the study are extremely satisfying for the unit at different levels, particularly in terms of quality, process control, and resource optimization. Furthermore, the integration of maintenance management into the process has proven essential to ensure the reliability and sustainability of the installation, thereby contributing significantly to the overall efficiency of the unit. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Optimization Preventive maintenance Process capability Quality Statistical process control SPC Wet grinding

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Quality Process Control and Preventive Maintenance Optimization of a Grinding Unit: A Case Study 10

Quality Process Control and Preventive Maintenance Optimizat...

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10th International Conference on Optimization and Applications, ICOA 2024

作者： Sadraoui, Youssef Er-Ratby, Mohamed Kadiri, Moulay Saddik Kobi, Abdessamad National School of Applied Sciences Sultan Moulay Slimane University LIPIM Laboratory of Process Engineering Computer Science and Mathematics Khouribga Morocco National School of Applied Sciences Sultan Moulay Slimane University LASTI Laboratory of Science and Engineering Techniques Khouribga Morocco Polytech Angers University of Angers LARIS Angevin Laboratory for Systems Engineering Research France

ISBN: (纸本)9798350387353

The studied process is a grinding and hydroclassification unit designed to ensure that the grain size it processes is in line with the specifications for the pipe's transport mode. Obstacles to the unit's operational process include frequent stops, plant's low reliability, product delivery with an inappropriate grain size, and a lack of control over operating parameters. These factors have a significant impact on the unit's relevance and, consequently, its performance. We created a quality culture among operators with '0 grain quality loss' through the implementation of the quality and processes control study, which was essential to improve the situation. Thus, by completing the seven project stages, we were able to enhance and optimize the grinding and hydroclassification unit's overall performance and capability to regulate the grain size quality. We noticed a notable improvement in regulating the several parameters that affect the targeted particle size after we established tools for unit quality control, such as control and monitoring charts, reaction matrices, intervention flow charts and operational standards. The unit has seen a great deal of satisfaction from the project in terms of quality, process control and resources optimization. Moreover, the incorporation of preventive maintenance into this process has shown to be crucial in guaranteeing the reliability and durability of the installation, thereby substantially improving the unit's overall efficiency. © 2024 IEEE.

关键词： Preventive maintenance

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A Sound and Complete Tableau System for Fuzzy Halpern and Shoham’s Interval Temporal Logic 30

A Sound and Complete Tableau System for Fuzzy Halpern and Sh...

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30th International Symposium on Temporal Representation and Reasoning, TIME 2023

作者： Conradie, Willem Monego, Riccardo Muñoz-Velasco, Emilio Sciavicco, Guido Stan, Ionel Eduard School of Mathematics University of the Witwatersrand Johannisburg South Africa Department of Mathematics and Computer Science University of Ferrara Italy Department of Applied Mathematics University of Málaga Spain Faculty of Engineering Free University of Bozen-Bolzano Italy

ISBN: (纸本)9783959772983

Interval temporal logic plays a critical role in various applications, including planning, scheduling, and formal verification;recently, interval temporal logic has also been successfully applied to learning from temporal data. Halpern and Shoham’s interval temporal logic, in particular, stands out as a very intuitive, yet expressive, interval-based formalism. To address real-world scenarios involving uncertainty and imprecision, Halpern and Shoham’s logic has been recently generalized to the fuzzy (many-valued) case. The resulting language capitalizes on many-valued modal logics, allowing for a range of truth values that reflect multiple expert perspectives, but inherits the bad computational behaviour of its crisp counterpart. In this work, we investigate a sound and complete tableau system for fuzzy Halpern and Shoham’s logic, which, although possibly non-terminating, offers a semi-decision procedure for the finite case. 2012 ACM Subject Classification Theory of computation → Theory and algorithms for application domains © Willem Conradie, Riccardo Monego, Emilio Muñoz-Velasco, Guido Sciavicco, and Ionel Eduard Stan

关键词： Many valued logics

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Anomaly Detection in PV Modules: A Comparative Study of DBSCAN, k-means, Isolation Forest, and LOF 7

Anomaly Detection in PV Modules: A Comparative Study of DBSC...

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7th IEEE International Congress on Information Science and Technology, CiSt 2023

作者： Hairach, Mohamed Limam El Bellamine, Insaf Tmiri, Amal Laroseri Chouaïb Doukkali University Department of Computer Science Eljadida Morocco Mohammed v University National School of Arts and Crafts Department of Applied Mathematics and Computer Engineering Rabat Morocco

ISBN: (纸本)9781665461337

The increasing adoption of photovoltaic (PV) modules for renewable energy generation highlights the importance of maintaining their performance and efficiency. Anomalies in PV modules can lead to energy losses and reduced system reliability. Classical approaches for detecting PV module anomalies include current-voltage (IV) curve analysis, visual inspection, infrared thermography, and electroluminescence imaging. However, these methods often require employees, manual intervention, and may lack scalability and *** this study, we explore unsupervised machine learning techniques for detecting anomalies in PV modules using data from a PV plant, aiming to develop more efficient and accurate solutions compared to classical approaches. We evaluate four unsupervised machine learning algorithms - Isolation Forest, Local Outlier Factor (LOF), K-Means and DBSCAN, and compare their effectiveness based on metrics such as accuracy, precision, recall, and F1 *** findings reveal that the K-Means and DBSCAN algorithms, when properly tuned, demonstrate superior performance in detecting anomalies, while other algorithms show promise with further optimization. This study provides insights into the potential of unsupervised machine learning algorithms as an alternative to classical approaches for anomaly detection in PV modules, contributing to the development of efficient, accurate, and scalable anomaly detection systems in the solar energy sector and enhancing PV system performance and reliability. © 2023 IEEE.

关键词： Solar energy

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Differential Evolution-Boosted Sine Cosine Golden Eagle Optimizer with Lévy Flight

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Journal of Bionic engineering 2022年第6期19卷 1850-1885页

作者： Gang Hu Liuxin Chen Xupeng Wang Guo Wei Department of Applied Mathematics Xi’an University of TechnologyXi’an710054People’s Republic of China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048People’s Republic of China School of Art and Design Xi’an University of TechnologyXi’an710054China Department of Mathematics and Computer Science University of North Carolina at PembrokePembrokeNC28372USA

Golden eagle optimizer(GEO)is a recently introduced nature-inspired metaheuristic algorithm,which simulates the spiral hunting behavior of golden eagles in ***,the GEO suffers from the challenges of low diversity,slow iteration speed,and stagnation in local optimization when dealing with complicated optimization *** ameliorate these deficiencies,an improved hybrid GEO called IGEO,combined with Lévy flight,sine cosine algorithm and differential evolution(DE)strategy,is developed in this *** Lévy flight strategy is introduced into the initial stage to increase the diversity of the golden eagle population and make the initial population more abundant;meanwhile,the sine-cosine function can enhance the exploration ability of GEO and decrease the possibility of GEO falling into the local ***,the DE strategy is used in the exploration and exploitation stage to improve accuracy and convergence speed of ***,the superiority of the presented IGEO are comprehensively verified by comparing GEO and several state-of-the-art algorithms using(1)the CEC 2017 and CEC 2019 benchmark functions and(2)5 real-world engineering problems *** comparison results demonstrate that the proposed IGEO is a powerful and attractive alternative for solving engineering optimization problems.

关键词： Golden eagle optimizer Lévy flight Sine cosine algorithm Differential evolution strategy engineering design Bionic model

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Exploring advanced feature selection techniques: an application to dialectal Arabic data

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4637-4649页

作者： Abdellah, Ait Elouli Ouahi, Hassan Cherrat, El Mehdi Bekkar, Abdellatif Department of Computer Science Laboratory of Innovation in Mathematics and Intelligent Systems Faculty of Applied Sciences Ibn Zohr University Ait Melloul Morocco Laboratory of Innovation in Mathematics and Intelligent SystemsFaculty of Applied Sciences Ibn ZohrUniversity Ait Melloul Morocco Laboratory of Systems Engineering and Information Technology National School of Applied Sciences Ibn Zohr University Agadir Morocco Department of Computer ScienceLIM LaboratoryFaculty of Sciences and Technics Hassan II University of Casablanca Mohammedia Casablanca Morocco

In the field of automated language processing, distinguishing between Moroccan Arabic (Darija) in multilingual contexts is a major challenge. This study addresses this challenge by exploiting feature selection techniques to improve the accuracy of tongue detection. Using a comprehensive methodology integrating various feature selection methods, including TF-idf, CBOW, Word2Vec for feature extraction, LASSO regression, decision trees for machine learning techniques, and statistical feature selection such as ANOVA, Pearson correlation coefficient, and Mutual information, alongside semantic techniques by developing our semantic encoders adapted to Arabic data, we strive to uncover the most important, relevant language features while attenuating the noise. As a result, we have obtained robust results by applying the famous XGBOOST, with conventional extraction methods and SVD as a dimensional reduction method with a very reasonable execution time. Our experimental results highlight the effectiveness of feature selection techniques to reinforce supervised learning tasks. This research not only advances the field of natural language processing but also highlights the central role of feature selection in deciphering complex linguistic landscapes. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Comparative Analysis Dialect Identification Dimensionality Reduction Moroccan Dialect Arabic NLP Standard Arabic

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Dimensional Homogeneity in Classifying Second-Order Differential Invariant Systems for Four-Dimensional Lie Algebras

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International Journal of applied and Computational mathematics 2024年第6期10卷 1-44页

作者： Ayub, Muhammad Sultan, Zahida Mahomed, F.M. Ijaz, Saima Department of Mathematics COMSATS University Islamabad Abbottabad Campus Abbottabad Pakistan Department of Mathematics Azad Jammu and Kashmir University Muzaffarabad Pakistan DSI-NRF Centre of Excellence in Mathematical and Statistical Sciences School of Computer Science and Applied Mathematics University of the Witwatersrand Wits 2050 Johannesburg South Africa Department of Mathematics University of Engineering and Technology Lahore 54000 Pakistan

This research investigates a specific mathematical structure called a nonsingular differential invariant structure, which plays a crucial role in describing physical phenomena through differential equations. Focusing on a four-dimensional Lie algebra in (1+3)-dimensional space, this study classifies these structures. Here, a method is developed to identify second-order differential invariants and showed how they translate into systems of three second-order ordinary differential equations for the specific case of a four-dimensional Lie algebra, and these invariants are presented in the form of list in Table 1. To further refine the analysis, these systems are classified based on their “dimensional homogeneity structure,” which reveals their behavior when scaling all variables. This sub-classification described in Table 2, goes beyond the initial broad categorization, potentially linking these structures to specific physical applications. Novelty: To best of our knowledge, the classification of nonsingular differential invariant structure in (1+3)-dimensional space of four-dimensional real Lie algebras and association of dimensional homogeneity structure in classification scheme are new contributions in algebraic classification problems in literature and never reported before this. Such type of classification scheme is fruitful in analysis of many physical systems. In addition, the algebraic properties of these peculiar invariant systems including integrability, are analysed in a detailed manner both mathematically and from a mechanics viewpoint. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： Classification Dimensional homogeneity Integrability Lie algebra Nonsingular differential invariants Systems of second-order ODEs

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