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

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 Teachers' Adoption of Web 2.0 Tools in a Thai University: A Case Study 8

Exploring Teachers' Adoption of Web 2.0 Tools in a Thai Univ...

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8th International Conference on Information technology, InCIT 2024

作者： Rongbutsri, Nikorn Khalid, Md. Saifuddin Mae Fah Luang University School of Information Technology Chiang Rai Thailand Technical University of Denmark Department of Applied Mathematics and Computer Science Copenhagen Denmark

ISBN: (纸本)9798350366303

This paper explores the integration of Web 2.0 tools into teaching and learning at a Thai university, focusing on the alignment of these tools with specific pedagogical approaches to enhance engagement and learning outcomes. The study, conducted within the context of a blended learning environment influenced by the COVID-19 pandemic, employed problem-tree analysis and semi-structured interviews with faculty, IT support staff, and students. Findings reveal challenges related to tool complexity, the need for comprehensive teacher training, and the importance of aligning technology with pedagogical strategies. The research highlights the need for a more streamlined approach to Web 2.0 tool integration, emphasizing careful selection, effective teacher training, and a thoughtful blending of online and on-site learning experiences to improve student engagement and learning. The study contributes to the ongoing conversation surrounding effective technology integration in higher education by offering practical insights for improving teaching and learning in diverse contexts. © 2024 IEEE.

关键词： Contrastive Learning

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Entropy generation optimisation for unsteady stagnation Casson nanofluid flow over a stretching sheet with binary chemical reaction and Arrhenius activation energy using the bivariate spectral quasi-linearisation method

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International Journal of Ambient Energy 2022年第1期43卷 6489-6501页

作者： Oyelakin, I.S. Mthethwa, H. Sithole Kameswaran, Peri K. Shaw, S. Sibanda, P. School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg Braamfontein South Africa School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Scottsville Pietermaritzburg South Africa Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Vellore India Department of Mathematics and Statistical Sciences Botswana International University of Science and Technology Palapye Botswana

The heat and mass transport with Arrhenius activation energy has countless uses such as in nuclear reactors, the synthesis of chemical compounds, geothermal artificial lakes, and retrieval of thermal lubricants. The focus of this work is the analysis of Arrhenius activation energy and binary chemical reaction in a stagnation point Casson nanofluid flow over a stretching sheet. The system is subjected to a nonlinear radiation and a magnetic field. The transport equations are simplified using the Williams and Rhyne non-similar transformation. The solution to the transformed coupled system of nonlinear partial differential equations is obtained using the bivariate spectral quasi-linearisation method. Entropy generation that measures the degenerate useful energy and deprivation of the performance of engineering systems is analysed. Numerical results describing changes in the fluid velocity, temperature, concentration, entropy generation and Bejan number are presented and discussed for different fluid parameters. Activation energy parameter boosts up the solute concentration at the surface while an opposite is observed far away from the surface. The entropy generation decreases with an increase in the unsteady parameter and Casson number, while the opposite response was observed for a higher Reynolds number. Brinkman number gears up the entropy generation while it shows an opposite phenomenon for Bejan number. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Reynolds number

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On Batching Variable Size Inputs for Training End-to-End Speech Enhancement Systems 48

On Batching Variable Size Inputs for Training End-to-End Spe...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Gonzalez, Philippe Sonne Alstrom, Tommy May, Tobias Technical University of Denmark Department of Health Technology Denmark Technical University of Denmark Department of Applied Mathematics and Computer Science Denmark

ISBN: (纸本)9781728163277

The performance of neural network-based speech enhancement systems is primarily influenced by the model architecture, whereas training times and computational resource utilization are primarily affected by training parameters such as the batch size. Since noisy and reverberant speech mixtures can have different duration, a batching strategy is required to handle variable size inputs during training, in particular for state-of-the-art end-to-end systems. Such strategies usually strive for a compromise between zero-padding and data randomization, and can be combined with a dynamic batch size for a more consistent amount of data in each batch. However, the effect of these strategies on resource utilization and more importantly network performance is not well documented. This paper systematically investigates the effect of different batching strategies and batch sizes on the training statistics and speech enhancement performance of a Conv-TasNet, evaluated in both matched and mismatched conditions. We find that using a small batch size during training improves performance in both conditions for all batching strategies. Moreover, using sorted or bucket batching with a dynamic batch size allows for reduced training time and GPU memory usage while achieving similar performance compared to random batching with a fixed batch size. © 2023 IEEE.

关键词： Speech enhancement

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 356-368页

作者： Jiaxing Li Zidong Wang Jun Hu Hongli Dong Hongjian Liu the Department of Applied Mathematics Harbin University of Science and Technology IEEE the Department of Computer Science Brunel University London the School of Automation Harbin University of Science and Technology the Artificial Intelligence Energy Research Institute Northeast Petroleum University the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University the National Key Laboratory of Continental Shale Oil of China Northeast Petroleum University the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University the School of Mathematics and Physics Anhui Polytechnic University

In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.

关键词： Amplify-and-forward (AaF) relays covariance intersection fusion cubature Kalman filtering multi-sensor systems uniform boundedness

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Adaptive Reversible Visible Watermarking Based on Total Variation for BTC-Compressed Images

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computers, Materials & Continua 2023年第3期74卷 5173-5189页

作者： Hengfu Yang Mingfang Jiang Zhichen Gao School of Computer Science Hunan First Normal UniversityChangsha410205China Hunan Provincial Key Laboratory of Informationization Technology for Basic Education Changsha410205China Department of Applied Mathematics and Statistics College of Engineering and Applied SciencesStony Brook UniversityNY 11794USA

Few previous Reversible Visible Watermarking(RVW)schemes have both good transparency and watermark *** adaptive RVW scheme that integrates Total Variation and visual perception in Block Truncation Coding(BTC)compressed domain,called TVB-RVW is proposed in this paper.A new mean image estimation method for BTC-compressed images is first developed with the help of Total ***,a visual perception factor computation model is devised by fusing texture and luminance *** adaptive watermark embedding strategy is used to embed the visible watermark with the effect of the visual perception factor in the BTC ***,a lossless embedding method of the encrypted visible watermark is exploited to deter illegal watermark *** visible watermark can be removed since the visual perception factor and the estimated mean image remain unchanged before and after watermark *** experiments validate the superiority of the proposed algorithm over previous RVW schemes in BTC in terms of the visual quality of watermarked images and watermark visibility,and it can achieve a good balance between transparency and watermark visibility.

关键词： Reversible visible watermark block truncation coding visual perception total variation

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FreqRISE: Explaining time series using frequency masking 6

FreqRISE: Explaining time series using frequency masking

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6th Northern Lights Deep Learning Conference, NLDL 2025

作者： Brüsch, Thea Wickstrøm, Kristoffer Knutsen Schmidt, Mikkel N. Alstrøm, Tommy Sonne Jenssen, Robert Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Physics and Technology UiT The Arctic University of Norway Norway Norwegian Computing Center Oslo Norway

Time series data is fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision-making. To develop explainable artificial intelligence in these domains therefore implies explaining salient information in the time series. Current methods for obtaining saliency maps assumes localized information in the raw input space. In this paper, we argue that the salient information of a number of time series is more likely to be localized in the frequency domain. We propose FreqRISE, which uses masking-based methods to produce explanations in the frequency and time-frequency domain, and outperforms strong baselines across a number of tasks. The source code is available here: https://***/theabrusch/FreqRISE. © NLDL *** rights reserved.

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Throughput Prediction Using LS-SVM For 4g-LTE Network and Beyond

Throughput Prediction Using LS-SVM For 4g-LTE Network and Be...

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Telecommunications and Intelligent Systems (ICTIS), International Conference on

作者： Mostefa Hamdani Ahmed Sahnoune Mokhtar Merzougui Abdelmalek Douara Fouad Khatemi Department of Mathematics and Computer Science Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology University El Wancharissi Department of Science and Technology Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology University El Wancharissi

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

The integration of Artificial Intelligence(AI) with Long-Term Evolution (LTE) networks offers substantial potential for improving communication infrastructure. By harnessing AI algorithms, it is possible to dynamically optimize network parameters, proactively predict and mitigate equipment failures, and enhance Quality of Service (QoS) by prioritizing critical services through real-time analysis of key performance indicators. This study focuses on predicting network throughput using Least Square Support Vector Machine (LS-SVM). The predictive models developed can trigger adaptive network reconfigurations, ensuring that the network responds promptly to changing conditions. Simulation results reveal that the integration of LS-SVM significantly improves prediction accuracy, enabling more effective implementation of self-organizing network strategies in real-time.

关键词： Support vector machines Correlation coefficient Accuracy Quality of service Predictive models Throughput Real-time systems Telecommunications Analysis of variance Testing

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Numerical Computation of SEIR Model for the Zika Virus Spreading

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computers, Materials & Continua 2023年第4期75卷 2155-2170页

作者： Suthep Suantai Zulqurnain Sabir Muhammad Asif Zahoor Raja Watcharaporn Cholamjiak Data Science Research Center Department of MathematicsFaculty of ScienceChiang Mai UniversityChiang Mai50200Thailand Research Group in Mathematics and Applied Mathematics Faculty of ScienceChiang Mai UniversityChiang Mai50200Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlinspan64002Taiwan School of Science University of PhayaoPhayao 56000Thailand

The purpose of this study is to present the numerical performancesand interpretations of the SEIR nonlinear system based on the Zika virusspreading by using the stochastic neural networks based intelligent computingsolver. The epidemic form of the nonlinear system represents the four dynamicsof the patients, susceptible patients S(y), exposed patients hospitalized inhospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIRmodel. The computing numerical outcomes and performances of the systemare examined by using the artificial neural networks (ANNs) and the scaledconjugate gradient (SCG) for the training of the networks, i.e., *** correctness of the ANNs-SCG scheme is observed by comparing theproposed and reference solutions for three cases of the SEIR model to solvethe nonlinear system based on the Zika virus spreading dynamics throughthe knacks of ANNs-SCG procedure based on exhaustive *** outcomes of the ANNs-SCG algorithm are found consistently in goodagreement with standard numerical solutions with negligible errors. Moreover,the procedure’s constancy, dependability, and exactness are perceived by usingthe values of state transitions, error histogram measures, correlation, andregression analysis.

关键词： SEIR nonlinear system Zika virus artificial neural networks scaled conjugate gradient statistical measures

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Incremental Learning Strategy with Multi-dimensional Knowledge Distillation for One-stage Object Detection 22

Incremental Learning Strategy with Multi-dimensional Knowled...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Li, Yimeng Liu, Xuejiao Xiang, Xueshuang Tan, Yu-An Meng, Weizhi School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Qian Xuesen Laboratory of Space Technology China Academy of Space Technology Beijing China Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark

ISBN: (纸本)9798331527471

In practical applications, object detectors often encounter unknown classes samples. As object detection datasets undergo continuous development, the need arises for object detectors to adeptly recognize an escalating array of new classes while retaining the original detection capability. This paper introduces a new class incremental object detection learning framework with multi-dimensional knowledge distillation, using Yolov5 as the foundational detection model. Specifically, we utilize local feature distillation, attention distillation and global feature distillation to preserve information in the original model’s feature maps. Additionally, we employ response distillation to ensure the model remains responsive to previous classes. In comparison to the state-of-the-art methods, our approach achieves higher overall accuracy on PASCAL VOC 2007. Especially in the "19+1" incremental task, our approach gets the highest mAP@0.5 of 0.697 with a minimum mean AP decline rate of 4.45%. For the "4+3" and "6+1" tasks on KITTI, our approach preserves the model’s responsiveness to the original task more effectively while achieving the highest overall accuracy. © 2024 IEEE.

关键词： Contrastive Learning

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