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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Somerville, Alexander Lynar, Tim Joiner, Keith Wild, Graham UNSW Canberra School of Engineering and Technology CanberraACT2612 Australia UNSW Canberra School of Systems and Computing CanberraACT2612 Australia UNSW Canberra School of Science CanberraACT2612 Australia

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This study examines how effective simulator-based training systems are in improving drone piloting skills and performance. Using a quasi-experimental research design, it assesses the impact on accuracy, efficiency, and perceived workload among UAV pilots. By drawing on historical simulation practices from crewed aviation and applying instructivist educational principles, the research evaluates whether structured simulator training can enhance real world drone operation skills. The findings show a significant improvement in flight performance for those who underwent simulator training, emphasizing the importance of integrating advanced simulation technologies and instructivist methods in drone pilot training programs to address the evolving needs of both industry and academia. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Drones

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Simplified prescribed performance tracking control of uncertain nonlinear systems

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Science China(Information Sciences) 2022年第8期65卷 291-292页

作者： Quan-Yong FAN Shuoheng XU Bin XU Jianlong QIU School of Automation Northwestern Polytechnical University School of Automation and Electrical Engineering Linyi University Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong Linyi University

Dear editor,In practical engineering, some certain transient and steady performance, such as overshoot, adjustment time, and steady-state error, need to meet specific requirements. In order to ensure that the system s...

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Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture

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Digital Communications and Networks 2024年第5期10卷 1245-1257页

作者： Abeer A.Z.Ibrahim Fazirulhisyam Hashim Aduwati Sali Nor K.Noordin Keivan Navaie Saber M.E.Fadul Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Wireless and Photonics Networks Research Centre(WiPNET) Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Department of Communication and Computer Engineering College of Engineering and Medical SciencesKhartoum11111Sudan School of Computing and Communications Lancaster UniversityLancasterLA14YWUnited Kingdom Department of Electrical and Electronics Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia

The deployment of distributed multi-controllers for Software-Defined Networking(SDN)architecture is an emerging solution to improve network scalability and ***,the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low ***,we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane *** fault-tolerance strategy has been applied to distributed SDN control architecture,which allows each switch to migrate to next controller to enhance network *** this paper,the Reliable and Dynamic Mapping-based Controller Placement(RDMCP)problem in distributed architecture is framed as an optimization problem to improve the system reliability,quality,and *** considering the bound constraints,a heuristic state-of-the-art Controller Placement Problem(CPP)algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular *** algorithm identifies the optimal controller location,minimum number of controllers,and the expected assignment costs after failure at the lowest effective cost.A metaheuristic Particle Swarm Optimization(PSO)algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and *** effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline *** findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.

关键词： Software defined networking Dynamic mapping Particle swarm optimization Reliability Multi-objective optimization Evolutionary

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A measured inclusion of an RTI tool for the South Pacific 24

A measured inclusion of an RTI tool for the South Pacific

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7th International Conference on Software engineering and Information Management, ICSIM 2024

作者： Sharma, Bibhya Reddy, Pritika Singh, Vineet School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Fiji Department of Computing Science and Information Systems Fiji National University Fiji

ISBN: (纸本)9798400709197

Good Governance of any country is reflected through transparency and accountability of its activities and the availability of information and open data in a timely manner to its citizens. The Right to Information (RTI) Act guides the government and its citizens to achieve the objectives availability, and effective communication between the government and society, hence leading to good governance. While there are about 135 countries globally who have adopted the RTI Act, the Act is yet to be fully enacted in Fiji and the advocacy and awareness about RTI is in the infancy stage. This study entails details of an RTI awareness initiative in Fiji through the design and implementation of an e-course. The RTI e-course was developed using Articulate Storyline 360 and integrated into a learning management platform - Moodle. The course was then piloted to the university students in Fiji, Tonga and Vanuatu. The effectiveness of the course was measured in terms of course completion and collected feedback using an online questionnaire. The results from the Cronbach Alpha test showed that the RTI e-course was valid and reliable. Furthermore, the participants were satisfied with the design, structure and content of the e-course. The RTI e-course is the first-ever course on RTI as such this can be used by South Pacific countries to create awareness and educate their populace on their right to information. The awareness created will assist the people of the South Pacific to be more aware of the activities carried out by the government and eventually improve civic engagement and participation of the South Pacific populace to economic development of their society. © 2024 ACM.

关键词： Transparency

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Digital Regimen for Parkinson Disease: An Informative Approach Using Mobile Application 2

Digital Regimen for Parkinson Disease: An Informative Approa...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Seya Prakash, P. Gunasekaran, G. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Vellore Institute Technology Department of Smart Computing Vellore India

ISBN: (纸本)9798350328202

Parkinson's disease (PD) is an advanced neuro-degenerative condition where a large number of people around the world are profoundly affected. The scarcity of precise data regarding the typical circumstances faced by humans with PD severely restricts ongoing research and therapeutic endeavours. This proposed work aims to investigate various innovations to address this issue comprehensively. Specifically, this proposed work intends to develop a versatile application tailored to help the PD patients and incorporating smart medication reminders. Moreover, this proposed work will focus on creating exercise/workout plans & diet plans based on continuous data collection and patient feedback to enhance prescription management while minimizing side effects. Simultaneously, initiatives aimed at reducing the stress level and improve mental health. The development and implementation of this versatile applications hold significant promise in improving patient outcomes. By addressing the dearth of accurate information and fostering a systematic support ecosystem, it aspires to enhance the lives of individuals grappling with PD on a global scale. © 2024 IEEE.

关键词： Neurodegenerative diseases

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An Integrated Approach for Fault Detection in Industrial Processes Using Approximate Entropy and Slow Feature Analysis 43

An Integrated Approach for Fault Detection in Industrial Pro...

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43rd Chinese Control Conference, CCC 2024

作者： Yang, Chuangyan Li, Peng Wu, Jiande Lang, Xun Ai, Mingxi Yunnan University School of Information Science and Engineering Yunnan Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing China

ISBN: (纸本)9789887581581

Multivariate statistical process monitoring has been widely used in industry process. However, traditional algorithms often ignore the complexity dynamic features of actual industry process. This study proposes a novel algorithm based on Approximate Entropy and Slow Feature Analysis (AE-SFA), which has completed the full-condition monitoring of a dynamic complexity system and quantitative divided static components and dynamic components. The algorithm achieves the optimal detection rate under T2 and SPE control limits. To enrich the experiment, we used the Tennessee Eastman process dataset to demonstrate the superiority of our algorithm. From the comprehensive score of the optimal detection rate, the accuracy of the proposed method is improved by 3.24% and 10.50% of T2 and SPE statistics, respectively. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Statistical process control

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How Do HCI Researchers Study Cognitive Biases? A Scoping Review 25

How Do HCI Researchers Study Cognitive Biases? A Scoping Rev...

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2025 CHI Conference on Human Factors in computing systems, CHI 2025

作者： Boonprakong, Nattapat Tag, Benjamin Goncalves, Jorge Dingler, Tilman School of Computing and Information Systems University of Melbourne ParkvilleVIC Australia School of Computer Science and Engineering University of New South Wales SydneyNSW Australia School of Computing and Information Systems University of Melbourne Melbourne Australia Industrial Design Engineering Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400713941

computing systems are increasingly designed to adapt to users' cognitive states and mental models. Yet, cognitive biases affect how humans form such models and, therefore, they can impact their interactions with computers. To better understand this interplay, we conducted a scoping review to chart how Human-Computer Interaction (HCI) researchers study cognitive biases. Our findings show that computing systems not only have the potential to induce and amplify cognitive biases but also can be designed to steer users' behaviour and decision-making by capitalising on biases. We describe how HCI researchers develop algorithms and sensing methods to detect and quantify the effects of cognitive biases and discuss how we can use their understanding to inform system design. In this paper, we outline a research agenda for more theory-grounded research and highlight ethical issues when researching and designing computing systems with cognitive biases in mind as they affect real-world behaviour. © 2025 Copyright held by the owner/author(s).

关键词： Real time systems

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Neuroimaging Precision: Crafting a Stacked VGG19-Logistic Fusion for Breakthrough Early Detection of Alzheimer's 9

Neuroimaging Precision: Crafting a Stacked VGG19-Logistic Fu...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Selvi, G. Anbu Karthick, T. Srmist Department of Computer Science And Engineering Chennai Kattankulathur India School of Computing Srmist Department of Data Science And Business Systems Chennai Kattankulathur India

ISBN: (纸本)9798350365092

Several medical investigations have demonstrated that Alzheimer's disease (AD) manifests itself long before the formal diagnosis of dementia. These studies have led to the identification of numerous ideal biomarkers associated with Alzheimer's symptoms. Consequently, the imperative for early diagnosis has become evident, necessitating a high-performance computational tool capable of efficiently processing vast amounts of data. Early detection of AD not only offers valuable insights into the condition's progression but also presents a significant opportunity to benefit from timely and effective treatment. This ground-breaking study pioneers an innovative approach to the early detection and diagnosis of AD by crafting a sophisticated model SVGGL, which blends the power of stacked VGG19 neural networks with the interpretability of logistic regression. Leveraging neuro-imaging data, specifically the insights provided MRI, and our model aims to discern subtle structural and functional alterations in the brains of individuals at risk for or in the early stages of AD. The combination of feature extraction from stacked VGG19 models and subsequent logistic regression classification yields a precise and efficient classifier with an outstanding accuracy of 98.24%. With such a high degree of accuracy, the model can reliably differentiate among healthy people and those with Alzheimer's, opening the door to potential early intervention and individualized treatment plans. The study not only sheds light on the intricate neuro pathological landscape of AD but also propels us toward a future where cutting-edge technology and data-driven insights, exemplified by our high-accuracy model, converge to tackle the challenges posed by this prevalent neurodegenerative disorder. © 2024 IEEE.

关键词： Neuroimaging

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An improved variational mode decomposition with adaptive initial center frequency for bearing fault feature extraction

An improved variational mode decomposition with adaptive ini...

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2024 China Automation Congress, CAC 2024

作者： Wang, Ying Lang, Xun Wu, Jiande Li, Yuzhen Li, Peng Zhang, Yufeng Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9798350368604

Variational mode decomposition (VMD) is extensively utilized in the field of industrial signal processing due to its superior anti-mixing and denoising capabilities. However, in practice, the performance of VMD is significantly influenced by the initial center frequency. An improper setting of can significantly reduce the stability of decomposition, resulting in the loss or masking of feature information by noise. To address the above issue, an improved VMD algorithm is proposed in this work. Our algorithm redefines based on the narrowband characteristics of intrinsic mode function (IMF). Under the new definition, can be adaptively adjusted based on the signal spectrum processed by Wiener filtering. This adjustment significantly enhances the flexibility and anti-mixing performance of the original VMD. To validate our proposed method, comparative experiments were conducted using two sets of fault data from Case Western Reserve University (CWRU). The results demonstrate that our method is more effective and superior in extracting fault features. © 2024 IEEE.

关键词： Variational mode decomposition

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