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The Journal of Machine Learning Research 2024年第1期25卷 3032-3056页

作者： Janet Van Niekerk Håvard Rue Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia

Approximate inference methods like the Laplace method, Laplace approximations and variational methods, amongst others, are popular methods when exact inference is not feasible due to the complexity of the model or the abundance of data. In this paper we propose a hybrid approximate method called Low-Rank Variational Bayes correction (VBC), that uses the Laplace method and subsequently a Variational Bayes correction in a lower dimension, to the joint posterior mean. The cost is essentially that of the Laplace method which ensures scalability of the method, in both model complexity and data size. Models with fixed and unknown hyperparameters are considered, for simulated and real examples, for small and large data sets.

关键词： information processing rule INLA Laplace LGM variational Bayes

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Design and Implementation of 2-DOF Arm Manipulator Robot as Robotic Kinematic Learning Platform 6

Design and Implementation of 2-DOF Arm Manipulator Robot as ...

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6th International Conference on Vocational Education and electrical engineering, ICVEE 2023

作者： Widayaka, Parama Diptya Puspitaningayu, Pradini Alamsyah, Sayyidul Aulia Muktiadji, Rifqi Firmansyah Widodo, Arif Abdillah, Rifqi Universitas Negeri Surabaya Dept. of Electrical Engineering Surabaya Indonesia King Abdulaziz University Dept. of Electrical and Computer Engineering Jeddah Saudi Arabia Okayama University Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayamas Japan Institut Teknologi Sepuluh Nopember Surabaya Dept. of Informatics Surabaya Indonesia

ISBN: (纸本)9798350326642

Technological developments have impacts and challenges in human life. Technological advances have made human life easier. The impact of technological developments is the industrial manufacturing process getting faster with the help of robotics technology. Robots can shorten manufacturing process time and efficient. On the other hand, challenges with technological developments force us to keep learning and adaptive to every changes around us, especially in the education sector. In education sector, formal educational institutions or campuses must be able to facilitate students to be able to learn comprehensively in the field of technology, especially robotics, where we can make an innovative learning platform based on robotics technology. In this study, we discussed a 2-DOF arm manipulator robot as kinematics learning platform of industrial robot to increase student competencies in preparation for facing the industrial environment in century. Inverse kinematics equation has been applied in the robot by providing input coordinates on the x-Axis and y-Axis where the accuracy value on the x-Axis is 77% and the accuracy on the y-Axis is 83.1%. © 2023 IEEE.

关键词： Machine design

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SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARNING

SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARN...

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International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Liu, Linfeng Juwono, Filbert H. Wong, W.K. Purwanto, Erick Jin, Tingyan Zhen, Yuyang Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing China School of Instrument Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781837242689

In this study, we explore the classification and prediction capabilities of three models-Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)-on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction. © The Institution of engineering & Technology 2024.

关键词： Genetic programming

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In-Solution Germanium Selenide Nanosheets as Charge Trapping Layer in Flash Memories

In-Solution Germanium Selenide Nanosheets as Charge Trapping...

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IEEE Silicon Nanoelectronics Workshop (SNW)

作者： Nazek El-Atab Bashayr Alqahtani Program of Electrical and Computer Engineering Computer Electrical Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia

Germanium selenide (GeSe) is a highly promising material with several attractive characteristics, particularly in the field of ferroelectric and phase-change memories due to its outstanding electronic behavior. However, the potential of GeSe as a charge-trapping layer in flash memory has received less attention. Herein, the fabrication of a nonvolatile MOS memory device using GeSe nanosheets as a charge-trapping layer was demonstrated and the materials flakes were examined extensively. The electrical performance of the memory device was investigated. Intriguingly, it exhibited an extraordinarily wide memory window of 9 V under ±10 V electrical biasing. Additionally, the devices presented high endurance of $10^{4}$ programming and erasing cycles, and reliable charge storage of only 56% loss after 10 years.

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Evaluation of time-series database on microservice architecture for health monitoring system

Evaluation of time-series database on microservice architect...

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Simanjuntak, Eko Surantha, Nico Isa, Sani Muhamad Bina Nusantara University Binus Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia Tokyo City University Electronic and Communication Engineering Faculty of Engineering Department of Electrical Tokyo Japan

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

Recently, the research on daily health monitoring using a wearable sensor has been continually evolving. In the future, when this system is actually implemented, a vast amount of data transmission will be conducted from multiple sensors to the cloud server. A system capable of transmitting, storing, processing, and visualizing massive volumes of data is necessary for health monitoring. This research aims to develop a health monitoring system that can accommodate a high amount of data with high performance. The proposed techniques include numerous modifications to system architecture and technology. In the system architecture, a multi-database is proposed to enhance the reliability of the system. As an enhancement to the technology aspect, a time series database and MQTT server are proposed. As the result, the proposed system achieves significantly better performance in terms of throughput, database-writing speed, and database-reading speed compared to the existing system. © 2022 IEEE.

关键词： Database systems

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Hybrid Classical-Quantum Optimization for Ensemble Learning 9

Hybrid Classical-Quantum Optimization for Ensemble Learning

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9th International Conference on Advanced Informatics: Concepts, Theory and Applications, ICAICTA 2022

作者： Yulianti, Lenny Putri Santoso, Judhi Trisetyarso, Agung Surendro, Kridanto Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia Bina Nusantara University Program - Computer Science Bandung Indonesia

ISBN: (数字)9781665456685

ISBN: (纸本)9781665456685

Ensemble learning has gained attention due to its ability to improve predictive performance in classification and regression problems. However, there are several issues related to optimizing accuracy, diversity, and efficiency simultaneously in the ensemble learning process. Existing studies mostly focus on one or two of them, or focus on all three but only on the specific process of ensemble learning. Besides, the quantum approach has been expected to accelerate the optimization process while maintaining accuracy. The contribution of this study is to design a hybrid classical-quantum methodology for optimizing ensemble learning in a holistic way. This methodology combines classical (i.e. clustering, improved evaluation measures, and generalized weighted ensemble with internally tuned hyperparameters) and quantum (i.e. quantum annealing) approaches, and this methodology is analyzed to provide more optimal results. © 2022 IEEE.

关键词： Learning systems

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Bubble-pen lithography:Fundamentals and applications

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Aggregate 2022年第4期3卷 85-100页

作者： Pavana Siddhartha Kollipara Ritvik Mahendra Jingang Li Yuebing Zheng Walker Department of Mechanical Engineering The University of Texas at AustinAustinTexasUSA Department of Electrical and Computer Engineering The University of Texas at AustinAustinTexasUSA Material Science and Engineering Program Texas Materials InstituteThe University of Texas at AustinAustinTexasUSA

Developing on-chip functional devices requires reliable fabrication methods with high resolution for miniaturization,desired components for enhanced performance,and high throughput for fast prototyping and mass ***,laser-based bubble-pen lithography(BPL)has been developed to enable sub-micron linewidths,in situ synthesis of custom materials,and on-demand patterning for various functional components and *** exploits Marangoni convection induced by a laser-controlled microbubble to attract,accumulate,and immobilize particles,ions,and molecules onto different *** years have witnessed tremendous progress in theory,engineering,and application of BPL,which motivated us to write this ***,an overview of experimental demonstrations and theoretical understandings of BPL is ***,we discuss the advantages of BPL and its diverse applications in quantum dot displays,biological and chemical sensing,clinical diagnosis,nanoalloy synthesis,and *** conclude this review with our perspective on the challenges and future directions of BPL.

关键词： additive manufacturing capillary flow lithography marangoni convection microbubbles sensing

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Data Driven Traffic Density Monitoring: A UAV and ResNet50 Deep Learning Framework 21

Data Driven Traffic Density Monitoring: A UAV and ResNet50 D...

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21st IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2025

作者： Rubab, Saddaf Dahri, Fida Hussain Mustafa Abro, Ghulam E Tahir, Nooritawati Md Javed, Muhammad University of Sharjah College of Computing and Informatics Department of Computer Engineering Sharjah27272 United Arab Emirates Southeast University School of Computer Science and Engineering Nanjing China Interdisciplinary Research Centre for Aviation and Space Exploration Saudi Arabia College of Engineering Universiti Teknologi Mara School of Electrical Engineering Selangor Shah Alam Malaysia International Information Technology University Department of Cybersecurity Alamty Kazakhstan Donghua University College of Computer Science Department of Computer Science and Technology Shanghai200022 China

ISBN: (纸本)9798331522193

Urban traffic congestion necessitates innovative monitoring and control solutions. This study leverages UAV footage and deep learning techniques to classify and analyze traffic density levels autonomously. By employing a pre-trained ResN et50 model enhanced with additional layers, the research optimizes traffic density classification using UAV-captured data. A custom dataset of traffic scenarios in Dammam, Saudi Arabia, was developed, incorporating diverse conditions through advanced normalization and data augmentation techniques to improve resilience and robustness. The modified ResN et50 model demonstrated high classification accuracy across varied traffic scenarios, validating its reliability and scalability for real-world applications. Key findings include the superior adaptability of UAV-based monitoring for real-time urban traffic assessment and the enhanced classification precision achieved through model optimization. This research underscores the potential of integrating UAV technology with deep learning to advance urban traffic management and foster more efficient urban planning strategies. © 2025 IEEE.

关键词： Traffic congestion

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Tracking Time-Varying Parameters in Massive MIMO IoT Networks: A Linear Coherent Decentralized Approach

Tracking Time-Varying Parameters in Massive MIMO IoT Network...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kunwar Pritiraj Rajput Linlong Wu Bhavani Shankar M. R. Bjorn Ottersten Pramod K. Varshney Interdisciplinary Center for Security Reliability and Trust (SnT) University of Luxembourg Department of Electrical Engineering and Computer Science Syracuse University NY USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper investigates the integration of Internet of Things (IoT) networks with modern massive multiple-input multiple-output (MIMO) wireless systems to enable various new use cases. Given the dynamic nature of parameters monitored by IoT nodes, efficient techniques for tracking these time-varying parameters are required. In a typical IoT networks, each IoT node linearly precodes its observations and transmits them over a coherent multiple access channel to a fusion center (FC). These IoT networks are power and bandwidth constrained in nature. Therefore, designing transmit precoders for the IoT nodes and a combiner for the FC is essential. This work proposes online linear receive combiner and transmit precoder designs that minimize the mean square error (MSE) under transmit power constraints. Using an alternating optimization technique, we derive closed-form solutions for the combiners and transmit precoders. Our numerical results validate the effectiveness of the proposed algorithms.

关键词： Wireless communication Closed-form solutions Signal processing algorithms Massive MIMO Signal processing Vectors Real-time systems Internet of Things Speech processing Optimization

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

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IEEE Transactions on Mobile Computing 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In Nanyang Technological University College of Computing and Data Science Energy Research Institute @ NTU Interdisciplinary Graduate Program Singapore University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2002-2012 IEEE.

关键词： Resource allocation

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