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8th International Conference on Mechanical engineering and Automation Science, ICMEAS 2022

作者： Xi, Lu Mengya, Ai Yi, Xie Fan, Chen Shanghai Aircraft Design and Research Institute Department of Software and Electronic Component Engineering Shanghai China

ISBN: (数字)9781665463058

ISBN: (纸本)9781665463058

The aviation industry has paid increasing attention on technologies which can improve cabin safety and reduce the workload of cabin crew. To improve that situation, concept of smart and multi-functional overhead stowage compartment is proposed and designed by means of MBSE. The concept adopts stat-of-The-Art technologies of wireless communication and machine learning, which can be explored to further smart cabin scenarios. © 2022 IEEE.

关键词： Navigation

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An Integrated Calibration Scheme for Attitude Benchmark of Micro-nano Satellites and Its Experiments Based on In-Orbit Data 8

An Integrated Calibration Scheme for Attitude Benchmark of M...

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8th International Conference on Automation, Control and Robotics engineering, CACRE 2023

作者： Li, Mingqun Luo, Tong Wang, Shaokai Li, He Lei, Yongjun Jiang, Qinghua Beijing Institute of Control Engineering Research and Development Center Beijing China Beijing Institute of Control Engineering System Design Department Beijing China Beijing Institute of Control Engineering Software Development Department Beijing China

ISBN: (纸本)9798350302776

The integrated calibration method of micro-nano satellite attitude benchmark is studied. Considering the magnetic effects and residual magnetism caused by the complex magnetic environment of the satellite, an accurate measurement model for magnetometers was adopted. By using the relationship between the measured and theoretical values of the geomagnetic field, the residual magnetism and magnetic sensitivity coefficients in the magnetometer measurement model were calibrated, through the least square method of uncertain parameters of the calibration. After modeling the installation error of the solar sensor, the calibration of the solar sensor is completed, so as to realize the integrated calibration of the attitude benchmark of the micro-nano satellite. Finally, this article conducted ground experiments using telemetry data from an in orbit satellite. The experimental results showed that the proposed method reduced sensor measurement errors, improved sensor benchmark consistency, and improved attitude determination accuracy from around 10 degrees to better than 0.2 degrees, verifying the effectiveness of the proposed method. © 2023 IEEE.

关键词： Calibration

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From data to design:Social network insights for urban design and regeneration

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Frontiers of Architectural Research 2024年第6期13卷 1377-1399页

作者： Sepideh Zarepour Sohi Saeed Banihashemi Moslem Sheikhkhoshkar Poriya Abdollahi Roshan School of Urban Design and Planning University of TehranTehranIran University of Technology Sydney Ultimo NSWAustralia IAEM-Lorraine Doctoral School University of LorraineEpinalFrance Department of Software Engineering Islamic Azad University of HashtgerdIran

Urban public spaces are pivotal to the welfare and prosperity of modern *** their importance,this research addresses the critical gap in understanding and enhancing the qualities of these spaces through advanced analytics,focusing on Tehran’s main traditional market,the Bazaar.A novel methodological framework combining Social Network Analysis(SNA),and Strengths,Weaknesses,Opportunities,and Threats(SWOT)analysis,supported by location-based social media reviews,was *** innovative approach assessed the Bazaar’s comfort,vitality,and safety,analyzing real-time public interactions and perceptions through social media *** findings highlighted the Bazaar’s central role in Tehran’s urban landscape and identified the need for strategic design *** interventions aimed to improve walkability,comfort,safety,and diversity,and have been successfully implemented,significantly enhancing the Bazaar’s quality and *** study not only advances urban studies and planning by providing a model for urban public space analysis but also underscores the value of social media data in urban *** successful revitalization of Tehran’s Bazaar sets a precedent for enriching urban experiences and boosting city vitality through similar interventions in other urban spaces.

关键词： Urban regeneration Urban public space Social media data Social network analysis(SNA) Sustainable cities

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Development of Microcontroller-Based Pure Sine Wave Inverter with Overload and Overheating Protection 17

Development of Microcontroller-Based Pure Sine Wave Inverter...

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17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer engineering, TCSET 2024

作者： Holovatyy, Andriy Lobur, Mykhaylo Holovata, Sofia Lukaszewicz, Andrzej Nowakowski, Marek Kolesnyk, Kostyantyn Lviv Polytechnic National University Department of Computer-Aided Design Systems Lviv Ukraine Ukrainian National Forestry University Department of Software Engineering Lviv Ukraine Bialystok University of Technology Department of Machinery Design and Operation Bialystok Poland Military Institute of Armoured and Automotive Technology Development Department Sulejowek Poland Lviv Polytechnic National University Department of Computera-Aided Design Systems Lviv Ukraine

ISBN: (纸本)9798331520564

In the paper, hardware and software of the Arduino based pure sine wave inverter with overload and overheating protection have been developed. The inverter constantly monitors the consumption current, the battery voltage, the temperature of the power switches (radiator) and, if their parameters are normal, forms a sinusoidal voltage of 230 V, 50 Hz at the output of the inverter, similar to the network voltage. The inverter provides the display of information on the LCD screen about current and power consumption, battery voltage, temperature of the inverter power switches, as well as display of emergency icons when the permissible operating parameters are exceeded. The inverter generates sound and visual warning messages with LED indicators and buzzer. The inverter can be used as a lightweight low-power 100 W network inverter with sine-wave voltage for autonomous power supply of low-power electronics. © 2024 IEEE.

关键词： Microcontrollers

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Device Interoperability for Industrial IoT using Model-Driven Architecture 24

Device Interoperability for Industrial IoT using Model-Drive...

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24th International Multitopic Conference, INMIC 2022

作者： Amjad, Anam Azam, Farooque Anwar, Muhammad Waseem Nust Department of Computer and Software Engineering Islamabad Pakistan School of Innovation Design and Engineering Malardalen Unversity Vasteras Sweden

ISBN: (数字)9798350397109

ISBN: (纸本)9798350397109

Industrial Internet of Things (IIoT) is an emerging domain, converting common objects into connecting objects with ubiquitous internet access to automate industry. Due to different vendors, supporting different infrastructures, a set of communication protocols such as Zigbee, 6LowPAN, Wireless Fidelity (Wi-Fi), Hyper Text Transfer Protocol (HTTP), etc. are introduced for IIoT. Thus, a closed ecosystem for smart devices is created. Particularly, when two or more industrial IoT applications are developed using different application-layer protocols such as Constrained Application Protocol (CoAP), Advanced Message Queuing Protocol (AMQP), or MQ Telemetry Transport (MQTT), devices are called heterogeneous devices and interoperability becomes a major challenge. In the existing literature, device-level interoperability using different application-layer protocols is enhanced with the help of intermediators at the network layer which includes servers, brokers, or gateways/adapters to route communication. However, these intermediators lead to several other issues such as dependency on network layer components, load balancing, single point of failure, and scalability. Therefore, the interoperability issue needs to be addressed at the application layer using a device intermediator instead of utilizing network layer components. For this purpose, Model Driven engineering (MDE) is selected because less attention is paid to IIoT interoperable solutions development using MDE. To bridge this gap, a Model Driven Architecture (MDA) based approach is proposed that reduces the processing time and effort to develop these IIoT interoperable systems. For this purpose, (i) a metamodel, (ii) a UML profile, and (iii) transformation rules are developed to make heterogenous application-layer protocols interoperable using devices as intermediator. The initial feasibility of the proposed solution is demonstrated through a real-world case study i.e., a smart city. Results show that a complete solut

关键词： Interoperability

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Toward Understanding Federated Learning over Unreliable Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 80-97页

作者： Feng, Chenyuan Arafa, Ahmed Chen, Zihan Zhao, Mingxiong Quek, Tony Q. S. Yang, Howard H. EURECOM Department of Communication Biot06410 France University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC28223 United States Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Yunnan University Engineering Research Center of Cyberspace National Pilot School of Software Kunming650106 China Zhejiang University ZJU-UIUC Institute Haining314400 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ChampaignIL61820 United States

This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) - a machine learning method that preserves data privacy in the training process - over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance. © 2023 CCBY.

关键词： Federated learning

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Optimizing Railway Traffic Management Using Fuzzy Logic-Based Decision-Making Systems 2

Optimizing Railway Traffic Management Using Fuzzy Logic-Base...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Kumari, Nalli Vinaya Pradeep Ghantasala, G.S. Sharma R, Rajesh Ananthanagu, U. Vidyullatha, Pellakuri Pandiya, Dileep Kumar Jntuh Malla Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Zoominfo Technologies Principl Software Engineer Boston United States

ISBN: (纸本)9798350364699

Railway web page traffic manipulate is a complex and dynamic method that includes making real-time picks based mostly on various uncertain and difficult to understand factors, collectively with fluctuating passenger demand, diverse train schedules, and unpredictable weather conditions. Traditional decision-making strategies often battle to cope with the ones uncertainties, foremost to inefficiencies and potential safety risks. This paper proposes the software program of fuzzy properly judgment-based completely decision-making systems to optimize railway web page traffic manipulate. Fuzzy properly judgment, with its potential to model uncertainty and control difficult to understand data, offers a robust framework for boosting decision-making approaches in railway operations. By simulating various conditions and integrating real-time data, the proposed gadget can dynamically regulate train schedules, manipulate delays, and enhance everyday operational efficiency. The paper may present case studies demonstrating the effectiveness of fuzzy properly judgment in dealing with railway web page traffic, reducing delays, and improving passenger satisfaction. The findings advice that incorporating fuzzy properly judgment into railway web page traffic manipulates systems can purpose greater adaptive and resilient railway operations, ultimately contributing to greater steady and additional reliable rail networks. © 2024 IEEE.

关键词： Efficiency

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Advancements in 5G and 6G Technologies Shaping the Future of Global Connectivity and Communication 5

Advancements in 5G and 6G Technologies Shaping the Future of...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Pachar, Sunita Reddy, Satti Sudha Mohan Kompally, Gouthami Srinivasagopalan, Lakshmi Narasimhan Sangore, Rohidas Balu Katta, Sivakoteswararao GLA University IBM Department India SRKR Engineering College Department of ECE India Department of CSE-IOT India Software Development Enterprise Architecture PlanoTX United States Computer Science and Engineering MIT Art Design Technology University India KIETW Department of Mechanical Kakinada India

ISBN: (纸本)9798331530013

The evolution of wireless communication is poised for a major shift with the transition from 5G to 6G, a leap characterized by breakthroughs in speed, ultra-reliable low-latency communication, and AI-driven network optimization. This paper examines these advancements, focusing on sub-millisecond latency, terahertz spectrum utilization, and enhanced network capabilities tailored to industries such as smart infrastructure, healthcare, and autonomous vehicles. While 6G promises revolutionary improvements, it also presents new challenges, including increased computational complexity and energy demands, data privacy concerns, and efficient spectrum allocation. The proposed model aims to address these issues by integrating AI and machine learning for optimized resource management, resulting in a high-performance, cost-effective solution for real-time, data-intensive applications. To assess the model's effectiveness, comprehensive simulations and real-world trials will be conducted, measuring critical metrics such as network throughput, latency, reliability, and energy efficiency. This study offers an all-encompassing guide to understanding and harnessing the transformative potential of 6G technology, laying the groundwork for future wireless communication systems. © 2024 IEEE.

关键词： Terahertz spectroscopy

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A Study to Evaluate the Impact of LoRA Fine-tuning on the Performance of Non-functional Requirements Classification

arXiv

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arXiv 2025年

作者： Li, Xia Kim, Allen The Department of Software Engineering and Game Design and Development Kennesaw State University Marietta United States

Classifying Non-Functional Requirements (NFRs) in software development life cycle is critical. Inspired by the theory of transfer learning, researchers apply powerful pre-trained models for NFR classification. However, full fine-tuning by updating all parameters of the pre-trained models is often impractical due to the huge number of parameters involved (e.g., 175 billion trainable parameters in GPT-3). In this paper, we apply Low-Rank Adaptation (LoRA) fine-tuning approach into NFR classification based on prompt-based learning to investigate its impact. The experiments show that LoRA can significantly reduce the execution cost (up to 68% reduction) without too much loss of effectiveness in classification (only 2%-3% decrease). The results show that LoRA can be practical in more complicated classification cases with larger dataset and pre-trained models. © 2025, CC BY-NC-ND.

关键词： software design

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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