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Trajectory Prediction Method for Unmanned systems Based on Dynamic Graph Neural Network with Adaptive Weight Adjustment

Trajectory Prediction Method for Unmanned Systems Based on D...

引用

2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Mei, Yuankun An, Yu Geng, Liang Liu, Hao School of Science Hubei University of Technology Wuhan China Dept. of Systems Engineering Wuhan Digital Engineering Institute Wuhan China

ISBN: (纸本)9798350384185

In response to the complexity and uncertainty of trajectory prediction for unmanned systems in highly dynamic and uncertain complex environments, this study proposes a dynamic graph neural network (D-GNN) method based on adjustment mechanism. This method consists of a weight adjustment network module and a dynamic graph of unmanned systems. The weight adjustment network module dynamically adjusts the weights, enhancing the model's adaptability to complex environmental diversity and dynamics, thereby effectively addressing the dynamic relationship modeling between nodes and edges. Experimental results show that this method performs well in trajectory prediction tasks, achieving high prediction accuracy. This method demonstrates higher efficiency and accuracy in handling tasks of different scales and complexities compared to the original dynamic graph neural network (D-GNN) without the adaptive weight adjustment module. Experimental results show that the proposed method outperforms the baseline model in terms of prediction accuracy and computational efficiency, particularly in complex and dynamic environments. © 2024 IEEE.

关键词： Prediction models

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Analysing the Roles of Robotics in Manufacturing Organizations in the Era of Industry 4.0

Analysing the Roles of Robotics in Manufacturing Organizatio...

引用

2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adetunla, Adedotun Akinlabi, Esther Jen, Tien Chien Ajibade, Samuel-Soma University of Johannesburg Dept. of Mechanical Engineering Science South Africa Northumbria University Dept. of Mechanical & Construction Engineering Newcastle United Kingdom Sunway University School of Engineering & Technology Dept. of Computing & Information Systems Malaysia

ISBN: (纸本)9798350358155

The era of Industry 4.0 has ushered in a new approach in manufacturing, leveraging advanced technologies to transform traditional production methods. Robotics is the science of using robots to improve manufacturing processes with automation, flexibility, and efficiency. This article aims to comprehensively examine the role of robotics in manufacturing within the context of Industry 4.0. It seeks to explore the fundamental principles underlying robotics, their integration with Industry 4.0 technologies, applications across diverse manufacturing sectors, challenges faced in implementation, and the future directions of robotics in shaping the manufacturing landscape. © 2024 IEEE.

关键词： Smart manufacturing

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Harnessing the Power of Artificial Intelligence in Materials Science: An Overview

Harnessing the Power of Artificial Intelligence in Materials...

引用

2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

ISBN: (纸本)9798350358155

The integration of artificial intelligence (AI) into the realm of material science has ushered in a new era, by changing the process of material discovery and design. Leveraging advanced computational methods, machine learning algorithms, and predictive modeling, AI accelerates the identification of novel materials with tailored properties. From quantum simulations to high-throughput experimentation, AI-driven techniques enable rapid screening and prediction of material behaviors, significantly reducing the time and resources traditionally required for innovation. This synergy has paved the way for the creation of smart and adaptive materials, responsive to external stimuli and tailored for specific applications across industries. The marriage of AI and material science extends beyond discovery, encompassing efficient process optimization, manufacturing improvements, and the management of vast datasets through materials informatics. Challenges, including ethical considerations, data privacy, and responsible AI practices, must be navigated for sustainable integration. Looking forward, the collaborative potential of AI and material science promises continuous advancements. Ongoing research in machine learning, deep learning, and materials informatics anticipates breakthroughs in material applications, pushing the boundaries of innovation and sustainability. The union of AI and material science not only reshapes the landscape of scientific discovery but also holds the key to unlocking unprecedented opportunities for intelligent materials that will define the technological future. © 2024 IEEE.

关键词： Artificial Intelligence Machine Learning Materials Characterization Materials Science

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Development of a Tomato Fruit Anomalies Detector for a Small Greenhouse Drone Application 3

Development of a Tomato Fruit Anomalies Detector for a Small...

引用

3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2023

作者： Tantiborirak, Sittiporn Sukvichai, Kanjanapan Loraksa, Chanunya Kasetsart University Dept. of Electrical Engineering Bangkok Thailand Huachiew Chalermprakiet University Dept. of Computing Science and Digital Technology Samutprakarn Thailand

ISBN: (纸本)9781665473538

Tomatoes are an important commercial crop. Tomatoes are popular in many parts of the world because they are easy to grow and have a high nutritional value. The fact that tomato fruits are prone to disease, even when grown in greenhouses, is a major challenge in large-scale tomato production. Agricultural drones are now being used to construct precision farming systems that can automatically detect irregularities in tomatoes. The main challenge with using a drone in a greenhouse is that the drone must be tiny enough to carry a restricted load, including an onboard computer. Because of this, the algorithm used to detect tomato fruit anomalies must be compact and efficient. The tomato fruit abnormalities detecting method was proposed in this research. The method involved with CNNs and image processing algorithms. First the location of each tomato in tomato vines were identify and localized by using YOLOv4-tiny CNNs model, then, each tomato fruit was cropped. The background of the tomato fruit was removed by graph-like structure called GrabCut. Finally, all major defects were detected separately by the segmentation-based color thresholding technique. The experiments were conducted. The result showed that YOLOv4-tiny could detect tomato fruits with 98.68% AP and the color thresholding successfully detected the tomato anomalies. © 2023 IEEE.

关键词： Drones

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Canonical probabilistic principal component analysis based operating quality monitoring for Industrial Control systems

Canonical probabilistic principal component analysis based o...

引用

2024 China Automation Congress, CAC 2024

作者： Jin, Zhaoyang Li, Zhengxiao Fan, Haipeng Xu, Mingdi Dept. of Computing Environment Wuhan Institute of Digital Engineering Wuhan430020 China School of Automation China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798350368604

In the present day, industrial control systems (ICS) provide a significant contribution to the improvement of production efficiency. In the case of industrial control systems, the node arrangements are massive, security vulnerabilities are inevitable, and the operational status is complex, posing a threat to the stability of industrial processes. This study proposes a canonical variate analysis with probability principal component analysis monitoring method for ICS. Operational data are considered the most important information. The first step is to select significant variables that affect operating performance using one-way variance analysis. Through the application of probabilistic principal component analysis on the constantly linked canonical subspace and its residual subspace, a model for simultaneous monitoring is established. Thereafter, monitoring statistics and control limits based on online data are established. An analysis of the actual drilling process confirms the efficacy and superiority of the proposed strategy. The technology has superior sensitivity relative to traditional monitoring techniques. © 2024 IEEE.

关键词： Control system analysis

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Enhancing Performance in Hybrid Satellite-Terrestrial Networks: A Novel Joint NOMA-NC Approach

Enhancing Performance in Hybrid Satellite-Terrestrial Networ...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Karavolos, Michail Moraitis, Nektarios Tsipi, Lefteris Vouyioukas, Demosthenes University of the Aegean Dept. of Information and Communication Systems Engineering Samos83200 Greece National Technical University of Athens School of Electrical and Computer Engineering Zografou15773 Greece University of Piraeus Dept. of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350351255

An appealing alternative towards dealing with mobility and coverage challenges in future wireless networks, are hybrid satellite-terrestrial networks (HSTNs). The combination of network coding (NC) and non-orthogonal multiple access (NOMA) techniques, have recently gained popularity due to their performance gains in improving the quality of a wireless transmission and exploiting the accessible spectrum. This paper introduces a joint NOMA-NC approach, integrated to an HSTN comprising a satellite, a terrestrial base station (BS), and multiple ground-based mobile terminals (MTs). The suggested method facilitates a cluster of end-users to be concurrently served through NOMA from a satellite using optimal power allocation, whereas the BS utilizes random linear NC to augment the MTs' reception in the event of errors. The simulation results confirm the effectiveness of the proposed methodology in improving system sum throughput, demonstrating substantial enhancements compared with both OMA with NC, and standalone NOMA and OMA approaches. The resilience of the suggested method is proved, improving the sum throughput even at low power transmissions and in the worst case channel conditions. © 2024 IEEE.

关键词： Network coding

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Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study 59

Synthetic Time Series Data Generation for Healthcare Applica...

引用

59th Annual Conference on Information Sciences and systems, CISS 2025

作者： Jamshidi, Ainaz Arif, Muhammad Kalhoro, Sabir Ali Gelbukh, Alexander University of Maryland Baltimore County Dept. of Information Systems United States Colorado State University Dept. of Information Systems United States North Dakota State University Dept. of Electrical Engineering United States Instituto Politécnico Nacional Dept. of Computing Research Center Mexico

ISBN: (纸本)9798331513269

The generation of high-quality medical time series data is essential for advancing healthcare diagnostics and safeguarding patient privacy. Specifically, synthesizing realistic phonocardiogram (PCG) signals offers significant potential as a cost-effective and efficient tool for cardiac disease pre-screening. Despite its potential, the synthesis of PCG signals for this specific application received limited attention in research. In this study, we employ and compare three state-of-the-art generative models from different categories - WaveNet, DoppelGANger, and DiffWave - to generate high-quality PCG data. We use data from the George B. Moody PhysioNet Challenge 2022. Our methods are evaluated using various metrics widely used in the previous literature in the domain of time series data generation, such as mean absolute error and maximum mean discrepancy. Our results demonstrate that the generated PCG data closely resembles the original datasets, indicating the effectiveness of our generative models in producing realistic synthetic PCG data. In our future work, we plan to incorporate this method into a data augmentation pipeline to synthesize abnormal PCG signals with heart murmurs, in order to address the current scarcity of abnormal data. We hope to improve the robustness and accuracy of diagnostic tools in cardiology, enhancing their effectiveness in detecting heart murmurs. © 2025 IEEE.

关键词： Cardiology

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Energy Efficient joint user association and power allocation using Parameterized Deep DQN 9

Energy Efficient joint user association and power allocation...

引用

9th International Conference on Computer and Communication engineering, ICCCE 2023

作者： Mughees, Amna Tahir, Mohammad Sheikh, Muhammad Aman Amphawan, Angela Yap, Kian Meng Habaebi, Mohamed Hadi Islam, Md. Rafiqul Sunway University Dept. of Computing and Information Systems Petaling Jaya Malaysia University of Turku Dept. of Computing Turku Finland Cardiff Metropolitan University Dept. of Electronics and Computer Systems Engineering Cardiff United Kingdom International Islamic University Dept. of Electrical and Computer Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350325218

Using small cells to create an ultra-dense network for 5G and beyond is a promising strategy to improve network coverage, data demands and reduce latency. Despite using small cells, these dense wireless networks result in performance degradation and increased energy consumption. Energy consumption is a crucial parameter for sustainable future wireless networks. In order to improve quality of service (QoS) and Energy Efficiency (EE), efficient resource allocation strategies are required. This paper investigates a Parameterized Double Deep Q-Network (PDDQN) based framework for joint user association and power allocation to improve EE and throughput. Apart from other conventional machine learning approaches, considering single state space of the joint optimization problem, our proposed framework considers both discrete and continuous state spaces. Our proposed PDDQN technique also solves the generalization problem that occurs due to similar states. The simulation results indicate that the proposed work significantly improves energy EE and throughput in large-scale learning problems. © 2023 IEEE.

关键词： Energy efficiency

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Performance Evaluation of an Electronics Walking Stick for the Blind

Performance Evaluation of an Electronics Walking Stick for t...

引用

2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adetunla, Adedotun Akinlabi, Esther Jen, Tien Chien Oke, Wasiu Ajibade, Samuel-Soma University of Johannesburg Dept. of Mechanical Engineering Science South Africa Northumbria University Dept. of Mechanical & Construction Engineering Newcastle United Kingdom Afe Babalola University Dept. of Mechanical & Mechatronics Engineering Ado Nigeria Sunway University School of Engineering & Technology Dept. of Computing & Information Systems Malaysia

ISBN: (纸本)9798350358155

Millions of visually impaired or blind individuals face difficulties when navigating independently. While the white cane has been a traditional tool for blind navigation, recent efforts have sought to enhance its capabilities with the integration of sensors. Although blind individuals possess keen haptic sensitivity, challenges arise when using the white cane outdoors. Therefore, this study aims to improve the independence of blind individuals during both indoor and outdoor navigation. The key components employed in this research include ultrasonic sensors, Arduino Nano, Global System for Mobile Communication (GSM) Module, Global Positioning System (GPS) Module, among others. Testing of the developed electronic walking stick revealed that it not only facilitates a more convenient lifestyle for the blind or visually impaired but also serves as an alert system, notifying them of potential dangers.. © 2024 IEEE.

关键词： Global positioning system

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A review on the Challenges and Prospects in Harnessing Virtual Reality in Education

A review on the Challenges and Prospects in Harnessing Virtu...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adetunla, Adedotun Akinlabi, Esther Jen, Tien Chien Okokpujie, Imhade P. Ajibade, Samuel-Soma University of Johannesburg Dept. of Mechanical Engineering Science South Africa Northumbria University Dept. of Mechanical & Construction Engineering Newcastle United Kingdom Afe Babalola University Dept. of Mechanical & Mechatronics Engineering Ado Nigeria Sunway University School of Engineering & Technology Dept. of Computing & Information Systems Malaysia

ISBN: (纸本)9798350358155

Virtual Reality (VR) has emerged as a revolutionary technology with immense potential in transforming the landscape of education. This manuscript explores the integration of VR into educational settings, examining its benefits, challenges, and the promising future it holds in enhancing teaching and learning experiences across various disciplines. The result of this study shows that the existing literature showcases the diverse applications of VR, ranging from immersive simulations in medical training to enhancing language acquisition and making science, technology, engineering and mathematics (STEM) subjects more accessible. As educators and researchers continue to explore the possibilities offered by virtual reality, it is evident that this technology has the potential to redefine traditional educational paradigms. The emphasis on creating inclusive, interactive, and dynamic learning environments through VR not only caters to diverse learning styles but also addresses the ever-evolving needs of modern education. As this field evolves, ongoing research endeavors will undoubtedly uncover new insights, refine existing applications, and contribute to the continual improvement of educational methodologies in the digital age. © 2024 IEEE.

关键词： Virtual environments

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