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检索条件"任意字段=Scientific Conference on Mechatronics Engineering and Computer Science"

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Robust Sensor Fusion Algorithms for IoT

Robust Sensor Fusion Algorithms for IoT

引用

2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Subramanian, Sakthitharan Lokeshwari, Gandrakoti Jagadeesan, S. Leela, S. Pratheep, V.G. Venkatasudhahar, Murugesan School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai603203 India Geethanjali College of Engineering and Technology Department of Cybersecurity Telangana Hyderabad501301 India Department of Computer Science and Engineering Tamil Nadu Erode638052 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Kongu Engineering College Department of Mechatronics Engineering Tamil Nadu Perundurai638060 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mechanical Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798331543617

Sensor fusion is a critical component of the IoT ecosystem, as it combines a variety of data streams from multiple sensors to enhance the accuracy, reliability, and real-time decision-making. This research concentrates on the development of robust sensor fusion algorithms that are capable of effectively managing the heterogeneity, noise, and uncertainty that are inherent in data from Internet of Things (IoT) sensors. Our primary focus is on the ability of these algorithmic methodologies to manage data from multi-modal sensors. We investigate algorithms that are based on deep learning, Bayesian inference, and Kalman filtering. The proposed solutions aim to improve the performance of systems in sectors that are influenced by the Internet of Things (IoT), including healthcare, smart cities, industrial automation, and autonomous cars. This investigation investigates the scalability, energy efficiency, and computational burden of the Internet of Things (IoT) in the context of limited resources. Through simulations and practical applications, we demonstrate the enhancement of data precision, defect tolerance, and overall system dependability by these robust sensor fusion techniques. © 2024 IEEE.

关键词： Kalman filters

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Hyperspectral Image Endmember Extraction Algorithm Based on Manifold Learning and Superpixel Feature Extraction 6

Hyperspectral Image Endmember Extraction Algorithm Based on ...

引用

6th International conference on Communications, Information System and computer engineering, CISCE 2024

作者： Xu, Nan Sui, Jiazhi Yang, Huadong School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (纸本)9798350353174

Endmember extraction is the crucial process of hyperspectral unmixing. In this study, we introduce a nonlinear endmember extraction algorithm for hyperspectral remote sensing image analysis. The algorithm use the manifold learning technique to acquire the intrinsic nonlinear structure of hyperspectral data, and extract the endmembers based on convex simplex theory in low-dimensional manifolds. In order to achieve better manifold representations and reduce the computational burden, superpixel feature extraction is performed on the original hyperspectral data. Experimental results on synthetic and real data demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Superpixels

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Path Planning Algorithms For Unmanned Aerial Vehicle: Classification, Performance, and Implementation

Path Planning Algorithms For Unmanned Aerial Vehicle: Classi...

引用

2023 International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2023

作者： Ahmad, Ali Haidar Zahwe, Oussama Nasser, Abbass Clement, Benoit Ensta Bretagne Crossing Irl Cnrs 2010 Brest France American University of Culture and Education ICCS-Lab Computer Science Department Beirut Lebanon Ensta Bretagne Crossing Irl Cnrs 2010 Adelaide Australia

ISBN: (纸本)9798350322972

Path planning and obstacle avoidance form the basis of the UAV operations. The objective of an Unmanned Aerial Vehicle (UAV) is to navigate an optimal path towards its destination while ensuring the avoidance of the obstacles along the way. Several algorithms have been proposed by many researchers to achieve this objective. In this paper, we focused on Global path-planning algorithms for UAVs with obstacle avoidance. We compare various algorithms by highlighting their characteristics, advantages, and limitations. In addition, this paper implements four of the most well-known methods that tackle environmental challenges. Our results offer practical insights and guidance for researchers seeking to develop more effective path planning algorithms for UAVs. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Identifying Sources of Soil Pollution in Industrial Areas Using IoT Data and Neural Networks

Identifying Sources of Soil Pollution in Industrial Areas Us...

引用

2024 Asian conference on Intelligent Technologies, ACOIT 2024

作者： Kovarasan, Rajesh Kambattan Shibu, N.V. Sairam, A. Manivannan, K.K. Ishwarya, M.V. GaneshBabu, T.R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Mechatronics Engineering KCG College of Technology Tamil Nadu Chennai India Artificial Intelligence and Data science Department Agni College of Technology Tamil Nadu Chennai India Tamil Nadu Namakkal India

ISBN: (纸本)9798350374933

Quick and precise source identification is crucial for developing successful remediation solutions to address soil contamination in industrial locations, which presents serious dangers to both the environment and human health. To locate the origins of soil contamination, this research presents a new method that combines neural network models with data collected from the Internet of Things (IoT). To monitor soil type, pollution levels, and weather conditions in real-time, set up IoT devices. After that, advanced neural network algorithms are used to process and analyze the data to find connections and patterns that might indicate the origins of the pollution. Our findings show that this method is useful for identifying where soil pollution inside industrial zones is coming from. Achieve high accuracy in source identification by integrating IoT technology and machine learning approaches. This allows us to address environmental harm and protect public health via focused remediation activities. Policymakers, environmental scientists, and stakeholders engaged in sustainable development and pollution control may benefit greatly which highlight the potential of IoT data and neural networks in challenges difficult environmental concerns. © 2024 IEEE.

关键词： Environmental monitoring

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Promptable Leaf Segmentation in Plant Phenotyping: Research Perspectives and Challenges 30

Promptable Leaf Segmentation in Plant Phenotyping: Research ...

引用

30th International conference on mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Abe, John Wesley Ilao, Joel Foliente, Greg De La Salle University College of Computer Studies Manila Philippines University of Melbourne Faculty of Engineering and It Melbourne Australia

ISBN: (纸本)9798350391916

The transition to sustainable agriculture necessitates a greater understanding of plants in the field with automated plant phenotyping. Leaf segmentation is critical in this domain, enabling the accurate assessment of plant traits essential for managing the growth of economically viable crops and plants. This paper reviews recent developments in leaf segmentation methodologies, focusing on the emerging paradigm of promptable segmentation. Promptable segmentation, exemplified by Meta AI's Segment Anything Model (SAM), offers flexibility and versatility by allowing users to provide various prompts for segmentation tasks. However, challenges such as complex backgrounds, overlapping leaves, and computational complexity persist. The paper discusses strategies such as prompt engineering, post-processing of segmentation outputs, and fine-tuning segmentation models to address these challenges. Future research includes exploring occlusion handling techniques, adopting parameter-efficient fine-tuning methods, collecting and leveraging publicly available datasets, synthetic data generation, and embracing video-based data collection. By overcoming these challenges and harnessing the potential of promptable segmentation, researchers can create automated plant phenotyping for any plant species, leading to more accurate, efficient, and scalable solutions for agricultural sustainability. © 2024 IEEE.

关键词： Plant diseases

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Bytescience: Bridging Unstructured scientific Literature and Structured Data with Auto Fine-tuned Large Language Model in Token Granularity 24

ByteScience: Bridging Unstructured Scientific Literature and...

引用

24th IEEE International conference on Data Mining Workshops, ICDMW 2024

作者： Xie, Tong Zhang, Hanzhi Wang, Shaozhou Wan, Yuwei Razzak, Imran Kit, Chunyu Zhang, Wenjie Hoex, Bram GreenDynamics Australia University of New South Wales School of Photovoltaic and Renewable Energy Engineering Australia University of North Texas Dept. of Computer Science and Engineering Denton United States University of New South Wales School of Computer Science and Engineering Australia City University of Hong Kong Dept. of Linguistics and Translation Hong Kong Hong Kong

ISBN: (纸本)9798331530631

Natural Language Processing (NLP) is widely used to supply summarization ability from long context to structured information. However, extracting structured knowledge from scientific text by NLP models remains a challenge because of its domain-specific nature to complex data preprocessing and the granularity of multi-layered device-level information. To address this, we introduce Bytescience, a non-profit cloud-based auto finetuned Large Language Model (LLM) platform, which is designed to extract structured scientific data and synthesize new scientific knowledge from vast scientific corpora. The platform capitalizes on DARWIN, an open-source, fine-tuned LLM dedicated to natural science. The platform was built on Amazon Web Services (AWS) and provides an automated, user-friendly workflow for custom model development and data extraction. The platform achieves remarkable accuracy with only a small amount of well-annotated articles. This innovative tool streamlines the transition from the science literature to structured knowledge and data and benefits the advancements in natural informatics. Demo Video. © 2024 IEEE.

关键词： Cloud platforms

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A Stuxnet-style malware propagation modeling study based on Tesla autonomous vehicles 3

A Stuxnet-style malware propagation modeling study based on ...

引用

3rd International conference on computer Technology, Information engineering, and Electron Materials, CTIEEM 2023

作者： Wang, Junling Chang, Xinxin Zhong, Lei Zeng, Kaifa School of Information Engineering Jiangxi University of Science and Technology Ganzhou China

ISBN: (纸本)9781510672925

Autonomous vehicles are the core of future smart transportation, and Tesla is a leader in the field. As vehicles become intelligent, cyber attack defense becomes a concern. Tesla Autonomous vehicles may be challenged by Stuxnet-style malware, whose self-replication and propagation capabilities may threaten transportation security. This study proposes a spatiotemporal mathematical model for such malware. Based on computer virus propagation dynamics, it is combined with Tesla Autonomous vehicles for the first time. The propagation characteristics of the malware are analyzed, and scientific recommendations are provided for future prevention. © 2024 SPIE.

关键词： Autonomous vehicles

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Deep Reinforcement Learning for autonomous ground vehicle driving in mountainous environments with adverse environmental conditions

Deep Reinforcement Learning for autonomous ground vehicle dr...

引用

2023 International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2023

作者： Sarango, Roger Calderon-Cordova, Carlos Universidad Técnica Particular de Loja Dept. of Computer Science and Electronics Loja Ecuador

ISBN: (纸本)9798350322972

Currently, the implementation of autonomous vehicles remains a major challenge. In this context, the integration of a deep reinforcement learning (DRL) algorithm in an AV has been proposed for navigation in rough and uneven terrain found in rural areas of Ecuador that often lack adequate road infrastructure for vehicular traffic and are prone to considerable damage in case of natural disasters. In order to address these challenges, the autonomous driving system has been implemented in simulation using AirSim that allows emulating some environmental conditions present in these mentioned areas and its performance has been verified through testing. The integration of DRL in AV navigation represents an innovative and effective solution to overcome vehicle mobility limitations and terrain characteristics. In addition, this work has a significant impact on improving efficiency and safety in search and rescue tasks in rural areas of Ecuador, particularly in case of natural disasters. © 2023 IEEE.

关键词： Reinforcement learning

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Machine learning based models for detecting attacks in IoT systems

Machine learning based models for detecting attacks in IoT s...

引用

2023 International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2023

作者： Olaya, Maria Katherine Plazas Martínez, David Santiago Guerrero Tejada, Jaime Alberto Vergara Cobo, José Edinson Aedo University of Antioquia Department of Electronic Engineering Medellín Colombia

ISBN: (纸本)9798350322972

IoT platforms based on microservices are susceptible to vulnerabilities that can enable attackers to disrupt a system's normal operation. Recently, machine learning-based intrusion detection systems have garnered much interest from the scientific community. These techniques require reliable databases to carry out training. However, having balanced databases is not frequent, which could compromise the quality of the *** article proposes the combination of two network traffic databases, Bot-Iot and UNSW-NB15, to construct a new balanced database that allows for model training. This new database has been used to build and evaluate four machine learning models for attack detection: decision trees, random forests, logistic regression, and support vector machines (SVM). The attacks are detected by classifying network traffic as either normal or anomalous. The following metrics were used to evaluate the models: accuracy, sensitivity, precision, and f1-score. The experimental results show that the models have a performance of over 99% for the different metrics. © 2023 IEEE.

关键词： Smart city

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computer Aided Design of Graphene Based Lubricants for Electric Vehicle 4

Computer Aided Design of Graphene Based Lubricants for Elect...

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4th International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2024

作者： Washizu, Hitoshi Okabe, Yuya Tomokiyo, Takayuki Ishii, Yoshiki Kinoshita, Hiroshi Okamoto, Ryuichi Graduate School of Information Science University of Hyogo Kobe Japan School of Frontier Engineering Kitasato University Sagamihara Japan Graduate School of Engineering University of Hyogo Himeji Japan

ISBN: (纸本)9798350391183

Layered compounds, such as graphite and molybdenum disulfide, have a structure that molecular sheets are stacked in layers, and are commonly used as solid lubricants because they exhibit low friction. Among them, graphene oxide can be synthesized by oxidizing graphene, and is used in electronic devices because it is used as electrical insulator and has high mechanical strength. In addition, since it is hydrophilic than graphene, they can be easily dispersed in polar solvents. This mean it is usable both in water and oil environment. Moreover it has low environmental impact, and is low cost, so it is expected to be used in a wide range of applications. However, the mechanism of graphene oxide's low friction has not been elucidated. In this study, new molecular models are presented of graphite and multilayer graphene oxide sheets with oxygen contents of 20, 40, and 60 %, and calculate molecular motion. The results of structural relaxation calculations in this simulation confirmed that the film thickness and interlayer distance of graphene oxide increased with increasing oxygen content, is consistent with the results of experiments using XRD in previous studies. In addition, friction simulations are performed under both dry and with water added conditions. The results of the friction simulation confirmed that the friction behavior is quite different between graphite and graphene oxide. From this, it is suggested that graphene oxide does not have a low-friction mechanism like pure graphite. Especially, the effect of hydrogen bond of the lubricant water and the polar part of graphene oxide may be essential for the low friction. © 2024 IEEE.

关键词： Hydrogen bonds

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