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8th International Conference on computing, Control and Industrial engineering, CCIE 2024

作者： Gao, Meiyan Wei, Yuanzhou Li, Zhenglin Huang, Bolin Zheng, Changyang Mulati, Aliya College of Engineering and Computing Florida International University Miami United States Department of Computer Science and Engineering Texas A&M University College Station United States Viterbi School of Engineering University of Southern California CaliforniaCA United States School of Engineering Brown University Rhode Island United States Biological Science Florida International University Florida United States

ISBN: (纸本)9789819769339

This paper aims to explore various classification models and data processing techniques for classifying defects on steel plates. The study is based on a dataset of steel plates with seven different types of defects and seeks to identify the most effective combination of machine learning models and data processing techniques for automatic defect recognition. Key methodologies include data augmentation, feature selection, and dimensionality reduction techniques like SMOTE, PCA, and REF. Various models, including Naive Bayes, Logistic Regression, SVM, Decision Trees, Random Forests, and Neural Networks, are evaluated. The results indicate that Hyperparameter Tuning and REF significantly enhance model performance, with Random Forest and Neural Networks achieving the highest accuracy. Random Forest, due to its interpretability and efficiency, is identified as the best model for this classification task. This research lays a foundation for applying machine learning algorithms in industrial defect detection, promising improvements in automation, efficiency, and production quality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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IoT-Based Fire Alarm System and Motion Detector: A Comprehensive Protocol Analysis for Enhanced Performance

IoT-Based Fire Alarm System and Motion Detector: A Comprehen...

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International Conference for Convergence of Technology (I2CT)

作者： Sai Santhoshi Haneesha Samudrala Jaishitha Thambi Sai Rishisri Vadluri Shinu M. Rajagopal Sreebha Bhaskaran Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Fire alarm systems play a vital role in providing early warnings, facilitating prompt evacuation in emergency situations. These systems ensure the security of individuals by alerting them to possible risks and allowing rapid responses to minimize the impact of fires. The research investigates the integration of IoT devices into a Cisco Packet Tracer simulated environment for fire detectionwithin a centralized architecture using a star topology. The significance of an IoT system determines its ability to simulate and analyse diverse IoT device integrations, evaluate various protocols. The System incorporates smoke detection sensors for precise identification of fire events and motion-detecting sensors seamlessly integrated with cameras for motion capture. Performance of different protocols, including BGP, DHCP, EIGRP, HSRP, ICMP, OSPF, RIPis evaluated in terms of response time. The findings contribute valuable insights into optimizing protocol selection for enhanced real-time responsiveness in IoT-based fire alarm systems.

关键词： Performance evaluation Protocols Detectors Alarm systems Cameras Turning Sensor systems

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Decisioning-Based Approach for Optimising Control engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3212-3221页

作者： Mourad, Nahia Alsattar, Hassan A. Qahtan, Sarah Zaidan, Aws Alaa Deveci, Muhammet Sangaiah, Arun Kumar Pedrycz, Witold British University in Dubai Faculty of Engineering and IT Dubai United Arab Emirates University of Mashreq College of Administrative Science Department of Business Administration Baghdad10067 Iraq Middle Technical University College of Health and Medical Technology-Baghdad Department of Computer Center Baghdad10047 Iraq SP Jain School of Global Management SydneyNSW2141 Australia National Defence University Turkish Naval Academy Department of Industrial Engineering Istanbul34040 Turkey Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon International Graduate Institute of AI National Yunlin University of Science and Technology Yunlin64002 Taiwan University of Alberta Faculty of Engineering Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul34010 Turkey

The optimisation of control engineering tools based on digital twin capabilities and other cyber-physical metaverse manufacturing system (CPMMS) components are crucial for the successful performance. This study proposes a model for optimising control engineering tools using digital twin capabilities and other CPMMS components to solve the open issues. The main contributions and novelty aspects of the methodological process are outlined as follows: Formulated and developed is a decision matrix based on a utility procedure for 10 control engineering tools with digital twin capabilities and other three CPMMS components (Programmable-Logic-Controller and Human-Machine-Interface, Internet of Things connectivity and cybersecurity features). This matrix accounts for the uncertainty associated with tool assessment and transformation evaluation issue;formulated and develop an integrating fuzzy weighted with zero-inconsistency-interval-valued spherical fuzzy rough sets (IvSFRS-FWZIC) and combined compromise solution (CoCoSo) methods. The IvSFRS-FWZIC method is utilised to assign importance degrees to the digital twin capabilities and other CPMMS components. The applicability and robustness of the proposed approach are validated and evaluated through conducting sensitivity, correlation, and comparative analyses. The proposed approach can assist managers in analysing and selecting the most suitable tool for developing CPMMS. © 1975-2011 IEEE.

关键词： Internet of things

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Motor Bearings Fault Classification using CatBoost Classifier

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2022年 20卷 454-457页

作者： Irfan, Muhammad A, Alwadie. Awais, Muhammad Rahman, Saifur Mawgani, Abdulkarem Hussein Mohammed Al Saad, Nordin Sheikh, Muhammad Aman Electrical Engineering Department College of Engineering Najran University Najran61441 Saudi Arabia Department of Computer Science Edge Hill University St Helens Rd. OrmskirkL39 4QP United Kingdom Faculty of Computing and Engineering Quest International University Malaysia School of Engineering and Technology Sunway University Malaysia

Induction motors are used in all industries and are the major element of energy consumption. Faults in motor degrade the motor efficiency and result in more energy consumption. Bearing faults are reported to be the major reason for the motor breakdown and a lot of papers have been reported to focus on bearing fault diagnostics. However, low classification accuracy is the main hurdle in adopting the available fault classification algorithms. This paper has presented a novel classification algorithm using the Catboost classifier and time-domain features. The developed algorithm was tested on the laboratory test setup. The fault classification accuracy of 100 % was achieved through the proposed method. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Condition monitoring

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Accurate and Optimized Labelling of Fashion Products Through Attention Based Spiking Neural Networks

Accurate and Optimized Labelling of Fashion Products Through...

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International Conference on Advanced computing and Communication Systems (ICACCS)

作者： N Vithyatharshana Yedhoti Thrinayani Yogeshwar Nitin Kulkarni Rimjhim Padam Singh Sneha Kanchan Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Computer Science & Engineering Amrita School of Computing Bengaluru Department of Internet Engineering and Computer science Universiti Tunku Abdul Rahman Malaysia

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Automation of garment classification has become the backbone of e-commerce, influencing product search, personalization, and customer experience. This paper uses the Fashion MNIST dataset, a rich collection of clothing images to investigate the potential of Spiking Neural Networks (SNNs) for accurate and efficient classification. SNNs are an excellent choice for widespread usage and real-world integration due to their low power consumption and biomimicry. With the intention of revolutionizing product search, personalization, and customer experience, SNNs are made to address style differences, perspectives, and illumination. In order to fully utilize Fashion MNIST, this paper suggests strategic improvements to the SNN architecture. By addressing key challenges and introducing novel optimizations, our research contributes to a more robust and intelligent future for automated fashion product labelling. An SNN model with 93.6% accuracy has been developed as the base model for all the applications for the same. This data-driven approach leads the way to flawless product searches, highly customized recommendations, and a more intelligent fashion technology in future.

关键词： Accuracy Power demand Clothing Lighting Spiking neural networks computer architecture Encoding Labeling Electronic commerce Optimization

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Bayesian Network-based Active Learning for Ontology-based Video Retrieval using Hidden Markov Logic Model with Edge Histogram Descriptor

Bayesian Network-based Active Learning for Ontology-based Vi...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Sathiyaprasad, B. Sreeji, S. Satheeshkumar, B. Menaka, D. Babu, K. Jones, G. Maria Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600 119 India Galgotias University School of Computing Science and Engineering UP Greater Noida India SRM Institute of Science and Technology Katankulathur Department of Computational Intelligence Chennai603203 India

ISBN: (纸本)9798350331790

A large amount of data is collected, analysed, and used on a regular basis by a large number of people for web pages, books, journals, and files which make up a significant portion of the data on the Internet. The burden on significant content-based retrieval and other applications is increasing as the volume of surveillance video data grows. Therefore to reduce the burden during retrieval and to solve the problem of video surveillance an ontology based video retrieval by feature extraction and classification techniques is proposed. In the Initial step the examined form of description files in OWL (Web Ontology Language is fed as input. Then the video pre-processed and segmented before extracting the features. The features of video has been extracted using "Hidden Markov Logic Model With Edge Histogram Descriptor (HMLM-EHD)"in which the spatio based features are extracted with Shape-based features that define object shapes in image is extracted from object contours or regions. After the extraction of features, classification is done using Bayesian network based active learning. The retrieval subsystem evaluates the provided query, performs similarity matching operations (which can be done using the correlation Distance Algorithm), and finally provides the result to the end user for these categorised video frames. The experimental results shows retrieved video frames and their parametric analysis in terms of accuracy of 94%, precision of 85%, recall of 71 % and F-1 score of 73%. © 2023 IEEE.

关键词： Feature extraction

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Intelligent Solar Energy Harvesting and Management in IoT Nodes Using Deep Self-Organizing Maps 2

Intelligent Solar Energy Harvesting and Management in IoT No...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Shinkar, Anita Rajkumar Joshi, Drumil Praveen, R.V.S. Rajesh, Yelisela Boopalan, K. Singh, Dharmendra Dr. D.Y. Patil College of Engineering Department of Computer Engineering Maharashtra Varale India Southern Power Birmingham Alabama Monitoring & Diagnostics Department India Digital Engineering and Assurance Telangana Hyderabad India KLEF Department of Computer Science & Engineering Andhra Pradesh India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Tamilnadu India School of Management Sciences Department of Applied Sciences & Humanities Lucknow India

ISBN: (纸本)9798331534950

Smart city energy generation must be efficient and dependable. As a result of studies conducted in this field, reliable control schemes for microgrid management have been developed, which seamlessly integrate with smart building management systems. In order for a building microgrid's solar energy system to recover from problems, this article suggests reliable controllers and the hardware to install them. Training models and Internet of Things (IoT) sensors provide the backbone of this proposed approach. When it comes to organizational and industrial contexts, sensors have evolved significantly with the introduction of the IoT. Pressure, optical, temperature, chemical sensors, and proximity are just a few examples of the many types of data that IoT devices may collect and send through sensor networks, allowing for greater efficiency. The model achieves a 91.37% accuracy rate in solar energy harvesting prediction after being trained using the Extended DSOM. © 2024 IEEE.

关键词： Conformal mapping

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Overmind: Fast and Scalable Decentralized Learning Simulation for Mobile Environments

Overmind: Fast and Scalable Decentralized Learning Simulatio...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Sangsu Lee Haoxiang Yu Xi Zheng Christine Julien Department of Electrical and Computer Engineering University of Texas at Austin School of Computing Macquarie University

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Recent progress in decentralized machine learning provides a foundation for a future in which mobile devices collaboratively learn from the vast amount of crowdsourced data they produce without necessarily relying on a centralized server for coordination. However, testing novel decentralized learning algorithms requires many iterations that are very costly in terms of time and budget. We present Overmind, a fast simulation framework for testing the performance of decentralized learning algorithms in dynamic networks. The design of Overmind allows users to easily scale their scenarios in terms of both simulated nodes and the number of physical machines on which the simulation runs. Overmind supports an operational mode that expedites the simulation by approximating the interaction time between two nodes, which allows skipping events that are unlikely to happen (e.g, two nodes exchanging big model parameters on a connection that is maintained for a very short time period). Users can also configure Overmind with a dataset of connection traces of simulated nodes, which is especially helpful to test algorithms given realistic dynamic networks in which devices join and leave unpredictably. To leverage Overmind, users just need to implement the generic behavior that is invoked on participating nodes when they connect to or disconnect from other nodes. The implemented behaviors are invoked in Overmind according to a provided encounter trace. This node-centric programming model that Overmind provides, where users define node behavior based on an individual node's perspective, is an intuitive design for decentralized settings. In this paper, we discuss the design of Overmind and demonstrate that it enables fast, scalable, and easily configurable decentralized learning simulations.

关键词： Performance evaluation Machine learning algorithms Heuristic algorithms Machine learning Programming Smart systems Approximation algorithms Mobile handsets Servers Testing

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Dynamic Metasurface Antenna-Enabled Near-Field NOMA Communications

Dynamic Metasurface Antenna-Enabled Near-Field NOMA Communic...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Zheng Liu, Yuanwei Wang, Zhaolin Chen, Jian Xidian University School of Telecommunications Engineering Xi'an710071 China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom

ISBN: (纸本)9798350351255

A novel near-field transmission framework is proposed for dynamic metasurface antenna (DMA)-enabled non-orthogonal multiple access (NOMA) networks. The base station (BS) exploits the hybrid beamforming to communicate with multiple near users (NUs) and far users (FUs) using the NOMA principle. Based on this framework, a beam-steering scheme is proposed. The metric of beam pattern error (BPE) is introduced for the characterization of the gap between the hybrid beamformers and the desired ideal beamformers, where a two-layer algorithm is proposed to minimize BPE by optimizing hybrid beamformers. Then, the optimal power allocation strategy is obtained to maximize the sum achievable rate of the network. Numerical results validate that the proposed beamforming schemes exhibit superior performance compared with the existing imperfect-resolution-based beamforming scheme. © 2024 IEEE.

关键词： Beamforming

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An Integration of Indian Philosophy and Machine Learning for Human-like Cognition 2

An Integration of Indian Philosophy and Machine Learning for...

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2nd International Conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Sha, Akhbar Inture, Anvita Reddy Joseph, Jose Nair, Pavithra P.M. Sheeba, M.J. Anjali, T. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India T.K.M. College of Arts and Science University of Kerala India

ISBN: (纸本)9798350340235

This paper introduces a novel framework that melds the profound tenets of Indian philosophy with AI and ML. Our goal is to create models that replicate human cognition, paving the way for robots that think like us. Key concepts from Indian philosophy, including"buddhi,""manas,""chit," and"ahamkar," are woven into the framework, enhancing AI’s ability to understand and interact with the world in a more human-like way. The core of the framework consists of"Buddhi" and"Manas." Buddhi, a discriminative AI component, adeptly navigates sensory data, making decisions aligned with human discernment. Complementing it,"Manas" is a generative core, capable of crafting narratives and generating creative solutions. This synergy creates an iterative loop that encapsulates human thought, enabling machines to think, visualize, and even dream. In addition to advancing AI and ML research, we conducted an experiment using a modified News Aggregator dataset from the UCI repository. The experiment focused on word prediction, and our model, infused with Indian philosophy, outperformed RNN and LSTM models over 30 epochs. This framework has the potential to revolutionize AI and create more human-like machines, ushering in a new era of AI and robotics. © 2023 IEEE.

关键词： Artificial Intelligence Human way of thought Machine Learning Model of mind

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