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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

CAAI Transactions on Intelligence technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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Divide-Conquer Transformer Learning for Predicting Electric Vehicle Charging Events Using Smart Meter data

Divide-Conquer Transformer Learning for Predicting Electric ...

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IEEE General Meeting Power& Energy Society

作者： Fucai Ke Hao Wang Department of Human Centred Computing Faculty of Information Technology Monash University Australia Department of Data Science and AI Faculty of Information Technology Monash University Australia Monash Energy Institute Monash University Australia

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Predicting electric vehicle (EV) charging events is crucial for load scheduling and energy management, promoting seamless transportation electrification and decarbonization. While prior studies have focused on EV charging demand prediction, primarily for public charging stations using historical charging data, home charging prediction is equally essential. However, existing prediction methods may not be suitable due to the unavailability of or limited access to home charging data. To address this research gap, inspired by the concept of non-intrusive load monitoring (NILM), we develop a home charging prediction method using historical smart meter data. Different from NILM detecting EV charging that has already occurred, our method provides predictive information of future EV charging occurrences, thus enhancing its utility for charging management. Specifically, our method, leverages a self-attention mechanism-based transformer model, employing a "divide-conquer" strategy, to process historical meter data to effectively and learn EV charging representation for charging occurrence prediction. Our method enables prediction at one-minute interval hour-ahead. Experimental results demonstrate the effectiveness of our method, achieving consistently high accuracy of over 96.81% across different prediction time spans. Notably, our method achieves high prediction performance solely using smart meter data, making it a practical and suitable solution for grid operators.

关键词： Load monitoring Accuracy Transportation Predictive models Charging stations Parallel processing Transformers Electric vehicle charging Smart meters Energy management

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Descriptor: Egocentric Action Recognition for Bengali Sign Language Detection dataset (Ego-SLD)

IEEE Data Descriptions

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IEEE data Descriptions 2025年 2卷 102-112页

作者： Asfak Ali Bibek Das Ahmad Sami Al-Shamayleh Sujatro Sarkar Chinmoyee Ray Sayoni Mandal Saifuddin Sk Adnan Akhunzada Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata India Department of Data Science and Artificial Intelligence Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Electrical Engineering Jadavpur University Kolkata India College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar

Sign language recognition integrates computer vision and natural language processing to automatically interpret hand gestures and translate them into spoken or written Bengali. The primary goal is to bridge the communication gap between sign language users and non-users by recognizing gestures, movements, postures, and facial expressions that correspond to spoken language elements. Since hand gestures are the cornerstone of sign language communication, they play a pivotal role in improving the accuracy of sign language recognition systems. This article introduces Ego-SLD, a video dataset specifically designed for egocentric action recognition of Bangla sign language used in daily life situations. The dataset contains videos of 16 commonly used words, collected from 12 individuals (ten males and two females) aged between 16 and 42 years. Each participant provided two samples of each word in an indoor environment with standard lighting conditions. Ego-SLD is a crucial resource for the automatic recognition of Bangla sign language and holds significant potential to benefit the deaf community. Moreover, it serves as a valuable dataset for researchers focusing on vision-based sign language detection, hand gesture recognition, and egocentric action recognition. IEEE SOCIETY/COUNCIL Computer Society data TYPE/LOCATION Video; Jadavpur University, Kolkata, West Bengal, India data DOI/PID https://***/10.21227/kbeg-fa06

关键词： Sign language Hands Recording Annotations Machine learning Accuracy Lighting Face recognition Cleaning Training

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Divide-Conquer Transformer Learning for Predicting Electric Vehicle Charging Events Using Smart Meter data

arXiv

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arXiv 2024年

作者： Ke, Fucai Wang, Hao Department of Human Centred Computing Faculty of Information Technology Monash University Australia Department of Data Science and AI Faculty of Information Technology Monash University Australia Monash Energy Institute Monash University Australia

Predicting electric vehicle (EV) charging events is crucial for load scheduling and energy management, promoting seamless transportation electrification and decarbonization. While prior studies have focused on EV charging demand prediction, primarily for public charging stations using historical charging data, home charging prediction is equally essential. However, existing prediction methods may not be suitable due to the unavailability of or limited access to home charging data. To address this research gap, inspired by the concept of nonintrusive load monitoring (NILM), we develop a home charging prediction method using historical smart meter data. Different from NILM detecting EV charging that has already occurred, our method provides predictive information of future EV charging occurrences, thus enhancing its utility for charging management. Specifically, our method, leverages a self-attention mechanism-based transformer model, employing a "divide-conquer" strategy, to process historical meter data to effectively and learn EV charging representation for charging occurrence prediction. Our method enables prediction at one-minute interval hour-ahead. Experimental results demonstrate the effectiveness of our method, achieving consistently high accuracy of over 96.81% across different prediction time spans. Notably, our method achieves high prediction performance solely using smart meter data, making it a practical and suitable solution for grid operators. © 2024, CC BY.

关键词： Smart meters

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Federated Sequence-to-Sequence Learning for Load Disaggregation from Unbalanced Low-Resolution Smart Meter data

arXiv

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arXiv 2024年

作者： Li, Xiangrui The Department of Data Science and AI Faculty of Information Technology Monash Energy Institute Monash University ClaytonVIC3800 Australia

The importance of Non-Intrusive Load Monitoring (NILM) has been increasingly recognized, given that NILM can enhance energy awareness and provide valuable insights for energy program design. Many existing NILM methods often rely on specialized devices to retrieve high-sampling complex signal data and focus on the high consumption appliances, hindering their applicability in real-world applications, especially when smart meters only provide low-resolution active power readings for households. In this paper, we propose a new approach using easily accessible weather data to achieve load disaggregation for a total of 12 appliances, encompassing both high and low consumption, in scenarios with very low sampling rates (hourly). Moreover, We develop a federated learning (FL) model that builds upon a sequence-to-sequence model to fulfil load disaggregation without data sharing. Our experiments demonstrate that the FL framework - L2GD can effectively handle statistical heterogeneity and avoid overfitting problems. By incorporating weather data, our approach significantly improves the performance of NILM. © 2024, CC BY.

关键词： Strain measurement

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Big data-Enabled Federated Learning for Secure and Collaborative Industrial IoT in Industry 4.0

Big Data-Enabled Federated Learning for Secure and Collabora...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Asia Bataineh Hamzah Alqudah Hazem Bani Abdoh Fuad Fataftah Computer Science Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology American University of Madaba (AUM) Amman Jordan Computer Science Department Faculty of Computer Science and Information Technology Jerash university Jerash Jordan Mutlimedia systems and Virtual Reality School of Computer science Universiti Sains Malaysia (USM) Penang Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Integrating the Internet of Things (IoT), Artificial Intelligence and big data analytics in Industry 4.0 has revolutionized industrial processes, enabling enhanced operational efficiency, predictive maintenance, and innovation. However, the increasing volume of sensitive and decentralized data generated by Industrial IoT (IIoT) devices introduces significant challenges, including data fragmentation, privacy concerns, and interoper-ability issues. Traditional centralized data analysis methods often fail to address these challenges effectively. This paper proposes a novel privacy-preserving federated learning framework tailored for IIoT environments to bridge these gaps. The framework enables secure and decentralized distributed big data analysis while ensuring data sovereignty and minimizing communication overhead. The proposed approach enhances predictive maintenance and anomaly detection by integrating advanced deep learning models with edge-fog-cloud architectures, fostering cross-company collaboration and scalability. Experimental evaluations using real-world predictive maintenance datasets demonstrate the framework's effectiveness in achieving high accuracy, optimized resource utilization, and reduced runtime. Additionally, incor-porating clustering techniques improves model personalization, enhancing performance without compromising data privacy. This research establishes FL as a transformative solution for secure, collaborative intelligence in Industry 4.0 ecosystems, paving the way for sustainable and intelligent manufacturing environments.

关键词： data privacy Technological innovation Biological system modeling Scalability Collaboration Big data Fourth Industrial Revolution Optimization Industrial Internet of Things Predictive maintenance

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Understanding the spatio-temporal dynamics of ecosystem services under multiple future scenarios to assess the progress of Sustainable Development Goals implementation

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Journal of Geographical sciences 2025年第4期35卷 745-762页

作者： ZHANG Ze JIANG Weiguo LING Ziyan PENG Kaifeng WU Zhifeng LI Zhuo State Key Laboratory of Remote Sensing and Digital Earth Science Faculty of Geographical SciencesBeijing Normal UniversityBeijing 100875China School of Information and Control Engineering Qingdao University of TechnologyQingdao 266520ShandongChina College of Geographic and Environmental Sciences Tianjin Normal UniversityTianjin 300387China School of Geography and Remote Sensing Guangzhou UniversityGuangzhou 510006China National Marine Data and Information Service Tianjin 300171China

Ecosystem services in urban agglomerations are the environmental conditions under which human survival and development are *** assessment of ecosystem services and complex interactions can contribute positively to the achievement of the Sustainable Development Goals(SDGs)for urban ***,studies on the future contribution of multi-scenario ecosystem services to the SDGS are *** pronovel integrated modeling framework that integrates the CLUES,InVEST,SOM,and GWR approaches to address the complex relationship between ecosystem services over a long“past-present-future”time *** construct a novel ecosystem service bundle-based approach for measuring urban agglomerations progress towards achieving ecologically relevant sustainable development goals at multiple *** the future scenario,the water yield(WY),habitat quality(HQ),and soil conservation(SC)show similar spatial patterns,with comparable spatial grids,while carbon stock(CS)remains predominantly unchanged and the ecological protection scenario(EPS)improves more *** high-synergy regions are mainly distributed in bundle 4,and most of the trade-off regions appear in bundles 1 and *** the last 30 years,all but the water-related SDGs are declining in bundle 1 of the two urban agglomerations,which are 15%higher in the Guangxi Beibu Gulf(GBG)than in the Guangdong-Hong Kong-Macao Greater Bay Area(GBA).From 2020 to 2035,the three scenarios demonstrate that the optimization of the SDGs progresses most effectively under the future ecological protection scenario(EPS).In particular,bundles 3 and 4 are significantly *** critical new knowledge can be used in sustainable ecosystem management and decision-making in urban agglomerations.

关键词： Sustainable Development Goals(SDGs) ecosystem service bundle trade-offs and synergies InVEST model self-organizing mapping(SOM)algorithm urban agglomeration in south China

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Few-shot Domain-Adaptative Visually-fused Event Detection from Text

Few-shot Domain-Adaptative Visually-fused Event Detection fr...

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International Conference on information Fusion

作者： Farhad Moghimifar Fatemeh Shiri Reza Haffari Yuan-Fang Li Van Nguyen Department of Data Science and AI Faculty of Information Technology Monash University Melbourne Australia Information Sciences Division Defence Science and Technology Group Adelaide Australia

Incorporating auxiliary modalities such as images into event detection models has attracted increasing interest over the last few years. The complexity of natural language in describing situations has motivated researchers to leverage the related visual context to improve event detection performance. However, current approaches in this area suffer from data scarcity, where a large amount of labelled text-image pairs are required for model training. Furthermore, limited access to the visual context at inference time negatively impacts the performance of such models, which makes them practically ineffective in real-world scenarios. In this paper, we present a novel domain-adaptive visually-fused event detection approach that can be trained on a few labelled image-text paired data points. Specifically, we introduce a visual imaginator method that synthesises images from text in the absence of visual context. Moreover, the imaginator can be customised to a specific domain. In doing so, our model can leverage the capabilities of pre-trained vision-language models and can be trained in a few-shot setting. This also allows for effective inference where only single-modality data (i.e. text) is available. The experimental evaluation on the benchmark M2E2 dataset shows that our model outperforms existing state-of-the-art models, by up to 11 points.

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An Enhanced Evolutionary Algorithm for Optimized Real-Time Scheduling in Cloud Computing

An Enhanced Evolutionary Algorithm for Optimized Real-Time S...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Asia Bataineh Hamzah Alqudah Mohammad A. Alsharaiah Waleed Awwad Computer Science Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology American University of Madaba (AUM) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (JU) Amman Jordan Department of Computer Science College of Science Northern Border University (NBU) Arar KSA

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The exponential growth of cloud computing applications has posed significant challenges in achieving efficient resource management, reduced processing time, high through-put, and minimized energy consumption. Despite advancements, existing evolutionary algorithms often suffer from premature convergence, inefficient exploration-exploitation balance, and high energy overhead, particularly under heavy workloads. This paper proposes an enhanced hybrid Whale Optimization Algorithm (WOA) integrated with the Lévy Flight Mechanism for optimized resource management in cloud computing environments to address these challenges. The primary motivation for this work stems from the limitations observed in state-of-the-art algorithms. The research aims to bridge this gap by designing an evolutionary-based approach that improves resource utilization, reduces task execution delays, and optimizes energy consumption under varying workloads. The proposed model employs the WOA, enhanced with the Lévy Flight Mechanism, to ensure a more effective balance between exploration and exploitation during optimization. Extensive simulations were conducted on workloads ranging from 100 to 700 tasks, comparing the proposed method against leading algorithms. The results demonstrate that the proposed algorithm consistently outperforms the state-of-the-art techniques. Specifically, it achieves higher resource utilization (1.1 vs. 0.9), lower processing time, improved throughput (up to 500), and reduced energy consumption (50 joules compared to 70+ joules) under heavy workloads. These findings validate the enhanced WOA's effectiveness in addressing critical cloud computing optimization problems. The proposed approach provides a robust solution for dynamic resource allocation, ensuring energy efficiency and performance scalability, making it suitable for modern cloud-based environments with increasing computational demands.

关键词： Cloud computing Energy consumption Processor scheduling Scalability Evolutionary computation Throughput Whale optimization algorithms Real-time systems Resource management Optimization

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A Smart Blockchain Model for Supply Chain Management System 4th

A Smart Blockchain Model for Supply Chain Management System

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4th International Conference on data, Engineering, and Applications, IDEA 2022

作者： Ayo, Femi Emmanuel Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Mathematical Sciences Olabisi Onabanjo University Ogun State Ago Iwoye Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819724505

Industry 4.0 is the creation of smart industrial processes. Therefore, supply chain management describes a chain of activities from the purchase of raw materials to the distribution of finished goods. The problems with the existing manual supply chain system include consistency, timeliness, traceability, security, and trust among all supply chain actors. This study presented the integration between a rule-based expert system and blockchain technology called SupplyOnBlock for supply chain management. The rule-based expert system was proposed to create smart industrial processes and blockchain technology to create fast, traceable, and secure supply chain processes. The rule-based expert system is to create a smart individual process in the supply chain while the blockchain is to coordinate the individual smart processes in a network of blockchains. The proposed SupplyOnBlock model for supply chain management is designed to use the public key cryptographic hash function for encryption of key processes to avoid non-repudiation at any point in the SupplyOnBlock. The information to be transferred on the block is first hashed into a value to compress its volume then the public key cryptography is used to encode and decode the hashed information. The proposed encryption scheme should be able to validate the legitimacy of the sender using the private key of the public key cryptography algorithm. The receiver should be able to validate the legitimacy of the sender using the public key of the public key cryptography algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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