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2023 IEEE International Conference on Internet of Things and Intelligence systems, IoTaIS 2023

作者： Purcell, Richard Naik, Kshirasagar Lung, Chung-Horng Zaman, Marzia Sampalli, Srinivas Mutakabbir, Abdul Dalhousie University Faculty of Computer Science HalifaxNS Canada Electrical and Computer Engineering University of Waterloo WaterlooON Canada Systems and Computer Engineering Carleton University OttawaON Canada Research and Development Cistel Technology OttawaON Canada

ISBN: (纸本)9798350319040

Forest fires are a growing threat to human commu-nities. The Canadian Wildland Fire Information System gives realtime information to fire management agencies and the public. However, machine learning use for forest fire ignition classification prediction within the platform and ones like it, is yet to be fully realized. We propose a novel framework that uses federated machine learning combined with Internet of Things technologies, for forest fire ignition classification prediction. The framework incorporates distributed IoT weather stations deployed in an area prone to forest fires. We find comparable prediction accuracy between a federated machine learning system and a central server machine learning system. Our federated system, trained on an imbalanced dataset comprising 5,008,365 non-ignition cases and 45,411 ignition instances, has shown encouraging outcomes. It attained an Accuracy of around 0.76 and a ROC-AUC of about 0.80. The performance is on par with other systems, indicating that our approach is effective in classifying forest fire ignitions with a spatial resolution markedly superior to that of centralized systems. © 2023 IEEE.

关键词： Deforestation

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A Data Integration Framework with Multi-Source Big Data for Enhanced Forest Fire Prediction

A Data Integration Framework with Multi-Source Big Data for ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Kaur, Parveen Naik, Kshirasagar Purcell, Richard Sampalli, Srinivas Lung, Chung-Horng Zaman, Marzia Mutakabbir, Abdul University of Waterloo Electrical and Computer Engineering WaterlooON Canada Dalhousie University Faculty of Computer Science HalifaxNS Canada Carleton University Systems and Computer Engineering OttawaON Canada Research and Development Cistel Technology OttawaON Canada

ISBN: (纸本)9798350324457

Forest fires pose imminent threats to ecosystems and human lives, necessitating precise prediction for effective mitigation. The challenges include managing extensive big data and addressing data imbalance. This study introduces a data integration framework that integrates data from remote sensing satellites, ground-based weather stations, and other sources to create a comprehensive weather database spanning 18 years in Alberta, Canada. Machine learning methods, including Random Forest, eXtreme Gradient Boosting, and Multi-Layer Perceptron are employed to evaluate forest fire prediction performance, overcoming the challenge of data imbalance through changes in spatial resolution, spatio-subsamping, and downsampling techniques. XGBoost exhibits results with an ROC-AUC score of 87.2% and a sensitivity of 75%.Using meteorological data and fire history improves prediction, demonstrating big data and machine learning's role in addressing forest fire challenges. © 2023 IEEE.

关键词： Downsampling Forest Fire Imbalanced data Machine Learning Random Forest XGBoost

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Evaluation of micro-dispersion on oil recovery during low-salinity water-alternating-CO_(2)processes in sandstone cores:An integrated experimental approach

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Petroleum Science 2025年第1期22卷 277-295页

作者： Jia-Xin Wang Leng Tian Can Huang Xiao-Jiao Deng Daoyong Tony Yang Rui-Heng Wang Jia-Hao Lin Jin-Yang Wei CNOOC Research Institute Co. Ltd.Beijing100028China College of Petroleum Engineering China University of Petroleum(Beijing)Beijing102249China State Key Laboratory of Offshore Oil Exploitation Beijing100028China Research Center for Natural Gas Geology and Engineering China University of Petroleum(Beijing)Beijing102249China Exploration and Development Research Institute Jianghan Oilfield Branch CompanySinopecWuhan430223HubeiChina PetroChina Oil Gas and New Energy CompanyBeijing100007China Energy Systems Engineering Faculty of Engineering and Applied ScienceUniversity of ReginaReginaSaskatchewanS4S 0A2Canada Exploration and Development Research Institute of PetroChina Changqing Oilfield Company Xi'an710018ShaanxiChina No.3 Gas Production Plant of PetroChina Changqing Oilfield Company Wushenqi017300Inner MongoliaChina

Low-salinity water(LSW)and CO_(2) could be combined to perform better in a hydrocarbon reservoir due to their synergistic advantages for enhanced oil recovery(EOR);however,its microscopic recovery mechanisms have not been well understood due to the nature of these two fluids and their physical reactions in the presence of reservoir fluids and porous *** this work,well-designed and inte-grated experiments have been performed for the first time to characterize the in-situ formation of micro-dispersions and identify their EOR roles during a LSW-alternating-CO_(2)(CO_(2)-LSWAG)process under various ***,by measuring water concentration and performing the Fourier transform infrared spectroscopy(FT-IR)analysis,the in-situ formation of micro-dispersions induced by polar and acidic materials was ***,displacement experiments combining with nuclear magnetic resonance(NMR)analysis were performed with two crude oil samples,during which wettability,interfacial tension(IFT),CO_(2) dissolution,and CO_(2) diffusion were *** a CO_(2)-LSWAG pro-cess,the in-situ formed micro-dispersions dictate the oil recovery,while the presence of clay minerals,electrical double-layer(EDL)expansion and multiple ion exchange(MIE)are found to contribute *** formed micro-dispersions are induced by CO_(2) via diffusion to mobilize the CO_(2)-diluted oil,alter the rock wettability towards more water-wet,and minimize the density contrast between crude oil and water.

关键词： Low-salinity water-alternating-CO_(2)process Micro-dispersion Wettability alteration Clay minerals Electrical double-layer Multiple ion exchange

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Toward better semantic segmentation by retaining spectral information using matched wavelet pooling

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Neural Computing and Applications 2025年第10期37卷 7049-7066页

作者： El-Khamy, Said El-Bana, Shimaa Al-Kabbany, Ahmad Elragal, Hassan Department of Electrical Engineering Alexandria University Alexandria21544 Egypt Intelligent Systems Lab Arab Academy for Science Technology and Maritime Transport Alexandria21937 Egypt Department of Research and Development VRapeutic Inc. Cairo11613 Egypt Alexandria Higher Institute of Engineering and Technology Alexandria21311 Egypt

Pooling operations, such as average pooling, strided convolution, and max pooling, have become fundamental components of convolutional neural networks (CNNs) due to their ability to capture local features, expand receptive fields, and reduce computational costs. However, in the context of semantic segmentation, these pooling techniques can lead to the loss of crucial spatial details that are necessary for accurate pixel-level predictions. To tackle this issue, extensive research has focused on refining deep CNN models through architectural adaptations and novel training methods. Recent studies have demonstrated the importance of pooling layers, exemplified by innovations like the introduction of wavelet pooling. In our study, we highlight the value of incorporating our previously proposed matched wavelet pooling (MWP) into CNNs to enhance semantic segmentation pipelines. The core concept of MWP challenges the notion that including all sub-bands generated from wavelet decomposition consistently improves accuracy. Instead, we advocate for selecting specific sub-bands for the pooling process in each image during both training and testing. This approach introduces sub-band selection protocols customized for image-specific pooling, designed specifically for semantic segmentation CNN architectures, with a particular focus on the UNet and SegNet models. Across three widely used datasets, our proposed MWP- based pipeline, featuring the MWP-UNet architecture, consistently outperforms conventional pooling methods. It achieves a significant average improvement in intersection over union (IoU) of over 25% compared to recent literature. Additionally, our MWP-SegNet model outperformed the standard SegNet by 12.5% mIoU, further demonstrating the effectiveness of our matched wavelet pooling approach across different network architectures. © The Author(s) 2025.

关键词： Semantic Segmentation

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Log Preservation in Custody Dual Blockchain With Energy Regime and Obfuscation Shuffle

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IEEE Transactions on Network Science and engineering 2024年第4期11卷 3495-3511页

作者： Chew, Chit-Jie Lee, Wei-Bin Chen, Tzu-Hao Lin, Iuon-Chang Lee, Jung-San Feng Chia University Department of Information Engineering and Computer Science Taichung407 Taiwan Hon Hai Research Institute Information Security Research Center Taipei236 Taiwan National Chung Hsing University Department of Management Information Systems Taichung402 Taiwan

The rise of cybercrime has led to an explosion of threats in recent years, resulting in the increasingly important of digital forensics. To achieve successful investigation and prosecution, the log preservation is a critical aspect of incident response. The blockchain technology is considered as one of the powerful log storage mechanism, which bringing data transparency and non-tempering property aside from taking off the hypothesis of a trusted third party. Nevertheless, the sensitivity of the data makes it vulnerable to malicious attacks, and the adoption of blockchain has significantly burdened participants regarding consensus time and storage. To address these challenges, we aim to develop a custody dual blockchain (CDB) to secure the confidentiality and integrity of log data. The design of reputation consensus attains that only attractive participants need to offer computation and storage for efficiency enhancing, while the energy regime constructs a positive feedback loop that invigorates users to contribute to CDB. Moreover, the obfuscation shuffle effectively obscures the explicit log plaintext to prevent illegal access. Thus, the confidential data on the CDB nodes can even be used as evidence of digital forensics. Simulation results have demonstrated that the ledger overhead can be significantly reduced, and CDB could pass the robust examination of two formal proof tools: Proverif (Blanchet et al. 2021) and AVISPA. © 2013 IEEE.

关键词： Blockchain

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Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance

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computers, Materials & Continua 2023年第1期74卷 1523-1540页

作者： Sandeep Kumar MohdAnul Haq Arpit Jain C.Andy Jason Nageswara Rao Moparthi Nitin Mittal Zamil S.Alzamil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramAP522502India Department of Computer Science College of Computer and Information SciencesMajmaah University11952Al-MajmaahSaudi Arabia Department of Computer Science and Engineering Teerthanker Mahaveer UniversityMoradabadUttar Pradesh244001India Department of Electronics and Communication Engineering Sreyas Institute of Engineering and TechnologyHyderabad500068India University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia

Day by day,biometric-based systems play a vital role in our daily *** paper proposed an intelligent assistant intended to identify emotions via voice message.A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert *** proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes(LED)alert signals and it also keep track of the places like households,hospitals and remote areas,*** proposed approach is able to detect seven emotions:worry,surprise,neutral,sadness,happiness,hate and *** key elements for the implementation of speech emotion recognition are voice processing,and once the emotion is recognized,the machine interface automatically detects the actions by buzzer and *** proposed system is trained and tested on various benchmark datasets,i.e.,Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS)database,Acoustic-Phonetic Continuous Speech Corpus(TIMIT)database,Emotional Speech database(Emo-DB)database and evaluated based on various parameters,i.e.,accuracy,error rate,and *** comparing with existing technologies,the proposed algorithm gave a better error rate and less *** rate and time is decreased by 19.79%,5.13 *** the RAVDEES dataset,15.77%,0.01 s for the Emo-DB dataset and 14.88%,3.62 for the TIMIT *** proposed model shows better accuracy of 81.02%for the RAVDEES dataset,84.23%for the TIMIT dataset and 85.12%for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM)and Support Vector Machine(SVM)Model.

关键词： Speech emotion recognition classifier implementation feature extraction and selection smart assistance

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Extending OpenStack Monasca for Predictive Elasticity Control

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Big Data Mining and Analytics 2024年第2期7卷 315-339页

作者： Giacomo Lanciano Filippo Galli Tommaso Cucinotta Davide Bacciu Andrea Passarella Scuola Normale Superiore Pisa 56126Italy the Real-Time Systems Laboratory(RETIS) TelecommunicationsComputer Engineeringand Photonics Institute(TeCIP)Scuola Superiore Sant’AnnaPisa 56127Italy Department of Computer Science University of PisaPisa 56127Italy National Research Council Pisa56127Italy

Traditional auto-scaling approaches are conceived as reactive automations,typically triggered when predefined thresholds are breached by resource consumption *** such rules at scale is cumbersome,especially when resources require non-negligible time to be *** paper introduces an architecture for predictive cloud operations,which enables orchestrators to apply time-series forecasting techniques to estimate the evolution of relevant metrics and take decisions based on the predicted state of the *** this way,they can anticipate load peaks and trigger appropriate scaling actions in advance,such that new resources are available when *** proposed architecture is implemented in OpenStack,extending the monitoring capabilities of Monasca by injecting short-term forecasts of standard *** use our architecture to implement predictive scaling policies leveraging on linear regression,autoregressive integrated moving average,feed-forward,and recurrent neural networks(RNN).Then,we evaluate their performance on a synthetic workload,comparing them to those of a traditional *** assess the ability of the different models to generalize to unseen patterns,we also evaluate them on traces from a real content delivery network(CDN)*** particular,the RNN model exhibites the best overall performance in terms of prediction error,observed client-side response latency,and forecasting *** implementation of our architecture is open-source.

关键词： OpenStack monitoring elasticity control auto-scaling predictive operations Monasca

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Hazelnut mapping detection system using optical and radar remote sensing:Benchmarking machine learning algorithms

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Artificial Intelligence in Agriculture 2024年第2期12卷 97-108页

作者： Daniele Sasso Francesco Lodato Anna Sabatini Giorgio Pennazza Luca Vollero Marco Santonico Mario Merone Department of Engineering Research Unit of Computer Systems and BioinformaticsUniversitàCampus Bio-Medico di RomaviaÁlvaro del PortilloRome 00128Italy Department of Science and Technology for Sustainable Development and One Health Research Unit of Electronics for Sensor SystemsUniversitàCampus Bio-Medico di RomaviaÁlvaro del PortilloRome 00128Italy Department of Engineering Research Unit of Electronics for Sensor SystemsUniversitàCampus Bio-Medico di RomaviaÁlvaro del PortilloRome 00128Italy Department of Biology University of Naples FedericoⅡNaples 80138Italy

Mapping hazelnut orchards can facilitate land planning and utilization policies,supporting the development of cooperative precision farming *** present work faces the detection of hazelnut crops using optical and radar remote sensing data.A comparative study of Machine Learning techniques is *** system proposed utilizes multi-temporal data from the Sentinel-1 and Sentinel-2 datasets extracted over several years and processed with cloud *** provide a dataset of 62,982 labeled samples,with 16,561 samples belonging to the‘hazelnut’class and 46,421 samples belonging to the‘other’class,collected in 8 heterogeneous geograph-ical areas of the Viterbo *** different comparative tests are conducted:firstly,we use a Nested 5-Fold Cross-Validation methodology to train,optimize,and compare different Machine Learning algorithms on a single *** a second experiment,the algorithms were trained on one area and tested on the remaining seven geo-graphical *** developed study demonstrates how AI analysis applied to Sentinel-1 and Sentinel-2 data is a valid technology for hazelnut *** the results,it emerges that Random Forest is the classifier with the highest generalizability,achieving the best performance in the second test with an accuracy of 96%and an F1 score of 91%for the‘hazelnut’class.

关键词： Remote sensing Crop detection Hazelnut Machine learning Classification

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A Novel Random Pulse Width Modulation for PMSM Drive System Based on Unified Voltage Modulation 6

A Novel Random Pulse Width Modulation for PMSM Drive System ...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Kan, Jinpeng Meng, Yongqing Shi, Yixin Li, Fengjiao Chen, Wenjie Li, Changyi Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798350377798

The electromagnetic interference (EMI) problem caused by power electronic switching devices and pulse width modulation (PWM) affects the normal operation of the motor drive system. In this paper, a random PWM based on unified modulation is proposed for permanent magnet synchronous motor (PMSM) drive system. The theoretical basis of unified modulation and the implementation scheme of random modulation are analyzed. Finally, the effectiveness of the proposed random PWM is verified by simulation. © 2024 IEEE.

关键词： Pulse width modulation

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Prediction of Radiated Electromagnetic Interference Based on Four-module Motor Drive System 6

Prediction of Radiated Electromagnetic Interference Based on...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Shi, Yixin Chen, Wenjie Li, Fengjiao Kan, Jinpeng Meng, Yongqing Yang, Guang Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798350377798

In recent years, wide bandgap semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) have been increasingly applied in electric drive systems, effectively enhancing system power density. However, the high-frequency and high-speed characteristics of SiC devices exacerbate electromagnetic interference (EMI) issues within the system. Additionally, the cables in electric drive systems exhibit significant antenna effects, radiating conducted EMI into the surrounding space, thereby affecting the normal operation of other sensitive equipment. Therefore, it is essential to research motor drive system topologies that can actively suppress EMI and predict radiated interference in motor drive systems. The topology of a four-module motor paired with parallel inverters can achieve active suppression of EMI at the source. This paper conducts a simulation-based prediction of radiated interference for a parallel inverter-driven four-module motor system. It analyzes the coupling mechanisms of common mode and differential mode radiated interference and validates the radiated interference from both DC and AC cables through simulations, predicting the impact on surrounding sensitive equipment in practical systems. © 2024 IEEE.

关键词： Gallium nitride

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