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Coupled THMC model‑based prediction of hydraulic fracture geometry and size under self‑propping phase‑transition fracturing

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International Journal of Coal Science & technology 2024年第6期11卷 29-53页

作者： Nanlin Zhang Fushen Liu Liangliang Jiang Pinqiang Mo Jingwen Xiao Qi Song Yuhao Luo Computing Center for Geotechnical Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058ZijingangChina Research Center of Coastal and Urban Geotechnical Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058ZijingangChina Engineering Research Center of Urban Underground Development of Zhejiang Province Hangzhou 310058China Department of Chemical and Petroleum Engineering University of CalgaryCalgaryAB T2N1N4Canada State Key Laboratory of Intelligence Construction and Healthy Operation and Maintenance of Deep Underground Engineering School of Mechanics and Civil EngineeringChina University of Mining and TechnologyXuzhou 221116JiangsuChina Safety Environment and Technology Supervision Research InstitutePetro China Southwest Oil and Gas Field CompanyChengdu 610041China

The Self-Propping Phase-transition Fracturing technology(SPFT)represents a novel and environmentally friendly approach for a cost-effective and efficient development of the world’s abundant unconventional resources,especially in the context of a carbon-constrained sustainable *** involves the coupling of Thermal,Hydraulic,Mechanical,and Chemical(THMC)fields,which makes it challenging to understand the mechanism and path of hydraulic fracture *** study addresses these challenges by developing a set of THMC multifield coupling models based on SPFT parameters and the physical/chemical characteristics of the Phase-transition Fracturing Fluid System(PFFS).An algorithm,integrating the Finite Element Method,Discretized Virtual Internal Bonds,and Element Partition Method(FEM-DVIB-EPM),is proposed and validated through a case *** results demonstrate that the FEM-DVIB-EPM coupling algorithm reduces complexity and enhances solving *** length of the hydraulic fracture increases with the quantity and displacement of PFFS,and excessive displacement may result in uncontrolled fracture *** the parameters considered,a minimal difference in fracture length is observed when the PFFS amount exceeds 130 m^(3),that means the fracture length tends to *** study contributes to understanding the hydraulic fracture propagation mechanism induced by SPFT,offering insights for optimizing hydraulic fracturing technology and treatment parameters.

关键词： Sustainable development,Self Propping phase Transition fracturing technology,Thermal Hydraulic Mechanical Chemical coupling,FEM DVIB EPM,Hydraulic fracture propagation

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Hybrid Whale Optimization Algorithm for Resource Optimization in Cloud E-Healthcare Applications

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Computers, Materials & Continua 2022年第6期71卷 5659-5676页

作者： Punit Gupta Sanjit Bhagat Dinesh Kumar Saini Ashish Kumar Mohammad Alahmadi Prakash Chandra Sharma School of Computing&Information Technology Manipal University JaipurJaipur303007India School of Basic Sciences Manipal University JaipurJaipur303007India Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia

In the next generation of computing environment e-health care services depend on cloud *** Cloud computing environment provides a real-time computing environment for e-health care *** these services generate a huge number of computational tasks,real-time computing and comes with a deadline,so conventional cloud optimizationmodels cannot fulfil the task in the least time and within the *** overcome this issue many resource optimization meta-heuristic models are been proposed but these models cannot find a global best solution to complete the task in the least time and manage utilization with the least simulation *** order to overcome existing issues,an artificial neural-inspired whale optimization is proposed to provide a reliable solution for healthcare *** this work,two models are proposed one for reliability estimation and the other is based on whale optimization technique and neural network-based binary *** predictive model enhances the quality of service using performance metrics,makespan,least average task completion time,resource usages cost and utilization of the *** as compared to existing algorithms the proposedANN-WHOalgorithms prove to improve the average start time by 29.3%,average finish time by 29.5%and utilization by 11%.

关键词： Cloud computing whale optimization health care resource optimization

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FL@FM-TheWebConf’24: International Workshop on Federated Foundation Models for the Web 24

FL@FM-TheWebConf’24: International Workshop on Federated Fo...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： King, Irwin Long, Guodong Xu, Zenglin Yu, Han Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Faculty of Engineering and IT University of Technology Sydney Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400701726

In the evolving landscape of machine learning research, two significant developments have emerged to prominence: foundation models and federated learning. The FL@FM-TheWebConf’24 workshop provides an exciting forum for researchers and practitioners to discuss promising works and chart potential trajectories forward for this emerging field. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Frequency modulation

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An Interpretable Artificial Intelligence Based Smart Agriculture System

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Computers, Materials & Continua 2022年第8期72卷 3777-3797页

作者： Fariza Sabrina Shaleeza Sohail Farnaz Farid Sayka Jahan Farhad Ahamed Steven Gordon School of Engineering and Technology Central Queensland UniversitySydneyAustralia College of Engineering Science and EnvironmentThe University of NewcastleSydneyAustralia School of Social Sciences Western Sydney UniversitySydneyAustralia Department of Environmental Sciences Macquarie UniversitySydneyAustralia School of Computing Data and MathematicalSciencesWestern Sydney UniversitySydneyAustralia School of Engineering and Technology Central Queensland UniversityCairnsAustralia

With increasing world population the demand of food production has increased *** of Things(IoT)based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in *** can be an important factor to make such systems trusted and easily adopted by *** this paper,we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop *** strength of the proposed system is in its interpretability which makes it easy for farmers to understand,trust and use *** use of fuzzy logic makes the system customisable in terms of types/number of sensors,type of crop,and adaptable for any soil types and weather *** proposed system can identify anomalous data due to security breaches or hardware malfunction using machine learning *** ensure the viability of the system we have conducted thorough research related to agricultural factors such as soil type,soil moisture,soil temperature,plant life cycle,irrigation requirement and water application timing for Maize as our target *** experimental results show that our proposed system is interpretable,can detect anomalous data,and triggers actions accurately based on crop requirements.

关键词： Explainable artificial intelligence fuzzy logic internet of things machine learning sensors smart agriculture

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques 2

Predictive Analysis of Consumer Behavior Using Supervised Le...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Rekha, K. Sashi Amutha, T. Usharani, R. Pushparani, S. Sivagami, V.M. Saveetha Institute of Medical and Technical Sciences Simats Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace. © 2024 IEEE.

关键词： Self-supervised learning

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A New Parameter Estimation Method Using System Steady-state Sinusoidal Output Measurements in the Frequency Domain

A New Parameter Estimation Method Using System Steady-state ...

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2023 International Conference on Advanced Mechatronic Systems, ICAMechS 2023

作者： Zhang, Dongsheng Man, Zhihong Zheng, Jinchuan Cao, Zhenwei Wang, Hai Wang, Song Swinburne University of Technology School of Science Computing and Engineering Technologies VIC3122 Australia Murdoch University Discipline of Engineering and Energy College of Science Health Engineering Education WA6150 Australia School of Mechanical Electrical & Information Engineering Weihai China

ISBN: (纸本)9798350307474

In this paper, a new parameter estimation method using system steady-state sinusoidal output (SSSO) measurements is proposed for a broad class of second-order linear systems. It is shown that when the input to the system is chosen as the linear combination of a group of sinusoids, the amplitudes of system SSSO components can be extracted using any fundamental period of system SSSO measurement data. Unknown system parameters can then be estimated with the SSSO amplitude information in the frequency domain. The advantages of the new parameter estimation method are three fold: (i) only a fundamental period of system SSSO measurement data is required;(ii) the estimation process has good performance against sensor noise;(iii) it provides a global optimization mechanism, in any fundamental period at steady-state, to accurately estimate unknown system parameters. Simulation examples are implemented to show the effectiveness of the proposed parameter estimation method. © 2023 IEEE.

关键词： Global optimization

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Aqua Optimize Transformation of Water Management With Cloud-Powered Efficiency

Aqua Optimize Transformation of Water Management With Cloud-...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent computing, MoSICom 2024

作者： Ignisha Rajathi, G. Priya, L.R. Vedhapriyavadhana, R. Deepthyka, K. Manipal Institute of Technology Manipal Academy of Higher Education Department of Information Technology Bengaluru Manipal India Anna University Francis Xavier Engineering College Department of Electronics and Communication Engineering Chennai India University of the West of Scotland School of Computing Engineering and Physical Sciences London United Kingdom

ISBN: (纸本)9798331533311

Effective water quality monitoring is crucial to ensuring the sustainability and safety of water resources, particularly in fields such as municipal water management, aquaculture, and agriculture. Traditional methods often result in water wastage, environmental degradation, and public health risks due to delayed responses and manual inefficiencies. With the rising focus on sustainable resource management and the impacts of climate change, there is a pressing need for innovative solutions that enable real-time water quality assessment. This research presents an Internet of Things (IoT)-based water quality monitoring system that connects multiple sensors to cloud-based platforms for continuous data analysis. The system monitors key parameters - temperature, flow rate, pH, and water depth - and transmits data using the MQTT protocol to visualization tools like Grafana and Power BI. These tools provide stakeholders with interactive dashboards, displaying real-time water quality trends, anomalies, and alerts for immediate action. The system recommends scalability to adapt a range of industrial applications, providing a cost-effective solution for both small and large-scale water management needs, based on its modularity. Compared to traditional methods, the proposed system improves water quality assessment accuracy, reducing human error and manual inefficiencies by over 30%. Cloud-based data storage and visualization reduce processing times by 40%, while the automated alert system accelerates response times by 25%, allowing prompt interventions when water quality falls below critical thresholds. The system's adaptable, modular architecture allows for seamless integration with existing water management frameworks in urban water distribution, wastewater treatment, and agriculture. This research demonstrates the system's potential to enhance water resource management, lower operational costs, and promote environmental sustainability across diverse sectors. © 2024 IEEE.

关键词： Industrial water treatment

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Extended Kalman Filter Algorithm to Detect False Data Injection in Wireless Sensor Network 2

Extended Kalman Filter Algorithm to Detect False Data Inject...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Babuji, R. Ganesan, J. Jeya Lekha, D. Karthikeyan, M. Kalai Selvi, D.M. Rmk Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Wireless Sensor Networks (WSN) contains spatially distributed sensor nodes that collaborate with each other. However, the WSN is susceptible since the wireless medium is unpredictable. Several conventional approaches are used to distinguish the abnormal in the WSN, and all have difficulties. An Extended Kalman Filter Algorithm (EKFA) to detect false inject data in WSN is proposed in this paper. It addresses two issues such as abnormality recognition and obstacle detection. It also distinguishes false insert data. Particularly, it observes neighbors' behaviors and then applying EKFA to forecast their future states. The node behavior is observed by incorporating abnormal node examination and system examination elements. The Path Optimization Method (POM) is a shortest route that is used to discover the obstacle in the WSN. The EKFA mechanism minimizes the forwarder count and delay by applying POM. Simulation results demonstrate that the EKFA approach enhances abnormal node detection and minimizes the false negative ratio. © 2023 IEEE.

关键词： Extended Kalman filters

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Securing Neural Network-Based Personalized Medicine for Advanced Liver Cancer Detection in Healthcare

Securing Neural Network-Based Personalized Medicine for Adva...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Kasturi, Salvadi Singh, Jagendra Agarwal, Mohit Kumar, Ashwini Mishra, Komal Sharma, Yogita Cvr College of Engineering Department of Computer Science Engineering Vastunagar India School of Computer Science Engineering & Technology Bennett University Greater Noida India Compucom Institute of Technology & Management Department of Computer Science Engineering Jaipur India Chandigarh University Department of University Institute of Computing Chandigarh India School of Management and Commerce Manav Rachna University Faridabad India

ISBN: (纸本)9798350382693

This research is aimed at discussing the critical meeting point of healthcare and artificial intelligence, including the need to protect the required treatment solution. Investigation into the utility of various deep learning techniques such as VGG 16, VGG 19, Convolutional Neural Networks (CNN), and Recurrent Neural Network (RNN) for the prediction of liver cancer stages and their respective treatments has been completed. The generation of 2340 of medical images from multiple sources, including the internet and records of hospitals, as a dataset was performed in a secure cloud. The security measures have allowed only authorized personnel to access the data and ensured the privacy and secrecy of data. The created dataset was then separated into two parts: training, which consisted of training the model on 70% of images, and testing, which implied that 30% of images were used to test the generated model. For each of the run models, performance evaluation measurements such as accuracy, precision, recall, and F1 score were calculated. The results have shown the highest efficiency of VGG 19 model, which resulted in 98.44% of accuracy. For the generated model, a fictitious ROC curve and specificity rate were also provided to showcase the discrimination abilities of each of the submitted models. The results show that the utility of neural network-based personalized medicine is beneficial for the further progression of the early prediction stage of liver cancer and its treatment. The result showed that the proposed models has successfully contributed to the field of healthcare informatics and further opportunities for artificial intelligence application to increase patient outcomes in the form of personalized medicine that may be performed. © 2024 IEEE.

关键词： Convolutional neural networks

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on Intelligence technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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