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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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Research on Kinematic Simulation of Digital Pumping Unit Based on Matlab/ Multibody

Research on Kinematic Simulation of Digital Pumping Unit Bas...

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Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), international conference on

作者： Wen Sun Xinhao Yan Wei Zhang Tao Ren School of Mechanical Engineering Xi’an Shiyou University Xi’an China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Intelligent pumping units are an important component of intelligent oil fields, and digital twin technology is an excellent solution for achieving intelligent pumping units. After building the digital geometric model of the pumping unit on the base of SolidWorks software, based on the Matlab/Multibody software platform, the component model and assembly constraint relationship based on XML are imported to realize the construction and connection of the multibody model. the model driver program are set and calculation results are obtained to complete the kinematic simulation analysis; Meanwhile, compared with the kinematic analysis results of the pumping unit based on vector method, the correctness and feasibility of the multibody model of the pumping unit based on Matlab/Multibody were verified. the analysis results of this article lay an important foundation for data perception, data fusion, and prototype software system development of digital pumping units based on digital twin technology.

关键词： Analytical models Solid modeling three-dimensional displays Oils Geometric modeling XML Kinematics Vectors Digital twins MATLAB

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State of Health Prediction for Battery Energy Storage Systems under Random Walk Operations

State of Health Prediction for Battery Energy Storage System...

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System Reliability and Safety engineering (SRSE), international conference on

作者： Alaa Selim Huadong Mo Hemanshu Pota Di Wu Daoyi Dong School of Engineering and Technology University of New South Wales Canberra Australia School of Systems and Computing University of New South Wales Canberra Australia Sichuan University and Peking University CIICADA Lab School of Engineering Australian National University ACT Australia

ISBN: (数字)9798350356083

ISBN: (纸本)9798350356090

this study addresses the challenge of predicting State of Health ($\mathbf{S o H}$) and capacity degradation in Battery Energy Storage Systems (BESS) that experience random walk phenomena due to frequent control algorithm adjustments. through meticulous feature engineering on diverse operational cycle data, this research identifies and utilizes critical predictors influencing battery behavior. We employ a Gaussian Process Regression model, renowned for its effectiveness in capturing complex, nonlinear interactions characteristic of BESS under dynamic conditions, which consistently achieves accurate prediction with R-squared value of 0.99. the results affirm the potential of advanced machine learning techniques to enhance the management and reliability of BESS, particularly in applications where control algorithms frequently induce battery cycling akin to random walk, thereby complicating SoH and capacity degradation assessments.

关键词： Degradation Machine learning algorithms Battery energy storage system Heuristic algorithms Machine learning predictive models Prediction algorithms Batteries Safety Reliability

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Blood Glucose Data Analysis based EMD-LSTM

Blood Glucose Data Analysis based EMD-LSTM

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6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Junjian Liang Weiren Zhao Bingo W. K. Ling Quan Sun School of Information Engineering Guangdong University of Technology Guangzhou China School of Physics and Optoeletronic Engineering School of Information Engineering Guangdong University of Technology Guangzhou China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

In modern hospitals, people can check their blood glucose data through minimally invasive instruments, and the data will be transmitted to the central mainframe of the hospital through the Internet of things. through minimally invasive instruments, blood glucose information of the human body can be measured. the blood glucose information data measured is a highly complex nonlinear system, and the signal does not advance in a uniform time process but has its own advance. To enhance prediction accuracy, the Empirical Mode Decomposition (EMD) algorithm is employed. Combined with the characteristics of LSTM (Long Short-Term Memory) recursive neural network, data were normalized and then pushed to LSTM network models with different LSTM layers and different numbers of hidden neurons at the same layer for training and testing. the appropriate number of LSTM layers and hidden neurons was found by comparing the evaluation index and prediction effect, resulting in an improvement in prediction accuracy by about 30%. the test results show that the model prediction accuracy improved significantly. this model can complete the patient's individual blood glucose data modeling in the Internet of things in big data, providing a beneficial reference to forecast movements in patients with blood glucose. It can also help people in understanding their actual blood glucose levels and building appropriate health maintenance strategies.

关键词： Accuracy Minimally invasive surgery Neurons predictive models Prediction algorithms Data models Glucose Internet of things Blood Long short term memory

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Task Context: A Tool for Predicting Code Context models for software Development Tasks

Task Context: A Tool for Predicting Code Context Models for ...

引用

international conference on software engineering - Companion, ICSE, Companion

作者： Yifeng Wang Yuhang Lin Zhiyuan Wan Xiaohu Yang Zhejiang University China

s- A code context model consists of code elements and their relations relevant to a development task. Previous studies found that the explicit formation of code context models can benefit software development practices, e.g., code navigation and searching. However, little focus has been put on how to proactively form code context models. In this paper, we propose a tool named Task Context for predicting code context models and implement it as an Eclipse plug-in. Task Context uses the abstract topological patterns of how developers investigate structurally connected code elements when performing tasks. the tool captures the code elements navigated and searched by a developer to construct an initial code context model. the tool then applies abstract topological patterns with the initial code context model as input and recommends code elements up to 3 steps away in the code structure from the initial code context model. the experimental results indicate that our approach can predict code context models effectively, with a significantly higher F-measure than the state-of-the-art (0.57 over 0.23 on average). Furthermore, the user study suggests that our tool can help practitioners complete development tasks faster and more often as compared to standard Eclipse mechanism. Demo video: https://***/3yEPh6uvHI8 Repository: https://***/icsoft-zju/Task_Context

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IIoT-based predictive Maintenance for Oil and Gas Industry 22

IIoT-based Predictive Maintenance for Oil and Gas Industry

引用

proceedings of the 2022 6th international conference on Electronic Information Technology and Computer engineering

作者： Zhiyang Jia Jihe Wang Cheng Deng Department of Computer Science China University of Petroleum-Beijing at Karamay China Department of Mathematics West Yunnan University of Applied Sciences China

ISBN: (纸本)9781450397148

the Industrial Internet of things (IIoT) promises to provide an expanded awareness of field assets and equipment, access to data from across locations, and actionable insights for maximizing operational performance and safety of the oil and gas industry. Using automation and machine learning, with the application of predictive maintenance, efficiencies can be boosted and problems can be mitigated sooner and more effectively. the proposed system is mainly based on the data collection, processing, analysis, and modeling of an enormous number of historical and real-time data generated during the operation of the equipment on the edge side. the data-driven predictive maintenance used machine learning models and deep learning models to predict the remaining useful life (RUL). Bi-LSTM based prediction model has been trained on the cloud, and deployed onto the edge devices. the predictive maintenance process includes data acquisition, data processing, training of machine learning model, equipment health assessment, remaining useful life prediction, strategy formulation, and strategy execution. the predictive maintenance solution driven by the IIoT helps oil and gas companies make predictions before equipment failures have a significant impact on their company's safety level and profits to improve asset reliability and promote cost savings.

关键词： Oil and Gas Industry IIot Long short-term memory predictive maintenance

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predictive and Explainable Modelling in Economics on the Case Study of Remittance Prediction Using the NeuDen AI Computational Architecture 19th

Predictive and Explainable Modelling in Economics on the Cas...

引用

19th international conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2024

作者： AbouHassan, Iman Kasabov, Nikola Trifonov, Roumen Popov, George Technical University of Sofia Sofia Bulgaria Central Bank of Lebanon Beirut Lebanon KEDRI SECMS Auckland University of Technology Auckland New Zealand IICT Bulgarian Academy of Sciences Sofia Bulgaria Dalian University Dalian China

ISBN: (纸本)9783031815416

the presence of complex time series and multimodal data in economics and finance necessitates the development of advanced analytical models capable of interpreting complex patterns and dynamics. Despite their fast development as LLM, the current AI systems cannot do that. this paper introduces how a novel computational architecture, NeuDen, which combines the strengths of evolving spiking neural networks (eSNNs) with the interpretability of evolving dynamic neuro-fuzzy systems (eDNFS), can be used to model high-dimensional temporal data in economics. the NeuDen model is applied to the analysis of remittance inflows, demonstrating its ability to not only model data but also uncover the underlying trends in intricate remittance data. this application highlights the model's potential as a tool for efficient modelling of economic data that also helps understand the dynamics of economic processes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neural network models

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Comparative Study of Directive-based Programming models on CPUs and GPUs for Scientific Applications 6th

Comparative Study of Directive-based Programming Models on C...

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6th international conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Navya, C. Sanjay, H.A. Salvi, Sanket Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (纸本)9789811613418

Graphics processing unit-based parallel programming architecture has shown increased popularity as a building block for high performance computing. With the increased processing required in the last years, the search for devices with better performance, started in computing a need for parallel programming, makes it possible to support the performance of software and algorithms requiring high processing patterns. However, the major hurdle is their programming complexity for their widespread adoption. For better performance, many directive-based GPU programming models have been proposed. Directive-based programming model requires a different level of programming efforts to port and optimize applications. In this paper, two popular directive-based programming models have been evaluated using popular scientific benchmark applications. the benchmark applications are implemented using OpenACC for GPUs and OpenMP for CPUs. Results show that the directive-based programming model, OpenACC for GPUs, shows reasonably high performance, compared to directive-based programming model, OpenMP on CPUs. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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PSOM: An Vector Primitive Shape Optimization Module

PSOM: An Vector Primitive Shape Optimization Module

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Artificial Intelligence, Networking and Information Technology (AINIT), international Seminar on

作者： JiaQi Liu Yongjian Liu Yanchun Ma Wuhan University of Technology Wuhan China Wuhan Vocational College of Software and Engineering Wuhan China

ISBN: (数字)9798331522285

ISBN: (纸本)9798331522292

Currently, the most advanced image vectorization approach is based on a learning model, which uses deep learning to predict the parameters of the vector primitives and adjust the shapes of the primitives. these methods mainly focus on the overall accuracy and performance of the vectorization task. However, they do not pay attention to the influence of primitive shape on the quality of generated graphs. the movement of Bezier curve control points will lead to the problem of selfintersection of curves, which makes the vector graphs present a complex topology. In order to solve the above problems, we propose a general primitive shape optimization module, which suppresses the self-intersection of curves and encourages primitives to form simple shapes by controlling the steering between adjacent control points, so as to improve the beauty of primitives and the quality of vector graphics. the experimental results show that the module can significantly improve the visual effect and evaluation indexes of vector images on the two public datasets of Fonts and Emoji.

关键词： Seminars Deep learning Shape predictive models Visual effects Vectors Topology Information technology Optimization Emojis

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Deep Multi-Modal Approach for Protein Function Prediction and Classification

Deep Multi-Modal Approach for Protein Function Prediction an...

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international conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Rakibul Islam MD Talukdar Sadman Rafid Saleh Md. Yousuf Tasnim Rahman Riasat Azim Department of CSE United International University Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Proteins play a crucial role in various biochemical activities, making accurate protein class and function prediction critical. Accurate protein function prediction is fundamental in computational biology for biological understanding. Relying solely on one modality for protein function prediction limits the model's ability to capture the protein's complete picture, potentially leading to inaccurate predictions. this paper addresses these challenges and presents a machine-learning model for precise protein class prediction, leveraging protein sequence, secondary structure, and interaction information. the model achieves a remarkable 100% accuracy in protein class prediction, highlighting the effectiveness of multimodal data. Addition-ally, GO notation prediction yields high accuracies of 96% for Biological Processes (BP), 97% for Cellular Components (CC), and 98% for Molecular Functions (MF). these results demonstrate the benefits of multimodal learning in capturing the intricate relationships within protein functions, enabling more accurate protein function annotation, and ultimately leading to characterization and downstream applications in biomedical research.

关键词： Proteins Protein engineering Biological system modeling Computational modeling Machine learning predictive models Protein sequence

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Disease Detection Algorithm of Concrete Bridges Based on Improved YOLOv7

Disease Detection Algorithm of Concrete Bridges Based on Imp...

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Natural Language Processing (ICNLP), international conference on

作者： BoWen Song Jing Gu School of Electronic Engineering Xi'an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Aiming at the problem that the disease detection algorithm of concrete bridges based on machine learning and deep learning is not high in recognition accuracy, this paper proposes an efficient three-dimensional coordinate attention mechanism (ETCA) based on the YOLOv7 network, which uses this structure to pay attention weighting to the feature information on feature maps of different sizes in the feature fusion network and fully extract effective features. At the same time, the Anchor Free detection algorithm is introduced, and a positive and negative sample allocation strategy without anchor frame is proposed, so that the model is no longer limited by the size and aspect ratio of the preset box, but directly generates the prediction results through the prediction box regression parameters of the network, which not only enhances the generalization ability of the model for diseases of different sizes in complex backgrounds, but also reduces the complexity of the model.

关键词： Bridges Object detection predictive models Feature extraction Natural language processing Hardware Data mining Resource management Detection algorithms Diseases

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