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

共 3288 条记录，以下是101-110 订阅

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the Role of Turbulence Damping in Horizontal Slug Regime Modelling 6

The Role of Turbulence Damping in Horizontal Slug Regime Mod...

引用

6th international Youth conference on Radio Electronics, Electrical and Power engineering, REEPE 2024

作者： Asiptsov, Yaroslav O. Hossain, Ismail Ivanovich, Velkin Vladimir Ural Federal University Named after the First President of Russia B.N.Yeltsin Department of Nuclear Power Plants and Renewable Energy Ekaterinburg Russia

ISBN: (纸本)9798350382891

this research endeavors to elucidate the understanding of turbulence damping's pivotal role in horizontal slug regime modeling, specifically focusing on twophase water-air flow dynamics. In this paper, it is our aim firstly, to comprehensively explore the conditions under which twophase water-air slug flows manifest, and secondly, to scrutinize the influence of turbulence damping on the formation and characteristics of these slug regimes. Methodologically, this study employs two-dimensional numerical simulations in Ansys Fluent software, utilizing the Reynolds-Averaged Navier-Stokes (RANS) approach for turbulence modeling. the SST k-ω turbulence model & realizable k-ϵ turbulence model are applied to delineate the slug flow area on the flow diagram. Conclusively, the study thoroughly analyzed slug flow parameters and conducted frequency analysis on four instances of slug flow. It highlights the necessity of considering turbulence damping to enhance the reliability of horizontal slug regime modeling in industrial applications. © 2024 IEEE.

关键词： Turbulence models

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Improving the Application of YOLOv8 in Image Object Detection 6

Improving the Application of YOLOv8 in Image Object Detectio...

引用

6th international conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Li, Jiafeng College of Software Engineering Sichuan University Chengdu China

ISBN: (纸本)9798350353174

As deep learning develops, the use of object detection to analyze image content has achieved stage-by-stage success. However, due to the problems of spatial occlusion and slow convergence, the accuracy is poor and models are complex. To address the above problems, a new YOLOv8s network structure that improves the accuracy with almost no increase in training overhead is proposed. First, the hierarchical feature extraction is utilized to enhance the positional information of the feature map by adding Coordinate Attention at backbone and neck networks layer-by-layer. then, GCNet is introduced at the last layer of neck network to model global context and merge hierarchical features. Finally, SIOU Loss is used to accelerate the convergence speed to further improve the accuracy. the experimental results demonstrate that the enhanced model improves mAP by +4.6% over YOLOv8s and accuracy by +2.7% over YOLOv9s. Additionally, it slightly decreases model complexity and training overhead by 0.2 GFLOPs. © 2024 IEEE.

关键词： Object recognition

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three-Phase PWM Rectifier Control System Design 6

Three-Phase PWM Rectifier Control System Design

引用

6th international conference on Power and Energy Technology, ICPET 2024

作者： Ran, Yukuan Qiao, Mingzhong Naval University of Engineering Wuhan China

ISBN: (纸本)9798350375855

the tidal current generator set has the characteristics that the amplitude and frequency of the generated Alternating Current (AC) change with the tidal current velocity in real time, so based on this issue improve the control strategy for three-phase Voltage Source PWM Rectifier (VSR). Introduce the variable exponential convergence law into the voltage outer loop Sliding Mode Control (SMC), and introduce the Phase Locked Loop (PLL) technology into the current inner loop predictive Current Control (PCC). Build a simulation platform in Matlab/Simulink software, compare and analyze the improved control strategy with the traditional PI control and "SMC+PCC" control, finnally draw the conclusion that it has significant advantages in response speed and anti-interference ability. the result also verifies the feasibility of using this control strategy for three-phase VSR to convert unstable AC power generated by power flow generators into stable Direct Current (DC) power. © 2024 IEEE.

关键词： MATLAB

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predictive Power of Two Data Flow Metrics in software Defect Prediction 18

Predictive Power of Two Data Flow Metrics in Software Defect...

引用

18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Roman, Adam Brozek, Rafal Hryszko, Jaroslaw Jagiellonian Univ Fac Math & Comp Sci Krakow Poland

ISBN: (纸本)9789897586477

Data flow coverage criteria are widely used in software testing, but there is almost no research on low-level data flow metrics as software defect predictors. Aims: We examine two such metrics in this context: dep-degree (DD) proposed by Beyer and Fararooy and a new data flow metric called dep-degree density (DDD). Method: We investigate the importance of DD and DDD in SDP models. We perform a correlation analysis to check if DD and DDD measure different aspects of the code than the well-known size, complexity, and documentation metrics. Finally, we perform experiments with five different classifiers on nine projects from the Unified Bug Dataset to compare the performance of the SDP models trained with and without data flow metrics. Results: 1) DD is noticeably correlated with many other code metrics, but DDD is not correlated or is very weakly correlated with other metrics considered in this study;2) both DD and DDD are highly ranked in the feature importance analysis;3) SDP models that use DD and DDD perform better than models that do not use data flow metrics. Conclusions: Data-flow metrics: DD and DDD can be valuable predictors in SDP models.

关键词： software Defect Prediction Data Flow Metrics Dep-Degree Data Flow Complexity Within-Project Defect Prediction

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Towards Holistic Crime Management through Descriptive Analysis and predictive Forecasting 6

Towards Holistic Crime Management Through Descriptive Analys...

引用

6th international conference on Advancements in Computing, ICAC 2024

作者： Wijethunge, Tinaya Edirisinghe, Sampath Jeon, Mansik Kim, Jeehyun Wijesinghe, Ruchire Silva, Bhagya Faculty of Engineering Department of Computer Engineering Nugegoda Sri Lanka Daegu Korea Republic of Faculty of Engineering Department of Electrical and Electronic Engineering Malabe Sri Lanka Malabe Sri Lanka Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

throughout the evolution of humankind, devising effective crime prevention strategies has been a concern in fostering safer communities. Although achieving a zero-crime rate is not possible yet, using data management methods can contribute to preventing and minimizing crimes. this article presents an integrated approach to proactive crime prevention. Data insights derived from analysis and visualization provide a comprehensive overview of historical trends and scenarios, aiding understanding of past and present situations. the forecasting of crime rates extends this comprehension to the future, spotlighting the severity of the potential threat to the safety of the community. three distinct prediction models are presented to predict the occurrence of violent/non-violent crimes, the most probable crime type, and the most probable crime location in a spatial-temporal context, facilitating optimal resource allocation of police patrolling. Combining these three analytical components, an application is designed to present this holistic solution for effective crime prevention, with a 73% accuracy in predicting violent crimes. To achieve higher accuracy in predicting the most probable crime types and locations, it is essential to go beyond spatiotemporal context and augment data on other factors that affect the occurrence of crimes. © 2024 IEEE.

关键词： Prediction models

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Estimation of Minimum Miscibility Pressure in Impure/Pure N2Based Enhanced Oil Recovery Process: A Comparative Study of Statistical and Machine Learning Algorithms 6

Estimation of Minimum Miscibility Pressure in Impure/Pure N2...

引用

6th international conference on Robotics, Control and Automation engineering, RCAE 2023

作者： Zhu, Xiuli Damarla, Seshu Kumar Huang, Biao University of Shanghai for Science and Technology Department of Control Shanghai China University of Alberta Department of Chemical and Materials Engineering AB Canada

ISBN: (纸本)9798350315301

Minimum miscibility pressure (MMP) prediction plays an important role in design and operation of nitrogen based enhanced oil recovery processes. In this work, a comparative study of statistical and machine learning methods used for MMP estimation is carried out. these methods include weighted k-nearest neighbors, wavelet neural network, general regression neural network, extreme learning machine, regression trees (RTs), random forest, bagging RTs, boosting RTs and Gaussian process regression. Most of the predictive models developed in this study exhibited superior performance over predictive models reported in literature. © 2023 IEEE.

关键词： Nitrogen

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A Novel Inception Deep Learning model for Mobile AI Stroke Prediction system: AI Research and Industry Perspectives for Connected Health 6

A Novel Inception Deep Learning model for Mobile AI Stroke P...

引用

6th international conference on Computing and Informatics, ICCI 2024

作者： Elbagoury, Bassant M. Maskeliuans, Rytis Zaghow, Marwa Kamel, Nabil Faculty of Computer and Information Sciences Ain Shams University Computer Science and Artificial Intelligence Programs Egypt Faculty of Computer Science and Engineering King Salman International University KSIU Egypt Kaunas University of Technology Faculty of Informatics Department of Multimedia Engineering Lithuania Dell Technologies EMEA Egypt

ISBN: (纸本)9798350373875

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. the main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed. © 2024 IEEE.

关键词： Learning systems

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IoT based Leakage Detection and Emergency Shutdown 6

IoT based Leakage Detection and Emergency Shutdown

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Abarna, P.S. Priya, S. Padma Pavalarajan, S. Sivatharani, S. Manshi, M. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331522667

the risk of gas leaks has grown significantly as a life threatening issue in industrial activities, cooking, and heating. this system integrates automatic reaction mechanisms, real-time monitoring capabilities, and advanced detection technologies in a multifaceted manner to address various dangers. the comprehensive gas leak detection and automatic cut off system is intended to improve safety in a variety of situations, including industrial and residential ones. To precisely identify the existence of dangerous gases like methane and propane, the proposed system combines infrared, electrochemical, and semiconductor types of gas sensors integrated with a mobile application, users may remotely administer and monitor the system in real-time. the system initiates a multi-tiered response upon detecting a gas leak;it sounds an alarm, notifies user-specified the mobile devices about the leak, and turns on an exhaust fan to release concentrated gas. A solenoid valve is used by the system to quickly shut off the gas supply and electricity from any location. the need of user-friendly controls and prompt notifications in emergency situations is highlighted in this paper's discussion of software architecture, sensor integration, and user interface design. Experimental results show that the system is reliable and capable of responding quickly under different circumstances. In future the application of machine learning algorithms for predictive maintenance and the possibility of integrating IoT to further strengthen security protocols. the results emphasize importance to deploy these kinds of extensive safety systems widely in order to reduce the risks associated with gas exposure. © 2025 IEEE.

关键词： Solenoid valves

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Robotic Arm Automatic Placement Workstation with AI Pneumatic Tube Fault Diagnosis 6

Robotic Arm Automatic Placement Workstation with AI Pneumati...

引用

6th international conference on Control and Robotics, ICCR 2024

作者： Lee, Jeng-Dao Lin, Hsu-Lun Ye, Yu-Cheng Hung, Ying-Hsiu National Formosa University Department of Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Yunlin County Taiwan

ISBN: (纸本)9798331518158

Traditional equipment failures are often identified only when they manifest during production, leading to costly downtime, increased defect rates, and safety hazards. this study focuses on the progressive stages and severity of AI pneumatic tube fault diagnosis, a critical component in industrial operations. By leveraging a sensor and a microphone to collect vibration and audio data, we developed a deep learning-based system to accurately classify and predict the progression of these failures. Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM) networks were trained to identify anomalies in the collected data, effectively distinguishing between different fault stages, from minor issues to major failures. TCN models, with their dilated convolutions, capture long-term dependencies in the data, allowing for early detection of subtle anomalies. LSTM models, using their gated architecture, retain and utilize critical historical information, enabling more precise fault classification. A comparative analysis of various equipment condition monitoring methods demonstrates the superiority of our proposed approach in terms of signal detection and classification accuracy. Additionally, system integration of a Robotic Arm Automatic Placement Workstation and a user-friendly Node-RED-based interface supports real-time monitoring and operator intervention. this integrated system allows seamless communication between hardware components and software, enhancing overall operational efficiency. this study highlights the potential of AI and deep learning to enhance predictive maintenance and operational efficiency in industrial settings, particularly for critical equipment like pneumatic tubes. © 2024 IEEE.

关键词： Robotic arms

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Document-level event extraction for fused contextual semantic paragraph segmentation 6

Document-level event extraction for fused contextual semanti...

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6th international conference on Information Science, Electrical, and Automation engineering, ISEAE 2024

作者： Zhu, Junguang Zhang, Lisheng School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781510682924

Current approaches to document-level event extraction face challenges such as scattered argument parameters across sentences and multiple event records within a single document. In this study, we propose a solution that combines context paragraph information modeling with argument extraction to tackle these issues. Firstly, we employ advanced techniques for segmenting lengthy documents into smaller parts. Additionally, we utilize pre-trained models to obtain paragraph vector matrices for each segment. By integrating these models, annotated sentence vector matrices and paragraph matrices are combined through a dot product fusion model and concatenated together to generate improved sentence vector embeddings using pooling operations. Next, the entities extracted through named entity recognition are used to select pseudo-trigger words and construct adjacency similarity matrices and composite graphs. Finally, the task of populating event table elements is transformed into a multi-label classification task to address the issue of overlapping roles. Experiments on the chFinAnn dataset demonstrate an overall F1 improvement of 1.5 percentage points, with a significant increase in the model's inference speed. © 2024 SPIE.

关键词： Semantics

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