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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 3372 条记录，以下是991-1000 订阅

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High-Speed Train Health Assessment Based on Degradation Stages and Fault Classification by using Dual Task LSTM with Attention Mechanism

High-Speed Train Health Assessment Based on Degradation Stag...

引用

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

作者： Ali Nawaz Sanjrani Hong Zhong Huang Sadiq Ali Shah Attaullah Narajo Kalsoom Bhagat Bilawal Ali School of Mechanical and Electrical Engineering (SMEE) University of Electronic Science and Technology of China (UESTC) Chengdu China Department of Mechanical Engineering Mehran University of Engineering and Technology MUET SZAB Campus Khairpur Mir’s Pakistan School of Computer Science and Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China Department of Electrical Engineering Mehran University of Engineering and Technology MUET SZAB Campus Khairpur Mir’s China School of Electronic Science and Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China

ISBN: (数字)9798350356083

ISBN: (纸本)9798350356090

Health and Safety Assessment is critical for high-speed trains to maintain their operation continuously. Health assessment based on degradation levels to predict RUL and fault diagnosis are two key task events to decide the end of life for bearing or decide maintenance actions. However, a gap exists as these tasks are predicted separately. this study proposes a dual-task LSTM model with an attention mechanism to predict RUL based on degradation levels and diagnose the fault classification. this enables more accurate fault assessment that helps reduce maintenance costs. the performance of the proposed model demonstrates and leads over other state-of-the-art methods and proves an excellent accuracy of its new application with the potential to decide the end of the Life of Bearing based on degradation levels with fault classification.

关键词： Degradation Training Adaptation models Accuracy Attention mechanisms predictive models Robustness Maintenance Prognostics and health management Long short term memory

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An End-to-End Deep Learning Based Approach for Plant Disease Recognition

An End-to-End Deep Learning Based Approach for Plant Disease...

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international conference on Communications, Information System and Computer engineering (CISCE)

作者： Mingyue Guo Qiutong Li Deyu Liu Department of Intelligent Science and Technology Chengdu Neusoft University Chengdu China Department of Network Engineering Chengdu Neusoft University Chengdu China Department of Software Engineering Chengdu Neusoft University Chengdu China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

the agricultural industry is essential in providing high-quality food and plays a significant role in growing economies and populations. Plant diseases can cause severe losses in food production and eliminate species diversity. Using automated detection techniques for diagnosing plant diseases can enhance food productivity and minimize economic losses in the early stage. Recent advancements in Deep Neural Networks have significantly enhanced the accuracy of object recognition systems. In this paper, we present an end-to-end deep learning based methodology to recognise different diseases of plants, not limited to specific plant species. We investigate the efficiency of different Visual Transformer models with the transfer learning strategy. the AI Challenger 2018 Crop Disease Detection and PlantVillage datasets are used for training and evaluation. Experimental results indicate the efficacy of our proposed approach in accurately recognizing various plant diseases.

关键词： Deep learning Industries Plant diseases Analytical models Visualization Biological system modeling Transfer learning

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Estimation of Lithium-ion Battery Health State Using Partial Charge-Discharge Data

Estimation of Lithium-ion Battery Health State Using Partial...

引用

Energy Systems and Electrical Power (ICESEP), international conference on

作者： Wusong Dong Zuxin Li Dingding Liu Lihao Xu School of Engineering Huzhou University Huzhou China School of Intelligent Manufacturing Huzhou College Huzhou China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

Data-driven methods are techniques for estimating the health state of lithium-ion batteries with the widest scope of application, yet the difficulty lies in obtaining complete charge/discharge process data in actual scenarios. In order to increase efficiency in both data utilization and health state prediction, efficient indirect health features of lithium-ion cells are exploited from charge/discharge data based on a time series method. these features stem from partial data within specific voltage ranges, and they are subsequently imported into a Long Short-Term Memory neural network model. the parameters of the model are automatically tuned by the Pelican Optimization Algorithm. this approach achieves high-accuracy health state estimation on different batteries. the work reveals potential for lithium-ion battery health state prediction with fragmented data.

关键词： Lithium-ion batteries Time series analysis Neural networks Voltage predictive models Prediction algorithms Data models

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Redundancy Design and Analysis of Digital Instrument Control System in Nuclear Power Plant

Redundancy Design and Analysis of Digital Instrument Control...

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IEEE international conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Weichao He Yuguang Niu Shuangfeng Dai Ming Du North China Electric Power University Beijing China China Techenergy Co. Ltd Beijing China Beijing Ander Medical Co. Ltd Beijing China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

As a means to improve the reliability of digital instrument control system of nuclear power plant, redundancy technology is more and more adopted by modern industrial designers, and it plays an increasingly important role in modern nuclear power industry production. the system designer can improve the reliability of the system through equipment redundancy, software redundancy and redundancy technology, and meet the requirements of safe operation of the important load system. this paper summarizes the typical application mode of the control system redundancy technology in nuclear power plant, models the typical system redundancy structure, and completes the failure probability analysis. Finally, other technologies of the reliability design of the instrument control system in nuclear power plant are described.

关键词： Instruments Redundancy Process control Production Control systems Reliability engineering software software reliability Reliability Power generation

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Distributionally Robust Deep Neural Network for Malicious App software Dynamic Detection

Distributionally Robust Deep Neural Network for Malicious Ap...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

作者： Jie Li Yunle Tang Linfeng Yang Shifei Chen The College of Big Data Guangxi Vocational & Technical College Nanning China The School of Computer Electronics and Information Guangxi University Nanning China The School of Electrical Engineering Guangxi University Nanning China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Malicious App software Detection (MASD) is a crucial step in ensuring device security, as it helps users identify and eliminate malicious software that may pose a threat to the system or data security. Based on the distributionally robust optimization theory, this paper constructs an ambiguity set by using Wasserstein distance, and furtherly proposes a distributionally robust Deep Neural Networks (DNN) model to deal with unseen data for the MASD problem. A computationally-efficient upper approximation of the proposed model is given based on Lipchitz constants of the DNN network. Various test results reveal that the proposed model has better performances than the corresponding DNN. And our model possesses outstanding robustness when the training data set is reduced.

关键词： Computational modeling Data security Training data Artificial neural networks Data models Robustness Malware Object recognition Optimization

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A predictive Model for Self-Directed Learning Ability that is Based on Learning Behaviors 6th

A Predictive Model for Self-Directed Learning Ability that i...

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6th international conference on Computer Science and Educational Informatization, CSEI 2024

作者： Wang, Huining Zhu, Zhengzhou Chen, Jian Peking University No.5 Yiheyuan Road Haidian District Beijing China

ISBN: (纸本)9789819637348

Currently, there is a shift from one-time academic education to lifelong learning, highlighting the necessity of self-directed learning ability. To cultivate learners with a high level of autonomy, it is essential to predict and diagnose their self-directed learning abilities. Given the limitations of existing diagnostic methods for self-directed learning ability, this paper focuses on students in the software engineering course at Peking University. It constructs a hybrid learning behavior feature on the basis of Zimmerman's self-regulated learning model and characteristic instructional design. A predictive model for self-directed learning ability based on learning behaviors is designed to enhance teaching quality and improve self-directed learning ability. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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AI-Orchestrated Data-Driven Modelling and Control in Battery Management Systems: A Review

AI-Orchestrated Data-Driven Modelling and Control in Battery...

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international conference on Industrial Artificial Intelligence (IAI)

作者： Xiaofei Han Kang Li School of Electronic and Electrical Engineering University of Leeds Leeds United Kingdom

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

Battery storage is playing an important role in supporting the low-carbon transition of the energy systems from the top to the tail. For example, Lithium-ion batteries have been widely used in electric vehicles and renewable energy storage systems. However, the efficient management and control of these batteries pose a multitude of challenges. Inappropriate battery operating activities could significantly damage the health of the battery systems, and even cause safety concerns. Constructing suitable battery models to predict the battery states and developing reliable control strategies to avoid unsafe operations are significantly important. the recent development of artificial intelligence technology provides novel tools for battery operation and control due to its ability to handle large datasets. this paper presents a brief review of the latest progress in AI-orchestrated methods in battery modelling and control. the paper in particular focuses on the latest progress in machine learning and deep neural networks for the battery management system.

关键词： Lithium-ion batteries Renewable energy sources Reviews Battery management systems Tail Machine learning predictive models Batteries Safety Reliability

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PartDensE: Knowledge Graph Embedding With Joint Use Of Specific Learning And Relation-aware Functions

PartDensE: Knowledge Graph Embedding With Joint Use Of Speci...

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Internet of things, Automation and Artificial Intelligence (IoTAAI), international conference on

作者： Qiuyue Dan Delong Cui Zhiping Peng Qirui Li Jieguang He JinBo Qiu School of Information and Control Engineering Jilin Institute of Chemical Technology China School of Electronic and Information Engineering Guangdong University of Petrochemical Technology Maoming China Jiangmen Polytechnic Jiangmen China School of Computer Guangdong University of Petrochemical Technology Maoming China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

Knowledge Graph Embedding (KGE) is important to the value of the Knowledge Graph (KG) in its application field. Currently, neural network-based models achieve the most advanced performance. However, these models rarely consider the potential information between entities and relations in KGs, and also suffer from the problems of complex structure and high consumption of computing resources. In this paper, we propose the simple and efficient model PartDensE by using dense network, combining specific sharing layer and relational awareness layer. Specifically, our model learns all entities and relationships individually and can mine hidden features to enrich global information. Also, learn all the information based on each relation and be able to accurately establish associations between entities and relations. PartDensE can capture global and local features and displayed and hide information, retaining as much valuable information as possible. We evaluated PartDensE with 4 indicators across on two baseline datasets. Numerous experimental data prove the effectiveness of our model.

关键词： Knowledge engineering Automation Accuracy Computational modeling Knowledge graphs predictive models Benchmark testing Data models Internet of things

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State of Health Prediction of Lithium-ion Battery Based on Feature Fusion

State of Health Prediction of Lithium-ion Battery Based on F...

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Energy Systems and Electrical Power (ICESEP), international conference on

作者： Dingding Liu Zuxin Li Wusong Dong Lihao Xu School of Engineering Huzhou University Huzhou China School of Intelligent Manufacturing Huzhou College Huzhou China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

Aiming at the problems of low accuracy and poor stability of existing methods, a feature fusion and algorithm fusion based estimation method for the state of health (SOH) of lithiumion batteries is proposed. Firstly, six health features with high correlation with SOH are extracted from the charge and discharge curves of the lithium-ion batteries. then, the extracted health features are classified and fused using principal component analysis to realize the reduction of the data dimensionality. Finally, the parameter of the bi-directional long and short-term memory network (BILSTM) is optimized by using Sparrow's algorithm (SSA) and Firefly's algorithm (FA) to obtain the SOH estimation model. To validate the usability of the method, experiments are done on a variety of battery aging data and compared with the BILSTM model, SSA-BILSTM model, and FA-BILSTM model. the experimental results show that the FA-SSA-BILSTM method can effectively improve the accuracy of SOH estimation.

关键词： Lithium-ion batteries Accuracy Estimation predictive models Feature extraction Prediction algorithms Data models

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A computational cardiopulmonary physiology simulator accurately predicts individual patient responses to changes in mechanical ventilator settings 44

A computational cardiopulmonary physiology simulator accurat...

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44th Annual international conference of the IEEE-engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Mistry, Sonal Brook, Bindi S. Saffaran, Sina Chikhani, Marc Hannon, David M. Laffey, John G. Scott, Tim E. Camporota, Luigi Hardman, Jonathan G. Bates, Declan G. Univ Warwick Sch Engn Coventry CV4 7AL W Midlands England Univ Nottingham Sch Math Sci Nottingham NG7 2RD England Univ Nottingham Sch Med Nottingham NG7 2RD England UCL Fac Engn Sci London WC1E 6BT England Nottingham Univ Hosp NHS Trust Nottingham NG7 2UH England NUI Galway Sch Med Dept Anaesthesia & Intens Care Med Galway Ireland ICT Ctr Royal Ctr Def Med Acad Dept Mil Anaesthesia & Crit Care Birmingham B15 2SQ W Midlands England Guys & St Thomas NHS Fdn Trust Dept Crit Care London SE1 7EH England

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

We present new results validating the capability of a high-fidelity computational simulator to accurately predict the responses of individual patients with acute respiratory distress syndrome to changes in mechanical ventilator settings. 26 pairs of data-points comprising arterial blood gasses collected before and after changes in inspiratory pressure, PEEP, FiO(2), and I:E ratio from six mechanically ventilated patients were used for this study. Parallelized global optimization algorithms running on a high-performance computing cluster were used to match the simulator to each initial data point. Mean absolute percentage errors between the simulator predicted values of PaO2 and PaCO2 and the patient data after changing ventilator parameters were 10.3% and 12.6%, respectively. Decreasing the complexity of the simulator by reducing the number of independent alveolar compartments reduced the accuracy of its predictions.

关键词： Ventilators Measurement uncertainty predictive models Ventilation Prediction algorithms Real-time systems Physiology

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