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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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Traffic Flow Prediction Based on Delayed Attention Neural Ordinary Differential Equation

Traffic Flow Prediction Based on Delayed Attention Neural Or...

引用

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

作者： Xin Zhao Li Lan School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

To address the issues of delayed effects and insufficient exploration of spatiotemporal correlation characteristics in traffic flow prediction, a dynamic graph neural delay differential equation model is proposed to improve prediction accuracy. First, traffic flow features across different periods are extracted. Next, the delay time between upstream and downstream roads is calculated, and a delay differential equation is introduced to capture the time-lagged characteristics among nodes in the road network. Finally, validation on real-world public traffic flow datasets, including PEMS04 and PEMS08, demonstrates that the proposed model achieves an average reduction of approximately 35% in mean absolute error (MAE) and 27% in root mean square error (RMSE).

关键词： Accuracy Roads predictive models Ordinary differential equations Feature extraction Mathematical models Delays Trajectory Spatiotemporal phenomena Root mean square

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Prediction of SOC Values for Sodium Batteries by Using the GRU and BiGRU Network models

Prediction of SOC Values for Sodium Batteries by Using the G...

引用

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

作者： Fuliang Cheng Xuan Wu Peng Li Liqian Qi Guangxi University of Science and Technology School of Mechanical and Automotive Engineering Liuzhou China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

To enhance the predicted accuracy of state of charge (SOC) values, this paper proposes the gated recurrent unit (GRU) network model and bidirectional gated recirculation unit (BiGRU) network model to predict the SOC values of sodium batteries. the dynamic characteristics of sodium batteries were measured under two operation conditions (DST and FUDS) to collecting the training data and test data. Here, the values of voltage and current are the input parameters and the SOC values are the output results. the comparison results indicate that the BiGRU network model is more accurate than the GRU network model. the RMSE value and MAE value can be decreased to 32.28% and 17.77% by using the BiGRU network model. therefore, the BiGRU network model can improve the prediction accuracy of SOC values for sodium batteries.

关键词： Accuracy Fluctuations Sodium Neural networks Estimation predictive models Logic gates

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Efficient Path Estimation via Sensor Fusion of IMU, GPS, and Wheel Encoder and Control Parameter Optimization

Efficient Path Estimation via Sensor Fusion of IMU, GPS, and...

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Advancements in Computing (ICAC), international conference on

作者： Nisal Weerasinghe Nushara Wedasingha Lasantha Senevirathne Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

In autonomous vehicles, precise estimation of control parameters is essential for ensuring safety, adaptability, and energy efficiency. However, standalone estimators often fall short due to their inability to detect sharp turns, wheel skidding, and slippage, along with their high computational demands. this study presents a fusion model that integrates GPS, IMU, and wheel encoder data to accurately estimate control parameters while maintaining safety and adaptability under varying environmental conditions. the model also determines the optimal tack of the vehicle using these estimated parameters. Extensive testing demonstrated the model's superior ability to detect subtle changes, identify wheel skidding and slippage, and outperform state-of-the-art stand alone and fusion models in computational efficiency. this research marks a significant advancement in au-tonomous vehicle technology, offering a robust and sophisticated tool for precise control parameter estimation and optimal path prediction.

关键词： Adaptation models Computational modeling Vehicle safety Wheels Estimation predictive models Data models Vehicle dynamics Autonomous vehicles Global Positioning System

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Application of Logic Control and predictive PID Control in Smart Grid Dispatching and Decision Making

Application of Logic Control and Predictive PID Control in S...

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

作者： Zhixin Ou Department of Urban Rail Transit and Information Engineering Anhui Communications Vocational & Technical College Hefei China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

the smart grid dispatching and decision-making system has the characteristics of smart power detection, data cloud storage, and information fusion. the disadvantage is that the difficulty of model estimation is high, significant fluctuations in energy storage and transmission, algorithm update iteration delay, and low accuracy of data fusion technology. First, models and algorithms for smart grids, improve the multiple domain value delay problem of output stability. Second, the nine-point logic control strategy is based on partitioning deviation and deviation variation based on pan Boolean dispatching, effectively solving the frequency and band amplitude issues of power grid transmission load fluctuations. When the integral variable or input signals change, compared with traditional PID control, it is concluded that, just adjusting according to the nine-point logic control strategy, can obtain ideal control and output results. Finally, establishing a simulation model, the results indicate that its control curve performs well in model matching, satisfactory dispatching in case of model mismatch, and ensures that the fluctuation amplitude and error accuracy meet the requirements. the control strategy has the characteristics of a fast output response, high stability and robustness, and a small overshoot for changing power grid models.

关键词： Analytical models Fluctuations PI control predictive models Dispatching Stability analysis Smart grids Logic PD control Load modeling

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Strategic Deployment of Honeypots in Blockchain-based IoT Systems

Strategic Deployment of Honeypots in Blockchain-based IoT Sy...

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IEEE international conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Daniel Commey Sena Hounsinou Garth V. Crosby Multidisciplinary Engineering Texas A&M University Texas USA Computer Science & Cybersecurity Metro State University Minnesota USA Engineering Technology & Industrial Distribution Texas A&M University Texas USA

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

this paper addresses the challenge of enhancing cybersecurity in Blockchain-based Internet of things (BIoTs) systems, increasingly vulnerable to sophisticated cyberattacks. It introduces an AI-powered system model for the dynamic deployment of honeypots, utilizing an Intrusion Detection System (IDS) integrated with smart contract functionalities on IoT nodes. this model enables the transformation of regular nodes into decoys in response to suspicious activities, thereby strengthening the security of BIoT networks. through a game-theoretic model, specifically Bayesian games, the paper analyses strategic interactions between potential attackers and the AI-enhanced IDS. the model focuses on understanding and predicting sophisticated attacks that may initially appear normal, emphasizing strategic decision-making, optimized honeypot deployment, and adaptive strategies in response to evolving attack patterns.

关键词： Adaptation models Biological system modeling Smart contracts Refining Intrusion detection Games predictive models

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Research on Evaluation and Prediction Model of Vehicle Interior Sound Quality Based on EEG

Research on Evaluation and Prediction Model of Vehicle Inter...

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

作者： Liping Zhu Haijun Zhao Xulong Wang Na Li School of Automobile and Transportation Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

In view of the fact that EEG (electroencephalogram) signals can reflect people's psychological state under auditory stimulation, the interior noise is used as the auditory stimulation of the evaluator. A method of establishing an evaluation and prediction model of interior sound quality based on EEG signals is proposed, which takes the relaxation degree as the evaluation index, the EEG features extracted from the relaxation degree in the field of EEG as the input, and the subjective evaluation results as the output. By extracting the power spectral density of the EEG feature a(8-13Hz) wave strongly related to the relaxed state, the mean and variance of the two time domain features are used as the feature input, and the subjective evaluation results are used as the output to construct different prediction classification models. By comparing their prediction accuracy, the CNN classification prediction model performed best at 55.56%, followed by KNN prediction accuracy of 49.50%.

关键词： Accuracy Noise predictive models Nearest neighbor methods Feature extraction Brain modeling Electroencephalography

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An Optimization Method for Short-Term Power Load Forecasting Based on IPSO-SVM

An Optimization Method for Short-Term Power Load Forecasting...

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

作者： Yaguang Shi Shaopeng Wang Haoran Huang Yingchun Yang Lijun Zhang China Electrical Equipment Xuji Electric Zhengzhou R&D Zhengzhou China China Electrical Equipment Xuji Electrical Software Company Xuchang China China Mobile Online Marketing Service Center Zhengzhou China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

To improve the accuracy of short-term power load forecasting, this paper presents an optimization method for short-term power load forecasting, which is based on Improved Particle Swarm Optimization algorithm and Support Vector Machine(IPSO-SVM) combined forecasting model to forecast power load, and constructs a complete short-term power load forecasting model. Compared with traditional PSO-SVM, the optimized IPSO-SVM prediction model has higher prediction accuracy, which has important practical reference value in the research of power load prediction, and provides a new method path for the field of power load forecasting.

关键词： Accuracy Load forecasting predictive models Prediction algorithms Search problems Vectors Space exploration

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Research on Factors Affecting Weight Based on Machine Learning Algorithm

Research on Factors Affecting Weight Based on Machine Learni...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Rujuan Huang Qunqiang Chen Mei Qian Hao Zheng Chen Chen Shixiao Wu School of Information Engineering Wuhan Business University Wuhan China School of Mechanical and Electrical Engineering Wuhan Business University Wuhan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

In view of the increasing prevalence of overweight and obesity in Chinese residents, in order to explore the factors affecting weight, three machine learning models based on logical regression algorithm, decision tree algorithm and random forest algorithm are constructed to explore the factors affecting weight. the model is continuously optimized by parameters and cross-validation, and the performance of the model is evaluated by using four indicators: accuracy, precision, recall and the harmonic average of the two. the results show that the accuracy of logistic regression algorithm, decision tree algorithm and random forest algorithm are 0.65, 0.75 and 0.8 respectively, which shows that the decision tree algorithm and random forest algorithm can better complete the classification and prediction of weight influencing factors, and the random forest algorithm has the best classification and prediction effect, which can be used for researchers and practitioners in the field of nutrition and health reference.

关键词： Logistic regression Visualization Machine learning algorithms Accuracy predictive models Prediction algorithms Harmonic analysis Classification algorithms Random forests Robots

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Analysis of Clinical Intelligent Diagnosis and Treatment of Hemorrhagic Stroke

Analysis of Clinical Intelligent Diagnosis and Treatment of ...

引用

international conference on Electronic engineering and Informatics (EEI)

作者： Lele Wang Tangju Yuan School of Chongqing Three Gorges University Chongqing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Among hemorrhagic stroke cases, acute cerebral hemorrhage conditions account for 10-15% of stroke cases. Its complex etiology is usually triggered by ruptured cerebral aneurysms, abnormalities of cerebral arteries, and other factors that cause blood to enter the brain tissue, resulting in mechanical damage and physiopathological reactions. this disease has an acute onset and rapid progression, the prognosis is usually poor, the mortality rate is high, and patients often suffer from neurological deficits that place a heavy burden on the patient and his/her socio-economy. Extension of the hematoma is one of the risk factors for poor prognosis and can be acutely life threatening. therefore, early identification and prediction of hematoma expansion and edema development are crucial for improving prognosis. Medical imaging and artificial intelligence provide powerful tools for monitoring brain injury [1]. Intelligent diagnostic models can predict poor prognostic risk factors and support personalized treatment.

关键词： predictive models Brain modeling Hemorrhaging Prognostics and health management Informatics Monitoring Brain injuries Blood Diseases Biomedical imaging

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the Impact of Temporal Contrastive Learning on Self-Supervised Action Recognition

The Impact of Temporal Contrastive Learning on Self-Supervis...

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international conference on Computational Intelligence and Networks (CINE)

作者： Vaidehi Gupta Vaibhav Sahay Rajat Kumar Behera School of Computer Engineering Kalinga Institute of Industrial Technology Odisha India

ISBN: (数字)9798331516796

ISBN: (纸本)9798331516802

Action recognition in videos is a crucial task in computer vision with applications in video surveillance, humancomputer interaction, and robotics. Traditional supervised learning methods often require extensive label data, which can be a major obstacle in practice due to their expense and timeconsuming tendencies. Self-supervised learning (SSL) can serve as a promising method for action recognition in video data and thus be an efficient cure to this issue. Self-supervised learning is built upon the concept of unsupervised learning but it instead tries to understand the unlabelled data fed to it on the very first iteration to generate signals that it can understand as supervisory signals. this work thus explores various SSL techniques to train models that can efficiently capture the temporal evolution of actions in video without relying on the sedated recorded data. this research contributes to the field by improving the performance of SSL and opening the way to develop more efficient and scalable models for video understanding tasks for future studies in the said area.

关键词： Training Analytical models Computer vision Statistical analysis Supervised learning Contrastive learning predictive models Video surveillance Unsupervised learning Videos

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