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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1041-1050 订阅

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Demonstrating Remote Synchronization: An Experimental Approach with Nonlinear Oscillators

Demonstrating Remote Synchronization: An Experimental Approa...

引用

international conference on Control and Robots (ICCR)

作者： Sanjeev Kumar Pandey Neetish Patel Electrical Engineering Department IIT Delhi New Delhi India

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

this study investigates remote synchronization in arbitrary network clusters of coupled nonlinear oscillators, a phenomenon inspired by neural synchronization in the brain. Employing a multi-faceted approach encompassing analytical, numerical, and experimental methodologies, we leverage the Master Stability Function (MSF) to analyze network stability. We provide experimental evidence of remote synchronization between two clusters of nonlinear oscillators, where oscillators within each cluster are also remotely connected. this observation parallels the thalamus-mediated synchronization of neuronal populations in the brain. An electronic circuit testbed, supported by nonlinear ODE modeling and LT Spice simulation, was developed to validate our theoretical predictions. Future work will extend this investigation to encompass diverse network topologies and explore potential applications in neuroscience, communication networks, and power systems.

关键词： Neuroscience Power system stability predictive models Stability analysis Power grids Circuit stability Synchronization Oscillators Numerical stability Robots

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Temperature Feedforward Model Based on Improved Stochastic Configuration Network for Coal Mill

Temperature Feedforward Model Based on Improved Stochastic C...

引用

international conference on Industrial Artificial Intelligence (IAI)

作者： Xiaodi Huang Yanchen Jiang Qiansheng Li Yongfu Wang School of Mechanical Engineering and Automation Northeastern University Shenyang Liaoning China Dalian Power Plant HUANENG Power Intl Inc Dalian China School of Mechanical Engineering and Automation Northeastern University Shenyang China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

Aiming at the practical needs for feedforward mod-eling of the coal mill outlet temperature setpoint under blended coal operation conditions, an improved stochastic configuration network (ISCN) is introduced for predictive modeling. Firstly, the stochastic configuration of hidden node parameters based on the supervision mechanism leads to uncertainty and cannot guarantee parameter optimality. Consequently, an improved pelican optimization algorithm (IPOA) is introduced to optimize the configuration of hidden node parameters, which not only enhances network learning efficiency but also lightweights the network structure. On the other hand, fully supervised SCN mod-eling demands large amount of high-cost experimental data. To reduce modeling expenses, a semi-supervised learning approach based on small labeled data is employed to categorize unlabeled data and expand the training dataset. Nextly, considering that there are some irrationalities in determining the coal mill outlet temperature setpoint using traditional methods. the ISCN introced in this paper is applied to feedforward model of coal mill outlet temperature setpoint. Finally, the validity and practicality of the method are verified by experiments.

关键词： Training Uncertainty Stochastic processes Coal Semisupervised learning predictive models Network architecture Prediction algorithms Power generation Optimization

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Optimization of a Stand-Alone Hybrid Renewable Energy System

Optimization of a Stand-Alone Hybrid Renewable Energy System

引用

international conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Huidrom Hilengamba Meetei Benjamin A Shimray Ingudam Chitrasen Meitei Electrical Engineering Department NIT Manipur Imphal

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

the uptake of renewable energy, the expansion of the energy markets, and business tactics all affect sustainable generation. For a hybrid microgrid system to be both economical and dependable, the right combination of different renewable energy sources and storage devices is essential. In this paper, off-grid hybrid energy systems (HES) in remote areas are examined from both a techno-economic and sustainable perspective. As the Ant Lion Optimizer algorithm (ALO) has advantages over other methods for lowering the Levelized Cost of Energy (LCOE) and Net Present Cost (NPC), it is utilized as an optimization-seeking algorithm. Furthermore, a sensitivity analysis has been conducted on every model, and the Hybrid Optimization Model for Electric Renewable (HOMER Pro) is used to conduct simulations to examine the system components.

关键词： Renewable energy sources Design engineering Analytical models Costs software packages Sensitivity analysis Wind speed

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A Pedestrian Trajectory Prediction Network Based on Trajectory Conditional Visual Attention 23

A Pedestrian Trajectory Prediction Network Based on Trajecto...

引用

proceedings of the 2023 6th international conference on Algorithms, Computing and Artificial Intelligence

作者： Xiaojie Tian Ruoxi Lin Shimeng Yang Kaiju Li Guangqiang Yin Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China School of Information and Communication Engineering University of Electronic Science and Technology of China China School of Information and Software Engineering University of Electronic Science and Technology of China China Kashgar Municipal Committee of Xinjiang China

ISBN: (纸本)9798400709203

Pedestrian trajectory prediction task can increase artificial intelligence’s understanding of human behavior perception and intention, and is widely used in traffic management, service robots, automatic driving and other fields, which has great research value and significance. Although there is more and more research on pedestrian trajectory prediction tasks, pedestrian trajectory prediction is still a very challenging task due to the uncertainty of pedestrian will and the complexity of interaction in the complex real scene. Most of the research focus on the interaction between the target pedestrian and other pedestrians, which has great computational cost in the crowded pedestrian scene. We proposed CDCNet, by observing the historical track of pedestrians, and using the Conditional Dual Coordinated Attention to obtain the information that pedestrians pay attention to when moving forward, so that the network pays attention to the information around pedestrians instead of the content in the entire video, so that the network can also have a lower computing cost under the crowded pedestrian scene. Our experiments on five publicly available datasets show that CDCNet has good predictive efficiency and advanced performance.

关键词： attention mechanism

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Prediction the Wax Precipitation Characteristics in Waxy Crude Oil with Heavy Fraction Characterization

Prediction the Wax Precipitation Characteristics in Waxy Cru...

引用

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

作者： Qing Quan Shouxi Wang Zhimin Gao Zhujun Liu College of Petroleum Engineering Xi'an Shiyou University Xi'an Shaanxi Province China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

the calculation of component content distribution and related basic physical parameters in the heavy fraction of waxy oil will affect the calculation results of thermodynamic phase equilibrium and have an important impact on the prediction of the final wax precipitation characteristic parameters. Aiming at the problem that conventional separation models are not suitable for waxy oil with high content of heavy components, this paper compares and analysis the classical Ahmed model, Hosein model, Pengyang model, Katz model and Pedersen model, and points out that Hosein model has the highest precision. Based on the Hosein model, Hosseini Far-Jamhidi is used to calculate the physical properties of the separated SCN components, Whitson's formula is used to merge the separated virtual components, and Hong mixing rule is used to calculate the physical parameters of the merged virtual components. the prediction results of wax precipitation characteristics of each oil sample with or without characterization of heavy fraction are analyzed by PVT-Sim, which verifies the importance of heavy fraction characterization to improve the prediction accuracy of wax precipitation characteristics of waxy crude oil.

关键词： Analytical models thermodynamics Precipitation Accuracy Oils predictive models

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Deep Learning-Based Water Quality Parameter Prediction

Deep Learning-Based Water Quality Parameter Prediction

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

作者： Haiyang Fu Xiao Chen Zhenxing Li Chengdong Yang School of Information Science and Engineering Linyi University Linyi China School of Automation and Electrical Engineering Linyi University Linyi China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Water quality prediction is the basis and premise of environmental protection and water resource management. However, due to the complex nonlinear and dynamic nature of water quality data, accurate prediction faces many challenges. therefore, a hybrid model for short-term water quality parameter prediction based on multiplicative decomposition model and sample convolution exchange network (SCINet) is proposed in this paper. through experiments, MD_SCINet prediction model can effectively extract water quality characteristics and make time series prediction. the experimental results show that the prediction error of this model is significantly lower than that of TCN and CNN-LSTM, and the mean absolute error (MAE) is 35% and 44% lower than that of TCN and CNN-LSTM, respectively. the results show that this method is effective in short-term water quality parameter prediction and provides effective technical support for environmental protection and water resources management.

关键词： Accuracy Convolution Computational modeling Time series analysis Water quality predictive models Protection Water resources Faces

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Traffic Speed Forecasting using GCN and BiGRU

Traffic Speed Forecasting using GCN and BiGRU

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international conference on Electronic engineering and Informatics (EEI)

作者： Yue Wu Yingjun Chen Wei Yao School of Information Engineering Chang'an University Xi'an China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Traffic forecasting is a component of the intelligent transportation system (ITS), which is of great significance to improve traffic operation efficiency and strengthen traffic safety. But, because of the complex and ever-changing temporal and spatial relationships between traffic flow, traffic forecasting is still challenging. To reduce prediction errors and effectively represent the spatiotemporal features of traffic data, a traffic speed forecasting model utilizing graph convolutional neural networks and bidirectional gated recurrent units (GCN-BiGRU) is proposed. On the one hand, graph convolutional neural network is used to extract the traffic data's spatial features, which allow them to capture the topology and semantic information between the traffic nodes. Additionally, a spatial embedding and an attention mechanism are introduced simultaneously to enhance spatial correlation between the nodes. On the other hand, in order to extract the temporal characteristics of the traffic information, bi-directional gated recurrent unit is utilized to obtain information from both forward and backward propagation, which improves the model's forecasting ability in the time dimension. the results of experiments on real datasets demonstrate that GCN-BiGRU has a good forecasting effect and performs well in terms of forecasting accuracy at time steps of 15min, 30min and 60min.

关键词： Correlation Roads predictive models Logic gates Feature extraction Data models Spatiotemporal phenomena Data mining Convolutional neural networks Forecasting

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IACO-DQ Path Planning for AGV in Complex and Dynamic Environments

IACO-DQ Path Planning for AGV in Complex and Dynamic Environ...

引用

Robotics and Computer Vision (ICRCV), international conference on

作者： Huayang Wang Aihua Zhang Shijie Dong The School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

this study addresses the problem of autonomous navigation and obstacle avoidance for mobile robots in complex and dynamically changing environments. A path planning strategy is proposed, namely the IACO-DQ (I-ACO and Dyna-Q) algorithm, which utilizes I-ACO (Improved Ant Colony Optimization) to construct an initial global optimal path framework. this is combined with the model learning and planning capabilities of the Dyna-Q algorithm, allowing the AGV to predict future states through the environment model during actual exploration, thus achieving online real-time dynamic path planning. the goal is to provide AGV with an efficient and flexible path planning solution to handle dynamic obstacles, ensuring safe and rapid task completion in ever-changing environments. Simulation results demonstrate that the IACO-DQ algorithm significantly improves the AGV's obstacle avoidance capabilities and path planning efficiency in dynamic environments, showcasing enhanced robustness and adaptability.

关键词： Heuristic algorithms Simulation Transportation predictive models Prediction algorithms Turning Path planning Safety Collision avoidance Autonomous robots

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Residual life prediction of submarine cable based on health index and GM(1,1)

Residual life prediction of submarine cable based on health ...

引用

Energy, Power and Grid (ICEPG), international conference on

作者： Dong Liu Qian Li Lingtao Gao Wei Lei Southwest Petroleum University Chengdu China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

With the rapid development of Marine energy and communication technology, the submarine cable is an important link between the offshore platform and the land, and the accurate prediction of its health state and remaining life has become the key to ensure the safe operation of offshore engineering. therefore, on the basis of the current submarine cable operation status assessment technology, this paper proposes a comprehensive submarine cable life prediction method by integrating the equipment health index theory and GM(1,1) gray prediction model. A three-core submarine cable HYJQF41 on an offshore oil and gas platform is used as an experimental object to verify the reliability and accuracy of the model. this method provides important decision support for the long-term operation of submarine cables, enricfies the theoretical system of submarine cable life prediction, provides technical support for the safe operation and sustainable development of Marine engineering, and contributes to the further development and utilization of Marine resources.

关键词： Underwater cables Accuracy Offshore installations Oils predictive models Reliability theory Prediction algorithms Indexes Sustainable development Underwater vehicles

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Deep Learning-Based predictive Model of Molecular Properties

Deep Learning-Based Predictive Model of Molecular Properties

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Robotics and Computer Vision (ICRCV), international conference on

作者： Mingyu Yang Xiangyang Chen Weijie Lv School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

With the rapid development of computer hardware and machine learning algorithms in recent decades, artificial intelligence has created a boom in various industries. Computer technology has been widely used in all stages of drug discovery, from machine learning to deep learning, and scientists have conducted numerous studies on the physicochemical properties of drugs. With the advancement of computer technology and deep learning algorithms, it has become possible to obtain large-scale molecular characterization data, extract different types of molecular representations, and construct more subtle models. Among them, molecular graph structure data is increasingly used in deep learning to predict molecular properties. It directly extracts the two-dimensional structural information of molecules, avoiding the defects of indirect structural information extraction in traditional methods. However, the performance of different algorithms based on molecular fingerprints, descriptors, molecular pictures and molecular graph structures is currently controversial. Some studies have found that descriptor-based models outperform graph-based models, while others do the opposite. this shows the difference in the impact of different tasks and datasets on the models. In this experiment, molecular graphs and molecular fingerprints are combined, and molecular graphs are fed into graph neural networks to extract features after the attention mechanism, molecular fingerprints are fed into a fully connected layer to extract features, and the features extracted from the two molecular representations are fused together and a specific activation function is adopted to see if the accuracy and stability of the model can be effectively improved.

关键词： Deep learning Machine learning algorithms Computational modeling Fingerprint recognition predictive models Feature extraction Prediction algorithms Information retrieval Stability analysis Data mining

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