Artificial neural network-based predictive model for supersonic ejector in refrigeration system

Zhu, Huihui; Liu, Jiapeng; Yu, Jinpeng; Yang, Peng

doi:10.1016/j.csite.2023.103313

Cited by 7 publications

(2 citation statements)

References 39 publications

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“…Additionally, the model, which is constructed based on the summarizing features extracted from the data, still exhibits differences in details after the introduction of noise and random variables, even though the generated waveforms maintain a certain degree of similarity. Deep learning algorithms [33,34] are widely applied in areas such as time series generation, prediction, and performance prediction [35]. However, the question of how to train models to produce the desired CWM signals with limited data remains an issue that warrants in-depth exploration.…”

Section: Limitation Of the Proposed Modelmentioning

confidence: 99%

Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System

Fan,

Deng

2024

Sensors

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An increasing number of studies on non-contact vital sign detection using radar are now beginning to turn to data-driven neural network approaches rather than traditional signal-processing methods. However, there are few radar datasets available for deep learning due to the difficulty of acquiring and labeling the data, which require specialized equipment and physician collaboration. This paper presents a new model of heartbeat-induced chest wall motion (CWM) with the goal of generating a large amount of simulation data to support deep learning methods. An in-depth analysis of published CWM data collected by the VICON Infrared (IR) motion capture system and continuous wave (CW) radar system during respiratory hold was used to summarize the motion characteristics of each stage within a cardiac cycle. In combination with the physiological properties of the heartbeat, appropriate mathematical functions were selected to describe these movement properties. The model produced simulation data that closely matched the measured data as evaluated by dynamic time warping (DTW) and the root-mean-squared error (RMSE). By adjusting the model parameters, the heartbeat signals of different individuals were simulated. This will accelerate the application of data-driven deep learning methods in radar-based non-contact vital sign detection research and further advance the field.

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Section: Limitation Of the Proposed Modelmentioning

confidence: 99%

Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System

Fan,

Deng

2024

Sensors

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“…A BPNN is a kind of multilayer feedforward network trained by an error backpropagation algorithm [20]. It is one of the most widely used neural network models.…”

Section: Bpnn Model Descriptionmentioning

confidence: 99%

Enhanced Neural Network for Rapid Identification of Crop Water and Nitrogen Content Using Multispectral Imaging

Peng,

He,

Zheng

et al. 2023

Agronomy

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Precision irrigation and fertilization in agriculture are vital for sustainable crop production, relying on accurate determination of the crop’s nutritional status. However, there are challenges in optimizing traditional neural networks to achieve this accurately. This paper aims to propose a rapid identification method for crop water and nitrogen content using optimized neural networks. This method addresses the difficulty in optimizing the traditional backpropagation neural network (BPNN) structure. It uses 179 multi−spectral images of crops (such as maize) as samples for the neural network model. Particle swarm optimization (PSO) is applied to optimize the hidden layer nodes. Additionally, this paper proposes a double−hidden−layer network structure to improve the model’s prediction accuracy. The proposed double−hidden−layer PSO−BPNN model showed a 9.87% improvement in prediction accuracy compared with the traditional BPNN model. The correlation coefficient R2 for predicted crop nitrogen and water content was 0.9045 and 0.8734, respectively. The experimental results demonstrate high training efficiency and accuracy. This method lays a strong foundation for developing precision irrigation and fertilization plans for modern agriculture and holds promising prospects.

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Numerical investigation of exit pressure on flow structure in steam ejector by considering condensation and evaporation process

Wu,

Gang

2024

Proc.Indian Natl. Sci. Acad.

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Artificial neural network-based predictive model for supersonic ejector in refrigeration system

Cited by 7 publications

References 39 publications

Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System

Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System

Enhanced Neural Network for Rapid Identification of Crop Water and Nitrogen Content Using Multispectral Imaging

Numerical investigation of exit pressure on flow structure in steam ejector by considering condensation and evaporation process

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