Transfer Learning Neural Network Research Articles

A novel approach to estimate land surface temperature from landsat top-of-atmosphere reflective and emissive data using transfer-learning neural network

Land Surface Temperature (LST) is a crucial parameter in studies of urban heat islands, climate change, evapotranspiration, hydrological cycles, and vegetation monitoring. However, conventional satellite-based approaches for LST retrieval often require additional data like land surface emissivity (LSE). Meanwhile, traditional machine learning (ML) techniques face challenges in acquiring representative training data and leveraging data from varied sources effectively. To address these issues, we introduce a novel transfer-learning (TL) neural network approach for LST retrieval using top-of-atmosphere (TOA) reflective and emissive data from Landsat. This method not only improves LST retrieval by integrating various data types but also demonstrates the potential of shortwave data in surrogating LSE information, thereby reducing dependence on explicit LSE data. Our TL approach utilized extensive simulations from the radiative transfer model (RTM) and measurements from the real world. The simulations are comprehensive, covering a wide range of atmospheric and surface scenarios, and the inclusion of real-world data mitigates the discrepancy between simulations and actual observations. When applied to a decade of Landsat-8 observations and ground measurements from 241 stations across diverse regions, our TL method significantly outperforms ML, single-channel (SC), and split-window (SW) algorithms in terms of root mean square error (RMSE), with improvements of 0.46 K, 0.84 K, and 0.57 K, respectively. This superiority underscores the advantage of integrating simulated and observed data, as well as the benefit of utilizing both reflective and emissive data without relying on uncertain LSE inputs. Our findings present a promising new TL framework for estimating LST directly from TOA data, offering a robust approach that we have made publicly available through Google Earth Engine (GEE) for broader use. The LST data retrieved by our proposed method can provide valuable insights for environmental research.

Transfer learning neural network for reconstructing temperature field in film cooling with scarce local measurements

Film cooling is widely adopted for thermal protection of gas turbine blades. Obtaining the surface temperature field is essential for film cooling design. However, it is generally challenging because temperature measurement points are scarce in practice. Although machine learning models can be trained to reconstruct the temperature field, their accuracy is limited when the measurement points are insufficient and the explicit physical constraints are lacking. To address the problem, a transfer learning neural network (TLNN) is proposed in present study. The main idea is to learn the general characteristics of temperature field from abundant data in other working conditions as implicit constraints, and to make fine-tuning of the model for a specific working condition with scarce measurement points. Numerical simulations were conducted under various working conditions to generate dataset for model training and testing. With the blowing ratios of 1 and 0.5 used as the source and target working conditions, the mean relative L2-norm errors are 52 %, 41 %, and 32 % lower for 20, 10, and 5 measurement points, respectively, compared to the model without transfer learning. The results indicate that the proposed TLNN model can accurately reconstruct the temperature field in film cooling with scarce measurement points.

Bandwidth-Scalable Digital Predistortion of Active Phased Array Using Transfer Learning Neural Network

This paper proposes a transfer learning neural network (TLNN) approach for digital predistortion (DPD) of mm-Wave active phased arrays (APA) operated under variable signal bandwidth regimes. Compared with the conventional artificial neural network (ANN) method, the proposed approach can achieve similar linearization performance with much lower computational complexity by transferring part of a trained model from one bandwidth to another bandwidth. In the recently introduced 5G, the increased signal bandwidth triggers considerable memory effects in the APA. Moreover, dealing with different signal bandwidths typically requires a time-consuming recalculation of the predistorter parameters. In this paper, the authors propose to have those challenges solved by using a DPD model based on the transfer learning method. The proposed approach was validated with over-the-air (OTA) measurements on an APA excited with signals of varying bandwidth, namely from 20 MHz to 100 MHz. Experimental results show a significant reduction in the training time while ensuring good linearization performance. With the applied TLNN DPD, an 8.5 dB improvement of adjacent channel leakage ratio (ACLR) and 8.6 % points improvement of error vector magnitude (EVM) is achieved. Under the variable bandwidth regime, the complexity of the DPD model in terms of the number of multiplications is reduced from 199168 to 160. The proposed TLNN DPD proved to be robust concerning variation in the bandwidth of the APA excitation signal.

Diagnostic model optimization method for ADHD based on brain network analysis of resting-state fMRI images and transfer learning neural network.

Introduction: Attention deficit and hyperactivity disorder (ADHD) is a common inherited disease of the nervous system whose cause(s) and pathogenesis remain unclear. Currently, the diagnosis of ADHD is mainly based on clinical experience and guidelines that have laid out some diagnostic standards. Our study aimed to apply a learning-based classification method to assist the ADHD diagnosis based on high-dimensional resting-state fMRI.Methods: Our study selected the ADHD-200 Peking dataset of resting-state fMRI, which has an ADHD patient (n = 142) group and a typically developing control (TDC) healthy control (n = 102) group. We first used Pearson and partial correlation coefficients to perform functional connectivity (FC) analysis between ROIs. Then, the Pearson and partial correlation coefficient matrices were concatenated into a dual-channel feature to build a dual data channel as input to the transfer learning neural network (TLNN) architecture. Finally, we transferred the pretrained model from the auxiliary domain to our target domain and fine-tuned it.Results: Based on the Pearson correlation coefficient, FC between ROIs was detected in 22 brain regions, including the fusiform gyrus, superior frontal gyrus, posterior superior temporal sulcus, inferior parietal lobule, anterior cingulate cortex, and parahippocampal gyrus. Based on the partial correlation coefficient, we found FC in the salient network, default network, sensory-motor network, dorsal attention network, and cerebellum network. With the TLNN architecture, we solved the problem of insufficient training data and improved the sensitivity of the classification method. When the VGG model (fine-tuned transfer strategy, 1,024 fully connected layers) was applied, the accuracy of TLNN classification ultimately reached 82%.Conclusion: Our study suggests that completing the training of the target domain by transferring the prior knowledge of the auxiliary domain is effective in solving the classification problem of small sample datasets. Based on prior knowledge of FC analysis, TLNN classification may assist ADHD diagnosis in a new way.

HRSG early tube leak detection with a transfer learning neural network and Gramian Angular Difference Field

This paper proposes a novel heat recovery steam generator (HRSG) early tube leak detection model which leverages a convolution neural network classifier by utilising transfer learning with ResNet50 architecture. The design goal of this model was to achieve high classification accuracy with a minimal amount of leakage data. The model is also intended to be user-friendly and require minimal hyperparameter tuning. The proposed neural network was trained on the drum-specific conductivity time series data of HRSGs encoded in the Gramian Angular Difference Field (GADF). The model yielded a validation accuracy of 96.64%, true-positive rate of 93.28% and precision of 100% in regard to the validation set. The study included experiments on the influence of different encoding algorithms, Markov Transition Field (MTF) and Recurrence Plot (RP), and architectures on the performance of the model. This paper further discusses the viability of adapting the design to other time series classification problems.

Estimating global downward shortwave radiation from VIIRS data using a transfer-learning neural network

In recent years, machine learning (ML) has been successfully used in estimating downward shortwave radiation (DSR). To achieve global estimations, traditional ML models need sufficient ground measurements covering various atmospheric and surface conditions globally, which is difficult to accomplish. Training on the simulated data of a radiative transfer model (RTM) is a possible solution, but widely used RTMs ignore some complex cloud conditions which brings bias to simulations. In this study, a neural network applied with the transfer-learning (TL) concept is introduced to utilize both radiative transfer simulations and ground measurement data, achieving global DSR estimation with only top-of-atmosphere and surface albedo at local solar noon as inputs. The proposed method estimates both instantaneous and daily DSR from Visible Infrared Imaging Radiometer Suite (VIIRS) data at 750-m resolution, and both the estimates are validated by 40 independent stations globally. The root mean-square error and relative root mean square error of instantaneous DSR validation over 25 Baseline Surface Radiation Network, seven Surface Radiation Network, and eight Greenland Climate Network stations in 2013 were 91.2 (16.1%), 106.3 (18.3%), 75.0 (24.2%) W/m2, respectively, and the daily validation achieved 30.8 (15.5%), 33.5 (17.6%), and 31.3 (14.4) W/m2, respectively. The proposed method presents significant high accuracy over polar regions and similar performances over other areas compared with traditional ML models, physics models (e.g., look-up tables and direct estimations), and existing DSR products. The algorithm is also applied to VIIRS swath data to test its global efficacy. Instantaneous mapping captures the spatial pattern of the cloud-mask product, and daily mapping shows spatial patterns similar to the Clouds and the Earth's Radiant Energy System Synoptic TOA and surface fluxes and clouds product, but with more detail. Further analysis indicates that model performance is less sensitive to the quantity of training data after TL has been incorporated. This study demonstrates the advantages of TL on boosting both the generality and accuracy of DSR estimation, which can potentially be applied to other variable retrievals.

A Recognition Method for Cassava Phytoplasma Disease (CPD) Real-Time Detection based on Transfer Learning Neural Networks

A Recognition Method for Cassava Phytoplasma Disease (CPD) Real-Time Detection based on Transfer Learning Neural Networks

Fuzzy Comprehensive Evaluation Model of M&A Synergy Based on Transfer Learning Graph Neural Network.

With the rapid development of modern China and the influx of capital, the number of companies has gradually increased. However, most companies cannot operate for a long time due to various reasons. Therefore, mergers and acquisitions have occurred. Large companies merge small companies to some extent. The number of employees can be guaranteed, and the market can be stabilized. However, mergers and acquisitions also have higher risks. As the pace of mergers and acquisitions accelerates, there are more and more cases of failed mergers and acquisitions. The synergy effect of mergers and acquisitions is an important indicator to judge the performance of mergers and acquisitions. This article measures the synergy obtained by the main enterprise from the perspective of performance changes, establishes an evaluation model through the rate of change of financial indicators and migration learning, estimates it through a neural network model, and conducts an empirical analysis on it. The transfer learning neural network has been studied in depth. The research of this article is to accurately assess the synergy effect obtained after mergers and acquisitions and to analyze whether the company can profit from mergers and acquisitions, so as to provide a reference for subsequent mergers and acquisitions between companies.