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In recent years, the application of deep learning models for underwater target recognition has become a popular trend. Most of these are pure 1D models used for processing time-domain signals or pure 2D models used for processing time-frequency spectra. In this paper, a recent temporal 2D modeling method is introduced into the construction of ship radiation noise classification models, combining 1D and 2D. This method is based on the periodic characteristics of time-domain signals, shaping them into 2D signals and discovering long-term correlations between sampling points through 2D convolution to compensate for the limitations of 1D convolution. Integrating this method with the current state-of-the-art model structure and using samples from the Deepship database for network training and testing, it was found that this method could further improve the accuracy (0.9%) and reduce the parameter count (30%), providing a new option for model construction and optimization. Meanwhile, the effectiveness of training models using time-domain signals or time-frequency representations has been compared, finding that the model based on time-domain signals is more sensitive and has a smaller storage footprint (reduced to 30%), whereas the model based on time-frequency representation can achieve higher accuracy (1–2%). [ABSTRACT FROM AUTHOR]

In this paper, we forecasted the ionospheric total electron content (TEC) over the region of China using the bidirectional convolutional gated recurrent unit (BiConvGRU) model. We first generated the China Regional Ionospheric Maps (CRIMs) using GNSS observations provide by the Crustal Movement Observation Network of China (CMONOC). We then used gridded TEC maps from 2015 to 2018 with a 1 h interval from the CRIMs as the dataset, including quiet periods and storm periods of ionospheric TEC. The BiConvGRU model was then utilized to forecast the ionospheric TEC across China for the year 2018. The forecasted TEC was compared with the TEC from the International Reference Ionosphere (IRI-2016), Convolutional Long Short-Term Memory (ConvLSTM), Convolutional Gated Recurrent Unit (ConvGRU), Bidirectional Convolutional Long Short-Term Memory (BiConvLSTM), and the 1-day Predicted Global Ionospheric Map (C1PG) provided by the Center for Orbit Determination in Europe (CODE). In addition, indices including Kp, ap, Dst and F10.7 were added to the training dataset to improve the forecasting accuracy of the model (-A indicates no indices, while -B indicates with indices). The results verified that the prediction accuracies of the models integrated with these indices were significantly improved, especially during geomagnetic storms. The BiConvGRU-B model presented a decrease of 41.5%, 22.3%, and 13.2% in the root mean square error (RMSE) compared to the IRI-2016, ConvGRU, and BiConvLSTM-B models during geomagnetic storm days. Furthermore, at a specific grid point, the BiConvGRU-B model showed a decrease of 42.6%, 49.1%, and 31.9% in RMSE during geomagnetic quiet days and 30.6%, 34.1%, and 15.1% during geomagnetic storm days compared to the IRI-2016, C1PG, and BiConvLSTM-B models, respectively. In the cumulative percentage analysis, the BiConvGRU-B model had a significantly higher percentage of mean absolute error (MAE) within the range of 0–1 TECU in all seasons compared to the BiConvLSTM-B model. Meanwhile, the BiConvGRU-B model outperformed the BiConvLSTM-B model with lower RMSE for each month of 2018. [ABSTRACT FROM AUTHOR]

Ionospheric forecasts are critical for space-weather anomaly detection. Forecasting ionospheric total electron content (TEC) from the global navigation satellite system (GNSS) is of great significance to near-earth space environment monitoring. In this study, we propose a novel ionospheric TEC forecasting model based on deep learning, which consists of a convolutional neural network (CNN), long-short term memory (LSTM) neural network, and attention mechanism. The attention mechanism is added to the pooling layer and the fully connected layer to assign weights to improve the model. We use observation data from 24 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to model and forecast ionospheric TEC. We drive the model with six parameters of the TEC time series, Bz, Kp, Dst, and F10.7 indices and hour of day (HD). The new model is compared with the empirical model and the traditional neural network model. Experimental results show the CNN-LSTM-Attention neural network model performs well when compared to NeQuick, LSTM, and CNN-LSTM forecast models with a root mean square error (RMSE) and R2 of 1.87 TECU and 0.90, respectively. The accuracy and correlation of the prediction results remained stable in different months and under different geomagnetic conditions. [ABSTRACT FROM AUTHOR]

With the emergence of big data, the efficiency of data querying and data storage has become a critical bottleneck in the remote sensing community. In this letter, we explore hash learning for the indexing of large-scale remote sensing images (RSIs) with a supervised pairwise neural network with the aim of improving RSI retrieval performance with a few binary bits. First, a fully connected hashing neural network (FCHNN) is proposed in order to map RSI features into binary (feature-to-binary) codes. Compared with pixel-to-binary frameworks, such as DPSH (deep pairwise-supervised hashing), FCHNN only contains three fully connected layers and incorporates another new constraint, so it can be significantly accelerated to obtain desirable performance. Second, five types of image features, including mid-level and deep features, were investigated in the learning of the FCHNN to achieve state-of-the-art performances. The mid-level features were based on Fisher encoding with affine-invariant local descriptors, and the deep features were extracted by pretrained or fine-tuned CNNs (e.g., CaffeNet and VGG-VD16). Experiments on five recently released large-scale RSI datasets (i.e., AID, NWPU45, PatternNet, RSI-CB128, and RSI-CB256) demonstrated the effectiveness of the proposed method in comparison with existing handcrafted or deep-based hashing methods. [ABSTRACT FROM AUTHOR]

• A flexible optimization strategy is introduced to better learn pair-based similarity. • Imposing Weibull distribution-based constraint helps to reduce neighborhood ambiguous. • A maximum mean discrepancy quantization is proposed to minimize information loss. Hashing has been a promising technology for fast nearest neighbor retrieval in large-scale datasets due to the low storage cost and fast retrieval speed. Most existing deep hashing approaches learn compact hash codes through pair-based deep metric learning such as the triplet loss. However, these methods often consider that the intra-class and inter-class similarity make the same contribution, and consequently it is difficult to assign larger weights for informative samples during the training procedure. Furthermore, only imposing relative distance constraint increases the possibility of being clustered with larger average intra-class distance for similar pairs, which is harmful to learning a high separability Hamming space. To tackle the issues, we put forward deep Weibull hashing with maximum mean discrepancy quantization (DWH), which jointly performs neighborhood structure optimization and error-minimizing quantization to learn high-quality hash codes in a unified framework. Specifically, DWH learns the desired neighborhood structure in conjunction with a flexible pair similarity optimization strategy and a Weibull distribution-based constraint between anchors and their neighbors in Hamming space. More importantly, we design a maximum mean discrepancy quantization objective function to preserve the pairwise similarity when performing binary quantization. Besides, a class-level loss is introduced to mine the semantic structural information of images by using supervision information. The encouraging experimental results on various benchmark datasets demonstrate the efficacy of the proposed DWH. [ABSTRACT FROM AUTHOR]

• Render the transferred model more generalizable by enhancing appearance diversity. • Achieve feature decoupling with the aid of data augmentation and a multi-label assignment strategy. • Improve the reliability of pseudo labels by exploiting the correlation of multiple clustering results. Deep learning techniques have achieved impressive progress in the task of person re-identification. However, how to generalize a learned model from the source domain to the target domain remains a long-standing challenge. Inspired by the fact that the enrichment of data diversity and the utilization of miscellaneous semantic features can lead to better generalization ability, we design a model that integrates a novel data augmentation method with a multi-label assignment strategy to achieve semantic features decoupling in the source domain. The pre-trained model is employed to extract several kinds of semantic features from the target dataset, and each kind of semantic features is regarded as a specific domain. We then cluster features of each domain and exploit the connection between different clustering results to perform self-distillation for generating more reliable pseudo labels. Finally, the obtained pseudo labels are used to fine-tune the pre-trained model to achieve model transfer from the source domain to the target one. Extensive experiments demonstrate that our approach outperforms some state-of-the-art methods by a clear margin and even surpass some supervised methods. Source code is available at: https://www.github.com/flychen321/MDJL. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop and validate a predictive model for osteoporotic vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT imaging, and deep learning radiomics features from CT images.A total of 169 osteoporosis-diagnosed patients from three hospitals were randomly split into OVFs (n = 77) and Non-OVFs (n = 92) groups for training (n = 135) and test (n = 34). Demographic data, BMD, and CT imaging details were collected. Deep transfer learning (DTL) using ResNet-50 and radiomics features were fused, with the best model chosen via logistic regression. Cox proportional hazards models identified clinical factors. Three models were constructed: clinical, radiomics-DTL, and fusion (clinical-radiomics-DTL). Performance was assessed using AUC, C-index, Kaplan–Meier, and calibration curves. The best model was depicted as a nomogram, and clinical utility was evaluated using decision curve analysis (DCA).BMD, CT values of paravertebral muscles (PVM), and paravertebral muscles' cross-sectional area (CSA) significantly differed between OVFs and Non-OVFs groups (P < 0.05). No significant differences were found between training and test cohort. Multivariate Cox models identified BMD, CT values of PVM, and CSAPS reduction as independent OVFs risk factors (P < 0.05). The fusion model exhibited the highest predictive performance (C-index: 0.839 in training, 0.795 in test). DCA confirmed the nomogram's utility in OVFs risk prediction.This study presents a robust predictive model for OVFs risk, integrating BMD, CT data, and radiomics-DTL features, offering high sensitivity and specificity. The model's visualizations can inform OVFs prevention and treatment strategies.Methods: This study aimed to develop and validate a predictive model for osteoporotic vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT imaging, and deep learning radiomics features from CT images.A total of 169 osteoporosis-diagnosed patients from three hospitals were randomly split into OVFs (n = 77) and Non-OVFs (n = 92) groups for training (n = 135) and test (n = 34). Demographic data, BMD, and CT imaging details were collected. Deep transfer learning (DTL) using ResNet-50 and radiomics features were fused, with the best model chosen via logistic regression. Cox proportional hazards models identified clinical factors. Three models were constructed: clinical, radiomics-DTL, and fusion (clinical-radiomics-DTL). Performance was assessed using AUC, C-index, Kaplan–Meier, and calibration curves. The best model was depicted as a nomogram, and clinical utility was evaluated using decision curve analysis (DCA).BMD, CT values of paravertebral muscles (PVM), and paravertebral muscles' cross-sectional area (CSA) significantly differed between OVFs and Non-OVFs groups (P < 0.05). No significant differences were found between training and test cohort. Multivariate Cox models identified BMD, CT values of PVM, and CSAPS reduction as independent OVFs risk factors (P < 0.05). The fusion model exhibited the highest predictive performance (C-index: 0.839 in training, 0.795 in test). DCA confirmed the nomogram's utility in OVFs risk prediction.This study presents a robust predictive model for OVFs risk, integrating BMD, CT data, and radiomics-DTL features, offering high sensitivity and specificity. The model's visualizations can inform OVFs prevention and treatment strategies.Results: This study aimed to develop and validate a predictive model for osteoporotic vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT imaging, and deep learning radiomics features from CT images.A total of 169 osteoporosis-diagnosed patients from three hospitals were randomly split into OVFs (n = 77) and Non-OVFs (n = 92) groups for training (n = 135) and test (n = 34). Demographic data, BMD, and CT imaging details were collected. Deep transfer learning (DTL) using ResNet-50 and radiomics features were fused, with the best model chosen via logistic regression. Cox proportional hazards models identified clinical factors. Three models were constructed: clinical, radiomics-DTL, and fusion (clinical-radiomics-DTL). Performance was assessed using AUC, C-index, Kaplan–Meier, and calibration curves. The best model was depicted as a nomogram, and clinical utility was evaluated using decision curve analysis (DCA).BMD, CT values of paravertebral muscles (PVM), and paravertebral muscles' cross-sectional area (CSA) significantly differed between OVFs and Non-OVFs groups (P < 0.05). No significant differences were found between training and test cohort. Multivariate Cox models identified BMD, CT values of PVM, and CSAPS reduction as independent OVFs risk factors (P < 0.05). The fusion model exhibited the highest predictive performance (C-index: 0.839 in training, 0.795 in test). DCA confirmed the nomogram's utility in OVFs risk prediction.This study presents a robust predictive model for OVFs risk, integrating BMD, CT data, and radiomics-DTL features, offering high sensitivity and specificity. The model's visualizations can inform OVFs prevention and treatment strategies.Conclusion: This study aimed to develop and validate a predictive model for osteoporotic vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT imaging, and deep learning radiomics features from CT images.A total of 169 osteoporosis-diagnosed patients from three hospitals were randomly split into OVFs (n = 77) and Non-OVFs (n = 92) groups for training (n = 135) and test (n = 34). Demographic data, BMD, and CT imaging details were collected. Deep transfer learning (DTL) using ResNet-50 and radiomics features were fused, with the best model chosen via logistic regression. Cox proportional hazards models identified clinical factors. Three models were constructed: clinical, radiomics-DTL, and fusion (clinical-radiomics-DTL). Performance was assessed using AUC, C-index, Kaplan–Meier, and calibration curves. The best model was depicted as a nomogram, and clinical utility was evaluated using decision curve analysis (DCA).BMD, CT values of paravertebral muscles (PVM), and paravertebral muscles' cross-sectional area (CSA) significantly differed between OVFs and Non-OVFs groups (P < 0.05). No significant differences were found between training and test cohort. Multivariate Cox models identified BMD, CT values of PVM, and CSAPS reduction as independent OVFs risk factors (P < 0.05). The fusion model exhibited the highest predictive performance (C-index: 0.839 in training, 0.795 in test). DCA confirmed the nomogram's utility in OVFs risk prediction.This study presents a robust predictive model for OVFs risk, integrating BMD, CT data, and radiomics-DTL features, offering high sensitivity and specificity. The model's visualizations can inform OVFs prevention and treatment strategies. [ABSTRACT FROM AUTHOR]

Damping technology has been widely used because of its good vibration control effect. However, due to the strong nonlinearity of the added dampers, accurately predicting the seismic response of damped structures remains a challenge. This study investigates the application of interpretable machine learning (ML)-based and deep learning-based approaches to the prediction of the maximum inter-storey displacement of a damped structure. A comprehensive database consisting of 13,855 structural responses to ground motions was collected. Seven traditional interpretable ML algorithms including random forest and extreme gradient boosting (XGBoost), a convolutional neural network based on large receptive field, and seismic wave transformer (SWT) model based on a transformer network were developed. The predictions show that the error of the SWT based on unsupervised feature extraction is reduced by 50.90% compared with that of the optimal XGBoost in ensemble learning. Although the SWT has the highest global accuracy, XGBoost is found to have a smaller error when the structure is in a linear state with peak ground acceleration as partition index, so an aggregation model (AM)-based structural response prediction method was also proposed. The accuracy of the AM improved by 27.95% compared with that of the SWT. In contrast with other ML models, the proposed AM is more advantageous in terms of computational efficiency and accuracy. • Numerical model of a damped steel frame was developed using OpenSees based on shaking table test results. • A prediction dataset was built based on the numerical results of a damped structure. • Seven interpretable machine learning-based techniques were utilised to predict the maximum structural inter-storey drift. • The deep learning algorithm based on unsupervised feature extraction can obtain more satisfactory results when the structure enters strong nonlinearity. • The developed aggregation model exhibits higher prediction accuracy. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning, the performance of fine-grained image classification has experienced unprecedented improvement. However, for fine-grained image classification, quickly and effectively focusing on subtle discriminative details that make the sub-classes different from each other has always been challenging. In this paper, we propose a novel Multi-Scale Erasure and Confusion (MSEC) method to tackle the challenge of fine-grained image classification. Firstly, the input image is divided into several sub-regions, and the confidence scores of those sub-regions are calculated by the confidence function. The sub-regions with lower confidence scores are then erased by the Region Erasure Module (REM) and the erased image is confused once by the Multi-scale Region Confusion Module (Multi-scale RCM). Secondly, the sub-regions with higher confidence scores are divided and confused again by the Multi-scale RCM, and then generate an image with multi-scale information. Finally, features in the erased image and the "destructed" image are extracted by the backbone network, and the whole network is optimized by the multi-loss function to realize classification tasks. Extensive experiments on three standard fine-grained benchmark datasets, including Stanford Dogs, CUB-200-2011 and FGVC-Aircraft, show that MSEC can improve the accuracy of fine-grained image classification. [ABSTRACT FROM AUTHOR]

[Display omitted] • Innovative use of generative technology established the recognition models of cumin and fennel from three regions. • This study also established the models for cumin and fennel in the same region. • This study provides a new method for classification of NIR spectroscopy. Cumin (Cuminum cyminum) and fennel (Foeniculum vulgare) are widely used seasonings and play a very important role in industries such as breeding, cosmetics, winemaking, drug discovery, and nano-synthetic materials. However, studies have shown that cumin and fennel from different regions not only differ greatly in the content of lipids, phenols and proteins but also the substances contained in their essential oils are also different. Therefore, realizing precise identification of cumin and fennel from different regions will greatly help in quality control, market fraud and production industrialization. In this experiment, cumin and fennel samples were collected from each region, a total of 480 NIR spectra were collected. We used deep learning and traditional machine learning algorithms combined with near infrared (NIR) spectroscopy to identify their origin. To obtain the model with the best generalization performance and classification accuracy, we used principal component analysis (PCA) to reduce spectral data dimensionality after Rubberband baseline correction, and then established classification models including quadratic discriminant analysis based on PCA (PCA-QDA) and multilayer perceptron based on PCA (PCA-MLP). We also directly input the spectral data after baseline correction into convolutional neural networks (CNN) and generative adversarial networks (GAN). The experimental results show that GAN is more accurate than the PCA-QDA, PCA-MLP and CNN models, and the classification accuracy reached 100%. In the cumin and fennel classification experiment in the same region, the four models achieve great classification results from three regions under the condition that all model parameters remain unchanged. The experimental results show that when the training data are limited and the dimension is high, the model obtained by GAN using competitive learning has more generalization ability and higher classification accuracy. It also provides a new method for solving the problem of limited training data in food research and medical diagnosis in the future. [ABSTRACT FROM AUTHOR]

Cumin and fennel are two types of food raw materials that have similar appearances but great differences in efficacy. Therefore, the efficient identification of these two types of food is very important to the related food processing industry and the development of food detection technology. In this study, a deep learning algorithm was designed to process Fourier NIR spectral data for the classification of cumin and fennel. The method was based on the difference in the signal intensity in NIR spectra between cumin and fennel, and the identification was performed by combining the improved multiscale fusion convolutional neural network (MCNN) and the bidirectional long-short-term memory network (BILSTM) based on NIR spectra. The classification accuracies of the MCNN and BILSTM models were 100% and 98.57%, respectively. The models have high sensitivity and fast analysis speed, which directly promote the development of real-time identification technology of cumin and fennel and verify the feasibility of deep learning algorithms in the field of food detection. [ABSTRACT FROM AUTHOR]