As one promising branch of biometrics, palmprint recognition has received significant attention and made extraordinary progress in the past decades. The crucial step of palmprint recognition is to ...extract the discriminative features for the subsequent identification or verification task. However, neither the traditional hand-crafted descriptors nor the deep convolutional neural network (CNN) with the original softmax loss shows satisfactory generalization ability under open-set settings. In this paper, we proposed an end-to-end method for open-set palmprint recognition by applying CNN with a novel loss function, namely, centralized large margin cosine loss (C-LMCL). The modified loss function compels the feature vectors from different classes to uniformly and separately distribute in the hyper feature space. At the same time, it makes intra-class feature vectors compactly gather to their corresponding class centers. Consequently, such trained model has the ability to generalize across unseen subjects and different datasets. Finally, a lot of experiments are conducted on two public palmprint datasets- Tongji and PolyU datasets. In particular, all the evaluations are made under open-set protocols that are more complex and challenging compared to the previous close-set scenarios. The experimental results on the Tongji and PolyU datasets indicate the superiority of our algorithm over the state-of-the-art performance. It effectively confirmed the bright prospects of employing palmprint information in biometric authentication.
Juvenile hormone (JH) plays crucial roles in many aspects of insect life. The Methoprene-tolerant (Met) gene product, a member of the bHLH-PAS family of transcriptional regulators, has been ...demonstrated to be a key component of the JH signaling pathway. However, the molecular function of Met in JH-induced signal transduction and gene regulation remains to be fully elucidated. Here we show that a transcriptional coactivator of the ecdysteroid receptor complex, FISC, acts as a functional partner of Met in mediating JH-induced gene expression. Met and FISC appear to use their PAS domains to form a dimer only in the presence of JH or JH analogs. In newly emerged adult female mosquitoes, expression of some JH responsive genes is considerably dampened when Met or FISC is depleted by RNAi. Met and FISC are found to be associated with the promoter of the early trypsin gene (AaET) when transcription of this gene is activated by JH. A juvenile hormone response element (JHRE) has been identified in the AaET upstream regulatory region and is bound in vitro by the Met-FISC complex present in the nuclear protein extracts of previtellogenic adult female mosquitoes. In addition, the Drosophila homologs of Met and FISC can also use this mosquito JHRE to activate gene transcription in response to JH in a cell transfection assay. Together, the evidence indicates that Met and FISC form a functional complex on the JHRE in the presence of JH and directly activate transcription of JH target genes.
This study proposes a numerical investigation for rapid bridge damage detection based on a semi-supervised deep learning (DL) model and a damage index (DI)-based Gaussian process. The proposed damage ...detection method uses bridge response data (acceleration and displacement data) from various damage scenarios within a simply supported girder bridge subjected to a two-axle moving vehicle load. As for semi-supervised learning, we used a one-class convolutional neural network (OC-CNN) model. This model combines a one-class (OC) classification algorithm with a simple one-dimensional convolutional neural network (1D CNN) configuration. The performance of the proposed OC-CNN model was evaluated through a numerical example of a vehicle-bridge coupling system. The proposed OC-CNN model trained using acceleration data showed promising results for different vehicle weights and speeds. These results offer confidence in using the prediction error loss of the proposed OC-CNN model as an ideal damage-sensitive feature for rapid bridge damage detection. In addition, the Gaussian process used in the DI can classify the prediction error losses resulting from the change induced by different damage severities (10%, 20%, and 30%) and different types of damage scenarios (single damage, double damages, and multiple damages). These results emphasize the potential of the proposed damage detection method to monitor the state of bridges in practical engineering.
microRNAs (miRNAs) are increasingly recognized as important regulators of many biological processes in mosquitoes, vectors of numerous devastating infectious diseases. Identification of bona fide ...targets remains the bottleneck for functional studies of miRNAs. In this study, we used CLEAR-CLIP assays to systematically analyze miRNA-mRNA interactions in adult female Anopheles gambiae mosquitoes. Thousands of miRNA-target pairs were captured after direct ligation of the miRNA and its cognate target transcript in endogenous Argonaute-miRNA-mRNA complexes. Using two interactions detected in this manner, miR-309-SIX4 and let-7-kr-h1, we demonstrated the reliability of this experimental approach in identifying in vivo gene regulation by miRNAs. The miRNA-mRNA interaction dataset provided an invaluable opportunity to decipher targeting rules of mosquito miRNAs. Enriched motifs in the diverse targets of each miRNA indicated that the majority of mosquito miRNAs rely on seed-based canonical target recognition, while noncanonical miRNA binding sites are widespread and often contain motifs complementary to the central or 3' ends of miRNAs. The time-lapse study of miRNA-target interactomes in adult female mosquitoes revealed dynamic miRNA regulation of gene expression in response to varying nutritional sources and physiological demands. Interestingly, some miRNAs exhibited flexibility to use distinct sequences at different stages for target recognition. Furthermore, many miRNA-mRNA interactions displayed stage-specific patterns, especially for those genes involved in metabolism, suggesting that miRNAs play critical roles in precise control of gene expression to cope with enormous physiological demands associated with egg production. The global mapping of miRNA-target interactions contributes to our understanding of miRNA targeting specificity in non-model organisms. It also provides a roadmap for additional studies focused on regulatory functions of miRNAs in Anopheles gambiae.
Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System ...(GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the close relationship between the GNSS multipath errors and measurement environment in combination with the noise reduction characteristics of different filtering algorithms, the researchers proposed an AFEC mixed filtering algorithm, which is an combination of autocorrelation function-based empirical mode decomposition (EMD) and Chebyshev mixed filtering to extract the real vibration displacement of the bridge structure after system error correction and filtering de-noising of signals collected by the GNSS. The proposed AFEC mixed filtering algorithm had high accuracy (1 mm) of real displacement at the elevation direction. Next, the traditional random decrement technique (used mainly for stationary random processes) was expanded to non-stationary random processes. Combining the expanded random decrement technique (RDT) and autoregressive moving average model (ARMA), the modal frequency of the bridge structural system was extracted using an expanded ARMA_RDT modal identification method, which was compared with the power spectrum analysis results of the acceleration signal and finite element analysis results. Identification results demonstrated that the proposed algorithm is applicable to analyze the dynamic displacement monitoring data of real bridge structures under ambient excitation and could identify the first five orders of the inherent frequencies of the structural system accurately. The identification error of the inherent frequency was smaller than 6%, indicating the high identification accuracy of the proposed algorithm. Furthermore, the GNSS dynamic deformation monitoring method can be used to monitor dynamic displacement and identify the modal parameters of bridge structures. The GNSS can monitor the working state of bridges effectively and accurately. Research results can provide references to evaluate the bearing capacity, safety performance, and durability of bridge structures during operation.
We demonstrate a dual-wavelength passively mode-locked soliton fiber laser based on the single-wall carbon nanotube saturable absorber. By using a simple scheme of adjusting the intracavity loss, the ...gain profile of the erbium-doped fiber laser is effectively controlled. Besides operating at a single wavelength, the laser is able to simultaneously generate sub-picosecond pulses at both ~1532 and 1557 nm wavelength. The mode-locking wavelength can also be quickly switched from one wavelength to the other by changing the intracavity loss with a tunable attenuator.
This paper mainly improves the visual geometry group network-16 (VGG-16), which is a classic convolutional neural network (CNN), to classify the surface defects on cement concrete bridges in an ...accurate manner. Specifically, the number of fully connected layers was reduced by one, and the Softmax classifier was replaced with a Softmax classification layer with seven defect tags. The weight parameters of convolutional and pooling layers were shared in the pre-trained model, and the rectified linear unit (ReLU) function was taken as the activation function. The original images were collected by a road inspection vehicle driving across bridges on national and provincial highways in Jiangxi Province, China. The images on surface defects of cement concrete bridges were selected, and divided into a training set and a test set, and preprocessed through morphology-based weight adaptive denoising. To verify its performance, the improved VGG-16 was compared with traditional shallow neural networks (NNs) like the backpropagation neural network (BPNN), support vector machine (SVM), and deep CNNs like AlexNet, GoogLeNet, and ResNet on the same sample dataset of surface defects on cement concrete bridges. Judging by mean detection accuracy and top-5 accuracy, our model outperformed all the contrastive methods, and accurately differentiated between images with seven classes of defects such as normal, cracks, fracturing, plate fracturing, corner rupturing, edge/corner exfoliation, skeleton exposure, and repairs. The results indicate that our model can effectively extract the multi-layer features from surface defect images, which highlights the edges and textures. The research findings shed important new light on the detection of surface defects and classification of defect images.
The deck pavement, as an important structure of the bridge, cushions the wheel actions on the bridge, prevents the main girder from rain erosion, and ensures the flatness and slip resistance for ...vehicles. The asphalt concrete has been widely used in bridge decks. However, the conventional crack detection methods cannot identify the defects on asphalt concrete bridge deck accurately and efficiently, due to the dark color of the deck and the complexity, different types of defects. The objective of this paper is to develop a weakly supervised network for the segmentation and detection of cracks in asphalt concrete deck. Firstly, the data were differentiated by the autoencoder, and the unlabeled data features were highlighted, so that the original data autonomously generate a weakly supervised start point for convergence. Secondly, the features were classified by k-means clustering (KMC). Thirdly, the cracks in the bridge deck defects images were subjected to semantic segmentation under weak supervision. A dataset of six types of defects on asphalt concrete bridge deck which was set up the defects in the dataset were labeled manually. The experimental results show that the proposed achieved outstanding segmentation effects on all six types of defects was better than the other existed methods reported in the references.
Ultra-high-performance concrete (UHPC) is suitable for repairing and strengthening damaged normal strength concrete (NSC) structures due to its excellent qualities. However, a successful repair ...relies on whether the UHPC–NSC interface can offer a superb bonding performance under varying working conditions. Therefore, predicting the interface bond strength between substrate NSC and repair UHPC with sufficiently high accuracy has become essential for evaluating and maintaining NSC structures. This study utilized four different machine learning (ML) techniques, support vector machine (SVM), artificial neural network (ANN), multiple linear regression (MLR), and stepwise regression (SWR) to predict the UHPC–NSC interface bond strength. The ML models established the relationship between input variables and target bond strength and predicted the UHPC–NSC interface bond strength. Random search techniques were used to tune the selected algorithms hyperparameters, and the
k
-fold cross-validation technique was employed to ensure generalizability. Two datasets containing the UHPC–NSC bond strength test results from splitting-tensile and slant-shear tests were used to train and test the performance of the selected ML models. Results show that SVM and ANN models are more effective than the MLR and SWR models based on the two datasets. Besides, all the four ML models developed have better prediction accuracy than the empirical model given by the design codes. The correlation between the input variables and target bond strength was evaluated through partial dependence plots. The ML approach explored in this study has proven viable and effective in predicting UHPC–NSC bond strength and provided the basis for designing UHPC–NSC composite elements.
This research presents an innovative methodology aimed at monitoring jet trajectory during the jetting process using imagery captured by unmanned aerial vehicles (UAVs). This approach seamlessly ...integrates UAV imagery with an offline learnable prompt vector module (OPVM) to enhance trajectory monitoring accuracy and stability. By leveraging a high-resolution camera mounted on a UAV, image enhancement is proposed to solve the problem of geometric and photometric distortion in jet trajectory images, and the Faster R-CNN network is deployed to detect objects within the images and precisely identify the jet trajectory within the video stream. Subsequently, the offline learnable prompt vector module is incorporated to further refine trajectory predictions, thereby improving monitoring accuracy and stability. In particular, the offline learnable prompt vector module not only learns the visual characteristics of jet trajectory but also incorporates their textual features, thus adopting a bimodal approach to trajectory analysis. Additionally, OPVM is trained offline, thereby minimizing additional memory and computational resource requirements. Experimental findings underscore the method’s remarkable precision of 95.4% and efficiency in monitoring jet trajectory, thereby laying a solid foundation for advancements in trajectory detection and tracking. This methodology holds significant potential for application in firefighting systems and industrial processes, offering a robust framework to address dynamic trajectory monitoring challenges and augment computer vision capabilities in practical scenarios.
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