Search

At present, ginkgo leaves are still picked manually. A brush roller ginkgo leaf picker has been designed to improve harvesting efficiency and reduce losses caused by manual failure to pick leaves in time under large-scale planting areas. The ginkgo leaf picker is mainly composed of crawler chassis, gantry frame, brush roller picking parts, and collecting box. The kinematics of the brush roller are analyzed for the picking omission situation. An experimental platform for picking ginkgo biloba leaves was established. Three parameters, namely roller speed, moving speed, and roller inclination were selected for optimization. Then the maximum net harvest rate and the minimum damage rate were achieved. The orthogonal test showed that when the roller speed was 130 rev/min, the moving speed was 0.25 m/s, and the roller inclination was 32°, the picking effect was the best, the net harvest rate was 93.32%, the damage rate was 1.42%, and the damage degree of the trunks was slight. The experiment proved that the brush-roller ginkgo leaf picker has a good picking effect, which can provide a reference for the optimization design of ginkgo leaf harvesting machinery under the dwarf dense planting mode. [ABSTRACT FROM AUTHOR]

Background: Crop pests seriously affect the yield and quality of crops. Accurately and rapidly detecting and segmenting insect pests in crop leaves is a premise for effectively controlling insect pests. Methods: Aiming at the detection problem of irregular multi-scale insect pests in the field, a dilated multi-scale attention U-Net (DMSAU-Net) model is constructed for crop insect pest detection. In its encoder, dilated Inception is designed to replace the convolution layer in U-Net to extract the multi-scale features of insect pest images. An attention module is added to its decoder to focus on the edge of the insect pest image. Results: The experiments on the crop insect pest image IP102 dataset are implemented, and achieved the detection accuracy of 92.16% and IoU of 91.2%, which is 3.3% and 1.5% higher than that of MSR-RCNN, respectively. Conclusion: The results indicate that the proposed method is effective as a new insect pest detection method. The dilated Inception can improve the accuracy of the model, and the attention module can reduce the noise generated by upsampling and accelerate model convergence. It can be concluded that the proposed method can be applied to practical crop insect pest monitoring system. [ABSTRACT FROM AUTHOR]

Crop pests seriously affect the yield and quality of crop. To timely and accurately control crop pests is particularly crucial for crop security, quality of life and a stable agricultural economy. Crop pest detection in field is an essential step to control the pests. The existing convolutional neural network (CNN) based pest detection methods are not satisfactory for small pest recognition and detection in field because the pests are various with different colors, shapes and poses. A three-scale CNN with attention (TSCNNA) model is constructed for crop pest detection by adding the channel attention and spatial mechanisms are introduced into CNN. TSCNNA can improve the interest of CNN for pest detection with different sizes under complicated background, and enlarge the receptive field of CNN, so as to improve the accuracy of pest detection. Experiments are carried out on the image set of common crop pests, and the precision is 93.16%, which is 5.1% and 3.7% higher than ICNN and VGG16, respectively. The results show that the proposed method can achieve both high speed and high accuracy of crop pest detection. This proposed method has certain practical significance of real-time crop pest control in the field. [ABSTRACT FROM AUTHOR]

Side-scan sonar (SSS) is a vital sensor for marine survey, which is widely used in military and civilian fields. The accurate segmentation of SSS images is critical in sonar image intelligent interpretation. Existing SSS image segmentation methods have several limitations, such as insufficient feature extraction, relatively worse segmentation results for tiny target categories, and serious interference by seabed reverberation noise and bright shadow region. To overcome these issues, we propose a novel encoder–decoder architecture SSS image segmentation method based on convolution neural network (CNN). First, we extract the multiscale feature information contained in target region using the dynamic multiscale dilated convolution (DMDC_Conv). Second, to further obtain the global and detail feature information, we construct the adaptive receptive field mechanism block (ARFM_Block). Third, we design a feature fusion attention mechanism block (FFAM_Block) to fuse high-level and low-level feature information with different scales and suppress background information interference. Final, we construct a tree structure optimization module (TSOM) to solve the problem of pixel misclassification and obtain refine SSS image segmentation results. Extensive experiments are carried out on the constructed real scene SSS image dataset. The experimental results show that the proposed method achieves 93.24% and 90.82% of mean pixel accuracy (MPA) and mean intersection over union (MIoU), respectively, which outperforms the other state-of-the-art methods and has a substantial advantage in inference speed and calculation parameters. [ABSTRACT FROM AUTHOR]

[Display omitted] • Foam created by nano-Al(OH) 3 /CoatOsil-77/BS12 mixture shows shear thinning behavior. • Foam stability of nano-Al(OH) 3 /CoatOsil-77/BS12 mixture is enhanced by nano-Al(OH) 3. • Viscoelasticity of mixed liquid and foam increases with rising nano-Al(OH) 3 content. • Foam of mixed solution shows great thixotropic recovery in presence of nano-Al(OH) 3. Fluorine-free foam stabilized by nanoparticle (NP) is expected to become a new generation of environmental-friendly firefighting foam. Many problems on properties of NP-stabilized fluorine-free foams remain to be studied. The present paper focuses on the rheology and foam stability of fluorine-free foam stabilized by aluminum hydroxide NPs. The surface tension, conductivity, foamability, viscosity, and viscoelasticity of mixed dispersions were measured. The stability, viscosity, viscoelasticity, and thixotropy of foam was analyzed deeply by model fitting and mechanism explanation. The results show that the rheological properties and foam stability of mixed dispersions are both enhanced by the presence of NPs, and the enhancement amplitude increased with NP concentration. Both dispersions and NP-stabilized foams exhibit shear thinning behavior, and the viscosity variations in dispersions conform to the Cross model well. As NP concentration increases, the linear viscoelastic range of foam becomes wider, the flow point of NP foam is delayed, demonstrated enhanced viscoelasticity. The thixotropic recovery of foam decreases with the increasing NP concentration, and foam thixotropic recovery decreases with the increase of irreversible deformation degree. This study can provide design idea for the development of fluorine-free firefighting foam. [ABSTRACT FROM AUTHOR]

Protein–lipid interactions are vital for numerous transmembrane signaling pathways. However, simple tools to characterize these interactions remain scarce and are much needed to advance our understanding of signal transduction across lipid bilayers. To tackle this challenge, we herein engineer nanodisc as a robust fluorescent sensor for reporting membrane biochemical reactions. We circularize nanodiscs via split GFP and thereby create an intensity-based fluorescent sensor (isenND) for detecting membrane binding and remodeling events. We show that isenND responds robustly and specifically to the action of a diverse array of membrane-interacting proteins and peptides, ranging from synaptotagmin and synuclein involved in neurotransmission to viral fusion peptides of HIV-1 and SARS-CoV-2. Together, isenND can serve as a versatile biochemical reagent useful for basic and translational research of membrane biology. A fluorescent probe for detecting membrane protein binding and remodeling events is presented, which relies on split-GFP technology to generate circularized nanodiscs useful in membrane protein biophysics and structural biology. [ABSTRACT FROM AUTHOR]

Weeding is beneficial to the growth of the crops in field. At present, weeding in China mainly relies on chemical herbicide spraying on a large area, which leads to environmental pollution. Combined with digital image processing and pattern recognition technology, weed species identification in wheat seedling stage in field is of great significance to realize the variable spraying of herbicide, reduce the cost and protect the ecological environment. Weed species identification in field by machine vision is one of the challenging and hard topics because of the diversity and changeability of the weed in field. A weed species recognition approach is proposed combining modified Grabcut, adaptive fuzzy dynamic K-means algorithms and sparse representation classification (SRC). First, the original weed images are enhanced and noise is suppressed using filtering technique, and in the segmentation phase, each weed image is coarsely segmented by the modified GrabCut algorithm to remove most of background of the original image captured in the field, which can reduce the computing cost and recognition time. The original weed image is segmented by adaptive fuzzy dynamic K-means. Finally the weed species is recognized by SRC. Compared with the other weed recognition methods, the proposed method integrated the advantages of three approaches, (1) the improved Grabcut method does not require human interaction and can automatically segment the background, (2) the dynamic K-means algorithm introduces fitness function to evaluate clustering, which reduces the dependence of traditional K-means clustering algorithm on the initial value of clustering center to a certain extent, and avoids the problems such as dead zone center and center redundancy caused by local extremum, (3) SRC is utilized to classify the weed species. To test the proposed method, a lot of experiments are carried on the wheat weed image dataset. The results validate that the proposed method is effective for the weed species recognition, which can be used as a preliminary step for precision applying pesticide. [ABSTRACT FROM AUTHOR]

Plant species identification based on plant leaves is important for biological science, ecological science, and agricultural digitization. Because of the complexity and variation of the plant leaves, many classical plant species identification algorithms using plant leaf images are not enough for practical application. A modified local discriminant projection (MLDP) algorithm is proposed for plant species identification. MLDP aims to extract discriminant features for plant species identification by taking class label information into account based on the property of locality preserving. The MLDP can preserve the local geometrical structure of leaves and extract the strong discriminative ability. The experimental results on the public ICL leaf image database show the effectiveness and feasibleness of the proposed method. [ABSTRACT FROM AUTHOR]

• Advanced laser weld on bonding small glass pieces is experimentally and theoretically analysed. • 3D FEM of transient temperatures and stresses on laser-welded edge seal were analysed. • Laser average power and welding speed in relation to the temperature and stress fields were analyzed. • Optimal laser welded edge seal was achieved at 60 W and 90 mm/min showed good morphology. Laser welding of transparent materials, including glasses, established in the recent years. This study reports the results of the theoretical with experimental validation to transient temperature and stress fields on bonding small glass pieces to solder glass by laser welding. A 3D finite element model of bonding small glass pieces to solder glass by laser welding is developed and validated with experimental micro-structural analysis. An influence of laser average power and welding speed on the temperature field and stress field during welding is studied. A range of average laser power and welding speed, with a standard of the appropriate temperatures and ultimate stresses of sealing during laser welding, are determined. The results show that in the range of laser average power of 45~75 W and welding speed of 1–2 mm/s, the heat source central temperature increases with an increase of laser average power or the decrease of welding speed, and the corresponding maximum temperature exceeds 650 °C. The maximum transient thermal stress is calculated to be 152 MPa, it appeared at the boundary of the upper glass interface. The boundary stress at the front end of the heat source and the transient thermal stress at the inflection point are larger than the transient thermal stress at the middle point. The experimental and theoretical results show that the melting layer has excellent morphology and mechanical properties at the average laser power of 65 W and welding speed of 90 mm/min, which is applicable for the bonding of small glass pieces to solder glass by laser welding. [ABSTRACT FROM AUTHOR]

Plant plays an important role in agricultural, industrial, medicine, environmental and ecological protection. Recently, with global warming, biodiversity loss, rapid urban development and environmental damage, people have been seriously destroying the natural environments, which results in that a large number of plant species constantly dying and even dying out every year. It is essential to protect plant species. The first step of protecting plants is to recognize them and understand what they are and where they come from. But there are a large number of plant species that have been named on Earth, and many are still unknown yet, it is difficult to identifiy each species. To handle such huge information, develop a quick and efficient classification method has become a significant research. Plant species can be recognized by its leaf, flower, skin, fruit and seed, etc. Relatively speaking, using leaf to recognize plant species is very simple and convenient, and many leaf based plant species recognition methods have been proposed. In this paper, we mainly summarize the existing leaf based plant species identification methods, including plant leaf characteristic, public databases, feature extraction based methods, subspace learning based methods, sparse representation based methods, and deep learning based methods. The aim is to emphasize the importance of plant species identification, train people to know about plant species, and provide guidance and comprehensive study for the beginners in this field, in turn, to treasure and protect plant species. [ABSTRACT FROM AUTHOR]

Palmprint is one of the most reliable biometrics and has been widely used for human identification due to its high recognition accuracy and convenience for practical application. But the existing palmprint-based human identification system often suffers from image misalignment, pixel corruption and much computational time on the large database. An effective palmprint recognition method is proposed by combining hierarchical multi-scale complete local binary pattern (HMS-CLBP) and weighted sparse representation-based classification (WSRC). The hierarchical multi-scale local invariant texture features are extracted firstly from each palmprint by multi-scale local binary pattern (MS-LBP) and multi-scale complete local binary pattern (MS-CLBP) and are concatenated into one hierarchical multi-scale fusion feature vector. Then, WSRC is constructed by the Gaussian kernel distance, and use the Gaussian kernel distances between the fusion feature vectors of the training and testing samples. Finally, the sparse decomposition of testing samples is implemented by solving the optimization problem based on l1 norm, and the palmprints are recognized by the minimum residuals. The proposed method inherits the advantages of CLBP and WSRC and has good rotation, illumination and occlusion invariance. The results on the PolyU and CASIA palmprint databases illustrate the good performance and rationale interpretation of the proposed method. [ABSTRACT FROM AUTHOR]

• Dilated Inception convolution module is introduced into AlexNet to construct two subnetworks of Siamese network. • SDINet is exploited to improve apple disease recognition results using a small number of training images. • A large number of experiments are conducted to validate the performance of SDINet. The application of existing deep learning networks may prove difficult when datasets are small. Siamese network can achieve better accuracy on small dataset. A recognition method of apple leaf disease based on Siamese dilated Inception network (SDINet) with few training samples is proposed. Dilated Inception module is introduced into AlexNet to construct two subnetworks for SDINet, and two subnetworks are responsible for extracting multi-scale features from image pair in the same layer, and the global pooling instead of the fully connected layers are utilized to reduce the number of model parameters and ensure that the features are not lost. SDINet is trained with image pairs, each pair consisting of two real diseased leaf images or one real and one healthy leaf image. SDINet makes full use of the advantages of multi-scale dilated Inception to enrich and improve the information, enhance the adaptability of the model. Different from the existing deep CNNs, SDINet uses cosine distance learning to calculate the similarity between the leaf image pairs to recognize apple diseases. Experimental results on the apple diseased leaf image dataset validate that the proposed method is effective to recognize apple leaf disease using a small number of training samples. [ABSTRACT FROM AUTHOR]

• Dilated convolution kernel enlarges local receptive field and enhances feature extraction. • Global pooling layer reduces training parameters number and avoids overfitting problem. • Multi-scale convolutional kernels extract multi-scale features of the input image. • Improvement of recognition accuracy and robustness is verified by the experimental results. It is a challenging research topic to identify plant disease based on diseased leaf image processing techniques due to the complexity of the diseased leaf images. Deep learning models are promising for identifying plant disease based on leaf images and AlexNet is one of these models. Aiming at the problems of too many parameters of the AlexNet model and single feature scale, a global pooling dilated convolutional neural network (GPDCNN) is proposed in this paper for plant disease identification by combining dilated convolution with global pooling. Compared with the classical convolutional neural network (CNN) and AlexNet models, GPDCNN has three improvements: (1) the convolution receptive field are increased without increasing the computational complexity and without losing the discriminant formation by replacing fully connected layers with a global pooling layer; (2) dilated convolutional layer is employed to recover the spatial resolution without increasing the number of training parameters; (3) GPDCNN also integrates the merits of dilated convolution and global pooling. Experimental results on the datasets of six common cucumber leaf diseases demonstrate that the proposed model can effectively recognize cucumber diseases. [ABSTRACT FROM AUTHOR]

Plant disease leaf image segmentation plays an important role in the plant disease detection through leaf symptoms. A novel segmentation method of plant disease leaf image is proposed based on a hybrid clustering. The whole color leaf image is firstly divided into a number of compact and nearly uniform superpixels by superpixel clustering, which can provide useful clustering cues to guide image segmentation to accelerate the convergence speed of the expectation maximization (EM) algorithm, and then, the lesion pixels are quickly and accurately segmented from each superpixel by EM algorithm. The experimental results and the comparison results with similar approaches demonstrate that the proposed method is effective and has high practical value for plant disease detection. [ABSTRACT FROM AUTHOR]

Abstract The color information of diseased leaf is the main basis for leaf based plant disease recognition. To make use of color information, a novel three-channel convolutional neural networks (TCCNN) model is constructed by combining three color components for vegetable leaf disease recognition. In the model, each channel of TCCNN is fed by one of three color components of RGB diseased leaf image, the convolutional feature in each CNN is learned and transmitted to the next convolutional layer and pooling layer in turn, then the features are fused through a fully connected fusion layer to get a deep-level disease recognition feature vector. Finally, a softmax layer makes use of the feature vector to classify the input images into the predefined classes. The proposed method can automatically learn the representative features from the complex diseased leaf images, and effectively recognize vegetable diseases. The experimental results validate that the proposed method outperforms the state-of-the-art methods of the vegetable leaf disease recognition. [ABSTRACT FROM AUTHOR]

Highlights • It utilizes label information and data structure to combine multiple organs for plant recognition. • Two graphs measure inter-class separability and intra-class compactness in a local way. • Geodesic distance is introduced to 1-NN classifier. • Experiments real leaf&flower dataset to test the performance of the proposed method. Abstract Plant species recognition using a single organ, such as flower and leaf, is not sufficiently reliable, because different species may have very similar flowers or leaves, while the same species may have rather different flowers or leaves. Combining leaves and flowers to recognize plant species can produce positive results. Based on multi-modal learning scheme, an automatic plant species recognition method is proposed by combining leaves and flowers of plant. In the method, a modified local discriminant canonical correlation analysis (MLDCCA) is designed by incorporating the idea of local discriminant embedding (LDE) into canonical correlation analysis (CCA). Firstly, two neighbor graphs are constructed based on the exploration of the manifold that the input data lie on. Then, two projection matrices for dimensionality reduction are obtained by making the within-class neighbor samples most correlated and between-class neighbor samples least correlated, and meanwhile keeping the correlation between leaves and flowers of the same species maximum. Finally, 1-nearest neighbor classifier with geodesic distance is used to recognize the plant species. MLDCCA is a powerful supervised multi-modal dimensional reduction method which can extract the discriminant features from two plant organs, meanwhile preserve the discriminant information and the data structure well. Experimental results on a real leaf and flower image dataset validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

[Display omitted] • Hydrophobic aggregates are created due to interaction between NPs and surfactants. • Addition of NP with a concentration above 1.5 wt% delays foam drainage and coarsening. • Network structure formed by hydrophobic aggregates enhances foam thermal stability. Nanoparticles (NPs) have shown promising applications in developing the novel environmental-friendly firefighting foams. Hydrophilic silica NPs, siloxane surfactant (CoatOsil-77), and hydrocarbon surfactant (BS-12) were used to prepare mixed foam dispersions. The adsorption between surfactant molecules and NPs, and foamability of the mixed dispersions were analyzed. Thermal stability of foam was evaluated by using foam drainage, coarsening, and volume decay under thermal radiation. The strengthening mechanism of NPs on thermal stability of foam was analyzed. Results indicate that the intense interaction exists between NPs and surfactants. The presence of NPs with a concentration below 1 wt% leads to the deterioration of foam stability by accelerating foam drainage and coarsening, while a concentration of NPs above 1.5 wt% enhances foam stability. The foam coarsening, drainage, and foam volume decay were obviously delayed by NPs under thermal radiation. This research can give theoretical guidance for NP application in invention of fluorine-free firefighting foams. [ABSTRACT FROM AUTHOR]

A growing body of experimental evidence suggests that microRNAs (miRNAs) are closely associated with specific human diseases and play critical roles in their development and progression. Therefore, identifying miRNA related to specific diseases is of great significance for disease screening and treatment. In the early stages, the identification of associations between miRNAs and diseases demanded laborious and time-consuming biological experiments that often carried a substantial risk of failure. With the exponential growth in the number of potential miRNA-disease association combinations, traditional biological experimental methods face difficulties in processing massive amounts of data. Hence, developing more efficient computational methods to predict possible miRNA-disease associations and prioritize them is particularly necessary. In recent years, numerous deep learning-based computational methods have been developed and have demonstrated excellent performance. However, most of these methods rely on external databases or tools to compute various auxiliary information. Unfortunately, these external databases or tools often cover only a limited portion of miRNAs and diseases, resulting in many miRNAs and diseases being unable to match with these computational methods. Therefore, there are certain limitations associated with the practical application of these methods. To overcome the above limitations, this study proposes a multi-view computational model called MVNMDA, which predicts potential miRNA-disease associations by integrating features of miRNA and diseases from local views, global views, and semantic views. Specifically, MVNMDA utilizes known association information to construct node initial features. Then, multiple networks are constructed based on known association to extract low-dimensional feature embedding of all nodes. Finally, a cascaded attention classifier is proposed to fuse features from coarse to fine, suppressing noise within the features and making precise predictions. To validate the effectiveness of the proposed method, extensive experiments were conducted on the HMDD v2.0 and HMDD v3.2 datasets. The experimental results demonstrate that MVNMDA achieves better performance compared to other computational methods. Additionally, the case study results further demonstrate the reliable predictive performance of MVNMDA. [ABSTRACT FROM AUTHOR]

An Internet of things (IOT) based plant diseased leaf segmentation and recognition method is proposed based on Fusion of Super-pixel clustering, K-mean clustering and pyramid of histograms of orientation gradients (PHOG) algorithms. Firstly, the color diseased leaf image is divided into a few compact super-pixels by super-pixel clustering algorithm. Then K-means clustering algorithm is employed to segment the lesion image from each super-pixel. Finally, the PHOG features are extracted from three color components of each segmented lesion image and its grayscale image, and concatenate four PHOG descriptors as a vector. The experiment results on two plant diseased leaf image databases indicate that the proposed method is effective. This paper provides a feasible solution for plant diseased leaf image segmentation and plant disease recognition. [ABSTRACT FROM AUTHOR]

In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted. [ABSTRACT FROM AUTHOR]

Cucumber diseases can be detected and recognized automatically based on diseased leaf symptoms. In this paper, we propose a new method, combining superpixels, expectation maximization (EM) algorithm, and logarithmic frequency pyramid of histograms of orientation gradients (PHOG), to recognize cucumber diseases. The proposed method is composed of following steps. First, the superpixel operation is used to divide a diseased leaf image into a number of compact regions, which can dramatically accelerate the convergence speed of the EM algorithm that is adopted to segment the diseased leaf regions and obtain the lesion image. Second, the logarithmic frequency PHOG features are extracted from the segmented lesion image. Finally, Support Vector Machines (SVMs) are performed to classify and recognize different cucumber diseases. Conducted on a database of cucumber diseased leaf images, experimental results show the proposed method is effective and feasible for recognizing cucumber diseases. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) target detection plays a crucial role in military surveillance, earth observation, and disaster monitoring. With the development of deep learning (DL) and SAR imaging technology, numerous SAR target detection methods have been proposed and achieved better detection results. However, detecting different categories of SAR vehicle targets is still challenging due to the influence of coherent speckle noises and background clutter. This article presents a novel attention feature fusion awareness network (AFFNet) for vehicle target detection in SAR images. Specifically, we propose a multi-scale semantic attention (MSSA) module to obtain multi-scale and semantic features of target region; the variable multi-scale feature fusion (VMSFF) module is introduced to effectively fuse different feature information and alleviate target deformation interference by establishing feature correlation; the part feature awareness (PFA) module is used to obtain unique attribute of different vehicle targets to generate accurate anchor boxes. In addition, we design a candidate boundary box selection scheme, which can effectively adapt to SAR targets with different scales and categories. Overall, AFFNet is designed based on the SAR imaging mechanism and target physical feature information. To evaluate the performance of the proposed method, extensive experiments are conducted on the MSTAR dataset. The experiment results show that the proposed AFFNet obtains the mAP of 98.36 % and 97.26 % on standard operating conditions (SOCs) and extended operating conditions (EOCs), which is more efficient than the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Leaf based plant species recognition plays an important role in ecological protection, however its application to large and modern leaf databases has been a long-standing obstacle due to the computational cost and feasibility. Recognizing such limitations, we propose a Jaccard distance based sparse representation (JDSR) method which adopts a two-stage, coarse to fine strategy for plant species recognition. In the first stage, we use the Jaccard distance between the test sample and each training sample to coarsely determine the candidate classes of the test sample. The second stage includes a Jaccard distance based weighted sparse representation based classification(WSRC), which aims to approximately represent the test sample in the training space, and classify it by the approximation residuals. Since the training model of our JDSR method involves much fewer but more informative representatives, this method is expected to overcome the limitation of high computational and memory costs in traditional sparse representation based classification. Comparative experimental results on a public leaf image database demonstrate that the proposed method outperforms other existing feature extraction and SRC based plant recognition methods in terms of both accuracy and computational speed. [ABSTRACT FROM AUTHOR]

Aiming at the difficult problem of plant leaf recognition on the large-scale database, a two-stage local similarity based classification learning (LSCL) method is proposed by combining local mean-based clustering (LMC) method and local sparse representation based classification (SRC) (LWSRC). In the first stage, LMC is applied to coarsely classifying the test sample. k nearest neighbors of the test sample, as a neighbor subset, is selected from each training class, then the local geometric center of each class is calculated. S candidate neighbor subsets of the test sample are determined with the first S smallest distances between the test sample and each local geometric center. In the second stage, LWSRC is proposed to approximately represent the test sample through a linear weighted sum of all $$k\times S$$ samples of the S candidate neighbor subsets. Experimental results on the leaf image database demonstrate that the proposed method not only has a high accuracy and low time cost, but also can be clearly interpreted. [ABSTRACT FROM AUTHOR]

The groove shape characteristics of echelle gratings with high diffraction efficiency are investigated. Using the coordinate transformation method (C method), an r-2 aluminum echelle with 79 grooves/mm is optimized through rigorous numerical simulations and shows high diffraction efficiency of 76–81% in the high Littrow orders. A grating is found to be essentially an echelle if it contains a series of reflective facets with a specific tilt angle that are located far from the nonworking facet of the grating and have a deep groove depth; any groove shape that meets these conditions can be called an echelle grating. The underlying mechanism is analyzed phenomenologically using electromagnetic theory. The universal model proposed here, which represents a new cognitive understanding of the concept of the echelle, is ready for use in manufacturing applications and offers a new perspective for the fabrication of these gratings. [ABSTRACT FROM AUTHOR]

Most existing image-based crop disease recognition algorithms rely on extracting various kinds of features from leaf images of diseased plants. They have a common limitation as the features selected for discriminating leaf images are usually treated as equally important in the classification process. We propose a novel cucumber disease recognition approach which consists of three pipelined procedures: segmenting diseased leaf images by K-means clustering, extracting shape and color features from lesion information, and classifying diseased leaf images using sparse representation (SR). A major advantage of this approach is that the classification in the SR space is able to effectively reduce the computation cost and improve the recognition performance. We perform a comparison with four other feature extraction based methods using a leaf image dataset on cucumber diseases. The proposed approach is shown to be effective in recognizing seven major cucumber diseases with an overall recognition rate of 85.7%, higher than those of the other methods. [ABSTRACT FROM AUTHOR]

Apple leaf disease is one of the main factors to constrain the apple production and quality. It takes a long time to detect the diseases by using the traditional diagnostic approach, thus farmers often miss the best time to prevent and treat the diseases. Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision, where the key task is to find an effective way to represent the diseased leaf images. In this research, based on image processing techniques and pattern recognition methods, an apple leaf disease recognition method was proposed. A color transformation structure for the input RGB (Red, Green and Blue) image was designed firstly and then RGB model was converted to HSI (Hue, Saturation and Intensity), YUV and gray models. The background was removed based on a specific threshold value, and then the disease spot image was segmented with region growing algorithm (RGA). Thirty-eight classifying features of color, texture and shape were extracted from each spot image. To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification, the most valuable features were selected by combining genetic algorithm (GA) and correlation based feature selection (CFS). Finally, the diseases were recognized by SVM classifier. In the proposed method, the selected feature subset was globally optimum. The experimental results of more than 90% correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases, powdery mildew, mosaic and rust, demonstrate that the proposed method is feasible and effective. [ABSTRACT FROM AUTHOR]

The joint is an important component of the Edge Localized Modes (ELM) coils in fusion reactor, which is used to connect the ELM coils. Like the ELM coils, the joints work in an environment with high radiation levels, high temperature and high magnetic field. These joints are mainly subject to nuclear heat from the plasma and cyclic electromagnetic (EM) loads induced by the interaction of ELM coil current with magnetic fields. Take the joint of ITER ELM coil for example. In order to assure the structural integrity of joints under these loads, it is necessary to estimate the strength and fatigue of the joints. As a local model, the joint without ELM coil is investigated by the sub-model method. Results show that the finite element sub-model method is feasible and rapid for the joint design. The maximum magnetic flux intensity occurs in the axial direction for the joint local reference, which parallels with the current and corresponds to the toroidal direction of the ITER. The two areas at the top of the Inconel sleeve appear high temperature. For the joint, the conductor, jacket and sleeve can meet the static and fatigue criteria and the joint design is valid and feasible. The thermal load from the nuclear heat is the key factor for the joint design. [ABSTRACT FROM AUTHOR]

• Resblock is introduced to solve the problems of gradient disappearing and explosion. • Using two Respaths integrates the features between contraction and expansion path. • A lot of experiments are implemented to validate the effectiveness of MU-Net. Early detection and recognition of plant disease is a prerequisite for controlling plant disease, and one of the key steps is to segment plant diseased leaf images. However, this task is challenging because diseased leaf images are often very complex, with irregular shapes, variable sizes, various shapes, rich colors, fuzzy boundaries and messy backgrounds. An improved U-Net (MU-Net) is constructed for plant diseased leaf image segmentation by introducing a residual block (Resblock) and a residual path (Respath). Resblock is introduced into U-Net to overcome gradient disappearance and explosion problems, and 2 Respaths are used instead of 2 skip connections to improve the transformation of corresponding feature information between the contraction path and the expansion path. Furthermore, Resblock and Respath are combined, which can increase the network depth and improve the network's expression ability. Experimental results on a plant diseased leaf image dataset show that the proposed method can improve the accuracy and efficiency of plant diseased leaf image segmentation. [ABSTRACT FROM AUTHOR]

Plant classification based on the leaf images is an important and tough task. For leaf classification problem, in this paper, a new weight measure is presented, and then a dimensional reduction algorithm, named semi-supervised orthogonal discriminant projection (SSODP), is proposed. SSODP makes full use of both the labeled and unlabeled data to construct the weight by incorporating the reliability information, the local neighborhood structure and the class information of the data. The experimental results on the two public plant leaf databases demonstrate that SSODP is more effective in terms of plant leaf classification rate. [ABSTRACT FROM AUTHOR]

Plant leaf based plant disease recognition is becoming an important research topic in the pattern recognition and image processing. Many conventional methods are not effective for plant disease recognition because of the complexity of the disease leaf images. Singular value decomposition (SVD) has shown its advantage over other classical algorithms in terms of clustering accuracy and robustness to the data representation and feature extraction. Nonetheless, there is still considerable room to improve the practical application performance of SVD. To improve the recognition rate of cucumber disease, based on the Gglobal-Local SVD, a recognition method of cucumber disease is proposed. First, the spot image is segmented by the watershed algorithm from each cucumber disease leaf image. Second, each spot image is divided into a few blocks, and then the combining features of Global-Local singular values are extracted and ordered from each block by SVD. Third, the key-point vectors are constructed and their dimensionalities are adjusted to equal to each other. Finally, a SVM classifier is adopted to recognize the class of the unknown disease leaf image. Comparing with the existing cucumber disease recognition methods, the proposed one can extract the key-point features from the spot image whose dimension is significantly lower than that of the original space. Also, it can effectively avoid the key information loss in the usual stochastic feature extraction and selection process. The approach is tested on three kinds of cucumber disease leaf images. In the experiments, the original singular values are replaced by the nonnegative Global-Local Logarithmic singular values. The experimental results show that the proposed method is effective and feasible for the cucumber disease recognition, and has the highest recognition rate and more practical value. [ABSTRACT FROM AUTHOR]

Manifold learning based dimensionality reduction algorithms have been payed much attention in plant leaf recognition as the algorithms can select a subset of effective and efficient discriminative features in the leaf images. In this paper, a dimensionality reduction method based on local discriminative tangent space alignment (LDTSA) is introduced for plant leaf recognition based on leaf images. The proposed method can embrace part optimization and whole alignment and encapsulate the geometric and discriminative information into a local patch. The experiments on two plant leaf databases, ICL and Swedish plant leaf datasets, demonstrate the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Study on the classification and recognition methods of plant species by using plant leaf images plays an important role in protecting plant species and ecological environment. Designing a computer-aided plant recognition system is necessary and useful since it can facilitate plants recognition and classification, and understanding and managing plant species. Compared with other plant recognition methods, such as cell and molecule biology methods, plant recognition and classification based on leaf image processing is becoming a popular trend. In protecting plant perspective, leaf images have been used by plant protection researchers to diagnose plant diseases and this method has been proven to be reliable for years. Each kind of plant leaf has its own features and carries large significant information that can be used to recognize and classify the origin or the type of plant. Leaf shape is a prominent feature that most people use to recognize and classify a plant. The features, such as leaf area, perimeter, diameter, physiological length and physiological width, are basic geometry information that can be extracted from the leaf shape. In addition, leaf color, textures and vein pattern are also considered as important classifying features. All these features are useful for recognizing and classifying plant. Because of the complex and diversity of plant leaf images and the differences between within-class leaf images, many classification and recognition methods that use color, shape and texture of the leaves cannot meet the need of the plant automatic identification system. The feature extraction and dimensional reduction is a key step to plant classification. The classical linear dimensional reduction methods can not effectively applied to leaf image processing because the plant leaf images are general nonlinear data. Manifold learning based recognition methods have been successfully applied to face recognition. Based on manifold learning, a probability locality preserving discriminant projections (PLPDP) method was proposed for plant recognition in this study. The method was to learn a linear transformation, and focuses only on the pairwise points where the two points were neighbors to each other. First, the classification probability of each sample was computed, and the weighted matrix was defined by local information, label information and classifying probability. Then objective function was redefined. Then, through minimizing the function, an optimal projection matrix could be preserved in the low-dimensional feature space, such as the distribution information contained in the original data. Compared with the popular dimensionality reduction methods, such as linear discriminant analysis (LDA), locality preserving projections (LPP), discriminant LPP (DLPP) and supervised LPP(SLPP), the proposed method took three kinds of information into account (i.e., local information, label information and classifying probability) and refined the weighted matrix, which not only contained neighborhood information of samples, but also could reflect the probability from which a sample was correctly classified when its K-nearest-neighbors were selected. After projection, the neighborhood relationship of the intra-class samples which possessed more classification probability could be preserved, and the considered pairwise points in within-class were as close as possible, while those in between different classes were as far as possible, in which the global optimum could be effectively obtained. Finally, the experimental results on the public plant leaf image database demonstrate the superiority of the proposed algorithm compared with other algorithms. [ABSTRACT FROM AUTHOR]

• The shielding structure of hot cell shielding door is introduced. • The neutron shielding performance is studied by three methods. • The material and its thickness for hot cell shielding door is provided. Hot cell shielding door is an important component to ensure the safe operation of nuclear power plants. In this study, the shielding structure of hot cell shielding door is introduced. Using theoretical calculation of neutron shielding, Monte Carlo N-Particle Transport Code (MCNP) simulation analysis and experimental research, the neutron shielding performance of hot cell shielding door is studied. The results show that the neutron shielding ratio of both simulation and theoretical calculation reaches 98%, which meets the design requirements of neutron shielding performance. The experimental results show that when neutron energy is 5 MeV and thickness of boron containing plate is 40 mm, The neutron shielding ratio of three different composite materials, namely, steel plate + boron containing polyethylene plate + steel plate, steel plate + boron containing ultra-high molecular weight polyethylene plate + steel plate and steel plate + boron containing polypropylene plate + steel plate are 99.64%, and neutron shielding performance meets the index requirements. This study provides the choice of material and its thickness for hot cell shielding door. [ABSTRACT FROM AUTHOR]

Crop disease is one of the main disasters for Chinese agriculture and it seriously affects the yield and quality of crops, and causes economic losses to farmers. Early detection and prevention of crop diseases is critical to control the diseases, improve crop yields, reduce the economic losses and control pesticide pollution. Therefore, the research of recognition methods for crop diseases is necessary. In this study, a disease recognition method of cucumber disease, based on leaf image and environmental information, is proposed. In this method, the cucumber disease features, including environmental classifying features and disease leaf classifying features, were extracted by image processing and statistical analysis methods. The classifying features were then selected by SAS discriminant analysis, and the cucumber diseases were identified by using the rule of maximum membership degree. The cucumber leaves and their environmental information of Downy mildew, Brown Speck, and Anthracnose were collected for disease recognition. The diseased cucumber leaf images were processed by using a series of image pre-processing methods, such as image transforming, smoothing, and segmentation. White was chosen as the background of the diseased leaf images, a median filter was applied to effectively remove the disturbance of noise, and the color image segmentation method, based on statistical pattern recognition, was applied to separate the disease spot images from the diseased leaf images. The five features of environmental information were extracted, and the 35 statistical eigen vectors of color, shape, and texture of the diseased leaf images could be extracted by statistical analysis. Then, 40 disease union classifying features were obtained. Ten strong classifying features were then selected by the SAS discriminant analysis method. The feature vectors of the clustering center were then computed. Finally, three kinds of cucumber diseases were recognized by the maximum membership degree. The disease recognition method proposed in this study differs essentially from the traditional ones. Traditional methods only take into account features extracted from diseased leaf images, which makes the recognition rate of traditional methods low because the diseased leaf image is quite complex. However, the proposed disease recognition method contains not only the leaf image information, but also the environmental information, which improves the robustness and recognition rate of the proposed method. The recognition results of three kinds of cucumber diseases by the proposed method were more than 93 percent. The experiment results show that cucumber diseased leaf images obtained under different conditions were effectively recognized by the integrated application of image processing technology, analysis of image texture, color and figure characteristics, and analysis of the disease environmental information, etc. It has provided a technical basis and support for the automatic recognition of crop diseases with diseased leaf images and environmental information obtained in the field. The analysis and experimental results in this paper demonstrated that the crop disease recognition method is feasible by computer vision, statistics, and comprehensive utilization technology of spot color, texture, shape information, and crop environment information. As there are many factors affecting crop diseases, various diseases in different periods will show different symptoms. Therefore, how to use the computer vision technology and the disease environmental information to build a powerful and practical crop disease recognition method still needs further study. [ABSTRACT FROM AUTHOR]

Crop diseases often seriously affect both the quality and quantity of agricultural products and cause economic losses to farmers. How to accurately and quickly recognize the crop disease information is an important problem in preventing and controlling crop diseases. Crop disease recognition by crop leaf symptoms is a basic method of attempting to address this problem. Studies show that relying on pure naked-eye observing of the leaf symptoms by experts to detect the crop diseases can be prohibitively expensive, especially in developing countries. Automatic detection of crop diseases is an essential research topic, as it may prove benefits in monitoring large fields of crops, and thus automatically detect the symptoms of diseases as soon as they appear on crop leaves. In a research study of identifying and diagnosing crop diseases, the pattern of the disease is important part. Leaf spots are considered the important units indicating the existence of disease and regarded as indicators of crop diseases. A technique to detect the disease spot is needed. It is important to select a threshold of gray level for extracting the disease spot from the crop leaf. In order to classify disease leaf sample categories, a set of spot features for the classification and detection of the different disease leaves are required. The disease leaf images of the crop would be processed by using a series of image pre-processing methods, such as image transforming, image smoothing, and image segmentation. In this paper, crop disease leaf spots were segmented by the seeded region growing based region algorithm. Because the crop leaves look differ in many ways, most of classical pattern recognition methods are not effective to extract the disease features and reduce the dimensionality of diseased leaf images. A novel manifold learning algorithm called local discriminant projects (LDP) was proposed and was applied to crop disease recognition. After being projected into a low-dimensional subspace, the data points in the same class were close to each other, whereas the gaps between the data points from different classes became wider than before. In LDP, the class action was introduced to construct the objection function. There was no need to calculate the inverse matrix, so the small sample size problem occurring in traditional linear discriminant analysis was naturedly avoided, and much computational time would be saved by using LDP for dimensionality reduction. After each spot image was reorganized as one-dimensionality vector and its dimensionality was reduced by LDP, the nearest neighbor classifier was adopted to recognize crop disease. The extensive experiments were performed on a real maize disease leaf image database and compared with the traditional disease recognition methods and the supervised subspace learning algorithms in recognition performance. The mean correct classification rate of the proposed method was 94.4%. The proposed method was compared with the classical crop disease recognition methods (ANN, PCA+PNN, and Bayesian) and supervised subspace algorithms (LDE, DNE). The experiment results showed that the proposed method was effective and feasible for crop disease recognition. The preliminary study showed that there is a potential to establish an online field application in crop leaf disease detection based on leaf image processing techniques. [ABSTRACT FROM AUTHOR]

Gene expression profiles have great potential for accurate tumor diagnosis. It is expected to enable us to diagnose tumors precisely and systematically, and also bring the researchers of machine learning two challenges, the curse of dimensionality and the small sample size problems. We propose a manifold learning based dimensional reduction algorithm named orthogonal local discriminant embedding (O-LDE) and apply it to tumor classification. Comparing with the classical local discriminant embedding (LDE), O-LDE aims to obtain an orthogonal linear projection matrix by solving an optimization problem. After being projected into a low-dimensional subspace by O-LDE, the data points of the same class maintain their intrinsic neighbor relations, whereas the neighboring points of the different classes are far from each other. Experimental results on a public tumor dataset validate the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

1 Adding tungsten armor increases the eddy current density in the first wall. 2 Adding tungsten armor increases EM force and moment on the blanket. 3 The thickness of the tungsten armor increases the eddy current density. 4 Split size increases the eddy current density, temperature and stress. 5 Larger gap can decrease the eddy current density. Tungsten armors are arranged on the plasma side of the blankets to protect the first wall in a fusion device, such as China Fusion Engineering Test Reactor (CFETR) and European Demonstration Power Plant (EU DEMO). Tungsten armors will produce a huge eddy current and Electromagnetic (EM) load under plasma disruption due to their high conductivity and strong magnetic field environment, which will cause the blankets to be subjected to a substantial eddy current and thus heat load, thermal stress and mechanical stress, that potential could damage blankets. To evaluate the effect of the tungsten armor on the electromagnetic characteristics of blanket, electromagnetic calculation method verification is carried out. And the electromagnetic finite element model of blanket is established by using ANSYS, and the eddy current distribution in the first wall is calculated. Effects of the tungsten armor, split size and gap size of the tungsten armor on the eddy current of blanket first wall and effect of the tungsten armor on the EM force of blanket are researched. And the influence of different tungsten armor design on the thermal stress distribution of blanket first wall is also studied. The study results will provide an important reference for the blanket design of the fusion device. [ABSTRACT FROM AUTHOR]

Abstract: The label propagation has the benefits of nearly-linear running time and easy implementation. In this paper, we make use of the label propagation to propose a new weight measure, and present a supervised locality projection analysis (SLPA) method for plant leaf classification. Firstly, we apply Warshall algorithm to label propagation and get the label matrix, then incorporate it into the weight, which has a clear physical meaning. Secondly, multi-class data points in high-dimensional space are to be pulled or pushed by discriminant neighbors to form an optimum projecting to low dimensionality. Finally, the experimental results on two plant leaf databases show that the proposed method is quite effective and feasible. [Copyright &y& Elsevier]

Gene expression profiles have great potential for accurate tumor diagnosis. It is expected to enable us to diagnose tumors precisely and systematically, and also bring the researchers of machine learning two challenges, the curse of dimensionality and the small sample size problems. We propose a manifold learning based dimensional reduction algorithm named orthogonal local discriminant embedding (O-LDE) and apply it to tumor classification. Comparing with the classical local discriminant embedding (LDE), O-LDE aims to obtain an orthogonal linear projection matrix by solving an optimization problem. After being projected into a low-dimensional subspace by O-LDE, the data points of the same class maintain their intrinsic neighbor relations, whereas the neighboring points of the different classes are far from each other. Experimental results on a public tumor dataset validate the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: From the perspective of manifold learning, the weight between two nodes of graph plays an indispensable role, which provides the similarity between pairwise nodes, and can effectively reveal the intrinsic relationship between data classes. In the original Locality Preserving Projections (LPP), Unsupervised Discriminant Projection (UDP), Orthogonal LPP (OLPP), and other spectral mapping methods, the weight between two points is usually defined as a heat kernel or simply 0–1 weight, which cannot effectively reflect the sample class information. In Orthogonal Discriminant Projection (ODP), the weight between two points was defined based on their local information and class information, but it is not a monotonically decreasing with the increase of the distance between two nodes, so it is not very sound. In this paper, we first analyze the defect of the weight in ODP, then propose a novel weight measure between two nodes of a graph by combining their label information and local information, finally present a modified ODP algorithm following the ODP technique. The modified ODP algorithm can explore the intrinsic structure of original data and enhance the classification ability. The experimental results show that the modified ODP algorithm is effective and feasible. [Copyright &y& Elsevier]

Abstract: Based on locally linear embedding (LLE) and modified maximizing margin criterion (MMMC), a modified locally linear discriminant embedding (MLLDE) algorithm is proposed for plant leaf recognition in this paper. By MLLDE, the plant leaf images are mapped into a leaf subspace for analysis, which can detect the essential leaf manifold structure. Furthermore, the unwanted variations resulting from changes in period, location, and illumination can be eliminated or reduced. Different from principal component analysis (PCA) and linear discriminant analysis (LDA), which can only deal with flat Euclidean structures of plant leaf space, MLLDE not only inherits the advantages of locally linear embedding (LLE), but makes full use of class information to improve discriminant power by introducing translation and rescaling models. The experimental results on real plant leaf database show that the MLLDE is effective for plant leaf recognition. [Copyright &y& Elsevier]

Abstract: Semi-supervised dimensional reduction methods play an important role in pattern recognition, which are likely to be more suitable for plant leaf and palmprint classification, since labeling plant leaf and palmprint often requires expensive human labor, whereas unlabeled plant leaf and palmprint is far easier to obtain at very low cost. In this paper, we attempt to utilize the unlabeled data to aid plant leaf and palmprint classification task with the limited number of the labeled plant leaf or palmprint data, and propose a semi-supervised locally discriminant projection (SSLDP) algorithm for plant leaf and palmprint classification. By making use of both labeled and unlabeled data in learning a transformation for dimensionality reduction, the proposed method can overcome the small-sample-size (SSS) problem under the situation where labeled data are scant. In SSLDP, the labeled data points, combined with the unlabeled data ones, are used to construct the within-class and between-class weight matrices incorporating the neighborhood information of the data set. The experiments on plant leaf and palmprint databases demonstrate that SSLDP is effective and feasible for plant leaf and palmprint classification. [ABSTRACT FROM AUTHOR]

The mechanisms of abnormal high pressures are studied in this paper, and it is concluded that the undercompaction, hydrocarbon generation and stratum denudation are obviously effective to fluid pressure buildup. Because of the episodic difference, the hydrocarbon generation and stratum denudation are the main factors influencing oil-gas migration. On the basis of basin evolutionary analysis in the Dongying Sag, it is considered that the undercompaction mainly caused the abnormal pressure before the first denudation by the uplift in Late Paleogene, while hydrocarbon generation was the main factor of abnormal pressure after the denudation. The second denudation occurred in Late Neogene, which changed the pressure field and induced the fluid migration. The development of overpressures is the necessary condition to the formation of lithologic hydrocarbon reservoirs, which have positive correlations to overpressures. According to the fullness of the present reservoirs, the quantitative relations between oil-bearing property and driving forces of reservoir formation were determined, the latter were decided by dynamic source, reservoir capillary pressure, fluid pressure of surrounding rocks and the dynamic attenuation in different conducting systems. [ABSTRACT FROM AUTHOR]

In order to provide the guidance for the design and application of echelle gratings, the reason why the diffraction efficiency of the echelle grating can be improved by increasing the groove depth or reducing the apex angle is given. The polarization behaviors of gratings with different groove depths were simulated and analyzed via integral theory. To further demonstrate the polarization behavior, gratings with right and acute apex angles for classical and conical-Littrow incidence were simulated. Experimental polarization behaviors of gratings with 5.1-μm and 6.7-μm groove depths for classical incidence were obtained. The polarization degree of the echelle grating with the 6.7-μm groove depth was 6.5% at 405 nm and 2.52% at 632.8 nm, which smaller than 7.89% at 405 nm and 35.74% at 632.8 nm for the grating with a 5.1-μm groove depth. Therefore, the diffraction efficiency of an echelle grating can be improved by increasing the groove depth or reducing the apex angle, mainly because the polarization degree is restrained. [ABSTRACT FROM AUTHOR]

Based on the results of laboratory simulation experiments and field observations, the concept of ‘fault-fracture mesh petroleum play’ is proposed to explain the secondary petroleum accumulations in the Neogene reservoirs of the Jiyang Superdepression, Bohai Bay Basin, Eastern China. A well-defined fault-fracture mesh petroleum play usually contains three basic elements, an oil source network at the base, a transient storage for migrating hydrocarbons in the middle, and a petroleum accumulation network at the top. The oil source network provides fairways for hydrocarbons generated in a deep source to migrate vertically upward to shallow strata, often consisting of primary faults and fracture zones and/or regional unconformities in direct contact with mature petroleum source beds, and secondary faults intercepting lenticular fluvial sandstones. The transient storages for migrating hydrocarbons include the thick, porous, mat or sheet-like sandstones deposited under the fluvial, braided stream setting and occurring over relatively large geographic areas. These sandstones serve as temporary storage spaces, through which deep-sourced petroleum fluids migrate further, either laterally or vertically into shallower reservoirs. The upper petroleum accumulation network provides suitable habitats for oil and gas accumulation, comprising structural, lithologic, stratigraphic and combination traps. The Jiyang Superdepression of the Bohai Bay Basin hosts several excellent examples of the fault-fracture mesh petroleum play, where hydrocarbons in the Neogene reservoirs were derived either directly from Eocene-Oligocene source rocks, or from the redistribution of pre-existing oil accumulations in the deeper reservoirs. Reactivation of basement-involved faults and regional unconformities provided conduits for channeling the pre-Neogene hydrocarbons into the shallow strata. The magnitudes and frequencies of the faulting activities and their relative timings to those of peak oil generation and expulsion were critical factors for controlling the quantities of hydrocarbons transmitted to the Neogene strata. The thick sandstones and conglomerates in the lower Guantao Formation acted as excellent transient storages for the deep sourced oils, which were then trapped to form accumulations in the shallow structural, lithologic, stratigraphic and combination traps of the Guantao (Ng)–Minhuazhen (Nm) formations. With the recent discovery of several giant oilfields in the shallow Neogene reservoirs both onshore and offshore Bohai Bay Basin, the proposed ‘fault-fracture mesh petroleum play’ concept helps the exploration geologists to modify and clarify long held traditional concepts of petroleum play types in this basin, and expand upon the new exploration horizons of the shallow subtle petroleum traps. [Copyright &y& Elsevier]

Building extraction is an essential task due to its relevance to urban planning and automatic surveying mapping activities. Despite the existing convolutional neural network-based methods that have achieved remarkable progress on building extraction from remote sensing images, the accurate extraction of buildings with extremely large variations of scales and layouts is still challenging. In this study, a novel multiregion scale-aware network is proposed to address these issues. The network consists of two key components. First, a multiregion attention module is proposed to capture long-range context dependencies and exploit different regions’ attention information, alleviating the interference of cluttered backgrounds and variations in building layouts. With the multiscale features generated by a backbone network as input, a graph-based scale-aware structure is designed to model and reason the interactions between different scale features to enable a better understanding of multiscale features. Extensive experiments conducted on three datasets demonstrate that the proposed method achieves superior performance compared with the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Accurate identification of weed species is the premise for controlling weeds in field. But it is a challenging task due to the complexity and high-dimensional nonlinearity of the weed images in natural field. Convolutional neural networks (CNNs) model has been widely applied to image identification, but most of the CNNs models have the problems of large parameters, low identification accuracy, and single feature scale. This paper presents a novel deep neural network structure, named as MPF-Net for weed species identification. In MPF-Net, firstly, the weed images is sent into two different scales of depthwise separable convolution layers; secondly, the parallel output feature information is cross-fused, and uses the residual learning structure to increase the network model depth and feature extraction ability; finally the lightweight model PL-Model and the scale reduction module SR-Model are stacked together to construct the lightweight network. We have performed extensive experiments on real weed datasets, and compared the proposed MPF-Net against several variations of lightweight networks. The experimental results on the weed image dataset show that the proposed method is effective and feasible for weed species identification. [ABSTRACT FROM AUTHOR]

We treat wideband subwavelength guided-mode resonant gratings with grating-depth dependent duty cycles. By rigorous numerical computations, we visualize Bloch modal evolution while transforming the grating profile from trapezoid to triangle. Parametric optimization is achieved using the coordinate transformation method of Chandezon. With the increase of the profile base angle, the higher mixed resonant modes TM1 , 1 & 2 attract and combine and interact with the modes at both sides, forming wide reflection bands. This modal combination and interaction are the key determinant of broadband reflectivity spectral location and width. The optimized structure exhibits 99% reflectivity across a 613-nm spectral range, spanning a 1438- to 2051-nm wavelength range with a fractional bandwidth of ∼35 %. It is shown that gratings with trapezoidal profiles possess a good tolerance to groove depth variation, thus being easier to fabricate with a diamond-tip than triangular profile-based gratings. [ABSTRACT FROM AUTHOR]