摘要：
Leaf segmentation is crucial for plant recognition, especially for tree species identification. In natural environments, leaf segmentation can be very challenging due to the lack of prior information about leaves and the variability of backgrounds. In typical applications, supervised algorithms often require pixel-level annotation of regions, which can be labour-intensive and limited to identifying plant species using pre-labelled samples. On the other hand, traditional unsupervised image segmentation algorithms require specialised parameter tuning for leaf images to achieve optimal results. Therefore, this paper proposes an unsupervised leaf segmentation method that combines mutual information with neural networks to better generalise to unknown samples and adapt to variations in leaf shape and appearance to distinguish and identify different tree species. First, a model combining a Variational Autoencoder (VAE) and a segmentation network is used as a pre-segmenter to obtain dynamic masks. Secondly, the dynamic masks are combined with the segmentation masks generated by the mask generator module to construct the initial mask. Then, the patcher module uses the Mutual Information Minimum (MIM) loss as an optimisation objective to reconstruct independent regions based on this initial mask. The process of obtaining dynamic masks through pre-segmentation is unsupervised, and the entire experimental process does not involve any label information. The experimental method was performed on tree leaf images with a naturally complex background using the publicly available Pl@ntLeaves dataset. The results of the experiment showed that compared to existing excellent methods on this dataset, the IoU (Intersection over Union) index increased by 3.9%.
摘要：
Inspired by an earlier work, which showed that the Hankel matrix constructed by a global positioning system (GPS) coordinate time series has a low rank, this study proposes a low-rank matrix approximation based on a nonconvex log-sum function minimization for extracting seasonal signals from GPS coordinate time series. Compared to a convex nuclear norm minimization, the nonconvex log-sum function is more approximate for the rank function. Unlike a nuclear norm minimization, which can be solved by singular value thresholding, obtaining the solution of a nonconvex log-sum function minimization is challenging. By using the iterative reweighted nuclear norm algorithm, the nonconvex low-rank matrix approximation problem is addressed in this paper by iteratively updating the weighted nuclear norm minimization problem, which has a globally optimal solution under certain conditions. Correspondingly, the residuals were analyzed using a maximum likelihood estimation. Both the proposed method and the weighted nuclear norm minimization approach were compared with classical methods in the presence of time-correlated noise. Compared to the classical seasonally extracted methods, the proposed method provides a guaranteed root mean square error performance in the presence of time-correlated noise. The experimental analysis results of both the simulated time series and real station data demonstrated that the proposed method outperformed the weighted nuclear norm minimization approach, which outperformed the classical approaches under different noise levels.
摘要：
In recent years, the domain of diagnosing plant afflictions has predominantly relied upon the utilization of deep learning techniques for classifying images of diseased specimens; however, these classification algorithms remain insufficient for instances where a single plant exhibits multiple ailments. Consequently, we view the region afflicted by the malady of rice leaves as a minuscule issue of target detection, and then avail ourselves of a computational approach to vision to identify the affected area. In this paper, we advance a proposal for a Dense Higher-Level Composition Feature Pyramid Network (DHLC-FPN) that is integrated into the Detection Transformer (DETR) algorithm, thereby proffering a novel Dense Higher-Level Composition Detection Transformer (DHLC-DETR) methodology which can effectively detect three diseases: sheath blight, rice blast, and flax spot. Initially, the proposed DHLC-FPN is utilized to supersede the backbone network of DETR through amalgamation with Res2Net, thus forming a feature extraction network. Res2Net then extracts five feature scales, which are coalesced through the deployment of high-density rank hybrid sampling by the DHLC-FPN architecture. The fused features, in concert with the location encoding, are then fed into the transformer to produce predictions of classes and prediction boxes. Lastly, the prediction classes and the prediction boxes are subjected to binary matching through the application of the Hungarian algorithm. On the IDADP datasets, the DHLC-DETR model, through the utilization of data enhancement, elevated mean Average Precision (mAP) by 17.3% in comparison to the DETR model. Additionally, mAP for small target detection was improved by 9.5%, and the magnitude of hyperparameters was reduced by 324.9 M. The empirical outcomes demonstrate that the optimized structure for feature extraction can significantly enhance the average detection accuracy and small target detection accuracy of the model, achieving an average accuracy of 97.44% on the IDADP rice disease dataset.
摘要：
Sound event detection is sensitive to the network depth, and the increase of the network depth will lead to a decrease in the event detection ability. However, event localization has a deeper requirement for the network depth. In this paper, the accuracy of the joint task of event detection and localization is improved by decoupling SELD-TCN. The joint task is reflected in the early fusion of primary features and the enhancement of the generalization ability of the sound event detection branch as the DOA branch mask, while the advanced feature extraction and recognition of the two branches are carried out in different ways separately. The primary features extracted by resnet16-dilated instead of CNN-Pool. The SED branch adopts linear temporal convolution to realize sound event detection by imitating the linear classifier, and ED-TCN is used for the localization detection branch. The joint training of the DOA branch and the SED branch will affect each other badly. Using the most appropriate way for both branches and masking the DOA branch with the SED branch can improve the performance of both. In the TUT Sound Events 2019 dataset, the DOA error achieved an error effect of 6.73, 8.8 and 30.7 with no overlapping source data, with two and three overlapping sources, respectively. The SED accuracy has been significantly improved, and the DOA error has been significantly reduced.
摘要：
This paper proposes an anchor-free wheat ear detection method using ObjectBox with attention. First, on base of the backbone of ObjectBox, convolutional block attention module is used to improve the connection of each feature in the channel and space and enhance the feature extraction ability of the network. Second, in the neck part, ConvNeXtBlock is used to better fuse or extract the feature map given by the backbone. Last, the non-maximum suppression algorithm is improved to remove the center redundant detection box. The experimental results on the public global wheat head detection dataset show that the proposed method has an mean Average Precision (mAP) of 96.0%, an Precision of 94.5%, an Recall of 92.2% and
$$F_{1}$$
score of 93.3%. Compared with the original ObjectBox model, the improvement for mAP, Precision, Recall and
$$F_{1}$$
score is 2.0%, 1.3%, 2.9% and 2.1%, respectively. Compared with other existing wheat ear detection methods, it has higher detection accuracy.
期刊：
Journal of Fixed Point Theory and Applications,2023年25(2):1-31 ISSN：1661-7738
通讯作者：
Wang, CH
作者机构：
[Wang, Chunhua; Wang, CH] Cent China Normal Univ, Sch Math & Stat, Wuhan, Peoples R China.;[Wang, Chunhua; Wang, CH] Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan, Peoples R China.;[Wang, Qingfang] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;[Yang, Jing] Jiangsu Univ Sci & Technol, Sch Sci, Zhenjiang 212003, Peoples R China.
通讯机构：
[Wang, CH ] C;Cent China Normal Univ, Sch Math & Stat, Wuhan, Peoples R China.;Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan, Peoples R China.
摘要：
We study the following nonlinear critical elliptic equation
$$\begin{aligned} -\Delta u+\epsilon Q(y)u=u^{\frac{N+2}{N-2}},\;\;\; u>0\;\;\;\hbox { in } {\mathbb {R}}^N, \end{aligned}$$
where
$$\epsilon >0$$
is small and
$$N\ge 5.$$
Assuming that Q(y) is periodic in
$$y_1$$
with period 1 and has a local minimum at 0 satisfying
$$Q(0)>0,$$
we prove the existence and local uniqueness of infinitely many bubbling solutions of it. This local uniqueness result implies that some bubbling solutions preserve the symmetry of the potential function Q(y), i.e., the bubbling solution whose blow-up set is
$$\{(jL,0,\ldots ,0):j=0,\pm 1, \pm 2,\ldots , \pm m\}$$
must be periodic in
$$y_{1}$$
provided that
$$\epsilon $$
goes to zero and L is any positive integer, where m is the number of the bubbles which is large enough but independent of
$$\epsilon .$$
作者机构：
[Chen, Zhenhong; Xu, Peihua; Wang, Biqiang; Cheng, Chi] Hubei Meteorol Serv Ctr, Wuhan 430079, Peoples R China.;[Xu, Peihua] Cent China Normal Univ, Fac Artificial Intelligence Educ, Sch Educ Informat Technol, Wuhan 430079, Peoples R China.;[Zhang, Maoyuan] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.;[Liu, Renfeng] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
通讯机构：
[Maoyuan Zhang] H;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
discrete wavelet transform;autoencoder;bidirectional LSTM;wind power forecasting
摘要：
Due to the increasing proportion of wind power connected to the grid, day-ahead wind power prediction plays a more and more important role in the operation of the power system. This paper proposes a day-ahead wind power short-term prediction model based on deep learning (DWT_AE_BiLSTM). Firstly, discrete wavelet transform (DWT) is used to denoise the data, then an autoencoder (AE) technology is used to extract the data features, and finally, bidirectional long short-term memory (BiLSTM) is used for prediction. To verify the effectiveness of the proposed DWT_AE_BiLSTM model, we studied three different power stations and compared their performance with the shallow neural network model. Experimental analysis shows that this model is more competitive in forecasting accuracy and stability. Compared with the BP model, the proposed model has increased by 3.86%, 3.22% and 3.42% in three wind farms, respectively.
摘要：
Phthalides are a class of unique compounds such as ligustilide, butylphthalide and butyldenephthalide, which have shown to possess multiple bioactivities in new drug research and development. Phthalides are naturally distributed in different plants that have been utilized as herbal treatments for various ailments with a long history in Asia, Europe and North America. Their extensive biological activity has led to a dramatic increase in the study of phthalide compounds in recent years. This review summarizes the latest research progress of plant-derived phthalides, and a total of 133 phthalide compounds are described based on the characteristics of chemical structures. Pharmacological properties of plant-derived phthalides are associated with hemorheological improvement, vascular function modulation and central nervous system protection. Potential treatments for a variety of diseases mainly including cardio-cerebrovascular disorders and neurological complications such as Alzheimer's disease are also concluded. In addition, key metabolic pathways have been clearly elucidated. Further investigations on the molecular mechanisms involved in biological activity are recommended for offering new insights into profound comprehension of phthalide applications.
作者机构：
[Zhou, Jinbo; Li, Hao; Kang, Zhen; Zeng, Shan; Sheng, Zhongyin] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;[Chen, Yulong] Wuhan Polytech Univ, Coll Med & Hlth Sci, Wuhan 430023, Peoples R China.
通讯机构：
[Shan Zeng] S;School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
polished rice;RoI;YOLOv5;YOLACT
摘要：
The problem of small and multi-object polished rice image segmentation has always been one of importance and difficulty in the field of image segmentation. In the appearance quality detection of polished rice, image segmentation is a crucial part, directly affecting the results of follow-up physicochemical indicators. To avoid leak detection and inaccuracy in image segmentation qualifying polished rice, this paper proposes a new image segmentation method (YO-LACTS), combining YOLOv5 with YOLACT. We tested the YOLOv5-based object detection network, to extract Regions of Interest (RoI) from the whole image of the polished rice, in order to reduce the image complexity and maximize the target feature difference. We refined the segmentation of the RoI image by establishing the instance segmentation network YOLACT, and we eventually procured the outcome by merging the RoI. Compared to other algorithms based on polished rice datasets, this constructed method was shown to present the image segmentation, enabling researchers to evaluate polished rice satisfactorily.
摘要：
While deep learning based object detection methods have achieved high accuracy in fruit detection, they rely on large labeled datasets to train the model and assume that the training and test samples come from the same domain. This paper proposes a cross-domain fruit detection method with image and feature alignments. It first converts the source domain image into the target domain through an attention-guided generative adversarial network to achieve the image-level alignment. Then, the knowledge distillation with mean teacher model is fused in the yolov5 network to achieve the feature alignment between the source and target domains. A contextual aggregation module similar to a self-attention mechanism is added to the detection network to improve the cross-domain feature learning by learning global features. A source domain (orange) and two target domain (tomato and apple) datasets are used for the evaluation of the proposed method. The recognition accuracy on the tomato and apple datasets are 87.2% and 89.9%, respectively, with an improvement of 10.3% and 2.4%, respectively, compared to existing methods on the same datasets.
摘要：
Background: Migraine is a common neurovascular disorder with typical throbbing and unilateral headaches, causing a considerable healthcare burden on the global economy. This research aims to prepare chitosan-alginate (CS-AL) nanoparticles (NPs) containing Foshousan oil (FSSO) and investigate its potential therapeutic effects on the treatment of migraine.Methods: FSSO-loaded CS-AL NPs were prepared by using the single emulsion solvent evaporation method. Lipopolysaccharide (LPS)-stimulated BV-2 cells and nitroglycerin (NTG)-induced migraine mice were further used to explore anti-migraine activities and potential mechanisms of this botanical drug.Results: FSSO-loaded CS-AL NPs (212.1 & PLUSMN; 5.2 nm, 45.1 & PLUSMN; 6.2 mV) had a well-defined spherical shape with prolonged drug release and good storage within 4 weeks. FSSO and FSSO-loaded CS-AL NPs (5, 10, and 15 & mu;g/mL) showed anti-inflammatory activities in LPS-treated BV-2 cells via reducing the levels of pro-inflammatory cytokines such as tumor necrosis factor-& alpha; (TNF-& alpha;), interleukin-1 & beta; (IL-1 & beta;), interleukin-6 (IL-6), and nitric oxide (NO), but elevating interleukin-10 (IL-10) expressions. Moreover, FSSO-loaded CS-AL NPs (52 and 104 mg/kg) raised pain thresholds against the hot stimulus and decreased acetic acid-induced writhing frequency and foot-licking duration in NTG-induced migraine mice. Compared with the model group, calcitonin gene-related peptide (CGRP) and NO levels were downregulated, but 5-hydroxytryptamine (5-HT) and endothelin (ET) levels were upregulated along with rebalanced ET/NO ratio, and vasomotor dysfunction was alleviated by promoting cerebral blood flow (CBF) in the FSSO-loaded CS-AL NPs (104 mg/kg) group.Conclusion: FSSO-loaded CS-AL NPs could attenuate migraine via inhibiting neuroinflammation in LPS-stimulated BV-2 cells and regulating vasoactive substances in NTG-induced migraine mice. These findings suggest that the FSS formula may be exploited as new phytotherapy for treating migraine.
摘要：
This paper is concerned with the large time behavior of the solutions for 1D radiation hydrodynamic limit model without viscosity and its asymptotic stability of the viscous contact discontinuity wave under the smallness assumption of the strength of the contact wave and initial perturbations. The present pressure includes a fourth-order term about the absolute temperature from radiation effect which brings the main difficulty. Furthermore, the dissipative of the system is weaker for the lack of viscosity. All these make the problem more challenging. The prove is mainly based on the energy method, including normal and radial directions energy estimates.
期刊：
Structural and Multidisciplinary Optimization,2023年66(4):1-22 ISSN：1615-147X
通讯作者：
Liang Gao
作者机构：
[Zhang, Haobo; Xia, Zhaohui; Zhuang, Ziao; Gao, Liang] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, State Key Lab Digital Mfg Equipmesssnt & Technol, Wuhan 430074, Peoples R China.;[Yu, Chen] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;[Yu, Jingui] Wuhan Univ Technol, Sch Mech & Elect Engn, Wuhan 430070, Peoples R China.
通讯机构：
[Liang Gao] T;The State Key Lab of Digital Manufacturing Equipmesssnt and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
摘要：
Isogeometric analysis has been widely applied in topology optimization in recent years, and various methods have been derived. However, most methods are accompanied by significant computational costs, which make it difficult to deal with complex models and large-scale design problems. In this paper, an isogeometric topology optimization method based on deep neural networks is proposed. The computational time of optimization can be effectively reduced while ensuring high accuracy. With the IGA-FEA two-resolution SIMP method, the machine-learning dataset can be obtained during early iterations. Unlike existing data-driven methods, online dataset generation both significantly reduces data collection time and enhances relevance to the design problem. As the iterations process, the machine learning model can be updated online by continuously collecting new data to ensure that the optimized topology structures approach the standard results. Through a series of 2D and 3D design examples, the generality and reliability of the proposed model have been verified and its time-saving advantage becomes more pronounced as the design scale increases. Furthermore, the impacts of neural network parameters on the results are studied through several controlled experiments.
通讯机构：
[Xu, SY ] H;Huazhong Agr Univ, Coll Engn, Wuhan, Peoples R China.;Huazhong Agr Univ, Minist Agr, Key Lab Agr Equipment Middle & Lower Reaches Yangt, Wuhan, Peoples R China.
关键词：
3D Reconstruction;point clouds segmentation;rapeseed siliques;silique recognition;sparse-dense point clouds mapping
摘要：
In this study, we propose a high-throughput and low-cost automatic detection method based on deep learning to replace the inefficient manual counting of rapeseed siliques. First, a video is captured with a smartphone around the rapeseed plants in the silique stage. Feature point detection and matching based on SIFT operators are applied to the extracted video frames, and sparse point clouds are recovered using epipolar geometry and triangulation principles. The depth map is obtained by calculating the disparity of the matched images, and the dense point cloud is fused. The plant model of the whole rapeseed plant in the silique stage is reconstructed based on the structure-from-motion (SfM) algorithm, and the background is removed by using the passthrough filter. The downsampled 3D point cloud data is processed by the DGCNN network, and the point cloud is divided into two categories: sparse rapeseed canopy siliques and rapeseed stems. The sparse canopy siliques are then segmented from the original whole rapeseed siliques point cloud using the sparse-dense point cloud mapping method, which can effectively save running time and improve efficiency. Finally, Euclidean clustering segmentation is performed on the rapeseed canopy siliques, and the RANSAC algorithm is used to perform line segmentation on the connected siliques after clustering, obtaining the three-dimensional spatial position of each silique and counting the number of siliques. The proposed method was applied to identify 1457 siliques from 12 rapeseed plants, and the experimental results showed a recognition accuracy greater than 97.80%. The proposed method achieved good results in rapeseed silique recognition and provided a useful example for the application of deep learning networks in dense 3D point cloud segmentation.
摘要：
The Segment Anything Model (SAM) is a versatile image segmentation model that enables zero-shot segmentation of various objects in any image using prompts, including bounding boxes, points, texts, and more. However, studies have shown that the SAM performs poorly in agricultural tasks like crop disease segmentation and pest segmentation. To address this issue, the agricultural SAM adapter (ASA) is proposed, which incorporates agricultural domain expertise into the segmentation model through a simple but effective adapter technique. By leveraging the distinctive characteristics of agricultural image segmentation and suitable user prompts, the model enables zero-shot segmentation, providing a new approach for zero-sample image segmentation in the agricultural domain. Comprehensive experiments are conducted to assess the efficacy of the ASA compared to the default SAM. The results show that the proposed model achieves significant improvements on all 12 agricultural segmentation tasks. Notably, the average Dice score improved by 41.48% on two coffee-leaf-disease segmentation tasks.
通讯机构：
[Kang Zhou] C;College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
关键词：
Food quality assessment;Neural network rough-refinement optimization;Metaheuristic algorithm based on NNs;Data mining
摘要：
Food quality assessment is an important part of the food industry. The traditional food quality assessment technologies have the limitations of inconsistent and different technical defects for each method. Data mining technology has significant advantages in dealing with the problems of uncertainty and fuzziness. Therefore, this study proposes a food quality assessment model based on data mining, which aims to realize the standardization and consistency of food quality assessment, and can achieve or exceed the accuracy of existing technologies, so as to solve the obvious problems existing in traditional assessment methods. The core of the proposed model is to design a deep learning framework based on double layer rough-refinement optimization. The first layer is rough optimization, which introduces the thought of multi-objective optimization to optimize the topological structure of neural networks with various candidate types and candidate depths. The second layer is refinement adjustment, which uses meta heuristic algorithm to globally optimize the weight parameters of the network model. The combination of rough and refinement optimization can greatly reduce the computation of overall simultaneous optimization and globally optimize the neural network model with the highest accuracy from the neural network type, topology, and network parameters. Two kinds of food quality assessment problems are used to simulate and verify the proposed deep learning framework. The results prove that the framework is effective, feasible, and adaptability, and the proposed assessment model can well solve different types of food quality assessments.
作者：
Liu, Chunyan;Werner, Elisabeth M.;Ye, Deping;Zhang, Ning
期刊：
JOURNAL OF GEOMETRIC ANALYSIS,2023年33(8):1-25 ISSN：1050-6926
通讯作者：
Zhang, N
作者机构：
[Liu, Chunyan] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430048, Peoples R China.;[Werner, Elisabeth M.] Case Western Reserve Univ, Dept Math, Cleveland, OH 44106 USA.;[Ye, Deping] Mem Univ Newfoundland, Dept Math & Stat, St John, NF A1C 5S7, Canada.;[Zhang, Ning] Huazhong Univ Sci & Technol, Sch Math & Stat, 1037 Luoyu Rd, Wuhan 430074, Hubei, Peoples R China.
通讯机构：
[Zhang, N ] H;Huazhong Univ Sci & Technol, Sch Math & Stat, 1037 Luoyu Rd, Wuhan 430074, Hubei, Peoples R China.
关键词：
Affine surface area;Floating body;Log-concave functions;Ulam floating function;Ulam floating set
摘要：
We extend the notion of Ulam floating sets from convex bodies to Ulam floating functions. We use the Ulam floating functions to derive a new variational formula for the affine surface area of log-concave functions.
摘要：
Image segmentation is a crucial part in the automatic detection of rice appearance quality. Due to morphological characteristics of rice grains, missed detection and non-smooth boundaries may exist in the image segmentation of adhesive rice. To address the above issues, this study proposes a novel model named Swgan combined generative adversarial networks (GANs) with nested skip connections for obtaining accurate masks. In order to learn the mask distribution of each object in adhesive rice image and further avoid missed detection, the discriminator in GAN is used as a modifier of Cascade Mask R-CNN model to allow the generator to overcome the limitation of mask generation training, namely detecting multiply objects as a single target. Moreover, the Swgan utilizes Swin-Transformer as the backbone network and incorporates the Cascade Mask R-CNN framework and Nested Feature Pyramid Network (Nested-FPN) to maintain the mask's boundary smoothness during forward propagation. Experimental results indicate that the Swgan is used to obtain better segmentation results from objective detection and segmentation under complex conditions of adhesive rice when compared with state-ofart algorithms. Overall, the Swgan with satisfactory accuracy in image segmentation of adhesive rice combined with physical indicators detection provide reliable quality assessment of rice grains.
摘要：
This paper is concerned with an ideal polytropic model of non-viscous and heat-conductive gas in a one-dimensional half space. We focus our attention on the outflow problem when the flow velocity on the boundary is negative and we prove the stability of the viscous shock wave and its superposition with the boundary layer under some smallness conditions. Our waves occur in the subsonic area. The intrinsic properties of our system are more challenging in mathematical analysis, however, in the subsonic area, the lack of a boundary condition on the density provides us with a special manner for defining the shift for the viscous shock wave, and helps us to construct the asymptotic profiles successfully. New weighted energy estimates are introduced and the perturbations on the boundary are handled by some subtle estimates.
