摘要：
Weeds are a significant threat to agricultural productivity and the environment. The increasing demand for sustainable weed control practices has driven innovative developments in alternative weed control technologies aimed at reducing the reliance on herbicides. The barrier to adoption of these technologies for selective in-crop use is availability of suitably effective weed recognition. With the great success of deep learning in various vision tasks, many promising image-based weed detection algorithms have been developed. This paper reviews recent developments of deep learning techniques in the field of image-based weed detection. The review begins with an introduction to the fundamentals of deep learning related to weed detection. Next, recent advancements in deep weed detection are reviewed with the discussion of the research materials including public weed datasets. Finally, the challenges of developing practically deployable weed detection methods are summarized, together with the discussions of the opportunities for future research. We hope that this review will provide a timely survey of the field and attract more researchers to address this inter-disciplinary research problem.
摘要：
Hail, a highly destructive weather phenomenon, necessitates critical identification and forecasting for the protection of human lives and properties. The identification and forecasting of hail are vital for ensuring human safety and safeguarding assets. This research proposes a deep learning algorithm named Dual Attention Module EfficientNet (DAM-EfficientNet), based on EfficientNet, for detecting hail weather conditions. DAM-EfficientNet was evaluated using FY-4A satellite imagery and real hail fall records, achieving an accuracy of 98.53% in hail detection, a 97.92% probability of detection, a false alarm rate of 2.08%, and a critical success index of 95.92%. DAM-EfficientNet outperforms existing deep learning models in terms of accuracy and detection capability, with fewer parameters and computational needs. The results validate DAM-EfficientNet's effectiveness and superior performance in hail weather detection. Case studies indicate that the model can accurately forecast potential hail-affected areas and times. Overall, the DAM-EfficientNet model proves to be effective in identifying hail weather, offering robust support for weather disaster alerts and prevention. It holds promise for further enhancements and broader application across more data sources and meteorological parameters, thereby increasing the precision and timeliness of hail forecasting to combat hail disasters and boost public safety.
期刊：
Mathematical Methods in the Applied Sciences,2024年47(6):- ISSN：0170-4214
通讯作者：
Fan, LL
作者机构：
[Bai, Yinsong; Zhao, Huijiang] Wuhan Univ, Sch Math & Stat, Wuhan, Peoples R China.;[Bai, Yinsong] Xinjiang Univ, Coll Math & Syst Sci, Urumqi, Peoples R China.;[Fan, Lili] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Peoples R China.;[Fan, Lili; Fan, LL] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
通讯机构：
[Fan, LL ] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
关键词：
a hyperbolic system with Cattaneo's law;asymptotic nonlinear stability;boundary effect;initial-boundary value problem;small initial perturbation;viscous shock profiles;weighted energy method
摘要：
We consider the asymptotic nonlinear stability of viscous shock profiles for an initial-boundary value problem of the scalar conservation laws with an artificial heat flux satisfying Cattaneo's law in the negative half line Double-struck capital R-=(-infinity,0)$$ {\mathrm{\mathbb{R}}}_{-} equal to \left(-\infty, 0\right) $$ with Dirichlet boundary condition. When the nonlinear flux function is assumed to be strictly convex and the unique global entropy solution of the corresponding Riemann problem of the resulting scalar conservation laws consists of shock wave with negative speed, it is shown in this paper that the large time behavior of its global smooth solutions can be precisely described by the suitably shifted viscous shock profiles, where the time-dependent shift function is uniquely determined by both the boundary value and the initial data. We also show that the shift function converge to a constant time asymptotically. Our analysis is based on weighted L2-$$ {L} circumflex 2- $$energy method.
摘要：
In the realm of practical problem-solving, multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) are becoming increasingly prevalent. MOPRVIF involve determining the optimal decision variables that optimise multiple objectives, leveraging the relational data of a set of variables and multiple objectives. For these problems, this paper focuses on the following two issues: one is the demand for a unified computational model to solve this problem; the other is how to improve the algorithm's deep intelligent search capability. In this regard, this paper designs a dual data-driven multi-objective optimisation method. The method used consisted of four parts: elimination of redundant variables (ERV), objective function construction (OFC), selection evolution operator (SEO), and multi-objective evolutionary algorithm (MOEA). MOEA was the main focus of the method. ERV is data preparation and variable selection according to multiple objectives. OFC involves constructing the relationship model between variables and objectives, and a high-accuracy model is important for guaranteeing reliable results. Furthermore, SEO can adjust the evolution operator during a deep search. This is an important guarantee for deep, intelligent search. MOEA combined OFC and SEO to form the final solution algorithm-Dual Data Driven Multi-Objective Evolutionary Algorithm (DDMOEA). DDMOEA was explored using two different disciplinary problems of drug compound optimisation and wild blueberry cultivation and benchmarks were selected. The first two problem domains are distinct. The first problem is more complex than the second; however, both encompass redundant variables and indefinite objective functions. Benchmarks are utilised independently to gauge the profound intelligent search capability. The experiments affirm that the dual data -driven optimization approach proposed in this paper is effective, practical, and scalable.
摘要：
This paper aims to solve large-scale and complex isogeometric topology optimization problems that consume significant computational resources. A novel isogeometric topology optimization method with a hybrid parallel strategy of CPU/GPU is proposed, while the hybrid parallel strategies for stiffness matrix assembly, equation solving, sensitivity analysis, and design variable update are discussed in detail. To ensure the high efficiency of CPU/GPU computing, a workload balancing strategy is presented for optimally distributing the workload between CPU and GPU. To illustrate the advantages of the proposed method, three benchmark examples are tested to verify the hybrid parallel strategy in this paper. The results show that the efficiency of the hybrid method is faster than serial CPU and parallel GPU, while the speedups can be up to two orders of magnitude.
期刊：
International Journal of Remote Sensing,2023年44(22):6954-6980 ISSN：0143-1161
通讯作者：
Liu, CX
作者机构：
[Liu, Chaoxian; Zeng, Shan] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Peoples R China.;[Sui, Haigang] Wuhan Univ, State Key Lab Informat Engn Surveying, Mapping & Remote Sensing, Wuhan, Peoples R China.
通讯机构：
[Liu, CX ] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Peoples R China.
关键词：
Building damage assessment;aerial video;lightweight deep learning;ShuffleNet;damage levels
摘要：
A challenging problem for post-disaster emergency services is how to quickly and precisely acquire building damage with limited time and computation resources. With the development of unmanned aerial vehicle (UAV) technology and convolutional neural networks (CNN), using drone video and CNNs to determine the building damage has become one effective solution. In this research, we propose a stacked lightweight damage assessment model to overcome the current challenge in getting fast and precise post-disaster information based on aerial video datasets and artificial intelligence. Specifically, we developed an instance-level recognition building damage dataset based on aerial video. Then, we proposed a stacked lightweight ShuffleNet architecture that includes location and classification models to assess the building damage state. With regards to the location model, the lightweight network achieves a reduction in the model training time by approximately 37% and in the detection time by 44%, achieving similar location accuracy. As for the classification model, the trained model can achieve classification accuracy of approximately 83% for different damage levels by optimizing ShuffleNet. For post-disaster emergencies with limited time and computation resources, the proposed framework provides a valuable solution to better balance fast and precise building damage assessment.
摘要：
Building heterojunctions is a promising strategy for the achievement of highly efficient photocatalysis. Herein, a novel SnIn4S8@ZnO Z-scheme heterostructure with a tight contact interface was successfully constructed using a convenient two-step hydrothermal approach. The phase composition, morphology, specific surface area, as well as photophysical characteristics of SnIn4S8@ZnO were investigated through a series of characterization methods, respectively. Methylene blue (MB) was chosen as the target contaminant for photocatalytic degradation. In addition, the degradation process was fitted with pseudo-first-order kinetics. The as-prepared SnIn4S8@ZnO heterojunctions displayed excellent photocatalytic activities toward MB degradation. The optimized sample (ZS800), in which the molar ratio of ZnO to SnIn4S8 was 800, displayed the highest photodegradation efficiency toward MB (91%) after 20 min. Furthermore, the apparent rate constant of MB photodegradation using ZS800 (0.121 min-1) was 2.2 times that using ZnO (0.054 min-1). The improvement in photocatalytic activity could be ascribed to the efficient spatial separation of photoinduced charge carriers through a Z-scheme heterojunction with an intimate contact interface. The results in this paper bring a novel insight into constructing excellent ZnO-based photocatalytic systems for wastewater purification.
关键词：
Evolution of cooperation;Reinforcement learning;Differential privacy;Social network
摘要：
Cooperation is an essential behavior in multi-agent systems. Existing mechanisms have two common drawbacks. The first drawback is that malicious agents are not taken into account. Due to the diverse roles in the evolution of cooperation, malicious agents can exist in multi-agent systems, and they can easily degrade the level of cooperation by interfering with agent's actions. The second drawback is that most existing mechanisms have a limited ability to fit in different environments, such as different types of social networks. The performance of existing mechanisms heavily depends on some factors, such as network structures and the initial proportion of cooperators. To solve these two drawbacks, we propose a novel mechanism which adopts differential privacy mechanisms and reinforcement learning. Differential privacy mechanisms can be used to relieve the impact of malicious agents by exploiting the property of randomization. Reinforcement learning enables agents to learn how to make decisions in various social networks. In this way, the proposed mechanism can promote the evolution of cooperation in malicious social networks.
通讯机构：
[Zhang, F ] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
关键词：
Face anti -spoofing;Presentation attack;Disentangled representation;Deep learning;Face recognition
摘要：
Face recognition systems have been widely applied in security-related areas of our daily life. However, they are vulnerable to face spoofing attacks. Specifically, an attacker can fool a face recognition system into making false decisions, by presenting spoof face information (such as printed photos, replayed videos, etc.), rather than live face, to the face recognition system. Therefore, Face Anti-Spoofing (FAS) is critical for the security operation of a face recognition system.Deep learning-based FAS approaches show the best performance among existing FAS approaches. The basic idea of deep learning-based FAS approaches is to learn statistical representations capable of distin-guishing spoof faces from live ones, and then leverage the learned representations for live and spoof face classifications. Therefore, the learned representations play a key role in the performance of FAS. However, most existing approaches learn representations from representation-entangled spaces, in which critical and irrelevant representations for live and spoof face classifications are entangled with each other, thereby bringing a negative influence on the performance of a FAS system.To address the issue, we introduced a Twin Autoencoder Disentanglement (TAD) framework. Our TAD framework utilizes adversarial learning and a reconstruction strategy to disentangle both critical and irrelevant representations into two mutually independent representation spaces. In addition, to further suppress irrelevant representations that may remain in the critical representation space, we design a multi-branch supervision architecture (MSA) and embed it into TAD. MSA achieves the goal via imposing depth supervision and pattern supervision to the critical representation space. i.e., learning spatial rep-resentation (face depth information) and texture representation (face spoof pattern information).Experimental results on four typical public datasets, OULU-NPU, SiW, Replay-Attack, and CASIA-MFSD, demonstrate that our proposed TAD approach successfully disentangles critical and irrelevant represen-tations, and the two disentangled representations are more interpretable than state-of-the-art FAS meth-ods. The codes are available at https://github.com/TAD-FAS/TAD.& COPY; 2023 Elsevier B.V. All rights reserved.
摘要：
With the increasing popularity of online fruit sales, accurately predicting fruit yields has become crucial for optimizing logistics and storage strategies. However, existing manual vision-based systems and sensor methods have proven inadequate for solving the complex problem of fruit yield counting, as they struggle with issues such as crop overlap and variable lighting conditions. Recently CNN-based object detection models have emerged as a promising solution in the field of computer vision, but their effectiveness is limited in agricultural scenarios due to challenges such as occlusion and dissimilarity among the same fruits. To address this issue, we propose a novel variant model that combines the self-attentive mechanism of Vision Transform, a non-CNN network architecture, with Yolov7, a state-of-the-art object detection model. Our model utilizes two attention mechanisms, CBAM and CA, and is trained and tested on a dataset of apple images. In order to enable fruit counting across video frames in complex environments, we incorporate two multi-objective tracking methods based on Kalman filtering and motion trajectory prediction, namely SORT, and Cascade-SORT. Our results show that the Yolov7-CA model achieved a 91.3% mAP and 0.85 F1 score, representing a 4% improvement in mAP and 0.02 improvement in F1 score compared to using Yolov7 alone. Furthermore, three multi-object tracking methods demonstrated a significant improvement in MAE for inter-frame counting across all three test videos, with an 0.642 improvement over using yolov7 alone achieved using our multi-object tracking method. These findings suggest that our proposed model has the potential to improve fruit yield assessment methods and could have implications for decision-making in the fruit industry.
通讯机构：
[Yang, H ] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430040, Peoples R China.
关键词：
text classification;convolutional neural networks (CNN);attention mechanism;multi-layer feature fusion
摘要：
Text classification is one of the fundamental tasks in natural language processing and is widely applied in various domains. CNN effectively utilizes local features, while the Attention mechanism performs well in capturing content-based global interactions. In this paper, we propose a multi-layer feature fusion text classification model called CAC, based on the Combination of CNN and Attention. The model adopts the idea of first extracting local features and then calculating global attention, while drawing inspiration from the interaction process between membranes in membrane computing to improve the performance of text classification. Specifically, the CAC model utilizes the local feature extraction capability of CNN to transform the original semantics into a multi-dimensional feature space. Then, global attention is computed in each respective feature space to capture global contextual information within the text. Finally, the locally extracted features and globally extracted features are fused for classification. Experimental results on various public datasets demonstrate that the CAC model, which combines CNN and Attention, outperforms models that solely rely on the Attention mechanism. In terms of accuracy and performance, the CAC model also exhibits significant improvements over other models based on CNN, RNN, and Attention.
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.
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.
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.
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：
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.
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
$$\epsilon .$$
