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.
作者机构：
[Hao Li; Shan Zeng; Yaqin Li; Chaoxian Liu] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430048, China;Author to whom correspondence should be addressed.;[Yong Ma] Electronic Information School, Wuhan University, Wuhan 430072, China;[Xiaorui Xiong] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430048, China<&wdkj&>Author to whom correspondence should be addressed.
通讯机构：
[Xiaorui Xiong] S;School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430048, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
The immense representation power of deep learning frameworks has kept them in the spotlight in hyperspectral image (HSI) classification. Graph Convolutional Neural Networks (GCNs) can be used to compensate for the lack of spatial information in Convolutional Neural Networks (CNNs). However, most GCNs construct graph data structures based on pixel points, which requires the construction of neighborhood matrices on all data. Meanwhile, the setting of GCNs to construct similarity relations based on spatial structure is not fully applicable to HSIs. To make the network more compatible with HSIs, we propose a staged feature fusion model called SFFNet, a neural network framework connecting CNN and GCN models. The CNN performs the first stage of feature extraction, assisted by adding neighboring features and overcoming the defects of local convolution; then, the GCN performs the second stage for classification, and the graph data structure is constructed based on spectral similarity, optimizing the original connectivity relationships. In addition, the framework enables the batch training of the GCN by using the extracted spectral features as nodes, which greatly reduces the hardware requirements. The experimental results on three publicly available benchmark hyperspectral datasets show that our proposed framework outperforms other relevant deep learning models, with an overall classification accuracy of over 97%.
期刊：
Journal of Ambient Intelligence and Humanized Computing,2024年15(2):1365-1377 ISSN：1868-5137
通讯作者：
Zhenjiang Zhang
作者机构：
School of Software Engineering, Beijing Jiaotong University, Beijing, China;[Fuxing Song; Peng Zhang] Department of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing, China;School of Information Science and Engineering, Fujian University of Technology, Fuzhou, China;School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China;Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan
通讯机构：
[Zhenjiang Zhang] S;School of Software Engineering, Beijing Jiaotong University, Beijing, China<&wdkj&>Department of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing, China
关键词：
Big data;Text classification;Online feature selection;Neural network, Spark
期刊：
Journal of Flow Visualization and Image Processing,2024年31(1):33-52 ISSN：1065-3090
作者机构：
[Zunhai Gao] School of Information and Artificial Intelligence, Nanchang Institute of Science and Technology, Nanchang, 330108, China;[Hongtao Gao; Yuandong Xiang] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, 430048, China;[Zunhai Gao] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, 430048, China
摘要：
Existing deep learning methods for facial emotion recognition only focus on optimizing network structures, utilizing fixed receptive fields for different images, and relying on feature extraction based on a single scale of receptive fields. However, this approach fails to fully capture the most critical facial regions. To address this limitation, this paper presents a novel technique for facial emotion recognition that employs a selective kernel network. The proposed method introduces a dedicated module called the selective kernel network, which is trained using transfer learning. This module incorporates various components, such as a selective attention mechanism and channel-wise independent feature extraction and fusion. These components allow for the extraction of feature information from key facial regions. Unlike other methods, the selective convolutional kernel network extracts features with multiple scales of receptive fields and adapts to different spatial positions using a multilayer perceptron. This adaptability enhances useful features and suppresses noise. After extracting the features, they are combined, and the classification outcome is computed using the softmax function. Experimental results demonstrate that the suggested approach achieves an accuracy of 88.4 and 92.1% on the RAF-DB and KDEF datasets, respectively. These results confirm the efficacy of the proposed technique in comprehensively capturing the most crucial facial regions. Moreover, compared to alternative methods, this technique exhibits superior accuracy and enhanced resilience.
期刊：
Journal of Functional Analysis,2024年286(7):110316 ISSN：0022-1236
作者机构：
[Chuntai Liu] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, PR China
摘要：
The author of this paper and coauthors in 2022 studied a family of self-affine tiles in Rd with noncollinear digit sets, and gave a sufficient and necessary condition for such tiles to be tame balls. We in this paper mainly present a simpler proof of such equivalent condition. We replace quadric surfaces by some zigzag planes, and redefine the quasi-invariant plane which plays a key role in the construction of the desired homeomorphism. This adjustment greatly simplifies the proof.
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：
Electric Power Systems Research,2024年231:110273 ISSN：0378-7796
通讯作者：
Weiyong Zheng
作者机构：
[Fangxiu Wang; Jiemei Zhao] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, Hubei, China;[Weiyong Zheng] School of Foreign Languages, Shanghai University of International Business and Economics, Shanghai 201620, China;[Hadis Forghan] Moghadas Ardebili Institute of Higher Education, Ardabil, Iran
通讯机构：
[Weiyong Zheng] S;School of Foreign Languages, Shanghai University of International Business and Economics, Shanghai 201620, China
摘要：
In response to the escalating demand for sustainable energy solutions, the integration of diverse energy sources has gained prominence. Energy hubs, facilitating the amalgamation of multiple sources, enhance system efficiency and flexibility. Yet, coordinating the operation of such multi-energy systems remains challenging due to complexity and uncertainties, especially with renewable sources. This paper introduces a Cournot model-based approach for optimizing three interconnected energy hubs. The objective is to minimize total operating costs while ensuring reliability and efficiency. Leveraging the Cournot model, commonly employed in energy studies for competition and pricing, we capture the strategic behavior of energy hubs. The proposed solution employs a modified butterfly flame heuristic algorithm to navigate the non-linear and non-convex optimization problem, efficiently seeking global optimal solutions. Numerical experiments on a three-hub test case validate the method, demonstrating a 3.5 % reduction in total operation costs compared to the base case and over 2.5 % cost reduction for each hub. The proposed algorithm outperforms traditional methods in terms of convergence speed and solution quality, offering a promising avenue for optimizing the operation and management of multi-energy systems. In summary, this paper introduces a novel approach grounded in the Cournot model and a hybrid heuristic algorithm for optimizing three interconnected energy hubs, effectively addressing uncertainties associated with renewable sources and ensuring reliable and efficient system operation.
作者：
Fan Du;Qiaoqiao Hua;Chunhua Wang*;Qingfang Wang
期刊：
Journal of Differential Equations,2024年393:102-138 ISSN：0022-0396
通讯作者：
Chunhua Wang
作者机构：
[Fan Du; Qiaoqiao Hua] School of Mathematics and Statistics, Central China Normal University, Wuhan, 430079, PR China;[Chunhua Wang] School of Mathematics and Statistics & Key Laboratory of Nonlinear Analysis & Applications, Ministry of Education, Central China Normal University, Wuhan, 430079, PR China;[Qingfang Wang] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, 430023, PR China
通讯机构：
[Chunhua Wang] S;School of Mathematics and Statistics & Key Laboratory of Nonlinear Analysis & Applications, Ministry of Education, Central China Normal University, Wuhan, 430079, PR China
摘要：
We revisit the following nonlinear critical elliptic equation −Δu+V(|y′|,y″)u=uN+2N−2,u>0,u∈H1(RN), where (y′,y″)∈R3×RN−3 , V(|y′|,y″) is a bounded non-negative function in R+×RN−3 . Assuming that r2V(r,y″) has a stable critical point (r0,y0″) with r0>0 and V(r0,y0″)>0 , by using a modified finite-dimensional reduction method and various local Pohozaev identities, we prove that the problem above has multi-piece of bubble solutions, whose energy can be made arbitrarily large. Since there involves a new variable h¯ in the concentrated points {xj±}j=1m during the reduction process, we have to obtain a more precise estimate for the error term. And the bubble solutions are centered at points lying on the top and the bottom circles of a cylinder. Particularly, in one of these cases, the bubble solutions can concentrate at a pair of symmetric points relative to the origin. Our results present a new clustering type of blow-up phenomenon and we think the reason why this phenomenon can occur is mainly because that the function r2V(r,y″) has non-isolated critical points.
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.
作者机构：
[Shu H.; Zhou K.; Lyu X.; He Z.] College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, 430023, China;[Chen X.] School of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023, China
通讯机构：
[Kang Zhou] C;College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
关键词：
Evolutionary Algorithm;Tissue P systerm;Tri-objective VRPTW
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.
