期刊：
Applied Mathematics and Computation,2025年484:128994 ISSN：0096-3003
通讯作者：
Jiemei Zhao
作者机构：
[Shen, Yi; Zhao, Jiemei] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China;[Yu, Liqi] Mathematics Department, East University of Heilongjiang, Harbin 150066, China
通讯机构：
[Jiemei Zhao] S;School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
摘要：
This study is concerned with reachable set bounding of delayed second-order memristive neural networks (SMNNs) with bounded input disturbances. By applying an analytic method, some inequality techniques and an adaptive control strategy, a sufficient condition of reachable set estimation criterion is derived to guarantee that the states of delayed SMNNs are bounded by a compact ellipsoid. A non-reduced order method is employed to investigate the reachable set bounding problem instead of the reduced order method by variable substitution. In addition, the proposed result is presented in algebraic form, which is easy to test. Finally, a simulation is performed to demonstrate the validity of the proposed algorithm.
期刊：
Journal of Differential Equations,2024年409:817-850 ISSN：0022-0396
通讯作者：
Ruan, LZ
作者机构：
[Fan, Lili] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;[Ruan, Lizhi] Cent China Normal Univ, Sch Math & Stat, POB 71010, Wuhan 430079, Peoples R China.;[Ruan, Lizhi] Cent China Normal Univ, Key Lab NAA MOE, POB 71010, Wuhan 430079, Peoples R China.;[Ruan, Lizhi] Cent China Normal Univ, Hubei Key Lab Math Sci, POB 71010, Wuhan 430079, Peoples R China.;[Xiang, Wei] City Univ Hong Kong, Dept Math, Kowloon, Tat Chee Ave, Hong Kong, Peoples R China.
通讯机构：
[Ruan, LZ ] C;Cent China Normal Univ, Sch Math & Stat, POB 71010, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Key Lab NAA MOE, POB 71010, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Hubei Key Lab Math Sci, POB 71010, Wuhan 430079, Peoples R China.
关键词：
Radiative full Euler equations;Non-slip boundary condition;Rarefaction wave;Asymptotic stability
摘要：
This paper is devoted to studying the initial-boundary value problem for the radiative full Euler equations, which are a fundamental system in the radiative hydrodynamics with many practical applications in astrophysical and nuclear phenomena, with the non-slip boundary condition on an impermeable wall. Due to the difficulty from the disappearance of the velocity on the impermeable boundary, quite few results for compressible Navier-Stokes equations and no result for the radiative Euler equations are available at this moment. So the asymptotic stability of the rarefaction wave proven in this paper is the first rigorous result on the global stability of solutions of the radiative Euler equations with the non-slip boundary condition. It also contributes to our systematical study on the asymptotic behaviors of the rarefaction wave with the radiative effect and different boundary conditions such as the inflow/outflow problem and the impermeable boundary problem in our series papers including [5,6]. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
期刊：
Computers & Chemical Engineering,2024年188:108747 ISSN：0098-1354
通讯作者：
Wang, FX
作者机构：
[Wang, Fangxiu; Wang, FX; Zhao, Jiemei] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Hubei, Peoples R China.;[Van Hoang, Vo] Bialystok Tech Univ, Fac Elect Engn, Wiejska 45C, PL-15531 Bialystok, Poland.
通讯机构：
[Wang, FX ] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Hubei, Peoples R China.
关键词：
Tri ethylene glycol;Hybrid Boosting;XGBoost;Arithmetic optimization algorithm;Dehydration
摘要：
The extraction of gas from fields often involves impurities, necessitating natural gas processing to separate these impurities. This process typically entails the removal of acid gases (such as carbon dioxide and hydrogen sulfide) and dehydration, commonly achieved through absorption using triethylene glycol (TEG). Efforts to minimize BTEX emissions and maintain optimal dry gas water content are pivotal for enhancing the economic and environmental sustainability of natural gas processing. In this study, the accurate prediction of BTEX and dry gas water contents is aimed by boosting-based methods and hybridization techniques. Four hybrid models, including XGBoost, CatBoost, HGBR, and LightGBoost, were developed in conjunction with the Arithmetic Optimization Algorithm (AOA) meta-heuristic algorithm to optimize and fine-tune hyperparameters. Through a comprehensive case study and comparison of various evaluation indices, the XGBoost-AOA hybrid model emerged as the most accurate in predicting both outputs. Hence, we recommend the XGBoost algorithm optimized with the AOA algorithm as the preferred approach for predicting BTEX and dry gas water contents in natural gas processing.
摘要：
UAV swarm passive positioning technology only requires the reception of electromagnetic signals to achieve the positioning and tracking of radiation sources. It avoids the active positioning strategy that requires active emission of signals and has the advantages of good concealment, long acting distance, and strong anti-interference ability, which has received more and more attention. In this paper, we propose a new UAV swarm formation flight method based on pure azimuth passive positioning. Specifically, we propose a two-circle positioning model, which describes the positional deviation of the receiving UAV using trigonometric functions relative to the target in polar coordinates. Furthermore, we design a two-step adjustment strategy that enables the receiving UAV to reach the target position efficiently. Based on the above design, we constructed an optimized UAV swarm formation scheme. In experiments with UAV numbers of 8 and 20, compared to the representative comparison strategy, the proposed UAV formation scheme reduces the total length of the UAV formation by 34.76% and 55.34%, respectively. It demonstrates the effectiveness of the proposed method in the application of assigning target positions to UAV swarms.
摘要：
<jats:title>Abstract</jats:title><jats:p>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.</jats:p>
摘要：
Conventional segmentation methods based on visible images in intensive pig farming face various challenges. Examples include color differences between pig breeds, background interference and lighting conditions. To overcome these issues, we designed the infrared pig cascade segmentation (INPC) model for the first time on infrared images. The model uses a cascade structure. Each stage utilizes higher resolution feature maps to better preserve fine details. It also solves the problem of poor segmentation of small objects due to low resolution of infrared images. At the same time, the model's cross-guidance strategy enhances the interaction between bounding box regression and mask prediction. This reduces errors caused by interference like feces and urine. Additionally, a progressive mask branch refines mask prediction, improving segmentation in scenarios like imaging haze or pig adhesion. To facilitate model training and evaluation, we built the first largescale standardized infrared pig dataset. Experimental results demonstrate that INPC outperforms mainstream segmentation models in terms of average precision (AP), except for AP(0.5). Specifically, INPC achieves AP(0.5), AP(0.75), AP(0.5:0.95), AP(0.5:0.95s), and AP(0.5:0.95l) of 97.9%, 97.1%, 88.2%, 71.5%, and 90.1% respectively. Inference for a single image on a GPU takes only 0.197 s. Some datasets are available at https://github.com/HUBUwg96/INPC.
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：
This article proposes a novel soft multiprototype clustering algorithm (SMP) for high-dimensional data clustering with noisy and complex structural patterns. SMP integrates dimensionality reduction, multiprototype clustering, and multiprototype merge clustering under a two-layer seminonnegative matrix factorization (semi-NMF) architecture. Specifically, the first semi-NMF layer performs multiprototype clustering, which solves the problem that a single prototype cannot represent complex data structures. Meanwhile, the multiprototype fuzzy clustering constraints ensure that the multiprototypes better characterize the original data structure. The second semi-NMF layer performs multiprototype merge clustering to mitigate the issues of heavy computation burden and poor antinoise performance of the spectral clustering algorithm. The introduction of the Laplace graph matrix regularization constraint in this layer assists SMP in completing the merging of multiprototypes with complex data structures. Comprehensive experiments demonstrate that the proposed method outperforms the state-of-the-art algorithms.
摘要：
Hyperspectral imaging (HSI) has been effectively used in the nondestructive assessment of food quality in recent years. However, the identification of moldy objects using HSIs still faces challenges, including slow detection speed and poor identification accuracy. To address these challenges, this study proposes a three-dimensional hyperspectral mold detection (3D-HMD) approach. The model utilizes multiple 3D convolution (3DMC) modules as the backbone network for optimizing spectral-spatial feature extraction and introduces an attention mechanism to promote the feature information of different hyperspectral bands. A feature pyramid network (FPN) is then used to fuse classification features outputted from the backbone network for feature enhancement. To improve the recognition efficiency for moldy targets, a detection head module derived from the field of object detection is introduced to achieve HIS object-level classification. The experimental results indicate that the detection speed of the proposed model is nearly tenfold greater than that of traditional algorithms, such as 1DRNN and 3D-CNN, with a mean average precision (mAP) of 81.63 %. Overall, the 3D-HMD model demonstrates remarkable efficiency and accuracy in recognizing moldy peanuts, leading to suitable applications for food quality detection.
作者机构：
[Yujian Liu] Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;[Jian Lu] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China;[Guangwu Liu] Department of Orthopaedic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
通讯机构：
[Guangwu Liu] D;Department of Orthopaedic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
关键词：
Shoulder pain;Primary care physicians;Ambulatory care;Differences;NAMCS (National Ambulatory Medical Care Survey)
摘要：
The aim of this study was to identify the differences in the clinical management of shoulder pain by primary care physicians (PCPs) and non-primary care physicians (non-PCPs) from the National Ambulatory Medical Care Survey (NAMCS) dataset. This cross-sectional study included ambulatory care visits for shoulder pain by using NAMCS data from 2007 to 2019. Descriptive statistics were presented to assess patient-level and visit-level characteristics of the sampled visits. By controlling for patient-level and visit-level covariates, we conducted a multivariable logistic regression to evaluate the influence of primary care physician status on the utilization of health services (pain medications, PT referral, health education/counseling, and diagnostic imaging) for shoulder pain. There were 74.43 million ambulatory care visits by adults with shoulder pain during the study period, and nearly one-third of these shoulder visits were made to PCPs. As compared with non-PCPs, PCPs had higher adjusted odds of prescribing narcotic analgesics (adjusted odds ratio [OR] = 1.62, 95% confidence interval [CI]: 1.04–2.51), skeletal muscle relaxants (adjusted OR = 2.71, 95% CI: 1.65–4.45), other pain medications (adjusted OR = 1.87, 95% CI: 1.13–3.07), and lower odds of prescribing PT (adjusted OR = 0.34, 95% CI: 0.21–0.55) and MRI (adjusted OR = 0.46, 95% CI: 0.25–0.84). We observed significant differences in the services ordered or provided by PCPs versus non-PCPs for shoulder pain in ambulatory care settings. These results may reveal the higher reliance of pharmacological approaches, coupled with the potential under-utilization of PT during the ambulatory shoulder care provided by PCPs compared to non-PCPs in the United States.
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 (Corresponding author yonggao012@yeah.net); 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
