摘要：
This paper focuses on the exponential stabilisation problem of inertial memristive neural networks (IMNNs) with unbounded discrete time-varying delays. The considered IMNNs are modelled by second-order derivatives equations by introducing the inertial terms. By using nonsmooth analysis, Lyapunov stability theory, inequality techniques and integral-differential of Lyapunov functional method, a feedback controller is designed to guarantee pth moment exponential stabilisation of the addressed IMNNs under the framework of Filippov solutions. It is worth noticing that the considered time delays of IMNNs can be unbounded. Finally, a numerical example is presented to illustrate the effectiveness of the main theoretical result.
通讯机构：
[Zhang, C ] W;Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
关键词：
Contamination;Farming;Geographic information systems;Machine learning algorithms;Risk management;farmland protection;geographic information systems;heavy metals;machine learning algorithms;risk assessment;soil
摘要：
As a nationally protected land resource, farmland plays a crucial role in agriculture production and food safety, making the quality of soil and environmental health critically important. Therefore, studying the extent of soil heavy metal pollution in farmland is of great significance for understanding the growth environment of food crops and protecting agricultural land resources. This study addresses the challenge of accurately, quickly, and conveniently assessing the extent of soil heavy metal pollution across an entire research area using a limited number of soil samples. To tackle this issue, a novel soil heavy metal pollution risk hybrid intelligent evaluation model (HIEM) is proposed. The HIEM utilizes the Semi-Supervised Bayesian Regression (Semi-BR) model, trained through Bayesian Co-training, to predict the soil heavy metal content at unsampled points. It employs an improved Multiple Kernel Support Vector Machine (MKSVM) model to evaluate the pollution status of the soil. Additionally, Geographic Information System (GIS) techniques are employed for spatial analysis of the pollution situation in the research area. The study focuses on eight soil heavy metals: As, Cd, Cr, Hg, Pb, Zn, Cu, and Ni. The experimental verification of the model was conducted using field sampling data from the major agricultural areas of Huangpi and Xinzhou in Wuhan, Hubei Province, China. The experimental results show that the eastern region of Huangpi District is more severely contaminated, particularly the central area in the northeast, with moderate to high pollution levels. The hybrid intelligent evaluation model achieves an average accuracy of 96.66% in assessing single-factor pollution of the eight soil heavy metals and an overall evaluation accuracy of 97.42%. The hybrid intelligent evaluation model is able to accurately fit traditional single-factor index methods and Nemerow comprehensive pollution index method. The Geographic Information System representation reveals a consistent distribution trend of soil heavy metal pollution reflected by the hybrid intelligent evaluation model with the results obtained from single-factor index and Nemerow comprehensive pollution index evaluation, indicating the feasibility of using this evaluation method for assessing the risk of soil heavy metal pollution. The conclusion shows that the hybrid intelligent evaluation model needs at least 639 sets of sample data to achieve the highest accuracy when assessing the risk of soil heavy metal contamination in an area of about $3.7\times 10^{4}\,\,hm^{2}$ , and this paper provides a reference to solve the problem of realizing high-precision risk assessment of heavy metal contamination of agricultural soils in the case of small samples. This study is of great practical significance for soil pollution investigation, soil quality assessment and other practical work.
通讯机构：
[Dejun Li] H;Hubei Meteorological Service Center, Wuhan 430205, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
evaluation of artificial precipitation enhancement (EoAPE);UNET-GRU;rainfall estimation
摘要：
The evaluation of the effects of artificial precipitation enhancement remains one of the most important and challenging issues in the fields of meteorology. Rainfall is the most important evaluation metric for artificial precipitation enhancement, which is mainly achieved through physics-based models that simulate physical phenomena and data-driven statistical models. The series of effect evaluation methods requires the selection of a comparison area for effect comparison, and idealized assumptions and simplifications have been made for the actual cloud precipitation process, leading to unreliable quantitative evaluation results of artificial precipitation effects. This paper proposes a deep learning-based method (UNET-GRU) to quantitatively evaluate the effect of artificial rainfall. By comparing the residual values obtained from inverting the natural evolution grid rainfall of the same area under the same artificial rainfall conditions with the actual rainfall amount after artificial rainfall operations, the effect of artificial rainfall can be quantitatively evaluated, effectively solving the problem of quantitative evaluation of artificial precipitation effects. Wuhan and Shiyan in China are selected to represent typical plains and mountainous areas, respectively, and the method is evaluated using 6-min resolution radar weather data from 2017 to 2020. During the experiment, we utilized the UNET-GRU algorithm and developed separate algorithms for comparison against common persistent baselines (i.e., the next-time data of the training data). The prediction of mean squared error (MSE) for these three algorithms was significantly lower than that of the baseline data. Moreover, the indicators for these algorithms were excellent, further demonstrating their efficacy. In addition, the residual results of the estimated 7-h grid rainfall were compared with the actual recorded rainfall to evaluate the effectiveness of artificial precipitation. The results showed that the estimated rainfall was consistent with the recorded precipitation for that year, indicating that deep learning methods can be successfully used to evaluate the impact of artificial precipitation. The results demonstrate that this method improves the accuracy of effect evaluation and enhances the generalization ability of the evaluation scheme.
关键词：
day-ahead prediction;mutation rate;data augmentation;GAN model
摘要：
This study introduces a data augmentation technique based on generative adversarial networks (GANs) to improve the accuracy of day-ahead wind power predictions. To address the peculiarities of abrupt weather data, we propose a novel method for detecting mutation rates (MR) and local mutation rates (LMR). By analyzing historical data, we curated datasets that met specific mutation rate criteria. These transformed wind speed datasets were used as training instances, and using GAN-based methodologies, we generated a series of augmented training sets. The enriched dataset was then used to train the wind power prediction model, and the resulting prediction results were meticulously evaluated. Our empirical findings clearly demonstrate a significant improvement in the accuracy of day-ahead wind power prediction due to the proposed data augmentation approach. A comparative analysis with traditional methods showed an approximate 5% increase in monthly average prediction accuracy. This highlights the potential of leveraging mutated wind speed data and GAN-based techniques for data augmentation, leading to improved accuracy and reliability in wind power predictions. In conclusion, this paper presents a robust data augmentation method for wind power prediction, contributing to the potential enhancement of day-ahead prediction accuracy. Future research could explore additional mutation rate detection methods and strategies to further enhance GAN models, thereby amplifying the effectiveness of wind power prediction.
摘要：
The semantic segmentation of outdoor images is the cornerstone of scene understanding and plays a crucial role in the autonomous navigation of robots. Although RGB–D images can provide additional depth information for improving the performance of semantic segmentation tasks, current state–of–the–art methods directly use ground truth depth maps for depth information fusion, which relies on highly developed and expensive depth sensors. Aiming to solve such a problem, we proposed a self–calibrated RGB-D image semantic segmentation neural network model based on an improved residual network without relying on depth sensors, which utilizes multi-modal information from depth maps predicted with depth estimation models and RGB image fusion for image semantic segmentation to enhance the understanding of a scene. First, we designed a novel convolution neural network (CNN) with an encoding and decoding structure as our semantic segmentation model. The encoder was constructed using IResNet to extract the semantic features of the RGB image and the predicted depth map and then effectively fuse them with the self–calibration fusion structure. The decoder restored the resolution of the output features with a series of successive upsampling structures. Second, we presented a feature pyramid attention mechanism to extract the fused information at multiple scales and obtain features with rich semantic information. The experimental results using the publicly available Cityscapes dataset and collected forest scene images show that our model trained with the estimated depth information can achieve comparable performance to the ground truth depth map in improving the accuracy of the semantic segmentation task and even outperforming some competitive methods.
作者机构：
[Deng, Guotai] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.;[Deng, Guotai] Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan 430079, Peoples R China.;[Liu, Chuntai] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;[Ngai, Sze-Man] Hunan Normal Univ, Coll Math & Stat, Key Lab High Performance Comp & Stochast Informat, Minist Educ China, Changsha 410081, Hunan, Peoples R China.;[Ngai, Sze-Man] Georgia Southern Univ, Dept Math Sci, Statesboro, GA 30460 USA.
通讯机构：
[Ngai, SM ] H;Hunan Normal Univ, Coll Math & Stat, Key Lab High Performance Comp & Stochast Informat, Minist Educ China, Changsha 410081, Hunan, Peoples R China.;Georgia Southern Univ, Dept Math Sci, Statesboro, GA 30460 USA.
摘要：
As the research on deep learning methods gradually progresses, more and more classification models are applied in the classification of hyperspectral image (HSI). High-dimensional and low-resolution characteristics of HSI, however, make it difficult for conventional models to process its data effectively. In this article, a novel HSI classification model, namely, spatial–spectral pyramid network (SSPN), is designed by combining a 3-D convolutional neural network (3D CNN) with feature pyramid structure. SSPN taking advantage of 3-D convolution coupled with multiscale convolutional extraction is used to obtain a large set of diverse spatial–spectral features. Multiscale interfusion is also applied in SSPN to enrich the features contained in a single feature map and to improve the sensitivity on HSI spatial–spectral information, allowing it to better learn spatial–spectral features. Moreover, the losses of each combination based on multiscale interfusion are calculated via weighted average, which enables SSPN to avoid the excessive influence of single combination in the updating of model parameters. Four HSI public datasets and several comparison models are employed to validate the classification effect of SSPN. Experimental results show that SSPN achieves the highest overall accuracy (OA) in all datasets compared with other classification models, with 100%, 98.8%, 99.8%, and 98.7% on the datasets of Chikusei, Pavia University, Botswana, and Houston 2013, respectively. SSPN is demonstrated to possess higher classification accuracy and better generalization performance on HSI.
摘要：
During the rice quality testing process, the precise segmentation and extraction of grain pixels is a key technique for accurately determining the quality of each seed. Due to the similar physical characteristics, small particles and dense distributions of rice seeds, properly analysing rice is a difficult problem in the field of target segmentation. In this paper, a network called SY-net, which consists of a feature extractor module, a feature pyramid fusion module, a prediction head module and a prototype mask generation module, is proposed for rice seed instance segmentation. In the feature extraction module, a transformer backbone is used to improve the ability of the network to learn rice seed features; in the pyramid fusion module and the prediction head module, a six-layer feature fusion network and a parallel prediction head structure are employed to enhance the utilization of feature information; and in the prototype mask generation module, a large feature map is used to generate high-quality masks. Training and testing were performed on two public datasets and one private rice seed dataset. The results showed that SY-net achieved a mean average precision (mAP) of 90.71% for the private rice seed dataset and an average precision (AP) of 16.5% with small targets in COCO2017. The network improved the efficiency of rice seed segmentation and showed excellent application prospects in performing rice seed quality testing.
摘要：
In this paper we will compare the Plateau's problem with Cech and singular homological boundary conditions, we also compare these with the size minimizing problem for integral currents with a given boundary. Finally we get the agreement on the infimum values for all these Plateau's problems.& COPY; 2023 Elsevier Inc. All rights reserved.
摘要：
Image stitching task targets to derive a large panoramic image for obtaining extensive information. However, artifacts such as ghosting or geometric misalignment are inevitably generated. As a practical measure, optimal seamline detection strategies use the spatial information to obtain the optimal seam in RGB image stitching, but they cannot be directly used in hyperspectral image (HSI) stitching. Since the spatial information of numerous continuous bands of HSI is different, the detected seam of the traditional RGB-based method in each band of HSI is divergent, which will cause visual difference and spectral distortion. To solve this problem, we propose a novel optimal seamline detection strategy via graph cuts for HSI stitching in this work. First, we use robust feature matching and elastic warp to align multiple adjacent images into a common geometrical transformation. After that, we design a novel energy function composing both the spatial and spectral information of HSI to determine an optimal seam in continuous regions with high texture consistency. Finally, we use the graph cuts method to eliminate visible artifacts. Our method can determine a unique optimal seam in the whole HSI for stitching so as to obtain high-quality panoramic HSI without artifacts and reduce the spectral distortion. A series of experiments verify the effectiveness and superiority of the proposed method to several advanced approaches in HSI stitching.
通讯机构：
[Zhang, C ] W;Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430048, Peoples R China.
关键词：
personal audio system;sound field control;acoustic contrast;reconstruction error;array effort
摘要：
A personal audio system has a wide application prospect in people’s lives, which can be implemented by sound field control technology. However, the current sound field control technology is mainly based on sound pressure or its improvement, ignoring another physical property of sound: particle velocity, which is not conducive to the stability of the entire reconstruction system. To address the problem, a sound field method is constructed in this paper, which minimizes the reconstruction error in the bright zone, minimizes the loudspeaker array effort in the reconstruction system, and at the same time controls the particle velocity and sound pressure of the dark zone. Five unevenly placed loudspeakers were used as the initial setup for the computer simulation experiment. Simulation results suggest that the proposed method is better than the PM (pressure matching) and EDPM (eigen decomposition pseudoinverse method) methods in the bright zone in an acoustic contrast index, the ACC (acoustic contrast control) method in a reconstruction error index, and the ACC, PM, and EDPM methods in the bright zone in a loudspeaker array effort index. The average array effort of the proposed method is the smallest, which is about 9.4790, 8.0712, and 4.8176 dB less than that of the ACC method, the PM method in the bright zone, and the EDPM method in the bright zone, respectively, so the proposed method can produce the most stable reconstruction system when the loudspeaker system is not evenly placed. The results of computer experiments demonstrate the performance of the proposed method, and suggest that compared with traditional methods, the proposed method can achieve more balanced results in the three indexes of acoustic contrast, reconstruction error, and loudspeaker array effort on the whole.
通讯机构：
[Shan Zeng] S;School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, Hubei 430023, China
关键词：
Data augmentation;Anomaly detection;Industrial data
摘要：
Detecting the anomalies in a large amounts of high-dimensional data has been a challenging task. In the Industry 4.0 environment, large-scale high-dimensional monitoring data features the complex pattern of high level semantics. In order to provide enterprise-wide monitoring solutions, it is necessary to identify the high-level semantic patterns of the anomalies in these data without splitting them. Existing end-to-end deep neural networks for time series are capable of recognizing the high-level semantics in natural language or speech signals, but they are barely applied in real-time anomaly detection of industrial data because of the large time costs. In this paper, we leverage the self-supervised contrastive learning methodology and propose a Composite Semantic Augmentation Encoder (CSAE) to provide an appropriate representation of industrial data and implement quick detection of anomalies in industrial application environments. CSAE is a non-sequential deep neural network with two augmentation layers and a mandatory layer. The two layers of data-augmentation are built to expand the size of samples of both low-level semantic anomalies and high-level semantic anomalies, which enables CSAE to discover diverse anomalies and improves its accuracy of high-level semantic pattern recognition. The mandatory layer is built to compress and reserve the temporal information in the industrial data to accelerate the anomaly detection. Therefore, as a non-sequential contrastive learning model, CSAE has faster training convergence than the usual sequence models. The experiment results have verified that CSAE can achieve higher prediction accuracy with less time consumption than existing machine learning models in the tasks of high dimensional anomaly pattern detection. (C) 2022 Elsevier B.V. All rights reserved.
作者机构：
[Wu, Chenghu] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430048, Peoples R China.;[Fan, Mingbo] Huazhong Univ Sci & Technol, Dept Neurosurg, Cent Hosp Wuhan, Tongji Med Coll, Wuhan 430014, Peoples R China.;[Yu, Ailin] Wuhan Univ, Renmin Hosp, Dept Endocrinol, Wuhan 430060, Peoples R China.;[Chen, Yue] Anhui Med Univ, Sch Clin Med, Hefei 230000, Peoples R China.
通讯机构：
[Mingbo Fan] D;Department of Neurosurgrey, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
通讯机构：
[Chong Wang; Yinsheng Qiu] A;Authors to whom correspondence should be addressed.<&wdkj&>Hubei Key Laboratory of Animal Nutrition and Feed Science, School of Animal Science and Nutrition Engineering, Wuhan Polytechnic University, Wuhan 430023, China<&wdkj&>Authors to whom correspondence should be addressed.<&wdkj&>College of Animal Science and Technology & College of Veterinary Medicine, Zhejiang A & F University, Hangzhou 311300, China
摘要：
Aflatoxin M1 (AFM1), a group 1 carcinogen, is a risk factor to be monitored in milk. This study aimed to investigate the occurrence of AFM1 in milk in Xinjiang, China, and to assess the risk of exposure for milk consumers in different age-sex groups. A total of 259 milk samples including pasteurized milk (93 samples), extended-shelf-life (ESL) milk (96), and raw donkey milk (70) were collected in Xinjiang from January to March in 2022. The AFM1 content of the milk samples was detected using a validated ELISA method. Of the 259 total samples analyzed for AFM1, 84 (32.4%) samples were contaminated at levels greater than the detection limit of 5 ng/L, with the maximum level of 16.5 ng/L. The positive rates of AFM1 in pasteurized milk and ESL milk were 43.0% (n = 40) and 45.8% (n = 44), respectively, and AFM1 was undetectable in donkey milk. The estimated daily intakes of AFM1 in each age group were lower than the hazard limits and were similar between male and female milk consumers. Therefore, the AFM1 contamination of milk in Xinjiang is low but still needs to be continuously monitored considering that children are susceptible to AFM1.
作者机构：
[Zhang, Cong; Wang, Song] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430048, Peoples R China.
通讯机构：
[Cong Zhang] S;School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430048, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
sound field control;particle velocity;non-uniform loudspeaker systems
摘要：
In recent years, a variety of sound field control methods have been proposed for the generation of separated sound regions. Different algorithms control the physical properties of the generated sound field to different degrees. The existing methods mainly focus on sound pressure restoration and its related improvement. When the loudspeaker array is non-uniformly placed, the reconstruction system is not stable enough. To solve this problem, this paper proposes two sound field control methods related to particle velocity. The first method regulates the reconstruction error of particle velocity in the bright zone and the square of particle velocity in the dark zone; the second method regulates the reconstruction error of sound pressure and particle velocity in the bright zone and the square of sound pressure and particle velocity in the dark zone. Five channel and twenty-two channel non-uniform loudspeaker systems were used for two-dimensional and three-dimensional computer simulation testing. Experimental results show that the two proposed methods have better tradeoffs in terms of acoustic contrast, reproduction error and array effort than traditional methods, especially the second proposed method. In the two-dimensional experiment, the maximum reductions of the average array efforts generated by the proposed methods were about 10 dB and 11 dB compared with the average array efforts generated by two traditional methods. In the three-dimensional experiment, the maximum reductions of the average array efforts generated by the proposed methods were about 8 dB and 2 dB compared with the average array efforts generated by two traditional methods. The smaller the array effort, the more stable the loudspeaker system. Therefore, the reconstruction systems produced by the proposed methods are more stable than those produced by the traditional methods.
期刊：
INDIANA UNIVERSITY MATHEMATICS JOURNAL,2022年71(2):463-508 ISSN：0022-2518
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, Hubei Key Lab Math Sci, POB 71010, Wuhan 430079, Peoples R China.;[Xiang, Wei] City Univ Hong Kong, Dept Math, Kowloon, Hong Kong, Peoples R China.
摘要：
Urban sound event detection can automatically preload relevant information for a robot to ensure that it can be applied to various scene-activity tasks. To address the limitations of timbre similarity and scene recognition by audio collection devices, a fusion model based on the self-attention mechanism is proposed in this paper. The model consists of scattering transform and self-attention model. The scattering transform computes modulation spectrum coefficients of multiple orders through cascades of wavelet convolutions and modulus operators. It is learnable compared with Mel-scale Frequency Cepstral Coefficients (MFCC), and can be used to better restore the semantic features of some sound scenes with similar timbres. The transformer has an outstanding effect on Natural Language Processing (NLP) owing to its self-attention mechanism. In this paper, the self-attention mechanism in its encoder was used in the model, mainly to make the feature granularity consistent to refine the features. In addition, Focal Loss function was adopted in the model to curb the sample distribution imbalance. The Google Command and ESC-50 were used to supplement the scene categories of dataset UrbanSound8K. The model parameters of the learnable filters that performed well on the dataset UrbanSound8K were preserved to fine-tune the other two datasets with insufficient data volume and more target categories. The length of slice duration was further explored the in the model. The experimental results show that the model can achieve better performance in a large range of scene models.
摘要：
The crux of image deraining stems from the challenge of recognizing the diverse rain patterns within the rainy image. Most methods for image deraining remain visible rain residuals in the restored image, which suffers from insufficient modeling of rain streaks. In this work, we propose contrastive learning-based generative network (CLGNet), which follows a coarse-to-fine framework. In the coarse phase, our CLGNet employs the hierarchical encoder-decoder structure to remove obvious rain patterns, and first generates the coarse background image. Then, we introduce a well-designed multiscale feature aggregation module in the refining phase to extract and integrate global information dependencies from different scales. In additon, to facilitate the intra-stage and cross-stage information interaction, we propose the intra-stage feature fusion module and the cross-stage feature fusion module to encode broad contextual information. More importantly, we propose an innovative contrastive learning strategy and apply it to each stage of our proposed CLGNet to enhance the decoupling ability of the encoder and help the model recognize complex rain patterns. Extensive experiments on five benchmark datasets demonstrate the superiority of our proposed CLGNet than other state-of-the-art methods for single image deraining on both the visual quality and quantitative evaluation. (C) 2022 SPIE and IS&T
