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Artykuły w czasopismach na temat „Convolution root”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Xu, Hui, Sergey Foss, and Yuebao Wang. "Convolution and convolution-root properties of long-tailed distributions." Extremes 18, no. 4 (2015): 605–28. http://dx.doi.org/10.1007/s10687-015-0224-2.

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Li, Zhenxin, Yong Han, Zhenyu Xu, Zhihao Zhang, Zhixian Sun, and Ge Chen. "PMGCN: Progressive Multi-Graph Convolutional Network for Traffic Forecasting." ISPRS International Journal of Geo-Information 12, no. 6 (2023): 241. http://dx.doi.org/10.3390/ijgi12060241.

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Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a single predefined matrix or a single self-generated matrix. It is difficult to obtain deeper spatial information by only relying on a single adjacency matrix. In this paper, we present a progressive multi-graph convolutional network (PMGCN), which includes spatiotemporal attention, multi-graph convolution, and multi-scale convolution modules.

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Gobbi, Fabio. "Convolution Based Unit Root Processes: a Simulation Approach." International Journal of Statistics and Probability 5, no. 6 (2016): 22. http://dx.doi.org/10.5539/ijsp.v5n6p22.

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We propose a convolution based approach to the simulation of a modified version of a unit root process where the state variable $Y_{t-1}$ is dependent on the innovation $\varepsilon_t$. The dependence structure is given by a copula function $C$. We study by simulation the effect of a negative correlation on the properties of unit roots. We call this process C-UR(1).

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4

Sheng, Wanxing, Keyan Liu, Dongli Jia, Shuo Chen, and Rongheng Lin. "Short-Term Load Forecasting Algorithm Based on LST-TCN in Power Distribution Network." Energies 15, no. 15 (2022): 5584. http://dx.doi.org/10.3390/en15155584.

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In this paper, a neural network model called Long Short-Term Temporal Convolutional Network (LST-TCN) model is proposed for short-term load forecasting. This model refers to the 1-D fully convolution network, causal convolution, and void convolution structure. In the convolution layer, a residual connection layer is added. Additionally, the model makes use of two networks to extract features from long-term data and periodic short-term data, respectively, and fuses the two features to calculate the final predicted value. Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) are

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Gao, Yuan, Changhua Liu, and Xiaoming Wu. "Classification Method of Rape Root Swelling Disease Based on Convolution Neural Network." Journal of Physics: Conference Series 2138, no. 1 (2021): 012003. http://dx.doi.org/10.1088/1742-6596/2138/1/012003.

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Abstract Both the seedling stage and the adult plant stage of rape can be infected with root edema, and the damaged roots swell to form tumors of different sizes and shapes. The incidence of rape root swelling at the seedling stage reached 17%, and the average incidence at the adult plant stage was 15%, resulting in a 10.2% reduction in rape production. The average plant height, number of siliques, number of kernels per horn, 1000-seed weight and yield per plant of healthy plants were significantly higher than those of diseased plants. Grading root lesions can help trace the root causes of roo

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6

Lee, Hyung Won, Jiyoung Yu, Gwang-Gook Kim, et al. "Convolutional Neural Network Model for the Prediction of Back-Bead Occurrence in GMA Root Pass Welding of V-groove Butt Joint." Journal of Welding and Joining 39, no. 5 (2021): 463–70. http://dx.doi.org/10.5781/jwj.2021.39.5.1.

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Gas metal arc (GMA) welding is widely used in the machinery industry. The quality of a welded joint is affected by the penetration of root pass welding in the V-groove joint. Automation using GMA welding is continuously required, and root pass welding automation is required to automate the entire welding process. In particular, the development of a prediction model that can ensure full penetration back-bead is required for the automation of root pass welding. In this study, a convolutional neural network (CNN) model was applied to predict the occurrence of back-bead in V-groove butt joint GMA

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7

Szakács, Tamás. "Convolution of second order linear recursive sequences II." Communications in Mathematics 25, no. 2 (2017): 137–48. http://dx.doi.org/10.1515/cm-2017-0011.

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Abstract We continue the investigation of convolutions of second order linear recursive sequences (see the first part in [1]). In this paper, we focus on the case when the characteristic polynomials of the sequences have common root.

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8

Diamond, Harold G. "Convolution square root of 1 and the Prime Number Theorem." Annales Universitatis Scientiarum Budapestinensis de Rolando Eötvös Nominatae. Sectio computatorica, no. 47 (2018): 239–48. https://doi.org/10.71352/ac.47.239.

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Tian, Wei, Linhong Lai, Xianghua Niu, Xinxin Zhou, Yonghong Zhang, and Kenny Thiam Choy Lim Kam Kenny. "Estimation of Tropical Cyclone Intensity Using Multi-Platform Remote Sensing and Deep Learning with Environmental Field Information." Remote Sensing 15, no. 8 (2023): 2085. http://dx.doi.org/10.3390/rs15082085.

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Accurate tropical cyclone (TC) intensity estimation is crucial for prediction and disaster prevention. Currently, significant progress has been achieved for the application of convolutional neural networks (CNNs) in TC intensity estimation. However, many studies have overlooked the fact that the local convolution used by CNNs does not consider the global spatial relationships between pixels. Hence, they can only capture limited spatial contextual information. In addition, the special rotation invariance and symmetry structure of TC cannot be fully expressed by convolutional kernels alone. Ther

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10

Yang, Shiqiang, Qi Li, Duo He, Jinhua Wang, and Dexin Li. "Global Correlation Enhanced Hand Action Recognition Based on NST-GCN." Electronics 11, no. 16 (2022): 2518. http://dx.doi.org/10.3390/electronics11162518.

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Hand action recognition is an important part of intelligent monitoring, human–computer interaction, robotics and other fields. Compared with other methods, the hand action recognition method using skeleton information can ignore the error effects caused by complex background and movement speed changes, and the computational cost is relatively small. The spatial-temporal graph convolution networks (ST-GCN) model has excellent performance in the field of skeleton-based action recognition. In order to solve the problem of the root joint and the further joint not being closely connected, resulting

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11

Li, Peihong, Xiaozhi Liu, and Yinghua Yang. "Remaining Useful Life Prognostics of Bearings Based on a Novel Spatial Graph-Temporal Convolution Network." Sensors 21, no. 12 (2021): 4217. http://dx.doi.org/10.3390/s21124217.

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As key equipment in modern industry, it is important to diagnose and predict the health status of bearings. Data-driven methods for remaining useful life (RUL) prognostics have achieved excellent performance in recent years compared to traditional methods based on physical models. In this paper, we propose a novel data-driven method for predicting the remaining useful life of bearings based on a deep graph convolutional neural network with spatiotemporal domain convolution. This network uses the average sliding root mean square (ASRMS) as the health factor to identify the healthy and degraded

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12

Leonhardt, Viktor, Felix Claus, and Christoph Garth. "PEN: Process Estimator neural Network for root cause analysis using graph convolution." Journal of Manufacturing Systems 62 (January 2022): 886–902. http://dx.doi.org/10.1016/j.jmsy.2021.11.008.

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13

Katkovskaya, I. N., and V. G. Krotov. "Strong-Type Inequality for Convolution with Square Root of the Poisson Kernel." Mathematical Notes 75, no. 3/4 (2004): 542–52. http://dx.doi.org/10.1023/b:matn.0000023335.53027.30.

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Song, Zhihang, Tianzhang Zhao, and Jian Jin. "Early Identification of Root Damages Caused by Western Corn Rootworms Using a Minimally Invasive Root Phenotyping Robot—MISIRoot." Sensors 23, no. 13 (2023): 5995. http://dx.doi.org/10.3390/s23135995.

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Western corn rootworm (WCR) is one of the most devastating corn rootworm species in North America because of its ability to cause severe production loss and grain quality damage. To control the loss, it is important to identify the infection of WCR at an early stage. Because the root system is the earliest feeding source of the WCR at the larvae stage, assessing the direct damage in the root system is crucial to achieving early detection. Most of the current methods still necessitate uprooting the entire plant, which could cause permanent destruction and a loss of the original root’s structura

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Valls, Xavier, Lorenzo Moneta, Guilherme Amadio, and Arthur Tsang. "New developments in the ROOT fitting classes." EPJ Web of Conferences 214 (2019): 05043. http://dx.doi.org/10.1051/epjconf/201921405043.

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The ROOT Mathematical and Statistical libraries have been recently improved both to increase their performance and to facilitate the modelling of parametric functions that can be used for performing maximum likelihood fits to data sets to estimate parameters and their uncertainties. First, we report on the new functionalities introduced in ROOT’s TFormula and TF1 classes to build these models in a convenient way for the users. We show how function objects, represented in ROOT by TF1 classes, can be used as probability density functions and how they can be combined together—via an addition oper

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16

Sun, Guolun, Zhihua Huang, Li Wang, and Pengyuan Zhang. "Temporal Convolution Network Based Joint Optimization of Acoustic-to-Articulatory Inversion." Applied Sciences 11, no. 19 (2021): 9056. http://dx.doi.org/10.3390/app11199056.

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Articulatory features are proved to be efficient in the area of speech recognition and speech synthesis. However, acquiring articulatory features has always been a difficult research hotspot. A lightweight and accurate articulatory model is of significant meaning. In this study, we propose a novel temporal convolution network-based acoustic-to-articulatory inversion system. The acoustic feature is converted into a high-dimensional hidden space feature map through temporal convolution with frame-level feature correlations taken into account. Meanwhile, we construct a two-part target function co

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17

Haripriya, M., R. B. Sharma, and T. Ram Reddy. "$k^{th}$ root transformations for some subclasses of alpha convex functions defined through convolution." Novi Sad Journal of Mathematics 46, no. 1 (2016): 131–46. http://dx.doi.org/10.30755/nsjom.02459.

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Andrey, Ivashko, Liberg Igor, and Lunin Denis. "SYNTHESIS OF FAST-OPERATING DEVICES FOR DIGITAL SIGNAL PROCESSING BASED ON THE NUMBERTHEORETIC TRANSFORMS." Eastern-European Journal of Enterprise Technologies 1, no. 4 (103) (2020): 6–10. https://doi.org/10.15587/1729-4061.2020.194342.

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The selection of special-form moduli and their corresponding primitive roots have been proposed, which provide for a simplified structure of arithmetic devices using number-theoretic transforms. A method for determining moduli has been developed that ensures a minimum number of arithmetic operations when performing the modulo addition and multiplication operations. The structures of special-form modulo adders have been developed and modeled, which make it possible to perform the addition operation as quickly as possible. The modulo adders for the Fermat, Mersenne, and Golomb numbers have been

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19

Li, Weisheng, Dongwen Cao, Yidong Peng, and Chao Yang. "MSNet: A Multi-Stream Fusion Network for Remote Sensing Spatiotemporal Fusion Based on Transformer and Convolution." Remote Sensing 13, no. 18 (2021): 3724. http://dx.doi.org/10.3390/rs13183724.

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Remote sensing products with high temporal and spatial resolution can be hardly obtained under the constrains of existing technology and cost. Therefore, the spatiotemporal fusion of remote sensing images has attracted considerable attention. Spatiotemporal fusion algorithms based on deep learning have gradually developed, but they also face some problems. For example, the amount of data affects the model’s ability to learn, and the robustness of the model is not high. The features extracted through the convolution operation alone are insufficient, and the complex fusion method also introduces

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20

Cai, Jianxian, Li Wang, Jiangshan Zheng, Zhijun Duan, Ling Li, and Ning Chen. "Denoising Method for Seismic Co-Band Noise Based on a U-Net Network Combined with a Residual Dense Block." Applied Sciences 13, no. 3 (2023): 1324. http://dx.doi.org/10.3390/app13031324.

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To address the problem of waveform distortion in the existing seismic signal denoising method when removing co-band noise, further improving the signal-to-noise ratio (SNR) of seismic signals and enhancing their quality, this paper designs a seismic co-band denoising model Atrous Residual Dense Block U-Net (ARDU), which uses a U-shaped convolutional neural network (U-Net) as a basic framework and combines atrous convolution and the residual dense block (RDB). In the ARDU model, atrous convolution is connected with residual dense blocks to form the feature extraction unit of the model encoder.

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21

Lu, Huakang, Dongmin Huang, Youyi Song, Dazhi Jiang, Teng Zhou, and Jing Qin. "ST-TrafficNet: A Spatial-Temporal Deep Learning Network for Traffic Forecasting." Electronics 9, no. 9 (2020): 1474. http://dx.doi.org/10.3390/electronics9091474.

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This paper presents a spatial-temporal deep learning network, termed ST-TrafficNet, for traffic flow forecasting. Recent deep learning methods highly relate accurate predetermined graph structure for the complex spatial dependencies of traffic flow, and ineffectively harvest high dimensional temporal features of the traffic flow. In this paper, a novel multi-diffusion convolution block constructed by an attentive diffusion convolution and bidirectional diffusion convolution is proposed, which is capable to extract precise potential spatial dependencies. Moreover, a stacked Long Short-Term Memo

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22

Kumar, Ashok, Suvam Dhar, and Shreyas Pradeep Unhale. "Plant Health Detection Using Convolution Neural Network." Journal of Computational and Theoretical Nanoscience 17, no. 8 (2020): 3355–65. http://dx.doi.org/10.1166/jctn.2020.9185.

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When the various crops and plants grown in a country, which include both agricultural and horticultural produce are infested by pests or suffers from various different forms from diseases due to deficiency of various minerals and nutrients, the process of identifying and analyzing is been done by the farmer or the concerned person manually through the naked eye and from the limited knowledge. This makes it very difficult to specifically and correctly diagnose the root of the infestation or disease. To remove this problem of human inefficiency in identifying the problem and making a correct dia

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23

Falk, Kevin G., Talukder Zaki Jubery, Jamie A. O’Rourke, et al. "Soybean Root System Architecture Trait Study through Genotypic, Phenotypic, and Shape-Based Clusters." Plant Phenomics 2020 (June 9, 2020): 1–23. http://dx.doi.org/10.34133/2020/1925495.

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We report a root system architecture (RSA) traits examination of a larger scale soybean accession set to study trait genetic diversity. Suffering from the limitation of scale, scope, and susceptibility to measurement variation, RSA traits are tedious to phenotype. Combining 35,448 SNPs with an imaging phenotyping platform, 292 accessions (replications=14) were studied for RSA traits to decipher the genetic diversity. Based on literature search for root shape and morphology parameters, we used an ideotype-based approach to develop informative root (iRoot) categories using root traits. The RSA t

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24

Zhu, Tianhang, Yuxiang Mao, and Junzhi Zhang. "Adaptive Iterative Control Optimization ICP Algorithm for Robust Point Cloud Registration in Urban Environments." Applied and Computational Engineering 132, no. 1 (2025): 83–94. https://doi.org/10.54254/2755-2721/2024.20533.

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This paper puts forward an improved ICP algorithm to improve the robustness of point cloud registration in complex urban environment, and adopts adaptive iterative control to solve the limitations of traditional ICP algorithm such as premature convergence or over-fitting caused by static iteration. Sobel convolution enhances the response ability of the algorithm to the complexity of the environment, and dynamically adjusts the iteration limit according to the feature difference of the point cloud, thus improving the registration accuracy and calculation efficiency. According to a large number

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Cui, Zhaolei, Yuebao Wang, and Hui Xu. "Local Closure under Infinitely Divisible Distribution Roots and Esscher Transform." Mathematics 10, no. 21 (2022): 4128. http://dx.doi.org/10.3390/math10214128.

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In this paper, we show that the local distribution class Lloc∩OSloc is not closed under infinitely divisible distribution roots, i.e., there is an infinitely divisible distribution which belongs to the class, while the corresponding Lévy distribution does not. Conversely, we give a condition, under which, if an infinitely divisible distribution belongs to the class Lloc∩OSloc, then so does the Lévy distribution. Furthermore, we find some sufficient conditions that are more concise and intuitive. Using different methods, we also give a corresponding result for another local distribution class,

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Haefner, Stephan, and Reiner Thomä. "High Resolution Estimation of AoA, AoD and TdoA from MIMO Channel Sounding Measurements with Virtual Antenna Arrays: Maximum-Likelihood vs. Unitary Tensor-ESPRIT." International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems 7, no. 2 (2018): 27. http://dx.doi.org/10.11601/ijates.v7i2.254.

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Estimating the parameters of a geometric propagation model from MIMO channel sounding measurements will be considered, which requires the solution of an inverse problem. Thus, a model of the measured data is derived, which incorporates a model of the measurement system as well as the parameters of interest. Based on the data model a maximum-likelihood estimator will be derived to infer the model parameters. Because virtual antenna arrays are considerer, formed by step-wise rotating directive antennas at transmitter and receiver side, the MIMO measurements are conducted in the beam-space. Hence

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Ren, Shuzhan, and Craig A. Stroud. "Temperature Response from the Change of Surface Heat Flux and Vertical Diffusivity by Urbanization." Atmosphere 11, no. 9 (2020): 978. http://dx.doi.org/10.3390/atmos11090978.

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A 1-D diffusion model of temperature is employed to understand important features of temperature response to the changes of surface heat flux (SHF) and vertical diffusivity shown in 3-D model simulations. Analytical results show that the temperature response to the SHF change is the convolution of the SHF change and Green’s function (GF). Because the GF is inversely proportional to the square root of diffusion coefficient near the surface, weak/strong diffusivity in the early morning/noontime tends to generate a large/small temperature response by slowing/accelerating heat flow from surface to

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Zeng, Qingtian, Chao Wang, Geng Chen, and Hua Duan. "PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks." Wireless Communications and Mobile Computing 2021 (November 3, 2021): 1–10. http://dx.doi.org/10.1155/2021/7000986.

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Industrial parks are one of the main sources of air pollution; the ability to forecast PM2.5, the main pollutant in the industrial park, is of great significance to the health of the workers in the industrial park and environmental governance, which can improve the decision-making ability of environmental management. Most of the existing PM2.5 concentration forecast methods lack the ability to model the dynamic temporal and spatial correlations of PM2.5 concentration. In an industrial park environment, in order to improve the accuracy of PM2.5 concentration forecast, based on deep learning tec

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Pan, Yin, Zhenpeng Zhang, Xueyang Zhang, Zhi Zeng, and Yibin Tian. "YOLO-TARC: YOLOv10 with Token Attention and Residual Convolution for Small Void Detection in Root Canal X-Ray Images." Sensors 25, no. 10 (2025): 3036. https://doi.org/10.3390/s25103036.

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The detection of small voids or defects in X-ray images of tooth root canals still faces challenges. To address the issue, this paper proposes an improved YOLOv10 that combines Token Attention with Residual Convolution (ResConv), termed YOLO-TARC. To overcome the limitations of existing deep learning models in effectively retaining key features of small objects and their insufficient focusing capabilities, we introduce three improvements. First, ResConv is designed to ensure the transmission of discriminative features of small objects during feature propagation, leveraging the ability of resid

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Su, Siyuan, and Jian Wu. "GeometryFormer: Semi-Convolutional Transformer Integrated with Geometric Perception for Depth Completion in Autonomous Driving Scenes." Sensors 24, no. 24 (2024): 8066. https://doi.org/10.3390/s24248066.

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Depth completion is widely employed in Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SfM), which are of great significance to the development of autonomous driving. Recently, the methods based on the fusion of vision transformer (ViT) and convolution have brought the accuracy to a new level. However, there are still two shortcomings that need to be solved. On the one hand, for the poor performance of ViT in details, this paper proposes a semi-convolutional vision transformer to optimize local continuity and designs a geometric perception module to learn the positional

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Thangavel, Aravind, and Vijayakumar Govindaraj. "Forecasting Energy Demand Using Conditional Random Field and Convolution Neural Network." Elektronika ir Elektrotechnika 28, no. 5 (2022): 12–22. http://dx.doi.org/10.5755/j02.eie.30740.

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Electric load forecasting has been identified as an effective strategy to increase output and revenues in electrical manufacturing and distribution organizations. Several strategies for forecasting power consumption have been suggested; however, they all fail to account for small variations in power demand throughout the prediction. Therefore, the aim of this study was to develop a CRF-based power consumption prediction technique (CRF-PCP) to meet the difficulty of estimating energy consumption (EC). The EC of regions in the area is forecasted using convolution neural networks (CNNs) and condi

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Desai, Kavina S., Ankit V. Arora, Sonali V. Kapoor, Purnil B. Shah, and Yashrajsingh R. Rathore. "Cone-beam computed tomographic analysis of canal convolution in mesial root of mandibular second molars and a proposed new classification." Journal of Conservative Dentistry and Endodontics 27, no. 7 (2024): 714–18. http://dx.doi.org/10.4103/jcde.jcde_204_24.

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Aim: The aim is to evaluate the anatomical characteristics of mesiolingual and mesiobuccal canals in mandibular second molars particularly in terms of its exit direction, distance of confluence from the minor constriction, and the angle of confluence. Materials and Methods: The cone-beam computed tomography images of hundred mandibular second molars were analyzed. Endodontically treated teeth and those with anatomical variations such as C-shaped canal configuration were excluded from this study. The distance of the confluence from the minor constriction, angle of confluence, and the exit direc

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Salimov, Boris, Oleg Berngardt, Aleksey Hmelnov, Konstantin Ratovsky та Oleg Kusonsky. "Application of convolution neural networks for critical frequency fₒF2 prediction". Solar-Terrestrial Physics 9, № 1 (2023): 56–67. http://dx.doi.org/10.12737/stp-91202307.

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Ionosphere has an important impact on the quality of radio communication, radar, and global positioning. One of the essential characteristics describing the state of the ionosphere is its critical frequency fₒF2. Its prediction provides effective modes of operation of technical radio equipment as well as enables calculation of the corrections needed to improve the accuracy of its functioning. Different physical and empirical models are generally used for fₒF2 prediction. This paper proposes an empirical prediction technique based on machine learning methods and observational history. It relies

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Salimov, Boris, Oleg Berngardt, Aleksey Hmelnov, Konstantin Ratovsky та Oleg Kusonsky. "Application of convolution neural networks for critical frequency fₒF2 prediction". Solnechno-Zemnaya Fizika 9, № 1 (2023): 60–72. http://dx.doi.org/10.12737/szf-91202307.

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Ionosphere has an important impact on the quality of radio communication, radar, and global positioning. One of the essential characteristics describing the state of the ionosphere is its critical frequency fₒF2. Its prediction provides effective modes of operation of technical radio equipment as well as enables calculation of the corrections needed to improve the accuracy of its functioning. Different physical and empirical models are generally used for fₒF2 prediction. This paper proposes an empirical prediction technique based on machine learning methods and observational history. It relies

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Chang, Jiahao, Jiali Yin, Yanrong Hao, and Chengxin Gao. "STFDSGCN: Spatio-Temporal Fusion Graph Neural Network Based on Dynamic Sparse Graph Convolution GRU for Traffic Flow Forecast." Sensors 25, no. 11 (2025): 3446. https://doi.org/10.3390/s25113446.

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The characteristics of multivariate heterogeneity in traffic flow forecasting exhibit significant variation, heavily influenced by spatio-temporal dynamics and unforeseen events. To address this challenge, we propose a spatio-temporal fusion graph neural network based on dynamic sparse graph convolution GRU for traffic flow forecast (STFDSGCN), which incorporates a spatio-temporal attention fusion scheme with a gating mechanism. The dynamic sparse graph convolution gated recurrent unit (DSGCN-GRU) in this model is a novel component that integrates adaptive dynamic sparse graph convolution into

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Zhou, Jianzhong, Han Liu, Yanhe Xu, and Wei Jiang. "A Hybrid Framework for Short Term Multi-Step Wind Speed Forecasting Based on Variational Model Decomposition and Convolutional Neural Network." Energies 11, no. 9 (2018): 2292. http://dx.doi.org/10.3390/en11092292.

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Wind speed is an important factor in wind power generation. Wind speed forecasting is complicated due to its highly nonstationary character. Therefore, this paper presents a hybrid framework for the development of multi-step wind speed forecasting based on variational model decomposition and convolutional neural networks. In the first step of signal pre-processing, the variational model decomposition approach decomposes the wind speed data into several independent modes under different center pulsation. The vibrations of decomposed modes are useful for accurate wind speed forecasting. Then, th

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Man, Junfeng, Minglei Zheng, Yi Liu, Yiping Shen, and Qianqian Li. "Bearing Remaining Useful Life Prediction Based on AdCNN and CWGAN under Few Samples." Shock and Vibration 2022 (June 30, 2022): 1–17. http://dx.doi.org/10.1155/2022/1709071.

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At present, deep learning is widely used to predict the remaining useful life (RUL) of rotation machinery in failure prediction and health management (PHM). However, in the actual manufacturing process, massive rotating machinery data are not easily obtained, which will lead to the decline of the prediction accuracy of the data-driven deep learning method. Firstly, a novel prognostic framework is proposed, which is comprised of conditional Wasserstein distance-based generative adversarial networks (CWGAN) and adversarial convolution neural networks (AdCNN), which can stably generate high-quali

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Kovalenko, G. V., and I. A. Yadrov. "Application of Seq2Seq models for predicting the development of thunderstorm activity to enhance the pilot’s situational awareness in flight." Civil Aviation High Technologies 28, no. 1 (2025): 20–38. https://doi.org/10.26467/2079-0619-2025-28-1-20-38.

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The paper presents the results of application of Seq2seq models based on neural networks for nowcasting-forecasting with a lead time of up to 2 hours – of thunderstorm activity in order to increase situational awareness of aircraft crews. Various recurrent and convolutional recurrent models were created and trained on the basis of radar meteorological observations of thunderstorm cells. The results showed that convolutional recurrent neural networks (ConvRNN, ConvLSTM, ConvGRU) outperform classical recurrent models and improve the thunderstorm forecast by 25–30% in terms of RMSE (root mean squ

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Khozaimi, Ach, and Wayan Firdaus Mahmudy. "New insight in cervical cancer diagnosis using convolution neural network architecture." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3092. http://dx.doi.org/10.11591/ijai.v13.i3.pp3092-3100.

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<p>The Pap smear is a screening method for early cervical cancer diagnosis. The selection of the right optimizer in the convolutional neural network (CNN) model is key to the success of the CNN in image classification, including the classification of cervical cancer Pap smear images. In this study, stochastic gradient descent (SGD), root mean square propagation (RMSprop), Adam, AdaGrad, AdaDelta, Adamax, and Nadam optimizers were used to classify cervical cancer Pap smear images from the SipakMed dataset. Resnet-18, Resnet-34, and VGG-16 are the CNN architectures used in this study, and

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Zhou, Yujie, Ke Xu, and Fei He. "Root cause diagnosis in multivariate time series based on modified temporal convolution and multi-head self-attention." Journal of Process Control 117 (September 2022): 14–25. http://dx.doi.org/10.1016/j.jprocont.2022.06.014.

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Liang, Zhi, Gaojian Cui, Mingming Xiong, Xiaojuan Li, Xiuliang Jin, and Tao Lin. "YOLO-C: An Efficient and Robust Detection Algorithm for Mature Long Staple Cotton Targets with High-Resolution RGB Images." Agronomy 13, no. 8 (2023): 1988. http://dx.doi.org/10.3390/agronomy13081988.

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Under complex field conditions, robust and efficient boll detection at maturity is an important tool for pre-harvest strategy and yield prediction. To achieve automatic detection and counting of long-staple cotton in a natural environment, this paper proposes an improved algorithm incorporating deformable convolution and attention mechanism, called YOLO-C, based on YOLOv7: (1) To capture more detailed and localized features in the image, part of the 3 × 3 convolution in the ELAN layer of the backbone is replaced by deformable convolution to improve the expressiveness and accuracy of the model.

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Yang, Dan, Shijun Li, Yuyu Zhao, Bin Xu, and Wenxu Tian. "An EIT image reconstruction method based on DenseNet with multi-scale convolution." Mathematical Biosciences and Engineering 20, no. 4 (2023): 7633–60. http://dx.doi.org/10.3934/mbe.2023329.

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<abstract> <p>Electrical impedance tomography (EIT) is an imaging technique that non-invasively acquires the electrical conductivity distribution within a field. The ill-posed and nonlinear nature of the image reconstruction process results in lower quality of the obtained images. To solve this problem, an EIT image reconstruction method based on DenseNet with multi-scale convolution named MS-DenseNet is proposed. In the proposed method, three different multi-scale convolutional dense blocks are incorporated to replace the conventional dense blocks; they are placed in parallel to i

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Sooksatra, Sorn, Toshiaki Kondo, Pished Bunnun, and Atsuo Yoshitaka. "Redesigned Skip-Network for Crowd Counting with Dilated Convolution and Backward Connection." Journal of Imaging 6, no. 5 (2020): 28. http://dx.doi.org/10.3390/jimaging6050028.

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Crowd counting is a challenging task dealing with the variation of an object scale and a crowd density. Existing works have emphasized on skip connections by integrating shallower layers with deeper layers, where each layer extracts features in a different object scale and crowd density. However, only high-level features are emphasized while ignoring low-level features. This paper proposes an estimation network by passing high-level features to shallow layers and emphasizing its low-level feature. Since an estimation network is a hierarchical network, a high-level feature is also emphasized by

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Peterson, Amber M., Warren D. Helgason, and Andrew M. Ireson. "Estimating field-scale root zone soil moisture using the cosmic-ray neutron probe." Hydrology and Earth System Sciences 20, no. 4 (2016): 1373–85. http://dx.doi.org/10.5194/hess-20-1373-2016.

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Abstract. Many practical hydrological, meteorological, and agricultural management problems require estimates of soil moisture with an areal footprint equivalent to field scale, integrated over the entire root zone. The cosmic-ray neutron probe is a promising instrument to provide field-scale areal coverage, but these observations are shallow and require depth-scaling in order to be considered representative of the entire root zone. A study to identify appropriate depth-scaling techniques was conducted at a grazing pasture site in central Saskatchewan, Canada over a 2-year period. Area-average

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Peterson, A. M., W. D. Helgason, and A. M. Ireson. "Estimating field scale root zone soil moisture using the cosmic-ray neutron probe." Hydrology and Earth System Sciences Discussions 12, no. 12 (2015): 12789–826. http://dx.doi.org/10.5194/hessd-12-12789-2015.

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Abstract. Many practical hydrological, meteorological and agricultural management problems require estimates of soil moisture with an areal footprint equivalent to "field scale", integrated over the entire root zone. The cosmic-ray neutron probe is a promising instrument to provide field scale areal coverage, but these observations are shallow and require depth scaling in order to be considered representative of the entire root zone. A study to identify appropriate depth-scaling techniques was conducted at a grazing pasture site in central Saskatchewan, Canada over a two year period. Area-aver

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Zhao, Jin, Chengzhong Liu, Junying Han, Yuqian Zhou, Yongsheng Li, and Linzhe Zhang. "Real-Time Corn Variety Recognition Using an Efficient DenXt Architecture with Lightweight Optimizations." Agriculture 15, no. 1 (2025): 79. https://doi.org/10.3390/agriculture15010079.

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As a pillar grain crop in China’s agriculture, the yield and quality of corn are directly related to food security and the stable development of the agricultural economy. Corn varieties from different regions have significant differences inblade, staminate and root cap characteristics, and these differences provide a basis for variety classification. However, variety characteristics may be mixed in actual cultivation, which increases the difficulty of identification. Deep learning classification research based on corn nodulation features can help improve classification accuracy, optimize plant

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Zhang, Shaoxuan, Senxiang Lu, and Xu Dong. "Stress and Corrosion Defect Identification in Weak Magnetic Leakage Signals Using Multi-Graph Splitting and Fusion Graph Convolution Networks." Machines 11, no. 1 (2023): 70. http://dx.doi.org/10.3390/machines11010070.

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Weak magnetic flux leak detection is one of the most important non-destructive testing and measurement methods for pipelines. Since different defects cause different damage, it is necessary to classify the different types of defects. Traditional machine learning methods of defect type identification mainly use feature analysis methods and rely on expert a priori knowledge and the ability of designers. These methods have the following weaknesses: a priori knowledge needs to be designed iteratively, and a priori knowledge design relies on expert experience. In recent years, the rapid development

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Tian, Yukai, Jie Wen, Yanru Yang, Yuanhao Shi, and Jianchao Zeng. "State-of-Health Prediction of Lithium-Ion Batteries Based on CNN-BiLSTM-AM." Batteries 8, no. 10 (2022): 155. http://dx.doi.org/10.3390/batteries8100155.

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State-of-Health (SOH) prediction of lithium-ion batteries is crucial in battery management systems. In order to guarantee the safe operation of lithium-ion batteries, a hybrid model based on convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM) and attention mechanism (AM) is developed to predict the SOH of lithium-ion batteries. By analyzing the charging and discharging process of batteries, the indirect health indicator (HI), which is highly correlated with capacity, is extracted in this paper. HI is taken as the input of CNN, and the convolution and pooling operat

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Almaleh, Abdulaziz. "A Novel Deep Learning Approach for Real-Time Critical Assessment in Smart Urban Infrastructure Systems." Electronics 13, no. 16 (2024): 3286. http://dx.doi.org/10.3390/electronics13163286.

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The swift advancement of communication and information technologies has transformed urban infrastructures into smart cities. Traditional assessment methods face challenges in capturing the complex interdependencies and temporal dynamics inherent in these systems, risking urban resilience. This study aims to enhance the criticality assessment of geographic zones within smart cities by introducing a novel deep learning architecture. Utilizing Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal dependency modeling, the propos

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Hu, Chunwei, Xianfeng Liu, Sheng Wu, Fei Yu, Yongkun Song, and Jin Zhang. "Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure." Applied Sciences 13, no. 12 (2023): 7271. http://dx.doi.org/10.3390/app13127271.

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Accurate crowd flow prediction is essential for traffic guidance and traffic control. However, the high nonlinearity, temporal complexity, and spatial complexity that crowd flow data have makes this problem challenging. This research proposes a dynamic graph convolutional network model (Res-DGCN) based on the residual network structure for crowd inflow and outflow prediction in urban areas. Firstly, as the attention layer, the spatio-temporal attention module (SA) is employed for capturing the spatial relationship between the target node and the multi-order adjacent nodes by processing the fea

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