Vector-kernel convolutional neural networks

Ou, Jun; Li, Yujian

doi:10.1016/j.neucom.2018.11.028

Cited by 46 publications

(8 citation statements)

References 17 publications

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“…Among ANNs, convolutional neural networks (CNNs) take advantage of their input hierarchical shape [46][47][48][49] . CNNs have been applied to a large variety of research fields 50,51 , and are recently being exploited for turbulent-flow estimation. For instance, CNNs were used in Ref.…”

Section: Introductionmentioning

confidence: 99%

Sensing the turbulent large-scale motions with their wall signature

2019

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This study assesses the capability of extended proper orthogonal decomposition (EPOD) and convolutional neural networks (CNNs) to reconstruct large-scale and very-large-scale motions (LSMs and VLSMs respectively) employing wall-shear-stress measurements in wall-bounded turbulent flows. Both techniques are used to reconstruct the instantaneous LSMs evolution in the flow field as a combination of proper orthogonal decomposition (POD) modes, employing a limited set of instantaneous wall-shear-stress measurements. Due to the dominance of non-linear effects, only CNNs provide satisfying results. Being able to account for nonlinearities in the flow, CNNs are shown to perform significantly better than EPOD both in terms of instantaneous flow field estimation and turbulent statistics reconstruction. CNNs are able to provide a more effective reconstruction performance employing more POD modes, at larger distances from the wall and employing lower wall-measurement resolutions. Furthermore, the capability of tackling non-linear features of CNNs results in estimation capabilities that are weakly dependent on the distance from the wall.

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Section: Introductionmentioning

confidence: 99%

Sensing the turbulent large-scale motions with their wall signature

2019

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“…The architecture of this model is similar to that of CFNN, and the traditional convolutional layer is replaced with FIGURE 4. Network architecture of mYOLOv4-tiny a two-layer vector kernel convolutional layer [47] to further reduce the number of parameters and computational complexity of the model. (…”

Section: Vehicle Classification Using Cfnnmentioning

confidence: 99%

Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Lin

Jhang

2022

IEEE Access

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With the rapid pace of urbanization, the number of vehicles traveling between cities has increased significantly. Consequently, many traffic-related problems have emerged, such as traffic jams and excessive numbers and types of vehicles. To solve traffic problems, road data collection is important. Therefore, in this paper, we develop an intelligent traffic-monitoring system based on you only look once (YOLO) and a convolutional fuzzy neural network (CFNN), which record traffic volume, and vehicle type information from the road. In this system, YOLO is first used to detect vehicles and is combined with a vehicle-counting method to calculate traffic flow. Then, two effective models (CFNN and Vector-CFNN) and a network mapping fusion method are proposed for vehicle classification. In our experiments, the proposed method achieved an accuracy of 90.45% on the Beijing Institute of Technology public dataset. On the GRAM-RTM data set, the mean average precision and F-measure (F1) of the proposed YOLO-CFNN and YOLO-VCFNN vehicle classification methods are 99%, superior to those of other methods. On actual roads in Taiwan, the proposed YOLO-CFNN and YOLO-VCFNN methods not only have a high F1 score for vehicle classification but also have outstanding accuracy in vehicle counting. In addition, the proposed system can maintain a detection speed of more than 30 frames per second in the AGX embedded platform. Therefore, the proposed intelligent traffic monitoring system is suitable for real-time vehicle classification and counting in the actual environment.

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“…Besides the application of CNNs on image scenarios (data in 2D format), with several popular architectures such as AlexNet, VGG, GoogleNet, ResNet, and Inception, and worldwide image processing competitions (e.g., ILSVRC—ImageNet Large Scale Visual Recognition Challenge and VOC—PASCAL Visual Object Classes), CNNs structures make it possible to process other types of data, such as natural language, video, speech, or audio [ 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Neural Architecture Search for 1D CNNs—Different Approaches Tests and Measurements

Cordeiro

Raimundo

Postolache

et al. 2021

Sensors

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In the field of sensors, in areas such as industrial, clinical, or environment, it is common to find one dimensional (1D) formatted data (e.g., electrocardiogram, temperature, power consumption). A very promising technique for modelling this information is the use of One Dimensional Convolutional Neural Networks (1D CNN), which introduces a new challenge, namely how to define the best architecture for a 1D CNN. This manuscript addresses the concept of One Dimensional Neural Architecture Search (1D NAS), an approach that automates the search for the best combination of Neuronal Networks hyperparameters (model architecture), including both structural and training hyperparameters, for optimising 1D CNNs. This work includes the implementation of search processes for 1D CNN architectures based on five strategies: greedy, random, Bayesian, hyperband, and genetic approaches to perform, collect, and analyse the results obtained by each strategy scenario. For the analysis, we conducted 125 experiments, followed by a thorough evaluation from multiple perspectives, including the best-performing model in terms of accuracy, consistency, variability, total running time, and computational resource consumption. Finally, by presenting the optimised 1D CNN architecture, the results for the manuscript’s research question (a real-life clinical case) were provided.

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Vector-kernel convolutional neural networks

Cited by 46 publications

References 17 publications

Sensing the turbulent large-scale motions with their wall signature

Sensing the turbulent large-scale motions with their wall signature

Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Neural Architecture Search for 1D CNNs—Different Approaches Tests and Measurements

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