Cross-Domain Submesoscale Eddy Detection Neural Network for HF Radar

With the rapid development of deep learning, the neural network becomes an efficient approach for eddy detection. However, previous work employs a traditional neural network with a focus on improving the detecting accuracy only using limited data under a single scenario. Meanwhile, the experience of detecting eddies from one experiment is not directly inherited from the detection model for other experiments. Therefore, a cross-domain submesoscale eddy detection neural network (CDEDNet) based on the high-frequency radar (HFR) data of the Nansan and Xuwen region is proposed in this paper. Firstly, a fundamental deep eddy detection architecture CDEDNet-0 is constructed with a fully convolutional network (FCN). Secondly, for solving the problem of insufficient labeled eddy data, an instance-based domain adaption method is adopted in CDEDNet-1 to increase training samples. Thirdly, for tackling the problem of unable to inherit previous detection experience, parameter-based transfer learning is incorporated in CDEDNet-2 for multi-scene eddy detection. The experiment results demonstrate CDEDNet-1 and CDEDNet-2 perform better than CDEDNet-0 in terms of accuracy. Meanwhile, eddy characteristics including eddy type, radius, occurring time, merger, and dynamic trajectory are analyzed for the Nansan and Xuwen regions.

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A Multi-Scale Feature Extraction-Based Normalized Attention Neural Network for Image Denoising

Electronics ◽

10.3390/electronics10030319 ◽

2021 ◽

Vol 10 (3) ◽

pp. 319

Author(s):

Yi Wang ◽

Xiao Song ◽

Guanghong Gong ◽

Ni Li

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Image Denoising ◽

Color Image ◽

Rapid Development ◽

Similarity Index ◽

Structural Similarity ◽

Convolutional Network ◽

Scale Feature ◽

Multi Scale

Due to the rapid development of deep learning and artificial intelligence techniques, denoising via neural networks has drawn great attention due to their flexibility and excellent performances. However, for most convolutional network denoising methods, the convolution kernel is only one layer deep, and features of distinct scales are neglected. Moreover, in the convolution operation, all channels are treated equally; the relationships of channels are not considered. In this paper, we propose a multi-scale feature extraction-based normalized attention neural network (MFENANN) for image denoising. In MFENANN, we define a multi-scale feature extraction block to extract and combine features at distinct scales of the noisy image. In addition, we propose a normalized attention network (NAN) to learn the relationships between channels, which smooths the optimization landscape and speeds up the convergence process for training an attention model. Moreover, we introduce the NAN to convolutional network denoising, in which each channel gets gain; channels can play different roles in the subsequent convolution. To testify the effectiveness of the proposed MFENANN, we used both grayscale and color image sets whose noise levels ranged from 0 to 75 to do the experiments. The experimental results show that compared with some state-of-the-art denoising methods, the restored images of MFENANN have larger peak signal-to-noise ratios (PSNR) and structural similarity index measure (SSIM) values and get better overall appearance.

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Research on the Installation and Alignment Method of Ship Multi-support Bearings Based on Different Confidence-Level Training Samples

Journal of Ship Research ◽

10.5957/josr.11200056 ◽

2021 ◽

pp. 1-9

Author(s):

Yibin Deng ◽

Xiaogang Yang ◽

Shidong Fan ◽

Hao Jin ◽

Tao Su ◽

...

Keyword(s):

Neural Network ◽

Confidence Level ◽

Rapid Development ◽

Maximum Error ◽

Training Sample ◽

Measured Data ◽

Engine Room ◽

Training Samples ◽

Force Coupling ◽

Level Training

Because of the long propulsion shafting of special ships, the number of bearings is large and the number of measured bearing reaction data is small, which makes the installation of shafting difficult. To apply a small amount of measured data to the process of ship installation so as to accurately calculate the displacement value in the actual installation, this article proposes a method to calculate the displacement value of shafting intermediate bearing based on different confidence-level training samples. Taking a ro-ro ship as the research object, this research simulates the actual installation process, gives a higher confidence level to a small amount of measured data, constructs a new training sample set for machine learning, and finally obtains the genetic algorithm-backpropagation(GABP) neural network reflecting the actual installation process. At the same time, this research compares the accuracy between different confidence-level training sample shafting neural network and the shafting neural network without measured data, and the results show that the accuracy of shafting neural network with different confidence-level training samples is higher. Although as the adjustment times and the number of measured data increase, the network accuracy is significantly improved. After adding four measured data, the maximum error is within 1%, which can play a guiding role in the ship propulsion shafting alignment. Introduction With the rapid development of science and technology in the world, special ships such as engineering ships, official ships, and warships play an important role (Carrasco et al. 2020; Prill et al. 2020). Some ships of this special type are limited by various factors such as the stern line of engine room, hull stability, and operation requirements. They usually adopt the layout of middle or front engine room, which causes the propulsion system to have a longer shaft and the number of intermediate shafts and intermediate bearings exceeds two. This forms a so-called multisupport shafting (Lee et al. 2019) and it increases the difficulty of shafting alignment because of the force-coupling between the bearings (Lai et al. 2018a, 2018b). The process of the existing methods for calculating the displacement value is complex, and because of the influence of installation error and other factors, it is necessary to adjust the bearing height several times to make the bearing reaction meet the specification requirements(Kim et al. 2017, Ko et al. 2017). So how to predict the accurate displacement value of each intermediate bearing is the key to solving the problem of multisupport shafting intermediate bearing installation and calibration (Zhou et al. 2005, Xiao-fei et al. 2017).

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Multi-Scale Deep Neural Network for Mitosis Detection in Breast Cancer Histological Images

10.20944/preprints201711.0063.v1 ◽

2017 ◽

Author(s):

Boqian Wu ◽

Binwen Fan ◽

Qiao Xiao ◽

Tasleem Kausar ◽

Wenfeng Wang

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Training Sample ◽

Detection Accuracy ◽

Training Strategy ◽

Detection Model ◽

Multi Scale ◽

Mitosis Detection ◽

Training Samples ◽

Image Analyze

Accurate assessment of the breast cancer deterioration degree plays a crucial role in making medical plan, and the important basis for degree assessment is the number of mitoses in a given area of the pathological image. We utilized deep multi-scale fused fully convolutional neural network (MFF-CNN) combing with conditional random felid (CRF) to detect mitoses in hematoxylin and eosin stained histology image. Analyze the characteristics of mitotic detection ----scale invariance and sparsity, as well as the difficulties ---- small amount of data , inconsistent image staining and sample class unbalanced. Based on this, mitotic detection model is designed. In this paper, a tissue-based staining equalization method is used, and to establish an effective training sample set, we select training samples by using CNN. A mitotic detection model fusing multi-level and multi-scale features and context information was designed, and the corresponding training strategy was made to reduce over-fitting. As preliminarily validated on the public 2014 ICPR MITOSIS data, our method achieves a better performance in term of detection accuracy than ever recorded for this dataset.

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DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning

Sensors ◽

10.3390/s21010126 ◽

2020 ◽

Vol 21 (1) ◽

pp. 126

Author(s):

Fangyuan Liu ◽

Hao Zhou ◽

Biyang Wen

Keyword(s):

Surface Current ◽

Current Data ◽

Hf Radar ◽

Natural Phenomenon ◽

High Frequency Radar ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

Classical Statistics ◽

Oceanic Eddy ◽

Eddy Detection

Oceanic eddy is a common natural phenomenon that has large influence on human activities, and the measurement and detection of offshore eddies are significant for oceanographic research. The previous classical detecting methods, such as the Okubo–Weiss algorithm (OW), vector geometry algorithm (VG), and winding angles algorithm (WA), not only depend on expert’s experiences to set an accurate threshold, but also need heavy calculations for large detection regions. Differently from the previous works, this paper proposes a deep eddy detection neural network with pixel segmentation skeleton on high frequency radar (HFR) data, namely, the deep eddy detection network (DEDNet). An offshore eddy detection dataset is firstly constructed, which has origins from the sea surface current data measured by two HFR systems on the South China Sea. Then, a spatial globally optimum and strong detail-distinguishing pixel segmentation network is presented to automatically detect and localize offshore eddies in a flow chart. An eddy detection network based on fully convolutional networks (FCN) is also presented for comparison with DEDNet. Experimental results show that DEDNet performs better than the FCN-based eddy detection network and is competitive with the classical statistics-based methods.

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A Deep Convolutional Network for Multitype Signal Detection and Classification in Spectrogram

Mathematical Problems in Engineering ◽

10.1155/2020/9797302 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Weihao Li ◽

Keren Wang ◽

Ling You

Keyword(s):

Neural Network ◽

Deep Learning ◽

Wireless Communication ◽

Signal Detection ◽

Rapid Development ◽

Point Estimation ◽

Detection Methods ◽

Convolutional Network ◽

Signal Region ◽

Real Time Applications

Wideband signal detection is an important problem in wireless communication. With the rapid development of deep learning (DL) technology, some DL-based methods are applied to wireless communication and have shown great potential. In this paper, we present a novel neural network for detecting signals and classifying signal types in wideband spectrograms. Our network utilizes the key point estimation to locate the rough centerline of the signal region and recognize its class. Then, several regressions are carried out to obtain properties, including the local offset and the border offsets of a bounding box, which are further synthesized for a more fine location. Experimental results demonstrate that our method performs more accurate than other DL-based object detection methods previously employed for the same task. In addition, our method runs obviously faster than existing methods, and it abandons the candidate anchors, which make it more favorable for real-time applications.

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Automatic Bridge Crack Detection Using a Convolutional Neural Network

Applied Sciences ◽

10.3390/app9142867 ◽

2019 ◽

Vol 9 (14) ◽

pp. 2867 ◽

Cited By ~ 11

Author(s):

Hongyan Xu ◽

Xiu Su ◽

Yi Wang ◽

Huaiyu Cai ◽

Kerang Cui ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Crack Detection ◽

Transportation Safety ◽

Detection Accuracy ◽

Learning Technology ◽

Convolutional Network ◽

Detection Model ◽

Proposed Model ◽

Spatial Pyramid Pooling

Concrete bridge crack detection is critical to guaranteeing transportation safety. The introduction of deep learning technology makes it possible to automatically and accurately detect cracks in bridges. We proposed an end-to-end crack detection model based on the convolutional neural network (CNN), taking the advantage of atrous convolution, Atrous Spatial Pyramid Pooling (ASPP) module and depthwise separable convolution. The atrous convolution obtains a larger receptive field without reducing the resolution. The ASPP module enables the network to extract multi-scale context information, while the depthwise separable convolution reduces computational complexity. The proposed model achieved a detection accuracy of 96.37% without pre-training. Experiments showed that, compared with traditional classification models, the proposed model has a better performance. Besides, the proposed model can be embedded in any convolutional network as an effective feature extraction structure.

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Wind Speed Inversion in High Frequency Radar Based on Neural Network

International Journal of Antennas and Propagation ◽

10.1155/2016/2706521 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 5

Author(s):

Yuming Zeng ◽

Hao Zhou ◽

Hugh Roarty ◽

Biyang Wen

Keyword(s):

Neural Network ◽

Wind Speed ◽

High Frequency ◽

Dynamic Parameter ◽

Hf Radar ◽

Data Sets ◽

High Frequency Radar ◽

Wide Range ◽

Radar Echo ◽

Buoy Data

Wind speed is an important sea surface dynamic parameter which influences a wide variety of oceanic applications. Wave height and wind direction can be extracted from high frequency radar echo spectra with a relatively high accuracy, while the estimation of wind speed is still a challenge. This paper describes an artificial neural network based method to estimate the wind speed in HF radar which can be trained to store the specific but unknown wind-wave relationship by the historical buoy data sets. The method is validated by one-month-long data of SeaSonde radar, the correlation coefficient between the radar estimates and the buoy records is 0.68, and the root mean square error is 1.7 m/s. This method also performs well in a rather wide range of time and space (2 years around and 360 km away). This result shows that the ANN is an efficient tool to help make the wind speed an operational product of the HF radar.

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Neural Network Topology Construction and Classroom Interaction Benchmark Graph Based on Big Data Analysis

Wireless Communications and Mobile Computing ◽

10.1155/2021/2334443 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Congcong Luan ◽

Peng Shang

Keyword(s):

Neural Network ◽

Face Recognition ◽

Network Model ◽

Neural Network Model ◽

Rapid Development ◽

Video Recording ◽

Classroom Interaction ◽

Recognition Method ◽

Convolutional Network ◽

Status Analysis

With the rapid development of artificial intelligence and deep learning in recent years, many universities have put forward the goal of achieving digitalization, intelligent, and education informatization on campus. Throughout the lecture and learning process, the classroom status is an important reference factor to assess students’ acceptance of the course and the quality of lectures. However, at present, classroom status analysis is mainly conducted manually, which can distract teachers’ attention, so it is of great research significance to find a method that can improve the efficiency of classroom status analysis. In this paper, we choose an offline method to analyze the status of a classroom video recording in terms of students’ behavior and attendance in terms of frames, in which student behavior is identified by an improved target detection algorithm and attendance is analyzed by face recognition. By analyzing the structure of the neural network model, an improved neural network model is proposed for its characteristics of a large number of parameters and poor detection of small targets in the basic network. The backbone network is replaced by the improved neural network, and the depth-separable convolutional network is used to reduce the network parameters and increase the computation speed. The information in the deeper feature map is fused upward into the shallow layer to improve the accuracy of small target recognition. Finally, the optimization algorithm is incorporated into the network to optimize the network model and accelerate the model convergence speed. In addition, this paper incorporates the improved behavior recognition method and face recognition method into the system to realize the analysis of the offline classroom status. The system is divided into a teacher side and a management side, where the teacher side is responsible for uploading course recordings and the management side is responsible for randomly analyzing students’ status and attendance at any time, and the combination of the two forms a convenient and comprehensive classroom status analysis system platform. Users can upload classroom videos through the instructor interface and can view the classroom status analysis results of a course at any time by searching randomly in the administration. In this paper, the classroom status is mainly judged by the recognition of students’ behaviors.

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Mobile App Cross-Domain Recommendation with Multi-Graph Neural Network

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3442201 ◽

2021 ◽

Vol 15 (4) ◽

pp. 1-21

Author(s):

Yi Ouyang ◽

Bin Guo ◽

Xing Tang ◽

Xiuqiang He ◽

Jian Xiong ◽

...

Keyword(s):

Neural Network ◽

Mobile Apps ◽

Rapid Development ◽

Representation Learning ◽

Adaptive Method ◽

Mobile App ◽

Cross Domain ◽

Real World Datasets ◽

Graph Neural Networks ◽

Novel Model

With the rapid development of mobile app ecosystem, mobile apps have grown greatly popular. The explosive growth of apps makes it difficult for users to find apps that meet their interests. Therefore, it is necessary to recommend user with a personalized set of apps. However, one of the challenges is data sparsity, as users’ historical behavior data are usually insufficient. In fact, user’s behaviors from different domains in app store regarding the same apps are usually relevant. Therefore, we can alleviate the sparsity using complementary information from correlated domains. It is intuitive to model users’ behaviors using graph, and graph neural networks have shown the great power for representation learning. In this article, we propose a novel model, Deep Multi-Graph Embedding (DMGE), to learn cross-domain app embedding. Specifically, we first construct a multi-graph based on users’ behaviors from different domains, and then propose a multi-graph neural network to learn cross-domain app embedding. Particularly, we present an adaptive method to balance the weight of each domain and efficiently train the model. Finally, we achieve cross-domain app recommendation based on the learned app embedding. Extensive experiments on real-world datasets show that DMGE outperforms other state-of-art embedding methods.

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Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189290 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8927-8935

Author(s):

Bing Zheng ◽

Dawei Yun ◽

Yan Liang

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Behavior Pattern ◽

Rapid Development ◽

Video Data ◽

Support Vector ◽

Behavior Recognition ◽

Learning Methods ◽

The Impact ◽

Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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