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Ship Trajectory Prediction Based on BP Neural Network

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Journal on Artificial Intelligence 2019年第1期1卷 29-36页

作者： Hai Zhou Yaojie Chen Sumin Zhang School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan430065China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and TechnologyWuhan430065China School of Information Systems and Technology Drexel UniversityPhiladelphia19104The United States

In recent years,with the prosperity of world trade,the water transport industry has developed rapidly,the number of ships has surged,and ship safety accidents in busy waters and complex waterways have become more *** the movement of the ship and analyzing the trajectory of the ship are of great significance for improving the safety level of the *** at the multi-dimensional characteristics of ship navigation behavior and the accuracy and real-time requirements of ship traffic service system for ship trajectory prediction,a ship navigation trajectory prediction method combining ship automatic identification system information and Back Propagation(BP)neural network are *** to the basic principle of BP neural network structure,the BP neural network is trained by taking the characteristic values of ship navigation behavior at three consecutive moments as input and the characteristic values of ship navigation behavior at the fourth moment as output to predict the future ship navigation *** on the Automatic Identification System(AIS)information of the waters near the Nanpu Bridge in Pudong New Area,shanghai,the results show that the method is used to predict the ship's navigational behavior eigenvalues accurately and in real *** with the traditional kinematics prediction trajectory method,the model can effectively predict ship *** trajectory improves the accuracy of the ship's motion situation prediction,and has the advantages of high computational efficiency and strong versatility,and the error is within an acceptable range.

关键词： Maritime safety back propagation neural network automatic identification system track prediction

Fusing Motion Patterns and key Visual information for Semantic Event Recognition in Basketball Videos

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arXiv 2020年

作者： Wu, Lifang Yang, Zhou Wang, Qi Jian, Meng Zhao, Boxuan Yan, Junchi Chen, Chang Wen Beijing University of Technology Beijing China Beijing Municipal Key Lab of Computation Intelligence and Intelligent Systems Beijing China Shanghai Jiao Tong University Shanghai China School of Science and Engineering Chinese University of Hong Kong Shenzhen Hong Kong Department of Computer Science and Engineering SUNY Buffalo NY United States

Many semantic events in team sport activities e.g. basketball often involve both group activities and the outcome (score or not). Motion patterns can be an effective means to identify different activities. Global and local motions have their respective emphasis on different activities, which are difficult to capture from the optical flow due to the mixture of global and local motions. Hence it calls for a more effective way to separate the global and local motions. When it comes to the specific case for basketball game analysis, the successful score for each round can be reliably detected by the appearance variation around the basket. Based on the observations, we propose a scheme to fuse global and local motion patterns (MPs) and key visual information (KVI) for semantic event recognition in basketball videos. Firstly, an algorithm is proposed to estimate the global motions from the mixed motions based on the intrinsic property of camera adjustments. And the local motions could be obtained from the mixed and global motions. Secondly, a two-stream 3D CNN framework is utilized for group activity recognition over the separated global and local motion patterns. Thirdly, the basket is detected and its appearance features are extracted through a CNN structure. The features are utilized to predict the success or failure. Finally, the group activity recognition and success/failure prediction results are integrated using the kronecker product for event recognition. Experiments on NCAA dataset demonstrate that the proposed method obtains state-of-the-art performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

Phrase-based hashtag recommendation for microblog posts

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science China(information sciences) 2017年第1期60卷 132-144页

作者： Yeyun GONG Qi ZHANG Xiaoying HAN Xuanjing HUANG Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University

In microblogs, authors use hashtags to mark keywords or topics. These manually labeled tags can be used to benefit various live social media applications(e.g., microblog retrieval, classification). However,because only a small portion of microblogs contain hashtags, recommending hashtags for use in microblogs are a worthwhile exercise. In addition, human inference often relies on the intrinsic grouping of words into ***, existing work uses only unigrams to model corpora. In this work, we propose a novel phrase-based topical translation model to address this problem. We use the bag-of-phrases model to better capture the underlying topics of posted microblogs. We regard the phrases and hashtags in a microblog as two different languages that are talking about the same thing. Thus, the hashtag recommendation task can be viewed as a translation process from phrases to hashtags. To handle the topical information of microblogs, the proposed model regards translation probability as being topic specific. We test the methods on data collected from realworld microblogging services. The results demonstrate that the proposed method outperforms state-of-the-art methods that use the unigram model.

关键词： recommendation topic model translation phrase extraction hashtag

Multiple reconstruction compression framework based on PNG image

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arXiv 2019年

作者： Lu, Zhiqing Yin, Zhaoxia Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

It is shown that neural networks (NNs) achieve excellent performances in image compression and reconstruction. However, there are still many shortcomings in the practical application, which eventually lead to the loss of neural network image processing ability. Based on this, this paper proposes a joint framework based on neural network and zoom compression. The framework first encodes the incoming PNG or JPEG image information, and then the image is converted into binary input decoder to reconstruct the intermediate state image, next we import the intermediate state image into the zooming compressor and re-pressurize it, and reconstruct the final image. From the experimental results, this method can better process the digital image and suppress the reverse expansion problem, and the compression effect can be improved by 4 to 10 times as much as that of using RNN alone, showing better ability in application. In this paper, the method is transmitted over a digital image, the effect is far better than the existing compression method alone, the Human visual system can not feel the change of the effect. Copyright © 2019, The Authors. All rights reserved.

关键词： Image compression

Learning a Layout Transfer Network for Context Aware Object Detection

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arXiv 2019年

作者： Wang, Tao He, Xuming Cai, Yuanzheng Xiao, Guobao Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Information Science and Technology ShanghaiTech University Shanghai China College of Mathematics and Computer Science Fuzhou University NetDragon Fuzhou China

We present a context aware object detection method based on a retrieve-and-transform scene layout model. Given an input image, our approach first retrieves a coarse scene layout from a codebook of typical layout templates. In order to handle large layout variations, we use a variant of the spatial transformer network [1] to transform and refine the retrieved layout, resulting in a set of interpretable and semantically meaningful feature maps of object locations and scales. The above steps are implemented as a Layout Transfer Network which we integrate into Faster RCNN [2] to allow for joint reasoning of object detection and scene layout estimation. Extensive experiments on three public datasets verified that our approach provides consistent performance improvements to the state-of-the-art object detection baselines on a variety of challenging tasks in the traffic surveillance and the autonomous driving domains. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

Edge-guided non-local fully convolutional network for salient object detection

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arXiv 2019年

作者： Tu, Zhengzheng Ma, Yan Li, Chenglong Tang, Jin Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Institute of Physical Science and Information Technology Anhui University Hefei230601 China

Fully Convolutional Neural Network (FCN) has been widely applied to salient object detection recently by virtue of high-level semantic feature extraction, but existing FCN-based methods still suffer from continuous striding and pooling operations leading to loss of spatial structure and blurred edges. To maintain the clear edge structure of salient objects, we propose a novel Edge-guided Non-local FCN (ENFNet) to perform edge-guided feature learning for accurate salient object detection. In a specific, we extract hierarchical global and local information in FCN to incorporate non-local features for effective feature representations. To preserve good boundaries of salient objects, we propose a guidance block to embed edge prior knowledge into hierarchical feature maps. The guidance block not only performs feature-wise manipulation but also spatial-wise transformation for effective edge embeddings. Our model is trained on the MSRA-B dataset and tested on five popular benchmark datasets. Comparing with the state-of-the-art methods, the proposed method achieves the best performance on all datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

Robust Image Hashing via Random Gabor Filtering and DWT

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computers, Materials & Continua 2018年第5期55卷 331-344页

作者： Zhenjun Tang Man Ling Heng Yao Zhenxing Qian Xianquan Zhang Jilian Zhang Shijie Xu Guangxi Key Lab of Multi-Source Information Mining&Security Guangxi Normal UniversityGuilin541004China Shanghai Institute of Intelligent Electronics&Systems School of Computer ScienceFudan UniversityShanghai200433China School of Information Systems Singapore Management University178902Singapore

Image hashing is a useful multimedia technology for many applications,such as image authentication,image retrieval,image copy detection and image *** this paper,we propose a robust image hashing based on random Gabor filtering and discrete wavelet transform(DWT).Specifically,robust and secure image features are first extracted from the normalized image by Gabor filtering and a chaotic map called Skew tent map,and then are compressed via a single-level 2-D *** hash is finally obtained by concatenating DWT coefficients in the LL *** experiments with open image datasets are carried out and the results illustrate that our hashing is robust,discriminative and *** operating characteristic(ROC)curve comparisons show that our hashing is better than some popular image hashing algorithms in classification performance between robustness and discrimination.

关键词： Image hashing Gabor filtering chaotic map skew tent map discrete wavelet transform.

An experimental-based review of image enhancement and image restoration methods for underwater imaging

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arXiv 2019年

作者： Wang, Yan Song, Wei Fortino, Giancarlo Qi, Lizhe Zhang, Wenqiang Liotta, Antonio Academy for Engineering & Technology Fudan University China College of Information Technology Shanghai Ocean University Shanghai201306 China University of Calabria Italy Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Napier Edinburgh University School of Computing Edinburgh United Kingdom

Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium. Although major breakthroughs have been made recently in the general area of image enhancement and restoration, the applicability of new methods for improving the quality of underwater images has not specifically been captured. In this paper, we review the image enhancement and restoration methods that tackle typical underwater image impairments, including some extreme degradations and distortions. Firstly, we introduce the key causes of quality reduction in underwater images, in terms of the underwater image formation model (IFM). Then, we review underwater restoration methods, considering both the IFM-free and the IFM-based approaches. Next, we present an experimental-based comparative evaluation of state-of-the-art IFM-free and IFM-based methods, considering also the prior-based parameter estimation algorithms of the IFM-based methods, using both subjective and objective analysis (the used code is freely available at https://***/wangyanckxx/Single-UnderwaterImage-Enhancement-and-Color-Restoration). Starting from this study, we pinpoint the key shortcomings of existing methods, drawing recommendations for future research in this area. Our review of underwater image enhancement and restoration provides researchers with the necessary background to appreciate challenges and opportunities in this important field. Copyright © 2019, The Authors. All rights reserved.

关键词： Image reconstruction

Motif Discovery via Convolutional Networks with K-mer Embedding 15th

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Motif Discovery via Convolutional Networks with K-mer Embedd...

15th International Conference on intelligent Computing, ICIC 2019

作者： Wang, Dailun Zhang, Qinhu Yuan, Chang-An Qin, Xiao Huang, Zhi-Kai Shang, Li Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China College of Mechanical and Electrical Engineering Nanchang Institute of Technology NanchangJiangxi330099 China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China

ISBN: (纸本)9783030269685

With the rapid development of deep learning, some discriminative motif discovery methods based on deep neural network are gradually becoming the mainstream, which also bringing huge improvement of prediction accuracy. In this paper, we propose a convolutional neural network based architecture (eCNN), combining embedding layer with GloVe. Firstly, eCNN divides each single sequence of ChIP-seq datasets into multiple subsequences called k-mers by a sliding window, and then encoding k-mers into a relatively low dimension vectors by GloVe, and finally scores each vector using multiple convolutional networks. The experiment shows that our architecture can get good results on the task of motif discovery. © 2019, Springer Nature Switzerland AG.

关键词： Embeddings