Junyu Dong

Bio: Junyu Dong is an academic researcher from Ocean University of China. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 30, co-authored 399 publications receiving 3553 citations. Previous affiliations of Junyu Dong include Qingdao University.

Prediction of Sea Surface Temperature Using Long Short-Term Memory

Abstract: This letter adopts long short-term memory (LSTM) to predict sea surface temperature (SST), and makes short-term prediction, including one day and three days, and long-term prediction, including weekly mean and monthly mean The SST prediction problem is formulated as a time series regression problem The proposed network architecture is composed of two kinds of layers: an LSTM layer and a full-connected dense layer The LSTM layer is utilized to model the time series relationship The full-connected layer is utilized to map the output of the LSTM layer to a final prediction The optimal setting of this architecture is explored by experiments and the accuracy of coastal seas of China is reported to confirm the effectiveness of the proposed method The prediction accuracy is also tested on the SST anomaly data In addition, the model’s online updated characteristics are presented

Automatic Change Detection in Synthetic Aperture Radar Images Based on PCANet

Abstract: This letter presents a novel change detection method for multitemporal synthetic aperture radar images based on PCANet. This method exploits representative neighborhood features from each pixel using PCA filters as convolutional filters. Thus, the proposed method is more robust to the speckle noise and can generate change maps with less noise spots. Given two multitemporal images, Gabor wavelets and fuzzy $c$ -means are utilized to select interested pixels that have high probability of being changed or unchanged. Then, new image patches centered at interested pixels are generated and a PCANet model is trained using these patches. Finally, pixels in the multitemporal images are classified by the trained PCANet model. The PCANet classification result and the preclassification result are combined to form the final change map. The experimental results obtained on three real SAR image data sets confirm the effectiveness of the proposed method.

A CFCC-LSTM Model for Sea Surface Temperature Prediction

Abstract: Sea surface temperature (SST) prediction is not only theoretically important but also has a number of practical applications across a variety of ocean-related fields. Although a large amount of SST data obtained via remote sensor are available, previous work rarely attempted to predict future SST values from history data in spatiotemporal perspective. This letter regards SST prediction as a sequence prediction problem and builds an end-to-end trainable long short term memory (LSTM) neural network model. LSTM naturally has the ability to learn the temporal relationship of time series data. Besides temporal information, spatial information is also included in our LSTM model. The local correlation and global coherence of each pixel can be expressed and retained by patches with fixed dimensions. The proposed model essentially combines the temporal and spatial information to predict future SST values. Its structure includes one fully connected LSTM layer and one convolution layer. Experimental results on two data sets, i.e., one Advanced Very High Resolution Radiometer SST data set covering China Coastal waters and one National Oceanic and Atmospheric Administration High-Resolution SST data set covering the Bohai Sea, confirmed the effectiveness of the proposed model.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.