Tomoyuki Enomoto, Kazuhiko Kakuda, Shinichiro Miura

The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.2, pp. 36-39, 2019, DOI:10.32604/icces.2019.05292

Abstract In this paper, the nonlinear activation functions based on fluid dynamics are presented. We propose two types of activation functions by applying the so-called parametric softsign to the negative region. We apply the activation function to CNN (Convolutional Neural Network) which performs image recognition and approaches from multiple benchmark datasets such as MNIST, CIFAR-10. Numerical results demonstrate the workability and the validity of the present approach through comparison with other numerical performances. More >

A. Kuwata¹, H. Noguchi², H. Kawai³

The International Conference on Computational & Experimental Engineering and Sciences, Vol.2, No.3, pp. 87-92, 2007, DOI:10.3970/icces.2007.002.087

Abstract In this research, we constructed an e-learning system of the CAE (FEM) education. We analyzed about the usefulness of e-learning from each viewpoint of a software developer of the e-learning system, a system administrator of the system, and a user. In addition, we considered about the necessary technology in Web application. More >

Jun Wu^1,*, Kui Hu¹, Yiwei Cheng², Ji Wang¹, Chao Deng^2,*, Yuanhan Wang³

Structural Durability & Health Monitoring, Vol.13, No.3, pp. 317-329, 2019, DOI:10.32604/sdhm.2019.05571

Abstract Residual useful life (RUL) prediction is a key issue for improving efficiency of aircraft engines and reducing their maintenance cost. Owing to various failure mechanism and operating environment, the application of classical models in RUL prediction of aircraft engines is fairly difficult. In this study, a novel RUL prognostics method based on using ensemble recurrent neural network to process massive sensor data is proposed. First of all, sensor data obtained from the aircraft engines are preprocessed to eliminate singular values, reduce random fluctuation and preserve degradation trend of the raw sensor data. Secondly, three kinds of recurrent neural networks (RNN),… More >

Qiang Liu^1,*, Yanyun Zou^2,3, Xiaodong Liu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 617-637, 2019, DOI:10.32604/cmes.2019.06272

Abstract Haze-fog, which is an atmospheric aerosol caused by natural or man-made factors, seriously affects the physical and mental health of human beings. PM_2.5 (a particulate matter whose diameter is smaller than or equal to 2.5 microns) is the chief culprit causing aerosol. To forecast the condition of PM_2.5, this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM_2.5. Since the meteorological data and air pollutes data are typical time series data, it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network (SSHL-LSTMNN) containing memory capability… More >

Syed Falahuddin Quadri¹, Xiaoyu Li^1,*, Desheng Zheng², Muhammad Umar Aftab¹, Yiming Huang³

Journal on Big Data, Vol.1, No.1, pp. 1-7, 2019, DOI:10.32604/jbd.2019.05899

Abstract Given the glut of information on the web, it is crucially important to have a system, which will parse the information appropriately and recommend users with relevant information, this class of systems is known as Recommendation Systems (RS)-it is one of the most extensively used systems on the web today. Recently, Deep Learning (DL) models are being used to generate recommendations, as it has shown state-of-the-art (SoTA) results in the field of Speech Recognition and Computer Vision in the last decade. However, the RS is a much harder problem, as the central variable in the recommendation system’s environment is the… More >

Jiaxin Wang^1,*, Mengxin Cheng¹, Peng Wu¹, Beijing Chen^1,2

Journal of Information Hiding and Privacy Protection, Vol.1, No.2, pp. 87-93, 2019, DOI:10.32604/jihpp.2019.07189

Abstract Internet brings us not only the convenience of communication but also some security risks, such as intercepting information and stealing information. Therefore, some important information needs to be hidden during communication. Steganography is the most common information hiding technology. This paper provides a literature review on digital image steganography. The existing steganography algorithms are classified into traditional algorithms and deep learning-based algorithms. Moreover, their advantages and weaknesses are pointed out. Finally, further research directions are discussed. More >

Yongxiang Gu¹, Beijing Chen¹, Xu Cheng^1,*, Yifeng Zhang^2,3, Jingang Shi⁴

Journal of Information Hiding and Privacy Protection, Vol.1, No.1, pp. 23-33, 2019, DOI:10.32604/jihpp.2019.05938

Abstract Recently, deep learning has achieved great success in visual tracking. The goal of this paper is to review the state-of-the-art tracking methods based on deep learning. First, we categorize the existing deep learning based trackers into three classes according to network structure, network function and network training. For each categorize, we analyze papers in different categories. Then, we conduct extensive experiments to compare the representative methods on the popular OTB-100, TC-128 and VOT2015 benchmarks. Based on our observations. We conclude that: (1) The usage of the convolutional neural network (CNN) model could significantly improve the tracking performance. (2) The trackers… More >

Yuxi Jia¹, Feng Li^1,2, Fei Wang^1,2,*, Yan Gui^1,2,3

Journal of Information Hiding and Privacy Protection, Vol.1, No.1, pp. 11-21, 2019, DOI:10.32604/jihpp.2019.05979

Abstract The Brain-Computer Interfaces (BCIs) had been proposed and used in therapeutics for decades. However, the need of time-consuming calibration phase and the lack of robustness, which are caused by little-labeled data, are restricting the advance and application of BCI, especially for the BCI based on motor imagery (MI). In this paper, we reviewed the recent development in the machine learning algorithm used in the MI-based BCI, which may provide potential solutions for addressing the issue. We classified these algorithms into two categories, namely, and enhancing the representation and expanding the training set. Specifically, these methods of enhancing the representation of… More >

Fangming Bi^1,2, Xin Ma^1,2, Wei Chen^1,2,*, Weidong Fang³, Huayi Chen^1,2, Jingru Li^1,2, Biruk Assefa^1,4

Journal of New Media, Vol.1, No.2, pp. 63-74, 2019, DOI:10.32604/jnm.2019.06253

Abstract Video object tracking is an important research topic of computer vision, which finds a wide range of applications in video surveillance, robotics, human-computer interaction and so on. Although many moving object tracking algorithms have been proposed, there are still many difficulties in the actual tracking process, such as illumination change, occlusion, motion blurring, scale change, self-change and so on. Therefore, the development of object tracking technology is still challenging. The emergence of deep learning theory and method provides a new opportunity for the research of object tracking, and it is also the main theoretical framework for the research of moving… More >

Jiahui He¹, Chaozhi Wang¹, Hongyu Wu¹, Leiming Yan^1,*, Christian Lu²

Journal of New Media, Vol.1, No.2, pp. 51-61, 2019, DOI:10.32604/jnm.2019.06238

Abstract Multi-label text categorization refers to the problem of categorizing text through a multi-label learning algorithm. Text classification for Asian languages such as Chinese is different from work for other languages such as English which use spaces to separate words. Before classifying text, it is necessary to perform a word segmentation operation to convert a continuous language into a list of separate words and then convert it into a vector of a certain dimension. Generally, multi-label learning algorithms can be divided into two categories, problem transformation methods and adapted algorithms. This work will use customer's comments about some hotels as a… More >