Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (64)
  • Open Access


    Analysis of Campus Network Security

    Han Chu, Haoliang Lan*, Jie Xu, Xiao Feng Sun

    Journal of New Media, Vol.4, No.4, pp. 219-229, 2022, DOI:10.32604/jnm.2022.034830

    Abstract Campus network provides a critical stage to student service and campus administration, which assumes a paramount part in the strategy of ‘Rejuvenating the Country through Science and Education’ and ‘Revitalizing China through Talented Persons’. However, with the rapid development and continuous expansion of campus network, network security needs to be an essential issue that could not be overlooked in campus network construction. In order to ensure the normal operation of various functions of the campus network, the security risk level of the campus network is supposed to be controlled within a reasonable range at any moment. Through literature research, theory… More >

  • Open Access


    A New Intrusion Detection Algorithm AE-3WD for Industrial Control Network

    Yongzhong Li1,2,*, Cong Li1, Yuheng Li3, Shipeng Zhang2

    Journal of New Media, Vol.4, No.4, pp. 205-217, 2022, DOI:10.32604/jnm.2022.034778

    Abstract In this paper, we propose a intrusion detection algorithm based on auto-encoder and three-way decisions (AE-3WD) for industrial control networks, aiming at the security problem of industrial control network. The ideology of deep learning is similar to the idea of intrusion detection. Deep learning is a kind of intelligent algorithm and has the ability of automatically learning. It uses self-learning to enhance the experience and dynamic classification capabilities. We use deep learning to improve the intrusion detection rate and reduce the false alarm rate through learning, a denoising AutoEncoder and three-way decisions intrusion detection method AE-3WD is proposed to improve… More >

  • Open Access


    Application of Federated Learning Algorithm Based on K-Means in Electric Power Data

    Weimin He, Lei Zhao*

    Journal of New Media, Vol.4, No.4, pp. 191-203, 2022, DOI:10.32604/jnm.2022.032994

    Abstract Accurate electricity forecasting is the key basis for guiding the power sector to arrange operation plans and guaranteeing the profitability of electric power companies. However, with the increasing demand of enterprises and departments for data security, the phenomenon of “Isolated Data Island” becomes more and more serious, resulting in the accuracy loss of the traditional electricity prediction model. Federated learning, as an emerging artificial intelligence technology, is designed to ensure data privacy while carrying out efficient machine learning, which provides a new way to solve the problem of “Isolated Data Island” in terms of electricity forecasting. Nonetheless, due to the… More >

  • Open Access


    Build Gaussian Distribution Under Deep Features for Anomaly Detection and Localization

    Mei Wang1,*, Hao Xu2, Yadang Chen1

    Journal of New Media, Vol.4, No.4, pp. 179-190, 2022, DOI:10.32604/jnm.2022.032447

    Abstract Anomaly detection in images has attracted a lot of attention in the field of computer vision. It aims at identifying images that deviate from the norm and segmenting the defect within images. However, anomalous samples are difficult to collect comprehensively, and labeled data is costly to obtain in many practical scenarios. We proposes a simple framework for unsupervised anomaly detection. Specifically, the proposed method directly employs CNN pre-trained on ImageNet to extract deep features from normal images and reduce dimensionality based on Principal Components Analysis (PCA), then build the distribution of normal features via the multivariate Gaussian (MVG), and determine… More >

  • Open Access


    Remote Sensing Plateau Forest Segmentation with Boundary Preserving Double Loss Function Collaborative Learning

    Ying Ma1, Jiaqi Zhang2,3,4, Pengyu Liu1,2,3,4,*, Zhihao Wei5, Lingfei Zhang1, Xiaowei Jia6

    Journal of New Media, Vol.4, No.4, pp. 165-177, 2022, DOI:10.32604/jnm.2022.026684

    Abstract Plateau forest plays an important role in the high-altitude ecosystem, and contributes to the global carbon cycle. Plateau forest monitoring request in-suit data from field investigation. With recent development of the remote sensing technic, large-scale satellite data become available for surface monitoring. Due to the various information contained in the remote sensing data, obtain accurate plateau forest segmentation from the remote sensing imagery still remain challenges. Recent developed deep learning (DL) models such as deep convolutional neural network (CNN) has been widely used in image processing tasks, and shows possibility for remote sensing segmentation. However, due to the unique characteristics… More >

  • Open Access


    Semi-Supervised Medical Image Segmentation Based on Generative Adversarial Network

    Yun Tan1,2, Weizhao Wu2, Ling Tan3, Haikuo Peng2, Jiaohua Qin2,*

    Journal of New Media, Vol.4, No.3, pp. 155-164, 2022, DOI:10.32604/jnm.2022.031113

    Abstract At present, segmentation for medical image is mainly based on fully supervised model training, which consumes a lot of time and labor for dataset labeling. To address this issue, we propose a semi-supervised medical image segmentation model based on a generative adversarial network framework for automated segmentation of arteries. The network is mainly composed of two parts: a segmentation network for medical image segmentation and a discriminant network for evaluating segmentation results. In the initial stage of network training, a fully supervised training method is adopted to make the segmentation network and the discrimination network have certain segmentation and discrimination… More >

  • Open Access


    Microphone Array-Based Sound Source Localization Using Convolutional Residual Network

    Ziyi Wang1, Xiaoyan Zhao1,*, Hongjun Rong1, Ying Tong1, Jingang Shi2

    Journal of New Media, Vol.4, No.3, pp. 145-153, 2022, DOI:10.32604/jnm.2022.030178

    Abstract Microphone array-based sound source localization (SSL) is widely used in a variety of occasions such as video conferencing, robotic hearing, speech enhancement, speech recognition and so on. The traditional SSL methods cannot achieve satisfactory performance in adverse noisy and reverberant environments. In order to improve localization performance, a novel SSL algorithm using convolutional residual network (CRN) is proposed in this paper. The spatial features including time difference of arrivals (TDOAs) between microphone pairs and steered response power-phase transform (SRP-PHAT) spatial spectrum are extracted in each Gammatone sub-band. The spatial features of different sub-bands with a frame are combine into a… More >

  • Open Access


    Menu Text Recognition of Few-shot Learning

    Xiaoyu1,2, Tian Zhenzhen2, Xin Zihao2, Liu Suolan2, Chen Fuhua3, Wang Hongyuan2,*

    Journal of New Media, Vol.4, No.3, pp. 137-143, 2022, DOI:10.32604/jnm.2022.027890

    Abstract Recent advances in OCR show that end-to-end (E2E) training pipelines including detection and identification can achieve the best results. However, many existing methods usually focus on case insensitive English characters. In this paper, we apply an E2E approach, the multiplex multilingual mask TextSpotter, which performs script recognition at the word level and uses different recognition headers to process different scripts while maintaining uniform loss, thus optimizing script recognition and multiple recognition headers simultaneously. Experiments show that this method is superior to the single-head model with similar number of parameters in end-to-end identification tasks. More >

  • Open Access


    No-Reference Stereo Image Quality Assessment Based on Transfer Learning

    Lixiu Wu1,*, Song Wang2, Qingbing Sang3

    Journal of New Media, Vol.4, No.3, pp. 125-135, 2022, DOI:10.32604/jnm.2022.027199

    Abstract In order to apply the deep learning to the stereo image quality evaluation, two problems need to be solved: The first one is that we have a bit of training samples, another is how to input the dimensional image’s left view or right view. In this paper, we transfer the 2D image quality evaluation model to the stereo image quality evaluation, and this method solves the first problem; use the method of principal component analysis is used to fuse the left and right views into an input image in order to solve the second problem. At the same time, the… More >

  • Open Access


    Research on Image Quality Enhancement Algorithm Using Hessian Matrix

    Xi Chen1, Yanpeng Wu2,*, Chenxue Zhu2, Hongjun Liu3

    Journal of New Media, Vol.4, No.3, pp. 117-123, 2022, DOI:10.32604/jnm.2022.027060

    Abstract The Hessian matrix has a wide range of applications in image processing, such as edge detection, feature point detection, etc. This paper proposes an image enhancement algorithm based on the Hessian matrix. First, the Hessian matrix is obtained by convolving the derivative of the Gaussian function. Then use the Hessian matrix to enhance the linear structure in the image. Experimental results show that the method proposed in this paper has strong robustness and accuracy. More >

Displaying 1-10 on page 1 of 64. Per Page  

Share Link