Open Access

ARTICLE

Ying Ma¹, Jiaqi Zhang^2,3,4, Pengyu Liu^1,2,3,4,*, Zhihao Wei⁵, Lingfei Zhang¹, Xiaowei Jia⁶
Journal of New Media, Vol.4, No.4, pp. 165-177, 2022, DOI:10.32604/jnm.2022.026684 - 12 December 2022
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,… More >

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ARTICLE

Mei Wang^1,*, Hao Xu², Yadang Chen¹
Journal of New Media, Vol.4, No.4, pp. 179-190, 2022, DOI:10.32604/jnm.2022.032447 - 12 December 2022
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 More >

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547

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ARTICLE

Weimin He, Lei Zhao^*
Journal of New Media, Vol.4, No.4, pp. 191-203, 2022, DOI:10.32604/jnm.2022.032994 - 12 December 2022
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… More >

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Open Access

ARTICLE

Yongzhong Li^1,2,*, Cong Li¹, Yuheng Li³, Shipeng Zhang²
Journal of New Media, Vol.4, No.4, pp. 205-217, 2022, DOI:10.32604/jnm.2022.034778 - 12 December 2022
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 More >

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REVIEW

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 - 12 December 2022
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 More >

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