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学者姓名：徐艺文
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Abstract ：
State-of-the-art smart cities have been calling for economic but efficient energy management over a large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze, and control electric loads of all users in the system. In this study, a non-intrusive load monitoring method was designed for smart power management using computer vision techniques popular in artificial intelligence. First of all, one-dimensional current signals are mapped onto two-dimensional color feature images using signal transforms (including the wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods. Second, a deep neural network with multi-scale feature extraction and attention mechanism is proposed to recognize all electrical loads from the color feature images. Third, a cloud-based approach was designed for the non-intrusive monitoring of all users, thereby saving energy costs during power system control. Experimental results on both public and private datasets demonstrate that the method achieves superior performances compared to its peers, and thus supports efficient energy management over a large-scale Internet of Things network.
Keyword ：
computer vision computer vision electric load monitoring electric load monitoring load recognition algorithm load recognition algorithm smart city smart city smart electric energy management smart electric energy management
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| GB/T 7714 | He, Nian , Liu, Dengfeng , Zhang, Zhichen et al. Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management [J]. | SENSORS , 2024 , 24 (10) . |
| MLA | He, Nian et al. "Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management" . | SENSORS 24 . 10 (2024) . |
| APA | He, Nian , Liu, Dengfeng , Zhang, Zhichen , Lin, Zhiquan , Zhao, Tiesong , Xu, Yiwen . Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management . | SENSORS , 2024 , 24 (10) . |
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Nowadays, Versatile Video Coding (VVC) has achieved a superior performance than previous video coding standard (High Efficiency Video Coding). The Quadtree with Nested Multi-Type Tree (QTMT) coding block structure can enhance the coding performance. Nevertheless, this technique also leads to the significantly increasing complexity of VVC inter coding. Therefore, complexity optimization is an urgent problem to be optimized in the market application of VVC. To solve this issue, we propose a Supervised-Contrastive-Learningbased Inter Partitioning (SCLIP) method in this paper. Firstly, we define the above complexity optimization problem as a supervised classification task. Next, we develop a SCLIP Estimation Network (SCLIPEst-Net) with a supervised contrastive learning module and a classification module. After training on a newly established dataset, the SCLIPEst-Net can reasonably predict the mode partitioning. Finally, we propose an overall SCLIP algorithm that effectively determines the inter partitions of VVC with a low computational overhead. Experimental results indicate that our method achieves 45.14% average Time Saving (TS) with a 2.40% Bj & oslash;ntegaard Delta Bit Rate (BDBR) in Random Access (RA), outperforming the benchmarks.
Keyword ：
Complexity optimization Complexity optimization Inter prediction Inter prediction Supervised contrastive learning Supervised contrastive learning Versatile video coding Versatile video coding
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| GB/T 7714 | Lin, JieLian , Lin, Hongbin , Zhang, Zhichen et al. Efficient inter partitioning of versatile video coding based on supervised contrastive learning [J]. | KNOWLEDGE-BASED SYSTEMS , 2024 , 296 . |
| MLA | Lin, JieLian et al. "Efficient inter partitioning of versatile video coding based on supervised contrastive learning" . | KNOWLEDGE-BASED SYSTEMS 296 (2024) . |
| APA | Lin, JieLian , Lin, Hongbin , Zhang, Zhichen , Xu, Yiwen . Efficient inter partitioning of versatile video coding based on supervised contrastive learning . | KNOWLEDGE-BASED SYSTEMS , 2024 , 296 . |
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The recent advances in multimedia technology have significantly expanded the range of audio-visual applications. The continuous enhancement of display quality has led to the emergence of new attributes in video, such as enhanced visual immersion and widespread availability. Within media content, the video signals are presented in various formats including stereoscopic/3D, panoramic/360 degrees degrees and holographic images. The signals are also combined with other sensory elements, such as audio, tactile, and olfactory cues, creating a comprehensive multi-sensory experience for the user. The development of both qualitative and quantitative Quality of Experience (QoE) metrics is crucial for enhancing the subjective experience in immersive scenarios, providing valuable guidelines for system enhancement. In this paper, we review the most recent achievements in QoE assessment for immersive scenarios, summarize the current challenges related to QoE issues, and present outlooks of QoE applications in these scenarios. The aim of our overview is to offer a valuable reference for researchers in the domain of multimedia delivery.
Keyword ：
Immersive video Immersive video MULtiple SEnsorial MEDIA (MULSEMEDIA) MULtiple SEnsorial MEDIA (MULSEMEDIA) Quality of Experience (QoE) Quality of Experience (QoE) Video delivery Video delivery Video transmission Video transmission
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| GB/T 7714 | Chen, Weiling , Lan, Fengquan , Wei, Hongan et al. A comprehensive review of quality of experience for emerging video services [J]. | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2024 , 128 . |
| MLA | Chen, Weiling et al. "A comprehensive review of quality of experience for emerging video services" . | SIGNAL PROCESSING-IMAGE COMMUNICATION 128 (2024) . |
| APA | Chen, Weiling , Lan, Fengquan , Wei, Hongan , Zhao, Tiesong , Liu, Wei , Xu, Yiwen . A comprehensive review of quality of experience for emerging video services . | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2024 , 128 . |
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Emerging multimedia technologies significantly enhance the naturalness and immersion of human computer interaction. Currently, research on kinesthetic information has gained increasing attentions of multimedia community. However, effective interaction between kinesthetic and other multimedia signals remains a challenging task. In this paper, we propose a visual-kinesthetic interaction in Virtual Reality (VR) and real-world control tasks. First, we model the correlation between user's visual attention and kinesthetic positions under different tasks. Second, we utilize an attention-based Long Short-Term Memory network to predict the kinesthetic positions. Third, we build a VR system with robotic car control, which validates our model in VR interaction and control tasks. With a high task achievement rate, we envision the implementation of kinesthetic information in a more natural interaction system. The VR interaction system based on the proposed model can also provide guidance for the design of immersive robot teleoperation systems.
Keyword ：
Haptics Haptics Human-Computer Interaction (HCI) Human-Computer Interaction (HCI) Multimedia Multimedia Virtual Reality (VR) Virtual Reality (VR) Visual attention Visual attention
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| GB/T 7714 | Fang, Ying , Liu, Qian , Xu, Yiwen et al. Virtual reality interaction based on visual attention and kinesthetic information [J]. | VIRTUAL REALITY , 2023 , 27 (3) : 2183-2193 . |
| MLA | Fang, Ying et al. "Virtual reality interaction based on visual attention and kinesthetic information" . | VIRTUAL REALITY 27 . 3 (2023) : 2183-2193 . |
| APA | Fang, Ying , Liu, Qian , Xu, Yiwen , Guo, Yanmin , Zhao, Tiesong . Virtual reality interaction based on visual attention and kinesthetic information . | VIRTUAL REALITY , 2023 , 27 (3) , 2183-2193 . |
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Video quality assessment is critical in optimizing video coding techniques. However, the state-of-the-art methods have limited performance, which is largely due to the lack of large-scale subjective databases for training. In this work, a semi-automatic labeling method is adopted to build a large-scale compressed video quality database, which allows us to label a large number of compressed videos with manageable human workload. The resulting Compressed Video quality database with Semi-Automatic Ratings (CVSAR), so far the largest of compressed video quality database. We train a no-reference compressed video quality assessment model with a 3D CNN for SpatioTemporal Feature Extraction and Evaluation (STFEE). Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics and achieves promising generalization performance in cross-database tests. The CVSAR database has been made publicly available. It can be accessed at https://github.com/Rocknroll194/CVSAR.
Keyword ：
compressed video compressed video Databases Databases deep network deep network Feature extraction Feature extraction Image coding Image coding Labeling Labeling Quality assessment Quality assessment semi-auto rating semi-auto rating Video coding Video coding Video quality assessment Video quality assessment Videos Videos
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| GB/T 7714 | Lin, Liqun , Wang, Zheng , He, Jiachen et al. Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2023 , 33 (6) : 2616-2626 . |
| MLA | Lin, Liqun et al. "Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 33 . 6 (2023) : 2616-2626 . |
| APA | Lin, Liqun , Wang, Zheng , He, Jiachen , Chen, Weiling , Xu, Yiwen , Zhao, Tiesong . Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2023 , 33 (6) , 2616-2626 . |
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The ever-growing multimedia traffic has underscored the importance of effective multimedia codecs. Among them, the up-to-date lossy video coding standard, Versatile Video Coding (VVC), has been attracting attentions of video coding community. However, the gain of VVC is achieved at the cost of significant encoding complexity, which brings the need to realize fast encoder with comparable Rate Distortion (RD) performance. In this paper, we propose to optimize the VVC complexity at intra-frame prediction, with a two-stage framework of deep feature fusion and probability estimation. At the first stage, we employ the deep convolutional network to extract the spatial-temporal neighboring coding features. Then we fuse all reference features obtained by different convolutional kernels to determine an optimal intra coding depth. At the second stage, we employ a probability-based model and the spatial-temporal coherence to select the candidate partition modes within the optimal coding depth. Finally, these selected depths and partitions are executed whilst unnecessary computations are excluded. Experimental results on standard database demonstrate the superiority of proposed method, especially for High Definition (HD) and Ultra-HD (UHD) video sequences.
Keyword ：
Complexity theory Complexity theory Computational modeling Computational modeling Convolutional neural networks Convolutional neural networks Encoding Encoding Feature extraction Feature extraction Intra coding Intra coding Kernel Kernel rate-distortion (RD) rate-distortion (RD) versatile video coding (VVC) versatile video coding (VVC) video coding video coding Video coding Video coding
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| GB/T 7714 | Zhao, Tiesong , Huang, Yuhang , Feng, Weize et al. Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2023 , 25 : 6411-6421 . |
| MLA | Zhao, Tiesong et al. "Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation" . | IEEE TRANSACTIONS ON MULTIMEDIA 25 (2023) : 6411-6421 . |
| APA | Zhao, Tiesong , Huang, Yuhang , Feng, Weize , Xu, Yiwen , Kwong, Sam . Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation . | IEEE TRANSACTIONS ON MULTIMEDIA , 2023 , 25 , 6411-6421 . |
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为了精确地传输信号内容含义,实现智能识别与信号重建,针对振动触觉信号,提出了一种面向机器识别?人类感知的联合编码方案.在编码端,将三维振动信号转化为一维信号,采用短时傅里叶变换提取信号的语义信息,并实现语义信息高效压缩与表征.在解码端,基于语义信息采用全卷积神经网络实现触觉的智能识别;同时,将原始信号与基于语义信息的重构信号的残差值作为语义信息的补偿,逐步提高重构信号的质量,满足人类感知需求.实验结果表明,所提方案用较低比特率的语义信息实现触觉识别,同时在满足人类感知需求情况下,触觉数据的压缩效率有所提高.
Keyword ：
感知质量 感知质量 振动触觉 振动触觉 智能识别 智能识别 联合编码 联合编码 触觉 触觉 语义信息 语义信息
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| GB/T 7714 | 房颖 , 徐艺文 , 赵铁松 . 面向机器识别−人类感知的联合振动触觉编码 [J]. | 通信学报 , 2023 , 44 (5) : 42-51 . |
| MLA | 房颖 et al. "面向机器识别−人类感知的联合振动触觉编码" . | 通信学报 44 . 5 (2023) : 42-51 . |
| APA | 房颖 , 徐艺文 , 赵铁松 . 面向机器识别−人类感知的联合振动触觉编码 . | 通信学报 , 2023 , 44 (5) , 42-51 . |
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针对相控阵列辅助的无线通信系统中发射波束只依赖角度特性而导致的安全隐患问题，以及传统的迭代算法所带来的高计算复杂度问题，该文提出由深度学习(DL)和随机频率分集阵列(RFDA)辅助带有3维安全区域的安全传输方案。首先，推导在3维空间中带有安全区域的期望用户实现安全通信的传输条件。在此基础上，构建系统安全速率下界最大化问题。随后，提出基于深度学习的神经网络方案来设计最优的波束成形矢量和人工噪声(AN)矢量来降低计算复杂度。仿真结果表明：即便是在窃听者位于安全区域边缘的最差情况下，所提方案仍能够在实现3维安全传输，能够保证安全区域内接收到的信息不被窃听。
Keyword ：
安全传输 安全传输 深度学习 深度学习 随机频率分集阵列 随机频率分集阵列
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| GB/T 7714 | 胡锦松 , 蒋宛伶 , 陈由甲 et al. 深度学习辅助的随机频率分集阵列下的三维无线安全传输 [J]. | 电子与信息学报 , 2023 , 45 (06) : 2063-2070 . |
| MLA | 胡锦松 et al. "深度学习辅助的随机频率分集阵列下的三维无线安全传输" . | 电子与信息学报 45 . 06 (2023) : 2063-2070 . |
| APA | 胡锦松 , 蒋宛伶 , 陈由甲 , 徐艺文 , 赵铁松 , 束锋 . 深度学习辅助的随机频率分集阵列下的三维无线安全传输 . | 电子与信息学报 , 2023 , 45 (06) , 2063-2070 . |
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To solve the potential security issue caused by the fact that the transmitted beam in the phased array-assisted wireless communication systems only depend on angle characteristics and high computational complexity caused by the traditional iteration algorithms. A secure transmission scheme with 3D secure region assisted by Random Frequency Diverse Array (RFDA) and Deep Learning (DL) is proposed in this paper. Firstly, the requirements for the secure communication with the desired user within 3D secure zone are derived. Based on it, an optimization problem is formulated to maximize the lower bound of the secure rate of the considered system. Then, an optimization scheme based on deep learning is proposed to design the beamforming vector and Artificial Noise (AN) vector, so as to reduce the computational complexity. Simulation results show that even when the eavesdropper is located at the edge of the desired user's secure region, the proposed scheme can achieve the 3D secure transmission, and ensure the received confidential information in secure region.
Keyword ：
Deep Learning (DL) Deep Learning (DL) Random Frequency Diverse Array (RFDA) Random Frequency Diverse Array (RFDA) Secure transmission Secure transmission
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| GB/T 7714 | Hu, Jinsong , Jiang, Wanling , Chen, Youjia et al. 3D Wireless Secure Transmission under Random Frequency Diversity Array Assisted by Deep Learning [J]. | JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY , 2023 , 46 (6) : 2063-2070 . |
| MLA | Hu, Jinsong et al. "3D Wireless Secure Transmission under Random Frequency Diversity Array Assisted by Deep Learning" . | JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY 46 . 6 (2023) : 2063-2070 . |
| APA | Hu, Jinsong , Jiang, Wanling , Chen, Youjia , Xu, Yiwen , Zhao, Tiesong , Shu, Feng . 3D Wireless Secure Transmission under Random Frequency Diversity Array Assisted by Deep Learning . | JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY , 2023 , 46 (6) , 2063-2070 . |
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In order to accurately transmit the content meaning of vibrotactile signals and achieve intelligent recognition and signal reconstruction, a joint vibrotactile coding scheme for machine recognition and human perception was proposed. At the encoding end, the original three-dimensional vibrotactile signals were converted into one-dimensional signals. Then the semantic information of the signals was extracted using a short-time Fourier transform before being effectively compressed and transmitted. At the decoding end, a fully convolutional neural network was used to intelligently recognize based on the semantic information. The difference between the original signals and the reconstructed signals based on semantic information was used as compensation for the semantic information, and the quality of the reconstructed signals was gradually improved to meet human perceptual needs. The experimental results show that the proposed scheme achieve tactile recognition with semantic information at a lower bit rate while improving the compression efficiency of tactile data, thus satisfying human perceptual needs. © 2023 Editorial Board of Journal on Communications. All rights reserved.
Keyword ：
haptic haptic intelligent recognition intelligent recognition joint coding joint coding perceptual quality perceptual quality semantic information semantic information vibrotactile vibrotactile
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| GB/T 7714 | Fang, Y. , Xu, Y. , Zhao, T. . Joint vibrotactile coding for machine recognition and human perception; [面向机器识别−人类感知的联合振动触觉编码] [J]. | Journal on Communications , 2023 , 44 (5) : 42-51 . |
| MLA | Fang, Y. et al. "Joint vibrotactile coding for machine recognition and human perception; [面向机器识别−人类感知的联合振动触觉编码]" . | Journal on Communications 44 . 5 (2023) : 42-51 . |
| APA | Fang, Y. , Xu, Y. , Zhao, T. . Joint vibrotactile coding for machine recognition and human perception; [面向机器识别−人类感知的联合振动触觉编码] . | Journal on Communications , 2023 , 44 (5) , 42-51 . |
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