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检索条件"机构=Key Laboratory of Acoustic Visual Technology and Intelligent Control"

共 143 条记录，以下是101-110 订阅

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CTMC-based numerical analysis of cluster system with middle states in the case of common mode failure

CTMC-based numerical analysis of cluster system with middle ...

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International Conference on Computer and Information Science (ACIS)

作者： Zhiguo Hong Minyong Shi Yongbin Wang Faculty of Science and Engineering Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture Beijing China Beijing Key Laboratory of Modern Entertainment Technology Beijing China

ISBN: (纸本)9781509055081

By taking both CMF (Common Mode Failure) and middle states into account, numerical analysis of availability of cluster system is analyzed. Firstly, a CTMC (Continuous Time Markov Chain) model of cluster system containing three cluster nodes is constructed with failure rate (λ), repair rate (μ), CMF rate (γ), reconfiguration rate (β) and reset rate (δ) etc. Then the steady state solution of the CMTC model is concerned by solving the equations with the combination of several parameters. Subsequently, the effect of probability of being in automatic recovery mode (k) on availability of cluster system is analyzed and discussed via numerical values-based chart thereby. This work offers quantitative analysis of cluster system's availability.

关键词： Mathematical model Analytical models Numerical models Markov processes Steady-state Computers Reliability

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Vehicle Object Detection Based on RGB-Camera and Radar Sensor Fusion

Vehicle Object Detection Based on RGB-Camera and Radar Senso...

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Information, Media and Engineering (IJCIM), International Joint Conference on

作者： ZhongTong Li Ming Yan Wei Jiang Pin Xu School of Information and Communication Engineering Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

ISBN: (数字)9781728155869

ISBN: (纸本)9781728155876

Object detection has always played a major role in the field of computer vision. With the development of faster region-based convolutional neural network (faster-RCNN), single shot multi-box detector (SSD), You Only Look Once (Yolo) and other deep learning networks, 2D object detection has been developed well developed. However, in some key areas, such as driverless vehicles and artificial intelligence robots, cannot meet the requirements of tasks just by detecting two dimensional (2D) objects. So we need to get the three dimensional (3D) position of the object. In 3D object detection, it is difficult to obtain object position information only by red-green-blue (RGB) image. There are some methods to get the 3D position of the target through the point cloud data set. Therefore, this paper proposes a deep learning network, which can combine both color image information and point cloud geometry information, while using MobileNet V3 as the backbone network greatly enhances the operation speed. The region proposal networks (RPN) layer in faster-RCNN is migrated to the network. After testing, the method proposed by us improves obviously in speed and recognition accuracy compared with the previous methods.

关键词： Three-dimensional displays Two dimensional displays Feature extraction Cameras Radar Convolution Object detection

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Architectural Color Image Scale Computing of Beijing Forbidden City

Architectural Color Image Scale Computing of Beijing Forbidd...

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Culture-Oriented Science and technology (CoST), International Conference on

作者： Bowen Liu Wenli Huang Xiumeng Yu Zongliu Ma Ziyi Guo Shuang Wang Wei Jiang State Key Laboratory of Media Convergence and Communication Communication University of China China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Communication University of China China Beijing Key Laboratory of Modern Entertainment Technology Communication University of China China

ISBN: (数字)9798350380347

ISBN: (纸本)9798350380354

The traditional color system of Beijing Forbidden City is comprehensive and complex. The research focuses on collecting color data from the Forbidden City, analyzing subjective and objective color attributes, and compute architectural color image scale. First this research used K-means algorithm to extract the theme color cards of Beijing Forbidden City. Subsequently, single colors were extracted using the idea of community detection, and matching colors were extracted based on the thought of association rule mining. Then this research extracted objective attributes of colors and designed experiments to quantify subjective attributes. Finally, the color subjective and objective data fitting was completed. This outcome is helpful for the digital preservation of traditional colors and the advancement of color culture inheritance practices.

关键词： Costs Image color analysis Urban areas Semantics Fitting Color Data models

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Point Cloud Denoising Algorithm Based on Noise Classification

Point Cloud Denoising Algorithm Based on Noise Classificatio...

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Culture-oriented Science & technology (ICCST), International Conference on

作者： Chaohui Lv Min Li School of Information and Communication Engineering Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

ISBN: (数字)9781728181387

ISBN: (纸本)9781728181394

Aiming at the problem of different kinds of noise in 3D point cloud data, we propose a point cloud denoising method based on noise classification. This algorithm first divides noise in point cloud data into inner points and outer points, and uses radius filtering and statistical filtering to remove the outer points. Then, normal and curvature information of point cloud are estimated by the principal component analysis. At the same time, curvature information is introduced into the bilateral filtering factor to improve the existing algorithm. Ultimately, we smooth inner points mixed in point cloud by utilizing the modified algorithm. Comparing the improved algorithm with the bilateral filtering algorithm on the bunny, horse and dragon model, experimental results indicate that the maximum error and the average error are reduced. The algorithm in this paper makes models' features maintained better while models are smoothed.

关键词： Three-dimensional displays Filtering Filtering algorithms Classification algorithms Solid modeling Noise reduction Smoothing methods

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Music emotion prediction based on multidimensional feature fusion

Music emotion prediction based on multidimensional feature f...

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Information technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Xuanye Zhou Zhibin Su Hui Ren School of Information and Communication Engineering Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

Audience sentiment analysis is a popular research task in Music Information Retrieval (MIR). In this work, we aim to lean the prediction model of fine-grained music emotion for intelligent editing and retrieval. To satisfy the properties of time-series of music, the RNN-based model of BILSTM was selected as the bone. In particular, the traditional features and deep features extracted from the CNN architecture of spectrogram was fused with attention mechanism to enhance the recognition ability for different emotions. The ablation experiment has proved the necessary of the multi-dimensional features. Finally, the contrasting experiments on recent algorithms demonstrate that our proposed method for emotion prediction is effective, which is also suitable for small data sets.

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Semantic Analysis and Modeling of the Color of Stage Costume – A Case Study of “The Painting Journey”

Semantic Analysis and Modeling of the Color of Stage Costume...

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Culture-Oriented Science and technology (CoST), International Conference on

作者： Wenli Huang Bowen Liu Fang Sun Shuang Wang State Key Laboratory of Media Convergence and Communication Communication University of China China Key Laboratory of Acoustic Visual Technology and Intelligent Control System (Communication University of China) China Beijing Key Laboratory of Modern Entertainment Technology (Communication University of China) China

ISBN: (数字)9798350380347

ISBN: (纸本)9798350380354

The creation of Chinese dance dramas plays an important role in the dissemination of Chinese culture in the context of pluralistic unity. As one of the outstanding poetry dramas in the past two years, “The Journey of a Legendary Landscape Painting” (JLLP) has rich national cultural connotations and aesthetic value in its stage costume colors. This article first studied the data extraction method of stage costume colors based on clustering and association rule mining, and constructed the color dataset for JLLP. On this basis, the color semantic mining and extraction including subjective and objective factors were conducted. Finally, combined with color semantics, the character personality of JLLP was further analyzed. The research results show that the color extraction method designed in this article can accurately preserve the color characteristics of stage costumes. In addition, the costume colors of JLLP play a good supporting role in shaping the character personality.

关键词： Humanities Costs Correlation Image color analysis Databases Semantics Clustering algorithms

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Research on Comfort Perception and Evaluation of Immersive Experience Equipment in Cultural Tourism

Research on Comfort Perception and Evaluation of Immersive E...

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Culture-Oriented Science and technology (CoST), International Conference on

作者： Huibo Hu Zhibin Su Yujian Jiang Lingxi Qu School of Information and Communication Engineering Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

ISBN: (数字)9798350380347

ISBN: (纸本)9798350380354

Virtual Reality Head-Mounted Display (HMD), also known as VR glasses, is a typical amusement device that provides an immersive viewing experience based on virtual reality technology in cultural tourism equipment. Various factors may cause users to experience different degrees of sensory discomfort when using VR head-mounted displays, posing potential safety risks during performances. This study focuses on typical VR glasses used in cultural tourism performances and evaluates user comfort as the primary indicator. Through subjective evaluation experiment design, it examines the impact of motion conditions, internal scene settings, and VR glasses configuration on users’ comfort perception. The research aims to provide theoretical analysis and experimental data to reduce performance safety risks.

关键词： Performance evaluation visualization Time-frequency analysis Head-mounted displays Glass Resists Immersive experience

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Fault Prediction Method of Stage Lifts Based on Digital Twin

Fault Prediction Method of Stage Lifts Based on Digital Twin

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Culture-Oriented Science and technology (CoST), International Conference on

作者： Huiqin Wang Chunxi Zhang Zhen Li Mengjiao Xiao Ministry of Culture and Tourism Key Laboratory of Acoustic Visual Technology and Intelligent Control System Beijing China School of Information and Communication Engineering Communication University of China Beijing China

ISBN: (数字)9798350380347

ISBN: (纸本)9798350380354

To improve the safety of stage lifts’ operation and reduce the failure rate, a digital-twin assisted fault prediction method is proposed here. Signals such as vibration and temperature are collected from the virtual and physical models. A fault prediction model based on Graph Convolutional Networks (GCN) is constructed to identify the potential faults of stage lift. The experiment results illustrate the effectiveness of the proposed model. It would enhance the safety and stability of stage equipment, and provide reliable support for stage performances.

关键词： Fault diagnosis Vibrations Training Computational modeling Predictive models Stability analysis Real-time systems

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Research on Named Entity Recognition for Oral History Text

Research on Named Entity Recognition for Oral History Text

引用

Culture-Oriented Science and technology (CoST), International Conference on

作者： Chaoyu Hu Shilei Bai Mengfei Zhang School of Information and Communication Engineering Communication University of China Beijing China Beijing Key Laboratory of Modern Entertainment Technology Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture Beijing China

ISBN: (数字)9798350380347

ISBN: (纸本)9798350380354

Oral history involves conveying experienced events through spoken narratives, typically preserved and later studied by converting spoken language into text. Named entity recognition (NER) is the recognition of meaningful named entities in text, such as person names, locations, organizations, etc. Conducting NER research on oral history text is significant as it not only facilitates the verification of named entities by researchers but also allows for the extraction of key information from texts through named entities. By collecting oral history corpus, we establish an oral history text dataset, manually annotating all texts with six entity labels and repeatedly verifying them manually. Utilizing the concept of machine reading comprehension (MRC), a semantic fusion module was added, integrating label information into text information using a cross-attention mechanism, and decoding the corresponding entity labels through span decoding. The final model performs well on the oral history text dataset and also showed good performance on general Chinese datasets.

关键词： Costs Text recognition Semantics Named entity recognition Organizations Decoding History

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The Algorithm of Sequential Music Recommendation Based on BERT

The Algorithm of Sequential Music Recommendation Based on BE...

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Culture-Oriented Science and technology (CoST), International Conference on

作者： Zhen Li Kaiyue Li Qingqing Yang State Key Laboratory of Media Convergence of Communication Key Laboratory of Acoustic Visual Technology and Intelligent Control System (Communication University of China) Ministry of Culture and Tourism Beijing Key Laboratory of Modern Entertainment Technology Communication University of China Communication University of China Beijing China

Music recommendation algorithms aim to predict the songs that users might be interested in based on various factors, such as their listening history. The best existing method is Bert_Music, a sequential music recommendation model based on Bidirectional Encoder Representation from Transformer (BERT). This model leverages user sequence context information to improve recommendation performance. However, Bert_Music only considers the sequence information of songs and ignores their content characteristics. To address this limitation, we propose an improved version of Bert_Music, which is a sequential music recommendation model based on Bidirectional Encoder Representation from Transformer with tag information (BERT_MUSIC_T), uses song ID (Identity) and tag information to make more accurate recommendations. We compared the performance of Bert_Music_T with that of the Bert_Music model on the *** dataset and found that the Precision has increased by up to 2.85%. These results prove the validity of our method.

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