2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)最新文献_第7页

An Intelligent Vehicle Oriented EMC Fault Shooting Method Based on Semi-supervised Learning 一种基于半监督学习的智能汽车电磁兼容故障诊断方法

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660595

Lidong Chen, Yong Chai, Lingqiu Zeng, Jie Mu, Qingwen Han, L. Ye

引用次数: 1

Convolutional Neural Network Based Transmit Power Control for D2D Communication in Unlicensed Spectrum 基于卷积神经网络的无许可频谱D2D通信发射功率控制

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660479

Zhenyu Fan, Xinyu Gu

引用次数: 0

TAX-B2BREC: Multi-Stage Cascade Downstream Company Recommender System Based on Taxation Data TAX-B2BREC:基于税收数据的多级级联下游公司推荐系统

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660478

Shengmin Wang, Y. Chu, Zhenhao Qiao, Lan Ma, Hao Zhang, Zehao Wang, Yu Qiao

引用次数: 0

Analysis About Low Differential Mode Delay Based on Wavelength Dependence of Effective Refractive Index in Few Mode Fibers Around 1550nm 基于1550nm左右几种模式光纤有效折射率波长依赖性的低差模延迟分析

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660457

Shuo Chen, Huiping Tian

引用次数: 0

SDN-Based QoE Evaluation Methods for HTTP Adaptive Video Streaming 基于sdn的HTTP自适应视频流QoE评价方法

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660473

Mingqian Wang, Yumei Wang, Yu Liu

{"title":"SDN-Based QoE Evaluation Methods for HTTP Adaptive Video Streaming","authors":"Mingqian Wang, Yumei Wang, Yu Liu","doi":"10.1109/IC-NIDC54101.2021.9660473","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660473","url":null,"abstract":"Nowadays, video streaming has become the main traffic on the Internet. The demand of people for video services is growing higher and higher. In order to meet the requirements of users, the service providers such as network providers and application providers must guarantee good Quality of Experience (QoE). This paper reviews the influencing factors and main evaluation methods of user QoE, and introduces the research status and progress in this field. Then, combining with the most popular HTTP Adaptive Streaming (HAS) and SDN (Software-Defined Networking) technologies, we build an SDN-based QoE experimental platform for video streaming based some open-source software packages, in which we integrate a variety of QoE evaluation models and real extensible network environment modules. Finally, in the SDN-based virtual network, we perform videos multiple times, using different QoE evaluation methods each time, and obtain visual results. Our results prove that the platform architecture has the ability of helping researchers to carry out QoE evaluation testing in a variety of virtual network environments.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130288805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Adaptive Power Control Scheme for Noise Suppressing in Quantum-Secured Distribution Communication Networks 量子保密配电通信网中噪声抑制的自适应功率控制方案

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660547

Jianing Niu, Zhiyu Chen, Hua Wei, Guochun Li, Longchuan Yan, Yongmei Sun

引用次数: 0

Zero-Shot Voice Cloning Using Variational Embedding with Attention Mechanism 基于注意力机制的变分嵌入零采样语音克隆

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660599

Jaeuk Lee, Jiye G. Kim, Joon‐Hyuk Chang

{"title":"Zero-Shot Voice Cloning Using Variational Embedding with Attention Mechanism","authors":"Jaeuk Lee, Jiye G. Kim, Joon‐Hyuk Chang","doi":"10.1109/IC-NIDC54101.2021.9660599","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660599","url":null,"abstract":"Many voice cloning studies based on multi-speaker text-to-speech (TTS) have been conducted. Among the techniques of voice cloning, we focus on zero-shot voice cloning. The most important aspect of zero-shot voice cloning is which speaker embedding is used. In this study, two types of speaker embeddings are used. One is extracted from the mel spectrogram using a speaker encoder and the other is stored in an embedding dictionary, such as a vector quantized-variational autoencoder (VQ-VAE). To extract embedding from the embedding dictionary, an attention mechanism is applied, which we call attention- V AE (AT - V AE). By employing the embedding extracted by the speaker encoder as a query in the attention mechanism, the attention weights are calculated in the embedding dictionary. This mechanism allows the extraction of speaker embedding, which represents unseen speakers. In addition, training is applied to make our model robust to unseen speakers. Through the training stage, our system has developed further. The performance of the proposed method was validated in terms of various metrics, and it was demonstrated that the proposed method enables voice cloning without adaptation training.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114319105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Representative Multi-Domain Feature Selection Based Cross-Domain Few-Shot Classification 基于代表性多域特征选择的跨域小样本分类

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660577

Zhewei Weng, Chunyan Feng, Tiankui Zhang, Yutao Zhu, Ze-Sen Chen

引用次数: 1

Degree Matters: Assessing the Generalization of Graph Neural Network 程度问题:评估图神经网络的泛化

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660574

Hyunmok Park, Kijung Yoon

{"title":"Degree Matters: Assessing the Generalization of Graph Neural Network","authors":"Hyunmok Park, Kijung Yoon","doi":"10.1109/IC-NIDC54101.2021.9660574","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660574","url":null,"abstract":"Graph neural network (GNN) is a general framework for using deep neural networks on graph data. The defining feature of a GNN is that it uses a form of neural message passing where vector messages are exchanged between nodes and updated using neural networks. The message passing operation that underlies GNNs has recently been applied to develop neural approximate inference algorithms, but little work has been done on understanding under what conditions GNNs can be used as a core module for building general inference models. To study this question, we consider the task of out-of-distribution generalization where training and test data have different distributions, by systematically investigating how the graph size and structural properties affect the inferential performance of GNNs. We find that (1) the average unique node degree is one of the key features in predicting whether GNNs can generalize to unseen graphs; (2) the graph size is not a fundamental limiting factor of the generalization in GNNs when the average node degree remains invariant across training and test distributions; (3) despite the size-invariant generalization, training GNNs on graphs of high degree (and of large size consequently) is not trivial (4) neural inference by GNNs outperforms algorithmic inferences especially when the pairwise potentials are strong, which naturally makes the inference problem harder.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126771654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Small Range Ergodic Beamforming Method Based on Binocular Vision Positioning 基于双目视觉定位的小范围遍历波束形成方法

2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC) Pub Date : 2021-11-17 DOI: 10.1109/IC-NIDC54101.2021.9660448

Bo-cheng Yu, Xin Zhang

{"title":"A Small Range Ergodic Beamforming Method Based on Binocular Vision Positioning","authors":"Bo-cheng Yu, Xin Zhang","doi":"10.1109/IC-NIDC54101.2021.9660448","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660448","url":null,"abstract":"The increasing construction of 5G dense network creates the conditions for the application of Massive MIMO system. However, with the continuous expansion of business requirements, users put forward higher requirements for the number of antennas in MIMO system. With the increase of the number of antennas, the cost of traditional MIMO beamforming algorithm for channel detection and feedback will increase rapidly, which consumes more wire-less resources and greatly increases the computational burden of the system. The use of computer vision aids provides convenience for the beamforming method to track the target accurately under LOS condition. Combined with image tracking algorithm, the position of the target in each image frame can be calculated so that the angle information of LOS path and the best beam-forming scheme can be determined directly, which can reduce the cost and calculation of the system through wireless resource measurement and feed-back. As a result, the operation speed and accuracy of the system are improved. In this paper, a beamforming method based on binocular positioning is studied. Compared with the traditional method, this method can reduce the number of codeword searches and improve the channel capacity in high-density 5G network.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"14 5-6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116471315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1