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Chien-Chi Kao,Yung-Chang Lai,Jung Pei,Chih-Wei Chang,Fei-Hua Kuo,Jin-Yuan Shun 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
In recent years, IP surveillance networks are expected to enable various practical applications, such as finding suspects, monitoring pedestrians, and securing societies (e.g., securing a city, a company and a data center). With these applications, IP surveillance network is regarded as one of the potential technologies toward developing smart cities. To support the concept of IP surveillance networks, automatic attribute recognition systems have emerged as a promising intelligent management system. To automatically recognize attributes of pedestrians (e.g., gender and clothing), we apply deep convolutional neural networks (CNNs), and the main contributions of this paper are threefold: (1) we proposed a practical system architecture for intelligent management of surveillance networks; (2) we implemented different deep CNNs, and an ensemble-learning method that leverages these multiple deep-learning models; (3) we evaluated the models using the real data of IP surveillance networks.
Pang-Chen Liu,Shun-Kai Yang,Lung-Chin Huang,Huai-Eu Tseng,Fei-Hua Kuo,Tai-Chueh Shih 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
In recent years, due to customers have higher requirements for 4K/8K video and high-speed internet, telecom operators have begun to deploy FTTH network , but found that it is generally difficult to deploy fiber to the home, so G.fast technology has been favored by most telecom operators around the world and have begun to actively deploy. For the most widely deploy VDSL2 line with maximum rate that can only provide 100M internet service, a intelligent and accurate G.fast 300M high speed service prequalification technology, is a major research topic for telecom operators to promote 300M high-speed internet service. This paper proposes to use AI machine learning to estimate the G.fast line rate by using VDSL2 line attenuation to meet the real-site provision needs of telecommunications operators.