Management and performance evaluation of DSR aggregator based on a bi‐level optimization model

Li, Bosong; Jiang, Chuanwen; Zhang, Gao

doi:10.1002/tee.22584

Cited by 5 publications

(2 citation statements)

References 31 publications

(45 reference statements)

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“…Since the emergence of support vector machine, its solving problem has been a hot topic of the majority of scholars, and many solving methods have emerged, among which SMO algorithm is one of the best solving methods. It decomposes the quadratic programming problem in the process of SVM solution into a series of small quadratic programming problems with only two variables [17][18][19][20][21][22][23].…”

Section: Action Recognition Algorithm Based On Combination Of Smo and Artificial Intelligencementioning

confidence: 99%

An Empirical Study on Sports Combination Training Action Recognition Based on SMO Algorithm Optimization Model and Artificial Intelligence

Jiang

Tsai

2021

Mathematical Problems in Engineering

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In order to improve the accuracy of sports combination training action recognition, a sports combination training action recognition model based on SMO algorithm optimization model and artificial intelligence is proposed. In this paper, by expanding the standard action data, the standard database of score comparison is established, and the system architecture and the key acquisition module design based on 3D data are given. In this paper, the background subtraction method is used to process the sports video image to obtain the sports action contour and realize the sports action segmentation and feature extraction, and the artificial intelligence neural network is used to train the feature vector to establish the sports action recognition classifier. This paper mainly uses a three-stream CNN artificial intelligence deep learning framework based on convolutional neural network and uses a soft Vlad representation algorithm based on data decoding to learn the action features. Through the data enhancement of the existing action database, it uses support vector machine to achieve high-precision action classification. The test results show that the model improves the recognition rate of sports action and reduces the error recognition rate, which can meet the online recognition requirements of sports action.

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Section: Action Recognition Algorithm Based On Combination Of Smo and Artificial Intelligencementioning

confidence: 99%

An Empirical Study on Sports Combination Training Action Recognition Based on SMO Algorithm Optimization Model and Artificial Intelligence

Jiang

Tsai

2021

Mathematical Problems in Engineering

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“…With the rapid popularization of wireless portable devices (e.g., personal digital assistants, smartphones), a new peer‐to‐peer application scenario called opportunistic networks has emerged in recent years . The opportunistic network is a type of ad hoc network that realizes data transmission using the contact opportunities brought about by the movement of portable devices (called nodes below) in case of intermittent communication , which meets the thorough perception and ubiquitous interconnection of the Internet of Things.…”

Section: Introductionmentioning

confidence: 99%

An Efficient location‐aware routing approach in opportunistic networks

Wang

et al. 2020

IEEJ Transactions Elec Engng

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An opportunistic network is a kind of self‐organizing network that makes use of the encountering opportunities to realize short‐distance wireless communication. Because of the intermittent connectivity between nodes and dynamic network topology, how to devise a reliable and efficient routing approach has become a challenging issue. However, the periodic update mechanism of the node's location information in the existing works costs too much. To address this issue, we propose a location‐aware opportunistic routing (LAOR) approach. In the the proposed LAOR, we first establish a location‐aware prediction model based on the Markov process to predict the node's next location. Then we devise a decision‐making scheme of message forwarding to select the desired relay nodes based on the node's delivery probability. In addition, we introduce a dynamic buffer management strategy to determine the message priority. Finally, we validate the effectiveness of our proposed model and the high efficiency of the LAOR approach. Simulation results demonstrate that the proposed LAOR outperforms the existing Epidemic, Prophet, history‐based prediction routing, and location prediction‐based forwarding for routing using Markov chain algorithms in terms of delivery ratio, overhead ratio, and the number of messages dropped. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Construction and countermeasure discussion on government performance evaluation model of air pollution control: A case study from Beijing-Tianjin-Hebei region

Huang

Yin

et al. 2020

Journal of Cleaner Production

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Management and performance evaluation of DSR aggregator based on a bi‐level optimization model

Cited by 5 publications

References 31 publications

An Empirical Study on Sports Combination Training Action Recognition Based on SMO Algorithm Optimization Model and Artificial Intelligence

An Empirical Study on Sports Combination Training Action Recognition Based on SMO Algorithm Optimization Model and Artificial Intelligence

An Efficient location‐aware routing approach in opportunistic networks

Construction and countermeasure discussion on government performance evaluation model of air pollution control: A case study from Beijing-Tianjin-Hebei region

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