Multi-Branch Deep Residual Learning for Clustering and Beamforming in User-Centric Network

He, Yuan; Dai, Lingcheng; Zhang, Hongtao

doi:10.1109/lcomm.2020.3005947

Cited by 94 publications

(62 citation statements)

References 12 publications

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“…Relevant scholars use a third-order moving average filter to filter the noise of the acceleration signal [24][25][26] [27,28]. The deep learning model using the multilayer convolution kernel has a deep network structure.…”

Section: Related Workmentioning

confidence: 99%

Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

Chu

2021

Journal of Sensors

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This article analyzes the method of reading data from inertial sensors. We introduce how to create a 3D scene and a 3D human body model and use inertial sensors to drive the 3D human body model. We capture the movement of the lower limbs of the human body when a small number of inertial sensor nodes are used. This paper introduces the idea of residual error into the deep LSTM network to solve the problem of gradient disappearance and gradient explosion. The main problem to be solved by wearable inertial sensor continuous human motion recognition is the modeling of time series. This paper chooses the LSTM network which can handle time series as well as the main frame. In order to reduce the gradient disappearance and gradient explosion problems in the deep LSTM network, the structure of the deep LSTM network is adjusted based on the residual learning idea. In this paper, a data acquisition method using a single inertial sensor fixed on the bottom of a badminton racket is proposed, and a window segmentation method based on the combination of sliding window and action window in real-time motion data stream is proposed. We performed feature extraction on the intercepted motion data and performed dimensionality reduction. An improved Deep Residual LSTM model is designed to identify six common swing movements. The first-level recognition algorithm uses the C4.5 decision tree algorithm to recognize the athlete’s gripping style, and the second-level recognition algorithm uses the random forest algorithm to recognize the swing movement. Simulation experiments confirmed that the proposed improved Deep Residual LSTM algorithm has an accuracy of over 90.0% for the recognition of six common swing movements.

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Section: Related Workmentioning

confidence: 99%

Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

Chu

2021

Journal of Sensors

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“…The acquisition of comprehensive decision-making financial data is usually to collect other relevant data that affects the decision-making of enterprise managers, including the collection of information in the accounting system and financial management system, and uses the enterprise local area network to collect human resources, customers, and suppliers. Collect other information, and use the Internet to collect external government policies, social changes, industry development status, and financial information disclosed by other companies in the same industry [24].…”

Section: Wifi Technology the Full Name Of Wifi Is Wirelessmentioning

confidence: 99%

Financial Big Data Based on Internet of Things and Wireless Network Communication

Xue¹

2021

Wireless Communications and Mobile Computing

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With the advent of the era of big data, Internet of things technology and wireless communication technology have been in a state of rapid development. Opportunities and challenges in all walks of life are being subverted. Financial management, as the foundation of corporate governance, is important for improving economic efficiency and achieving sustainable business development which plays an important role. In order to realize the management and classification of financial big data, better identify the financial data of different enterprises, strengthen the safe storage of financial information, and provide early warning for the security issues involved, this article is based on the Internet of things and wireless communication networks. In the method part, this article introduces the framework of the Internet of things, Bluetooth, and infrared data transmission in wireless network communication and the principles of financial big data. The algorithm introduces a single-user MIMO system, free space propagation, and spectrum and energy efficiency. The analysis part analyzes the spectrum efficiency of different algorithms, social utility, average number of retransmissions, comprehensive scores of competitiveness in various fields of the Internet of things, and the significance of financial indicators. By comparing the data, it can be seen that the algorithm in this paper is superior to the two algorithms of IAN-CoMP and IA-CoMP. When the number of users is 100, the social utility of the algorithm in this paper is 4.45, while IAN-CoMP is 3.43 and IA-CoMP is 3.67. When the number of users increases to 700, the social utility of the algorithm in this paper is 28.34. The other two algorithms are, respectively, 24.45 and 25.99, and we know that the social utility of the algorithm in this paper is the best. Through comprehensive analysis, it is concluded that the financial big data model based on the Internet of things and wireless network communication in this paper can better realize data management and collection, so as to meet the needs of information developers.

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“…In recent years, the emergence of new technologies including blockchain, artificial intelligence, and machine learning and their applications has grown in the field of healthcare [1][2][3][4][5][6][7][8]. In 2020, Wang et al constructed a new efficient hybrid learning framework, namely the CMWOAFS-SVM [9][10][11][12][13][14][15], for support vector machine (SVM), which was successfully applied to diagnose different diseases, including breast cancer, diabetes, and ES [16][17][18][19][20][21]. In 2019, Zhao et al proposed a new efficient diagnostic approach to integrate machine learning and gas chromatography-mass spectrometry (GC-MS), named GEE, to identify paraquat (PQ) poisoned patients.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Classification Risks of Implementing Blockchain in the Drug Supply Chain with a New Hybrid Sorting Method

et al. 2021

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In blockchain technology, all registered information, from the place of production of the product to its point of sale, is recorded as permanent and unchangeable, and no intermediary has the ability to change the data of other members and even the data registered by them without public consensus. In this way, users can trust the accuracy of the data. Blockchain systems have a wide range of applications in the medical and health sectors, from creating an integrated system for recording and tracking patients’ medical records to creating transparency in the drug supply chain and medical supplies. However, implementing blockchain technology in the supply chain has limitations and sometimes has risks. In this study, BWM methods and VIKORSort have been used to classify the risks of implementing blockchain in the drug supply chain. The results show that cyberattacks, double spending, and immutability are very dangerous risks for implementation of blockchain technology in the drug supply chain. Therefore, the risks of blockchain technology implementation in the drug supply chain have been classified based on a literature review and opinions of the experts. The risks of blockchain technology implementation in the supply chain were determined from the literature review.

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Multi-Branch Deep Residual Learning for Clustering and Beamforming in User-Centric Network

Cited by 94 publications

References 12 publications

Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

Financial Big Data Based on Internet of Things and Wireless Network Communication

Evaluation and Classification Risks of Implementing Blockchain in the Drug Supply Chain with a New Hybrid Sorting Method

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