A Data Fusion and Data Cleaning System for Smart Grids Big Data

Lv, Zhining; Deng, Wei; Zhang, Zhihan; Guo, Ningxuan; Yan, Gangfeng

doi:10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00119

Cited by 19 publications

(13 citation statements)

References 5 publications

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“…Because of the expansion of wearable devices and the fact that they manage personal data [ 14 , 18 ], the research community focuses on privacy-preserving frameworks, as seen in the literature shown below.…”

Section: Related Literaturementioning

confidence: 99%

Privacy-Preserving Sports Wearable Data Fusion Framework

Zhang

2022

Computational Intelligence and Neuroscience

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When the sports industry has access to advanced training and preparation techniques, the sports sector is entering a new era, where real-time data processing services have a crucial priority in improving physical fitness and avoiding injuries to athletes. The primary sports support methodology is based on multiple sensors, mainly wearables, often of different types and technology, which collect somatometric data in real time and are usually analyzed with deep learning technologies. And while modern athletes train and prepare intelligently using the innovative techniques of available technology, there is considerable concern about the use of personal data. There is great concern about cyberattacks and possible data leaks that could affect the sports industry and sports in general. To secure the personal data of athletes collected and analyzed by sports wearables, this paper presents a privacy-preserving sports wearable data fusion framework. This is an advanced methodology based on Lagrange's relaxation method for the problem of multiple assignments and synthesis of information by numerous sensors and the use of differential privacy to access databases with personal information, ensuring that this information will remain personal without a third entity may disclose the identity of the athlete who provided the data.

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Section: Related Literaturementioning

confidence: 99%

Privacy-Preserving Sports Wearable Data Fusion Framework

Zhang

2022

Computational Intelligence and Neuroscience

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“…[1][2][3] The key to further integration of radar and electronic warfare is the design of a detection-jamming shared signal. With the rapid development of antenna array technology, [4][5][6][7] data fusion technology, [8][9][10] and power control technology, [11][12][13][14] advancements in new "detection-jamming sharing" signal styles and the realization of "power sharing" have become not only possible but extremely crucial and key research directions, and various experts and scholars have conducted related theoretical research.…”

Section: Introductionmentioning

confidence: 99%

Design of detection‐jamming shared waveform based on virtual force field algorithm

Xiong

Liu

Chen

2022

Engineering Reports

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Due to the technical barriers between radars and jammers and the poor performance of the traditional detection‐jamming shared signal in integrated radar‐electronic warfare systems, a new detection‐jamming shared signal waveform based on the virtual force field algorithm (VFFA) is proposed in this article. First, a multiobjective and multidimensional characteristic parameter optimization model, based on a virtual force field, is established, and then the design principle of the shared signal is presented in detail. The simulation results show that the detection‐jamming shared signal based on the VFFA presents the deceptive jamming of multiple false targets in a noncollaborative target's radar screen. Further, there is better detection performance with the advantages of multiple pulse repetition frequency (PRF) and pulse accumulation numbers, which are highly sensitive to the multijagged PRF signals emitted by the noncollaborative target. According to the VFFA described in this article, the optimum detection‐jamming shared signal waveform can be output in real time for specific free space targets, to improve the efficiency of integrated radar and electronic warfare systems.

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“…Today, the applications of intelligent systems and big data are spread throughout the world [1]. The concept of "Big Data" is based on high-speed criteria, mainly used for processing a huge quantity of compound information [2]. A large amount of real-time production data is gathered, and big data is applied to workshop techniques, including fault tracking, optimization of the process, and workshop scheduling [3].…”

Section: Introductionmentioning

confidence: 99%

A review on the impact of AI as big data in computer network environment

Ayub¹,

Mumtaz²,

Ahmad³

et al. 2021

J. adv. tec. eng. res.

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The rapid growth of Arti􀅫icial Intelligence (AI) with the evolution of network technology has improved and progressed to meet the needs of people. Big data place an important role in the 􀅫ield of AI due to enhanced utilization in computer networks. Many applications are used for big data-based AI in a computer network environment. The problems and the attacks (such as Ransomware, Phishing attacks, and AI attacks) occurring in Network Technology are widely associated with AI as big data along with the solutions such as detection and identi􀅫ication of given attacks and malicious activities. The computer network technology environment is used on a large scale and varies widely with the use of AI in the context of Big Data. However, more advanced technologies are applied with AI to ensure the security of Computer networks. Due to the vast amount of traf􀅫ic occurring in networks, the result leads to the occurrence of damage in the network, and intrusion detection is a process that proved to be the most essential to ensure the security provided to the system. The de􀅫ined attacks are speci􀅫ied as the main issues and will prevent vulnerable and malicious activities. The network requires a system to detect intrusions, which is further used to observe and identify the system and networks for anomalies and malicious intrusions as Intrusion Detection System (IDS), Firewall, Encryption, and Anti-spam technologies are used to identify any malicious activity and prevent it. For these attacks, Long Short Term Memory (LSTM) is used in deep learning, and many other technologies such as Machine learning and Data mining are associated with LSTM to detect intrusions. The computer networks with the application of big data as AI were made highly secure and ef􀅫icient systems by implementing the proposed LSTM method.

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A Data Fusion and Data Cleaning System for Smart Grids Big Data

Cited by 19 publications

References 5 publications

Privacy-Preserving Sports Wearable Data Fusion Framework

Privacy-Preserving Sports Wearable Data Fusion Framework

Design of detection‐jamming shared waveform based on virtual force field algorithm

A review on the impact of AI as big data in computer network environment

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