Image anomaly detection for IoT equipment based on deep learning

Hou, Rui; Pan, Mingming; Zhao, Yue; Yang, Yang

doi:10.1016/j.jvcir.2019.102599

Cited by 25 publications

(11 citation statements)

References 23 publications

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“…Time-series data [43,44,45,46,47,48,49,50] Smart city [51,52,53,54] Monitoring machinery health [55,5] Robotics and manufacturing [56,57,58] Detection applications for IoT sensors [59,60] Other industrial or manufacturing [61,62,63,64] Surveillance and video [65,66,67] General-purpose frameworks [68,69,70] Network and communication frameworks [71,72,73,74] User security and privacy frameworks [75,76,77] Other frameworks [78,79] Network traffic in IoT [80,81,82] Device and infrastructure security [83,21,84,85,86,87] Data transport security [88,…”

Section: Application Category Referencementioning

confidence: 99%

“…Inadequate monitoring of electrical equipment can result in massive economic losses and societal impacts. Hou et al [65] proposed an image anomaly detection method for IoT equipment based on deep learning for both personnel identification and fire smoke detection to address the problem. The researchers used the approach to monitor the safety of the power equipment's working environment and people identification, and fire smoke detection.…”

Section: Surveillance and Video Iot Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

IoT anomaly detection methods and applications: A survey

2022

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Section: Application Category Referencementioning

confidence: 99%

Section: Surveillance and Video Iot Applicationsmentioning

confidence: 99%

IoT anomaly detection methods and applications: A survey

2022

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“…The results showed that the algorithm can detect faults in an IOT system effectively and accurately. Hou et al [47] have applied a deep learning method to the real-time monitoring of power IOT equipment, which helps staff to locate faults quickly and accurately. Benkedjouh et al [48] have used deep learning to improve the accuracy of rotating machinery fault diagnosis in an Internet of Things framework.…”

Section: Literature Analysismentioning

confidence: 99%

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

Dou

Sun

Wu³

et al. 2021

IJGI

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In the era of big data, mass customization (MC) systems are faced with the complexities associated with information explosion and management control. Thus, it has become necessary to integrate the mass customization system and Social Internet of Things, in order to effectively connecting customers with enterprises. We should not only allow customers to participate in MC production throughout the whole process, but also allow enterprises to control all links throughout the whole information system. To gain a better understanding, this paper first describes the architecture of the proposed system from organizational and technological perspectives. Then, based on the nature of the Social Internet of Things, the main technological application of the mass customization–Social Internet of Things (MC–SIOT) system is introduced in detail. On this basis, the key problems faced by the mass customization–Social Internet of Things system are listed. Our findings are as follows: (1) MC–SIOT can realize convenient information queries and clearly understand the user’s intentions; (2) the system can predict the changing relationships among different technical fields and help enterprise R&D personnel to find technical knowledge; and (3) it can interconnect deep learning technology and digital twin technology to better maintain the operational state of the system. However, there exist some challenges relating to data management, knowledge discovery, and human–computer interaction, such as data quality management, few data samples, a lack of dynamic learning, labor consumption, and task scheduling. Therefore, we put forward possible improvements to be assessed, as well as privacy issues and emotional interactions to be further discussed, in future research. Finally, we illustrate the behavior and evolutionary mechanism of this system, both qualitatively and quantitatively. This provides some idea of how to address the current issues pertaining to mass customization systems.

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“…VR technology has been an important part of research, development and application since the 1990s. VR technology builds a realistic virtual environment using a computer, and supports its users to feel this virtual environment with natural skills [8]. VR technology has been introduced into firefighting as a new training method.…”

Section: Related Workmentioning

confidence: 99%

Fire Detection and Recognition Optimization Based on Virtual Reality Video Image

Xin-chu

2020

IEEE Access

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Fire detection technology based on video images can avoid many flaws in conventional methods and detect fires. To achieve this, the support vector machine (SVM) method in machine learning theory has unique advantages, while rough set (RS) theory and SVM complement each other in application. Thus, a new classifier could be created by organically combining these methods to identify fires and provide fire warnings, yielding excellent noise suppression and promotion. Therefore, in this study, an RS is used as the front-end system for the SVM method, yielding improved performance than only SVM. Recognition time is reduced, and recognition efficiency is improved. Experiments show that the RS-SVM classifier model based on parameter optimization proposed in this paper mitigates deficiencies in overfitting and determining local extremum with excellent reliability and stability, and enhances the forecast accuracy of fires. The method also reduces false fire-detection alarms and uses fire feature selection in virtual reality (VR) video images and fire detection and recognition.

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Image anomaly detection for IoT equipment based on deep learning

Cited by 25 publications

References 23 publications

IoT anomaly detection methods and applications: A survey

IoT anomaly detection methods and applications: A survey

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

Fire Detection and Recognition Optimization Based on Virtual Reality Video Image

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