A Deep Multimodal Adversarial Cycle-Consistent Network for Smart Enterprise System

Li, Peng; Laghari, Asif Ali; Rashid, Mamoon; Gao, Jing; Gadekallu, Thippa Reddy; Javed, Abdul Rehman; Yin, Shoulin

doi:10.1109/tii.2022.3197201

Cited by 10 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it had always been inefficient. Khan et al (2021) and Li et al (2022) adopted suppression method to cut off the correlation between user location and time to protect the trajectory privacy of users. In this method, k tasks were selected from n tasks completed by the user and uploaded to the server in an out-of-order combination (Nie et al, 2021;Khan et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

“…Traditional location privacy protection technologies include space concealment (Anh et al, 2010;Liu et al, 2021), location offset and blurring (Freudiger et al, 2013;Pournaras et al, 2016), and forging false location (Gao et al, 2022), etc. However, these technologies require forgery or modification of data acquisition location or time, which will affect the availability of crowdsensing task data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robot location privacy protection based on Q-learning particle swarm optimization algorithm in mobile crowdsensing

Kong

Chen

et al. 2022

Front. Neurorobot.

View full text Add to dashboard Cite

In the recent years, with the rapid development of science and technology, robot location-based service (RLBS) has become the main application service on mobile intelligent devices. When people use location services, it generates a large amount of location data with real location information. If a malicious third party gets this location information, it will cause the risk of location-related privacy disclosure for users. The wide application of crowdsensing service has brought about the leakage of personal privacy. However, the existing privacy protection strategies cannot adapt to the crowdsensing environment. In this paper, we propose a novel location privacy protection based on the Q-learning particle swarm optimization algorithm in mobile crowdsensing. By generalizing tasks, this new algorithm makes the attacker unable to distinguish the specific tasks completed by users, cuts off the association between users and tasks, and protects users' location privacy. The strategy uses Q-learning to continuously combine different confounding tasks and train a confounding task scheme that can output the lowest rejection rate. The Q-learning method is improved by particle swarm optimization algorithm, which improves the optimization ability of the method. Experimental results show that this scheme has good performance in privacy budget error, availability, and cloud timeliness and greatly improves the security of user location data. In terms of inhibition ratio, the value is close to the optimal value.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robot location privacy protection based on Q-learning particle swarm optimization algorithm in mobile crowdsensing

Kong

Chen

et al. 2022

Front. Neurorobot.

View full text Add to dashboard Cite

show abstract

“…With the rapid development of computer science and Internet technology, a large amount of data is produced all over the world all the time, such as, text, audio, pictures, videos and so on. Big data contains a lot of information, but this information is difficult to identify and organize manually (Li et al 2023;Huang et al 2021). Therefore, how to classify and identify information has been concerned and studied by many researchers.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Feature Fusion Model Based on Denoising Convolutional Neural Network and Attention Mechanism for Image Classification

Zhang

Lei

et al. 2023

International Journal of Swarm Intelligence Research

View full text Add to dashboard Cite

Spatial location features extracted by denoising convolutional neural network. At this time, an attention mechanism is introduced into denoising convolutional neural network. The dual attention model of local area is presented from two dimensions of channel and space—channel attention mechanism weights channel and spatial attention mechanism weights location. A variety of machine learning methods are used to classify and train different features. Multi-semantic features and heterogeneous features are fused by adaptive weighted fusion algorithm. Finally, the data sets Cifar-10, STL-10, Cifar-100 and GHIM-1OK are verified on the proposed method. Compared with a single semantic feature, the accuracy is improved by 10%-15%. Compared with several advanced algorithms, the performance has a significant advantage, which proves the complementarity of heterogeneous features and multi-network semantic features and the effectiveness of the adaptive weighted fusion algorithm.

show abstract

“…Data recorded in HMI during the DOL mode of operation was accessed remotely, monitoring and data analysis [63].…”

mentioning

confidence: 99%

Energy Saving Implementation in Hydraulic Press Using Industrial Internet of Things (IIoT)

Sumit

Gupta

Juneja³

et al. 2022

Electronics

View full text Add to dashboard Cite

With the growing cost of electrical energy, the necessity of energy-saving implementation in industries based on energy audits has become a major focus area. Energy audit results indicate energy-saving potential in an application and require the physical presence of the auditor’s team for monitoring and analyzing the energy consumption data. The use of Industrial Internet of Things (IIoT) for remote data monitoring and analysis is growing and new industrial applications based on IIoT are being developed and used by various industrial sectors. Possibilities of a mixed method of physical and remote energy audit using IIoT in industrial applications and its advantages as proposed in this research work needs to be explored. Existing hydraulic press machines running with direct online starter (DOL) can be run with variable speed drive (VSD) for energy saving but this requires an extensive energy audit. Key electrical and operational parameters of the hydraulic pump motor were monitored and analyzed remotely using IIoT in this research work by operating the hydraulic press with DOL and VSD motor control methods one by one. The input power factor of the hydraulic pump motor showed an improvement from 0.79 in DOL control to 0.9 in VSD control at different motor loads. The hydraulic pump motor starting current showed a reduction of 84% with VSD control. The hydraulic pump motor’s continuous current was reduced by 40% and 65% during the loading and unloading cycle, respectively, with VSD control. Electrical consumption was reduced by 24% as a result of operating the hydraulic pump motor at 35 Hz with VSD control without impacting the performance of the hydraulic press. These results indicated more efficient control by changing to VSD control in comparison with DOL control. A combination of physical and remote energy audits as performed in this research work using the proposed IIoT framework can be utilized for implementing energy saving in hydraulic presses thus motivating industries to adopt available more energy-efficient technologies at a faster pace.

show abstract

A Deep Multimodal Adversarial Cycle-Consistent Network for Smart Enterprise System

Cited by 10 publications

References 19 publications

Robot location privacy protection based on Q-learning particle swarm optimization algorithm in mobile crowdsensing

Robot location privacy protection based on Q-learning particle swarm optimization algorithm in mobile crowdsensing

A Multi-Feature Fusion Model Based on Denoising Convolutional Neural Network and Attention Mechanism for Image Classification

Energy Saving Implementation in Hydraulic Press Using Industrial Internet of Things (IIoT)

Contact Info

Product

Resources

About