Artificial intelligence for securing industrial-based cyber–physical systems

Lv, Zhihan; Chen, Dongliang; Lou, Ranran; Alazab, Ammar

doi:10.1016/j.future.2020.12.001

Cited by 87 publications

(32 citation statements)

References 26 publications

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“…Most scientists hold the viewpoint of the positive impact of the practical AI technology implementation on energy sector development and the economy as a whole [18,33,69,75] and human capital potential [77]. Within this spectrum, a dominant number of papers focus specifically on demonstrative assessments of the possible impact/effects of digitalization on particular industries and groups of industries in the global economy [12,13,18,33,72,77] and empirical assessments of digitalization effects of using machine intelligence [78][79][80]. Although, some scientists investigate the threats to energy security that AI technologies may carry.…”

Section: Artificial Intelligence In the Energy Sectormentioning

confidence: 99%

“…AI has already changed the methods that many industries operate, and the energy sector is not an exception. Energy companies and individual consumers use AI to collect and analyze data to identify and track trends in energy sector production and consumption [9][10][11][12][13]. Therefore, in light of the increasing energy sector popularity, through the active use of AI technologies, companies have a great opportunity to use them to reduce costs, improve the safety, reliability, and sustainability of their systems, and reduce risks to gain competitive advantages [9,11,14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Trends, Impacts, and Prospects for Implementing Artificial Intelligence Technologies in the Energy Industry: The Implication of Open Innovation

Dudnik

Vasiljeva²,

Kuznetsov

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

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This research aims to substantiate the impact of using open innovation (OI) in the energy sector in readiness to implement artificial intelligence (AI) technologies and their effectiveness. The empirical method was proposed to determine the readiness level of OI for the implementation of AI technologies by comparing Russian and French energy companies. Readiness level indicators of companies for AI implementation using the Fibonacci sequence, Student’s t-test, and the method of fuzzy sets were empirically determined. The integrated readiness indicator for AI implementation by companies was calculated using the method of fuzzy sets and expressed through variance, allowing for these significant factors. Russian companies are at a low level of developmental readiness to implement AI, which is in contrast to companies operating in a developed market where the determining factor is the AI technology cost. The example of the innovative business model “Energy-as-a-Service” shows the synergistic effects of OI use and AI technology introduction. This paper is novel because it seeks to contribute to the current debate in the literature, justifying the position that energy companies that have in the past actively applied the concept of open innovation in business, are the most competitive and most efficient in implementing AI technologies.

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Section: Artificial Intelligence In the Energy Sectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trends, Impacts, and Prospects for Implementing Artificial Intelligence Technologies in the Energy Industry: The Implication of Open Innovation

Dudnik

Vasiljeva²,

Kuznetsov

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

show abstract

“…Data mining is the process of abstracting unknown but latently useful information and knowledge hidden in numerous, uncompleted, noise-interweaving, blurry, stochastic and practical application data (Lou et al 2021a ). Based on data mining techniques including association rules mining, classification, clustering, sequence pattern, prediction and trend analysis, predicting QoE factors can be influenced on performance of virtual education systems for next semesters in universities and colleges (Lv et al 2021b ; Zuo et al 2017 ). According to above-mentioned issues, this paper presents a prediction model based on association rules mining as one important and basic technique and supervised techniques focused on behavioral aspects of teaching and learning to evaluate the QoE factors of virtual education systems.…”

Section: Introductionmentioning

confidence: 99%

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining

Tan

Lin

2021

Soft Comput

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Today, emerging technologies such as 5G Internet of things (IoT), virtual reality and cloud-edge computing have enhanced and upgraded higher education environments in universities, colleagues and research centers. Computer-assisted learning systems with aggregating IoT applications and smart devices have improved the e-learning systems by enabling remote monitoring and screening of the behavioral aspects of teaching and education scores of students. On the other side, educational data mining has improved the higher education systems by predicting and analyzing the behavioral aspects of teaching and education scores of students. Due to an unexpected and huge increase in the number of patients during coronavirus (COVID-19) pandemic, all universities, campuses, schools, research centers, many scientific collaborations and meetings have closed and forced to initiate online teaching, e-learning and virtual meeting. Due to importance of behavioral aspects of teaching and education between lecturers and students, prediction of quality of experience (QoE) in virtual education systems is a critical issue. This paper presents a new prediction model to detect technical aspects of teaching and e-learning in virtual education systems using data mining. Association rules mining and supervised techniques are applied to detect efficient QoE factors on virtual education systems. The experimental results described that the suggested prediction model meets the proper accuracy, precision and recall factors for predicting the behavioral aspects of teaching and e-learning for students in virtual education systems.

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“…The increasing use of digital twins (DTs) is one of the most important trends in the Industry 4.0 concept and industrial engineering [1], and some authors directly refer to the Industry 4.0 era as the era of DT [2]. The concept of Industry 4.0 extends the possibilities and use of DTs for, e.g., decision support and production planning [3], solving unexpected situations/problems or predicting such situations [4], as well as training and knowledge transfer of leadership, management, and executives [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…A CPS is a system consisting of physical entities controlled by computer algorithms that allow these entities to function completely independently, including autonomous decision making, i.e., they can control a given technological unit or be an independent member of complex production units. CPSs are often built on artificial intelligence and machine learning [6], using simulations [7] for decision making and other areas of computer science that are being developed within the Industry 4.0 concept.…”

Section: Introductionmentioning

confidence: 99%

Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods

2021

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The paper presents possible approaches for reducing the volume of data generated by simulation optimisation performed with a digital twin created in accordance with the Industry 4.0 concept. The methodology is validated using an application developed for controlling the execution of parallel simulation experiments (using client–server architecture) with the digital twin. The paper describes various pseudo-gradient, stochastic, and metaheuristic methods used for finding the global optimum without performing a complete pruning of the search space. The remote simulation optimisers reduce the volume of generated data by hashing the data. The data are sent to a remote database of simulation experiments for the digital twin for use by other simulation optimisers.

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Artificial intelligence for securing industrial-based cyber–physical systems

Cited by 87 publications

References 26 publications

Trends, Impacts, and Prospects for Implementing Artificial Intelligence Technologies in the Energy Industry: The Implication of Open Innovation

Trends, Impacts, and Prospects for Implementing Artificial Intelligence Technologies in the Energy Industry: The Implication of Open Innovation

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining

Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods

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