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

Dudnik, Olesya; Vasiljeva, Мarina V.; Kuznetsov, Nikolay; Podzorova, Marina; Nikolaeva, Irina; Vatutina, Larisa; Khomenko, E B; Ivleva, Marina I.

doi:10.3390/joitmc7020155

Cited by 17 publications

(4 citation statements)

References 64 publications

(90 reference statements)

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“…In the data division of the first stage, according to the discreteness characteristics of action data in basketball training of athletes in two different states, what is extracted are the athletes' action data and the sustained and instantaneous actions of athletes in a series of activities. Because there are many athletes' persistent movements and much need of unit movements, in the second stage of data division, according to the changes in the limbs angles of basketball players during the training process, it is finally realized of the data extraction of athletes' continuous movements (Dudnik et al, 2021 ).…”

Section: Research Model and The Methodologymentioning

confidence: 99%

Artificial Intelligence Technology in Basketball Training Action Recognition

Liang

Xu²,

Kuang³

2022

Front. Neurorobot.

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The primary research purpose lies in studying the intelligent detection of movements in basketball training through artificial intelligence (AI) technology. Primarily, the theory of somatosensory gesture recognition is analyzed, which lays a theoretical foundation for research. Then, the collected signal is denoised and normalized to ensure that the obtained signal data will not be distorted. Finally, the four algorithms, decision tree (DT), naive Bayes (NB), support vector machine (SVM), and artificial neural network (ANN), are used to detect the data of athletes' different limb movements and recall. The accuracy of the data is compared and analyzed. Experiments show that the back propagation (BP) ANN algorithm has the best action recognition effect among the four algorithms. In basketball training athletes' upper limb movement detection, the average accuracy rate is close to 93.3%, and the average recall is also immediate to 93.3%. In basketball training athletes' lower limb movement detection, the average accuracy rate is close to 99.4%, and the average recall is immediate to 99.4%. In the detection of movements of upper and lower limbs: the recognition method can efficiently recognize the basketball actions of catching, passing, dribbling, and shooting, the recognition rate is over 95%, and the average accuracy of the four training actions of catching, passing, dribbling, and shooting is close to 98.95%. The intelligent basketball training system studied will help basketball coaches grasp the skilled movements of athletes better to make more efficient training programs and help athletes improve their skill level.

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Section: Research Model and The Methodologymentioning

confidence: 99%

Artificial Intelligence Technology in Basketball Training Action Recognition

Liang

Xu²,

Kuang³

2022

Front. Neurorobot.

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“…Companies with comprehensive and extensive HR practices and systems (for example, career management, performance-based rewards, internal communication, training and development, performance appraisal, recruitment, and selection) are likely to have employees that are committed, competent, motivated, involved, empowered, flexible, and innovative [38]. These processes can be managed effectively with the help of innovations, digital technologies, and artificial intelligence [39].…”

Section: Globalizationmentioning

confidence: 99%

Sustainable Development of Employee Lifecycle Management in the Age of Global Challenges: Evidence from China, Russia, and Indonesia

Xiang

Косов

et al. 2023

Sustainability

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The COVID-19 pandemic has significantly affected the employee lifecycle management (ELM) sphere, leading to the adoption of new human resource (HR) technologies and policies. This study investigates the impact of megatrends, artificial intelligence, digital technologies, and innovation on ELM and human resource management (HRM) policies in China, Russia, and Indonesia. Data were collected through structured interviews and publicly available information from companies in these countries between 2021 and 2022. The study evaluates the effects of artificial intelligence (AI), digital transformation (DT), and innovations on the sustainable development of ELM and identifies differences in technological responses to ELM in companies depending on their level of digital maturity. The results show that the majority of companies have continued the process of ELM digital transformation, but the percentage varies based on the scope of activity, labor, and readiness of the country to implement new technologies. The study reveals that large companies in each analyzed country with over 10,000 employees have a greater need and opportunity to implement HR digital transformation, whereas small companies with up to 100 people can operate without automation. In addition, the findings of this study provide propositions for designing how AI and innovations contribute to ELM. This article contributes to the current debate in the literature by substantiating the positive impact of AI, digital technology, and innovation on ELM and HRM strategies, offering practical applications for companies to improve productivity. Overall, this study highlights the importance of adopting innovative HR technologies in response to global challenges and workplace trends.

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“…The need to develop a system of indicators has been confirmed by modern studies. Dudnik et al [30] examined the impact of the use of open innovation (OI) in the energy sector on readiness for the introduction of artificial intelligence (AI) technologies and their effectiveness. An empirical method was proposed to determine the level of readiness in companies participating in OI for the introduction of AI technologies by comparing Russian and French energy companies.…”

Section: Literature Reviewmentioning

confidence: 99%

Tool Development for Assessing the Strategic Development of Territorial Socio-Economic Systems for the Purposes of Energy Sector Digital Transformation

Gutman

Brazovskaia

2023

Energies

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This article addresses the issue of implementing and assessing the readiness of territorial and economic systems for digital innovations in the energy industry. To achieve the goal of this research, qualitative and quantitative methods were used, including systemic and comparative analyses, a balanced scorecard (BSC), and statistical data collection and processing methods. The study resulted in a list of indicators for monitoring the readiness of the energy sector for digitization, based on which a balanced scorecard reflecting the readiness of the energy industry in countries and regional industrial complexes (RICs) was developed. A strategic map for assessing the level of readiness of the power industry for digitisation in countries and RICs was constructed using the indicator pool. The uniqueness and novelty of this study lie in the adaptation of a balanced scorecard system, aligned with a country’s or RIC’s development strategy, to determine indicators for assessing the readiness level of the considered entity for digitising the power industry. The authors establish an approach to the overall assessment and subsequent monitoring of the energy industry’s readiness for digitisation in countries worldwide and RICs.

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

Cited by 17 publications

References 64 publications

Artificial Intelligence Technology in Basketball Training Action Recognition

Artificial Intelligence Technology in Basketball Training Action Recognition

Sustainable Development of Employee Lifecycle Management in the Age of Global Challenges: Evidence from China, Russia, and Indonesia

Tool Development for Assessing the Strategic Development of Territorial Socio-Economic Systems for the Purposes of Energy Sector Digital Transformation

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