Intelligent Transformation Research Articles

The Impact of Manufacturing Intelligence on Green Development Efficiency: A Study Based on Chinese Data

This study used provincial panel data from 2011 to 2020 to empirically analyze the impact of improvements in the manufacturing intelligence level on the efficiency of green development. The results show the following: improvements in manufacturing intelligence significantly increased the efficiency of green development. Following the “Guidelines for the Construction of the National Intelligent Manufacturing Standard System” proposed by China as a quasi-natural experiment, the double-difference method was used to prove the following: the promotional effect is greater in eastern regions after the policy was implemented. An improvement in manufacturing intelligence increases green development efficiency by improving the efficiency of technological innovation and energy use. The impact of the manufacturing intelligence level on green development efficiency shows a threshold effect. The promotion effect is more pronounced in regions where the level of technological innovation and the strength of the government’s role cross this threshold. Therefore, the government should vigorously promote the intelligent transformation of the manufacturing industry and improve the efficiency of China’s green development.

Intelligent transformation and sustainable innovation capability: Evidence from China

Previous studies overlook the impact of intelligence on enterprises' sustainable innovation capability. Our study shows that intelligent transformation enhances this ability (both inputs and outputs). We mitigate endogeneity through the instrumental variable, and the conclusion remains robust. The optimization of human capital structure and technological diversification constitute the underlying channels through which intelligent transformation promotes enterprises’ sustainable innovation. Further research indicates that the sustainable innovation effect of intelligent transformation is more significant in state-owned enterprises. These findings deepen our understanding of the dynamic effects of intelligent transformation on innovation.

Memristive hyperchaotic system-based complex-valued artificial neural synchronization for secured communication in Industrial Internet of Things

In this article, a memristive hyperchaotic system-based complex-valued Artificial Neural Network (ANN) synchronization for secured communication between Industrial Internet of Things (IIoT) nodes is proposed. The IIoT, an extension of the Internet of Things (IoT), has significant promise for resource use in the industrial sector and intelligent transformation. Security and privacy issues exist when industrial data is sent over an open network via sensor devices. For this, a secure key exchange protocol is necessary. Synchronized key exchange using an ANN is one of the solutions. Additionally, there is a dearth of research on the reciprocating training of ANNs and the use of a robust Pseudo-Random Number Generator (PRNG) to generate a shared input. This study explains how to use a hyperchaotic environment to swiftly assess how well ANNs finalize their coordination and synchronize for session key switch over. Reciprocating learning is used to synchronize two neural networks and transfer the ANN’s output across a public channel. When the ANNs have produced the same outputs in previous iterations, coordination is tested using that function. The proposed technique has lot of benefits, like: (1) using a hyperchaotic-guided PRNG to produce the ANN input sequences; (2) generating the session key via the public network using a reciprocal neuronal alignment of complex ANNs; and (3) allowing two communicating partners to identify complete synchronization more quickly when compared to earlier methodologies. The tested proposed technique outperforms comparable strategies in the literature, according to the results.

Does intelligent transformation trigger technology innovation in China's NEV enterprises?

With a sample of the listed New Energy Vehicle (NEV) enterprises in China (2008–2020), this paper investigates the impact of intelligent transformation on innovation in NEV enterprises. The results indicate that intelligent transformation has a positive and significant impact on the technology innovation in China's NEV enterprises, and the impacts are more significant in Non-State of Enterprises (non-SOEs) and those without fixed suppliers. Mediating mechanism reveals that intelligent transformation may trigger technology innovation in NEV enterprises through establishing new production function and incorporating consumer as external resources. The paper reveals an important question of how intelligent transformation trigger technology innovation in NEV enterprises, thus providing valuable suggestions on practice of intelligent transformation of NEV enterprises.

Review on intelligent pipeline technologies: A life cycle perspective

The pipeline is one of the important carriers in the petroleum industry, but facing with the development bottlenecks of low efficiency, high cost and high decision risk. With machine learning and advanced sensor, new technological breakthroughs are being opening up for intelligent pipeline systems to benefit. Nowadays, the intelligent transformation of pipeline is still in the initial stage. To accelerate this process, this paper elaborates the research on intelligent pipeline systems from the perspective of the full life cycle of pipeline. The life cycle is divided into six stages, including exploration, feasibility study, design, construction, operation and scrap. In each stage, the related theoretical research and key technologies have been systematically reviewed. It is observed that full digital construction and operation is an inevitable trend. The Internet of Things, big data, and multi-objective optimization are critical technologies. Finally, some future directions are put forward to prompt a pipeline into intelligence.

Research on the Influence Mechanism of Enterprise Industrial Internet Standardization on Digital Innovation

In the era of the digital economy, with the digital and intelligent transformation of the industry, technical complexity, and higher demand for the inter-operability of Industrial Internet platforms, standardization work faces challenges of timeliness and high quality. So, enterprises must improve the quality and efficiency of their digital innovation capabilities in the dynamic process of Industrial Internet standardization. Given this condition, based on the digital innovation theory, standardization theory, and social network theory, this paper takes 387 A-share-listed enterprises in the field of Industrial Internet industry as the research object and uses the secondhand cross-sectional data to carry out the research on the influence mechanism of enterprise Industrial Internet standardization on the digital innovation capabilities of enterprises. The results show that: enterprise Industrial Internet standardization exerts a positive influence on the digital innovation capability of enterprises; a standard alliance network in the enterprise Industrial Internet standardization plays an intermediary role in the digital innovation capability of enterprises; and the scale of enterprises has a positive moderating effect on the intermediary role of a standard alliance network between the enterprise Industrial Internet standardization and the digital innovation capability of enterprises. From the perspective of the alliance network effect, the research explores the economic consequences of standardization construction and enriches the appraisal of the standardization capability of enterprises for the purpose of providing targeted empirical evidence and practical strategies for the standard-based digital innovation of Industrial Internet enterprises.

Chasing Green: The Synergistic Effect of Industrial Intelligence on Pollution Control and Carbon Reduction and Its Mechanisms

The coordinated promotion of pollution control and carbon reduction is intended to build a more beautiful China. Intelligent industrial technology plays an important role in the fight against climate change and in improving the ecological environment. Based on panel data from 30 provinces in China from 2006 to 2020, we used a two-way fixed effects model to evaluate the synergistic effects of industrial intelligent transformation on pollution control and carbon reduction and its mechanisms. The results showed that the introduction and installation of industrial robots by enterprises significantly reduced carbon emissions and the concentration of fine particles in the air, as well as having the synergistic effect of reducing pollution and carbon. This conclusion was still robust after using instrumental variable methods to perform endogenous tests. The study also showed that industrial intelligence reduced pollution and carbon through mechanisms that promoted green technological innovation and improved energy efficiency. The conclusions of this study could provide evidence for the use of digital technologies to promote environmental protection and achieve the goal of carbon neutrality, as well as play a significant role in the promotion of economic and societal green transformation.

Transformation of Knowledge and Artificial Intelligence in the Digital Age: An Evaluation in Terms of Production and Power Relations

Transformation of Knowledge and Artificial Intelligence in the Digital Age: An Evaluation in Terms of Production and Power Relations

Coordination Decision-Making for Intelligent Transformation of Logistics Services under Capital Constraint

The intelligent transformation of logistics plays a significant role in meeting the diverse needs of customers, improving operational efficiency, and reducing carbon emissions in logistics activities. Therefore, to achieve sustainable development, logistics enterprises need to face the decision-making problem of intelligent logistics transformation. In this paper, we construct a Stackelberg game model between a financially constrained logistics-service provider (LSP) and a well-funded logistics-service integrator (LSI) and discuss the impact of the wholesale price contract, the cost-sharing contract, the revenue-sharing contract, the two-part tariff contract, and the hybrid cost-sharing and revenue-sharing contract on the intelligence level of logistics services, the profits of supply-chain members, and the channel for logistics-service demand. We found that the cost-sharing contract and the revenue-sharing contract cannot achieve Pareto improvement in the profits of supply-chain members. In addition, the increase in bank-loan interest rates would seriously weaken the level of intelligence and market demand for the entire logistics service. However, when consumers do not have high requirements for the intelligence of logistics services, the two-part power–price contract can create a win–win situation for supply-chain members and increase market demand within a certain range; on the contrary, a hybrid contract of cost sharing and revenue sharing is the best choice. Moreover, in the process of contract design for the intelligent transformation of logistics services, it is necessary to pay attention to the influence of the price-sensitivity coefficient on decision-making.

The Circular Economy, Big Data Analytics, and the Transformation of Urban Slums in Sub-Saharan Africa

Sub-Saharan Africa is currently experiencing growth in the number of people living in poverty, and the situation is worsening due to climate change and the COVID-19 pandemic. Cities are increasingly under stress because of urbanization and the demand for low-cost housing. Slum dwellers face daunting social, environmental, and economic challenges. Geospatial analysis of remote sensing, demographic, economic, social, and environmental data is being used to delineate slums. The application of circular economy guidelines for an intelligent transformation of slums combines technical and social innovation that reaches beyond the slums to the whole urban ecosystem. Examples of contributions to the circular economy are provided. Finally, some ideas are introduced on how the internet of things can improve access to goods and services and strengthen interconnectedness through the ability to participate more readily in the social dialogue of the city. The city of Accra in Ghana, West Africa, is discussed as a potential slum city to functional intelligent city transformation.

The impact of air quality on industrial intelligence: evidence from Chinese industrial firms

As Industry 4.0 develops, enterprises are investing in the industrial intelligent transformation. Using unique data from Chinese industrial enterprises, this paper investigates whether air quality affects the upgrading of industrial intelligence in enterprises. We find that decreased air quality improves industrial intelligence for enterprises, as enterprises increase their use of intelligent industrial robots when air pollution causes shortages of labour quantity and efficiency. Further analysis shows that large-scale enterprises and enterprises in areas with high economic development are more inclined to develop industrial intelligence in response to decreased air quality than their counterparts. In addition, the results indicate that the intelligent transformation of enterprises can improve their performance. This study provides a new perspective on the impact of environmental issues on corporate behaviour.

Electrical Appliance Recognition Algorithm Based on Neural Network and Kernel Density Estimation

With the rapid development of the social economy, the need for social intelligent transformation is increasingly urgent. In order to identify the electrical equipment quickly and accurately and reduce the false recognition rate, we propose a new identification algorithm, which uses the neural network model to pre-identify the connected electrical equipment and then conducts a secondary test through the kernel density estimation model. The experimental results show that the algorithm has a high recognition rate for the trained model and, at the same time, solves the misrecognition problem of the new electrical appliance neural network model in the production environment.

Intelligent Dynamic Test System for Vehicle Engine Based on 5G Internet of Things

With the development of the automobile industry, the improvement of the reliability test level and the intelligent fault diagnosis level of the engine, as the heart component of the automobile and the assembly with the highest failure rate, becomes more and more important. In a context where technology is being upgraded, the development of intelligent automotive engine inspection system based on IoT thinking is set to become the major trend in the future. The objective of this article would be to have better knowledge about smart car engine system in the 5G IoT era. It is also through the study, a dynamic testing and characterization of the engine. The performance of the automobile engine, especially the dynamic performance, is further optimized. In this article, we focus on the PUMAOPEN test system of AVL and the system under test, the state management of the test process, and the intelligent transformation of key components. The results show that: studies and analyses the dynamic working conditions of intelligent vehicles on the Internet of Things, and compares the dynamic working conditions with the steady working conditions from qualitative to quantitative. By analyzing the influence of throttle opening change rate and speed change rate on dynamic performance and engine dynamic torque estimation, the fast sensing and response-ability in the engine testing process is improved. When the engine is accelerated by the experimental method presented in this article, and when the throttle opening is more than 30% and the engine speed varies at 100 and 200 rpm/s, the output value of the torque is higher than the output value of the steady-state torque or has little difference with the output value of the steady-state torque, and tends to be stable. In deceleration performance, the dynamic output torque of the engine is asymmetric. And it will deviate from the steady-state output torque, showing different dynamic response characteristics in different working sections. Throttle delay, overshoot characteristics, mechanical inertia, inflation coefficient, air–fuel ratio, and ignition advance angle when engine speed changes sharply will make engine dynamic characteristics deviate from steady-state characteristics.

Still no Dunning-Kruger effect: A reply to Hiller

The primary criticism raised by Hiller (2023) pertained to the transformation of the self-assessed intelligence (SAI) scores applied by Gignac and Zajenkowski (2020), an investigation that suggested the Dunning-Kruger effect (DKE) may be primarily a statistical artefact. Hiller recommended an alternative transformation. Our re-analysis of Gignac and Zajenkowski's data with Hiller's recommended transformation failed to find evidence in favour of the DK hypothesis. Hiller also recommended SAI measurement with a percentile-based approach. Based on a review of the literature, one investigation has employed a percentile-based approach to SAI measurement, and it largely failed to support the DK hypothesis, when analysed with the non-linear regression approach recommended by Gignac and Zajenkowski. We conclude by encouraging researchers to continue to derive novel approaches to testing the DK hypothesis, especially approaches that overcome known methodological challenges.

Transformer state assessment and implementation based on online monitoring

Transformer is the key equipment of power grid, it's operation status has an important impact on the stability and reliability of the power system. The online monitoring data of the transformer can truly reflect its status information, and it is of great significance to evaluate its status in combination with the online monitoring data. At present, in the traditional transformer state assessment, there are some shortcomings such as uncertainty of the assessment system, singleness of index data, simple method for determining the weight of assessment indicators, the accuracy of assessment results, and insufficient timeliness. In view of the above situation, a real-time evaluation method of transformer based on online monitoring data is proposed. Firstly, a hierarchical evaluation system model based on quantitative index data such as online monitoring data and preventive experiments data and qualitative index data such as operating years and annual load rate is constructed; Secondly, a combined weighting method based on Modified Analytic Hierarchy Process(MAHP) and Critic method is proposed, which effectively overcomes the drawbacks caused by a single weight; then, a method to deal with various fault weights based on variable weight theory is proposed; Finally, the overall state of the transformer is obtained by combining the weights of each layer and the results of the normalization of the indicators. Through the verification of data examples, it can accurately, timely, and scientifically reflect the operating status of the transformer, and can provide scientific maintenance guidance for inspection personnel, which has practical application value. Under the background of digital and intelligent transformation of offshore oil and gas platforms, based on the QT development platform, MySQL database, and C++ language, an online transformer evaluation system is designed and developed, including online monitoring, state evaluation, and other major modules. At the same time, it provides users with visual analysis and helps the intelligent construction of offshore oil and gas platforms.

Melting the Psychological Boundary: How Interactive and Sensory Affordance Influence Users’ Adoption of Digital Heritage Service

As a result of the post-pandemic situation, enhancing digital heritage services has become one of the key issues for the recovery of tourism. Disruptive innovation in human–computer interaction technology has brought new opportunities for digitalization and intelligent transformation in the contemporary cultural tourism industry. Existing research on the adoption behavior of digital heritage services primarily focuses on users’ assessments of behavior results. There is a considerable gap in research about the interaction and value co-creation between users and digital intelligence services and users’ cognitive construction logic of digital heritage services. Following reciprocal determinism, we propose a conceptual model to deconstruct the detailed transmission path of interactive affordance and sensory affordance to digital heritage adoption. In Study 1, a lab experiment in an AI-assisted smart screen digital heritage service context revealed that interactive affordance and user adoption of digital heritage services were partially mediated by psychological distance. Findings from a between-subject online experiment in Study 2 confirmed that embodied cognition and psychological distance play a parallel intermediary role in the impact of sensory affordance on adoption. In Study 3, a lab experiment in a VR-based digital museum context further verified that information overload moderates the influence of embodied cognition on psychological distance. This research reveals the deep-bounded, rational decision-making logic of digital heritage service adoption and provides significant practical enlightenment for the optimization of the affordance experience.

Does Artificial Intelligence Promote or Inhibit On-the-Job Learning? Human Reactions to AI at Work

This paper examines how AI at work impacts on-the-job learning, shedding light on workers’ reactions to the groundbreaking AI technology. Based on theoretical analysis, six hypotheses are proposed regarding three aspects of AI’s influence on on-the-job learning. Empirical results demonstrate that AI significantly inhibits people’s on-the-job learning and this conclusion holds true in a series of robustness and endogeneity checks. The impact mechanism is that AI makes workers more pessimistic about the future, leading to burnout and less motivation for on-the-job learning. In addition, AI’s replacement, mismatch, and deskilling effects decrease people’s income while extending working hours, reducing their available financial resources and disposable time for further learning. Moreover, it has been found that AI’s impact on on-the-job learning is more prominent for older, female and less-educated employees, as well as those without labor contracts and with less job autonomy and work experience. In regions with more intense human–AI competition, more labor-management conflicts, and poorer labor protection, the inhibitory effect of AI on further learning is more pronounced. In the context of the fourth technological revolution driving forward the intelligent transformation, findings of this paper have important implications for enterprises to better understand employee behaviors and to promote them to acquire new skills to achieve better human–AI teaming.

Unemployment risk perception and knowledge hiding under the disruption of artificial intelligence transformation

Using social exchange theory, we proposed a moderated mediation model to explore the relationship between unemployment risk perception and employee knowledge hiding under the disruption of artificial intelligence transformation. We conducted an empirical analysis of data from 274 respondents who completed a two-stage survey with an interval of about 2 weeks. The results were as follows: First, unemployment risk perception was positively related to employee knowledge hiding. Second, psychological contract breach mediated the relationship between unemployment risk perception and employee knowledge hiding. Third, mindfulness negatively moderated the relationship between psychological contract breach and employee knowledge hiding. Fourth, mindfulness negatively moderated the strength of the mediated relationship between unemployment risk perception and employee knowledge hiding via psychological contract breach. The research conclusions can help companies formulate policies to guide employees to actively share knowledge in artificial intelligence transformation contexts.

Research on Green Intelligent Building Design Strategy for small infrastructure project of Fujian Electric Power Company of State Grid

In order to realize the green and intelligent development of small infrastructure projects of State Grid Fujian Electric Power Company, and help the national strategic development goals of carbon peak and carbon neutrality, this paper analyzes and summarizes the green and intelligent design strategies suitable for small infrastructure projects of State Grid Fujian Electric Power Company, and help the green, low-carbon and intelligent transformation of State Grid Fujian Electric Power Company.

Research on unbalanced mining of highway project key data based on knowledge graph and cloud model

Various stages of highway project construction process involve text, image, audio, video and other related data sources involving many participants, forming a huge amount of data. Accurately tracing the source of responsibility, refining and applying the unbalanced data in the highway project archives is of great significance for realizing the intelligent transformation of highway construction project management. This paper firstly sorts out the construction process of highway projects and the main data sources, constructs a data association network between construction entities and construction process, as well as a knowledge map of highway construction data. Then, according to the highway construction stage, an index system based on 12 key data is constructed by using the entropy weight cloud model method, and the importance of the data is evaluated. Thirdly, based on the unbalanced characteristics of highway project data, a method of mining big data in highway project archives using classification evaluation indexes is proposed, and the accuracy of this method is verified by case calculation. Finally, taking the Shizong Qiubei Expressway in China as an example, the intelligent management and control suggestions for key data of transportation projects are proposed. It is found that the key data with special importance rate in highway construction include construction data, supervision data and completion data. Boosting algorithm is more accurate than the traditional SMOTE algorithm for unbalanced data mining, which helps to save the project construction cost and improve the quality of data extraction in the project archives. This study provides a theoretical reference for key data traceability of highway project intelligent management and control platform and the improvement of intelligent management efficiency.