Intelligent Production Research Articles

A multi-skilled staff scheduling and team configuration optimisation model for artificial intelligence project portfolio considering competence development and innovation-driven

This paper emphasises the pivotal role of enhancing research and development (R&D) staff competence and boosting team innovation efficiency in configuring R&D project teams for artificial intelligence (AI) product development. Diverging from traditional studies primarily focused on time, quality, and cost objectives, this study proposes prioritising the skill increments of staff and team diversity, aligning with the overarching goals of the R&D cycle. Targeting multi-skilled R&D personnel for scheduling and configuration, a three-objective tradeoff optimisation model is established. The nonlinear mixed-integer constrained programming model incorporates a learning effect for calculating employee skill value and employs a heterogeneity efficiency formula for assessing team diversity, particularly emphasising the diversity of R&D personnel research backgrounds. An algorithm based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed to obtain the approximate Pareto optimal solution. Furthermore, it compares the algorithm with the multi-objective particle swarm optimisation (MOPSO), explores practical decision-making methods for Pareto solutions, and conducts sensitivity analyses on the learning rate and diversity level. Our research is relevant to enterprises seeking to enhance R&D capabilities with a certain degree of homogeneity among R&D employees. This paper exemplifies and validates the proposed model and solution approach using a new AI product from a healthcare company.

Theoretical Framework and Research Proposal for Energy Utilization, Conservation, Production, and Intelligent Systems in Tropical Island Zero-Carbon Building

This study aims to theoretically explore the technological systems of tropical island zero-carbon building (TIZCB) to scientifically understand the characteristics of these buildings in terms of energy utilization, energy conservation, energy production, and intelligent system mechanisms. The purpose is to address the inefficiencies and resource wastage caused by the traditional segmented approach to building energy consumption management. Thus, it seeks to achieve a comprehensive understanding and application of the zero-carbon building (ZCB) technology system. This article focuses on the demands for energy-efficient comfort and innovative industrialization in construction. Through an analysis of the characteristics of TIZCB and an explanation of their concepts, it establishes a theoretical framework for examining the system mechanisms of these buildings. Additionally, it delves into the energy utilization, energy conservation, energy production, and intelligent system from macro, meso, and micro perspectives. This approach results in the development of an implementation strategy for studying the mechanisms of energy usage, conservation, and intelligent production systems in TIZCB. The results show that: (1) this study delves into the theoretical underpinnings of TIZCB, emphasizing their evolution from a foundation of low-carbon and near-zero energy consumption. The primary goal is to achieve zero carbon emissions during building operation, with reliance on renewable energy sources. Design considerations prioritize adaptation to high-temperature and high-humidity conditions, integrating regional culture along with the utilization of new materials and technologies. (2) A comprehensive technical framework for TIZCB is proposed, encompassing energy utilization, conservation, production capacity, and intelligent systems. Drawing from systems theory, control theory, and synergy theory, the research employs a macro–meso–micro analytical framework, offering extensive theoretical support for the practical aspects of design and optimization. (3) The research implementation plan establishes parameterized models, unveiling the intricate relationships with building performance. It provides optimized intelligent system design parameters for economically viable zero-carbon operations. This study contributes theoretical and practical support for the sustainable development of TIZCB and aligns with the dual carbon strategy in China and the clean energy free trade zone construction in Hainan.

The impact of artificial intelligence technology innovation on economic development -- from the perspective of generative AI products

The new generation of artificial intelligence products, represented by ChatGPT and Wenyan Yixin, is rapidly and extensively integrating into human production and life at an unprecedented pace, breadth, and depth. Generative artificial intelligence possesses distinct characteristics and functionalities compared to rule-based artificial intelligence, exerting a more prominent dual impact on the high-quality development of China's economy. On the one hand, it can promote high-quality economic development through empowering effects such as innovation-driven effects, enhanced production efficiency, and industrial transformation and upgrading effects. On the other hand, it may give rise to deep-seated risks, including labor market disruptions, market monopolization issues, national security risks, and misinformation phenomena, which could hinder high-quality economic development. It is crucial to have a correct understanding and a scientific approach, leveraging its positive effects while proactively addressing negative impacts, guiding it to better serve the needs of China's high-quality economic development.

Age-appropriate design of domestic intelligent medical products: An example of smart blood glucose detector for the elderly with AHP-QFD Joint KE

BackgroundIn response to the rise of intelligent products and the increasing prevalence of urban "empty nesters," we have developed a product specifically tailored for elderly diabetic patients. This product fulfils functional requirements and addresses stylistic preferences, contributing to the age-friendly evolution of home intelligent medical devices, particularly in intelligent blood glucose monitoring. MethodsOur approach commenced with a comprehensive user experience analysis to ascertain the needs of elderly users regarding home blood glucose meters. This involved constructing a hierarchy of user demands, followed by analysing and prioritising these needs. Utilizing Quality Function Deployment (QFD), we aligned user requirements with design specifications, identifying specific product functionalities and service design elements. Further, we employed Kansei Engineering (KE) to select sample designs that resonate with the concept of sensual imagery, leading to the derivation of specific modelling intentions. Combining these design elements, we proposed product design strategies and conducted practical case studies. The effectiveness of these designs was then assessed through fuzzy evaluation methods, allowing for user feedback. ResultsEmploying the Analytic Hierarchy Process for goal analysis, along with Quality Function Deployment theory and Kansei Engineering, we developed a home intelligent blood glucose meter catered to the elderly. This device not only meets its users' physiological and psychological needs but also provides an operationally convenient, health-conscious, and aesthetically pleasing experience. ConclusionsThis methodology enhances the age-appropriate design of home-based smart glucose monitors for the elderly, offering innovative insights and optimization strategies for designing elderly-centric smart medical products.

A typology of artificial intelligence data work

This article provides a new typology for understanding human labour integrated into the production of artificial intelligence systems through data preparation and model evaluation. We call these forms of labour ‘AI data work’ and show how they are an important and necessary element of the artificial intelligence production process. We draw on fieldwork with an artificial intelligence data business process outsourcing centre specialising in computer vision data, alongside a decade of fieldwork with microwork platforms, business process outsourcing, and artificial intelligence companies to help dispel confusion around the multiple concepts and frames that encompass artificial intelligence data work including ‘ghost work’, ‘microwork’, ‘crowdwork’ and ‘cloudwork’. We argue that these different frames of reference obscure important differences between how this labour is organised in different contexts. The article provides a conceptual division between the different types of artificial intelligence data work institutions and the different stages of what we call the artificial intelligence data pipeline. This article thus contributes to our understanding of how the practices of workers become a valuable commodity integrated into global artificial intelligence production networks.

Formation of intellectual property commercialization strategies

The object of research is the process that forms strategies and methods for effective commercialization of intellectual products at industrial enterprises. The problems of introducing intellectual property objects into the economic circulation in the field of industrial production have been analyzed. The most important of them are economic and managerial problems, which consist in the lack of necessary funding and are determined by the difficulty of assessing the market competitiveness of an intellectual product. Special attention was paid to market, technical-technological, and legal problems, the presence of which inhibits the development of the technological market and significantly affects the efficiency of intellectual and innovative activities. The presence of these problems predetermined the main goal of the study – to devise methods and strategies for the commercialization of intellectual property objects. Three basic strategies for the commercialization of intellectual products have been studied: the strategy of promoting an intellectual product, the strategy of diffusion of innovations, and the strategy of accumulating maximum income. Based on them, an integrated strategy for the commercialization of intelligent products for enterprises of the machine-building industry, which is oriented towards achieving the goals of maximizing the economic and social effect of the production of an intelligent product, was formed and substantiated. The main component of an integrated strategy is an organizational mechanism that provides investment support for innovative activities and scientific and consulting support for an intellectual product. The research results could be used to form programs of intellectual and innovative activity of industrial enterprises and would serve as a basis for further scientific research on these issues

RUSSIA IN THE 2020S: INDUSTRY 4.0 AND SOCIO-POLITICAL ASPECTS OF ITS INTEGRATION IN MASS ESTIMATES OF THE POPULATION (BASED ON SOCIOLOGICAL RESEARCH)

Purpose of the study: to form an objective understanding of the socio-political issues of integrating Industry 4.0 technologies into modern society in general and Russian society in particular. Methodology. The author, using a wide range of the latest domestic and foreign sources, conducts a consistent analysis of the most general issues related to the topic of the work, gives a general description of the theoretical discourse on the integration of Industry 4.0 technologies into society, and considers the interpretation of socio-political aspects in a (joint) “smart” production of Industry 4.0, explores pressing problems with compliance with established socio-economic practices in joint intelligent production, explores the presence of “fear of the future” in society, attitude towards breakthrough innovations of Industry 4.0, using the example of a survey of European society, the attitude of Russian society to the perception of Innovation 4.0 is outlined. Conclusions. Based on the results of the analysis, the author comes to the logical conclusion that the traditional system of established socio-economic practices will undoubtedly face a whole range of new challenges that may lead to a change in views on its form and content, both theoretical and theoretical. and applied it. in terms of compliance and execution of the plan. Especially, according to the author, this can be clearly manifested in the corporate sphere, which in the foreseeable future will become increasingly complex, and in the judicial sphere, where it will be unclear who to file claims against as a result of a trial for restoration or compensation for harm based on the results of an established violation of civil law.

Prediction and visualization of moisture content in Tencha drying processes by computer vision and deep learning.

It is important to monitor and control the moisture content throughout the Tencha drying processing procedure so that its quality is ensured. Workers often rely on their senses to perceive the moisture content, leading to relative subjectivity and low reproducibility. Traditional drying methods, which are used for measuring moisture content, are destructive to samples. This research was conducted using computer vision combined with deep learning to detect moisture content during the Tencha drying process. Different color space components of Tencha drying sample images were first extracted by computer vision. The color components were preprocessed using MinMax and Z score. Subsequently, one-dimensional convolutional neural networks (1D-CNN), partial least squares, and backpropagation artificial neural networks models were built and compared. The 1D-CNN model and Z scorepreprocessing achieved superior predictive accuracy, with correlation coefficient of prediction (Rp) = 0.9548 for moisture content. The migration of moisture content during the Tencha drying process was eventually visualized by mapping its spatial and temporal distributions. The results indicated that computer vision combined with 1D-CNN was feasible for moisture prediction during the Tencha drying process. This study provides technical support for the industrial and intelligent production of Tencha. © 2024 Society of Chemical Industry.

A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare.

Trustworthy medical AI requires transparency about the development and testing of underlying algorithms to identify biases and communicate potential risks of harm. Abundant guidance exists on how to achieve transparency for medical AI products, but it is unclear whether publicly available information adequately informs about their risks. To assess this, we retrieved public documentation on the 14 available CE-certified AI-based radiology products of the II b risk category in the EU from vendor websites, scientific publications, and the European EUDAMED database. Using a self-designed survey, we reported on their development, validation, ethical considerations, and deployment caveats, according to trustworthy AI guidelines. We scored each question with either 0, 0.5, or 1, to rate if the required information was "unavailable", "partially available," or "fully available." The transparency of each product was calculated relative to all 55 questions. Transparency scores ranged from 6.4% to 60.9%, with a median of 29.1%. Major transparency gaps included missing documentation on training data, ethical considerations, and limitations for deployment. Ethical aspects like consent, safety monitoring, and GDPR-compliance were rarely documented. Furthermore, deployment caveats for different demographics and medical settings were scarce. In conclusion, public documentation of authorized medical AI products in Europe lacks sufficient public transparency to inform about safety and risks. We call on lawmakers and regulators to establish legally mandated requirements for public and substantive transparency to fulfill the promise of trustworthy AI for health.

A Review of Proposed Models for Cutting Force Prediction in Milling Parts with Low Rigidity

Milling parts with low rigidity (thin-walled parts) are increasingly attracting the interest of the academic and industrial environment, due to the applicability of these components in industrial sectors of strategic interest at the international level in the aerospace industry, nuclear industry, defense industry, automotive industry, etc. Their low rigidity and constantly changing strength during machining lead on the one hand to instability of the cutting process and on the other hand to part deformation. Solving both types of problems (dynamic and static) must be preceded by prediction of cutting forces as accurately as possible, as they have a significant meaning for machining condition identification and process performance evaluation. Since there are plenty of papers dealing with this topic in the literature, the current research attempts to summarize the models used for prediction of force in milling of thin-walled parts and to identify which are the trends in addressing this issue from the perspective of intelligent production systems.

Research on the Application of THz-TDS in Coal–Rock Interface Recognition

The recognition of coal–rock interface is very crucial for research in the intelligent production of coal mines. To this end, the study investigated the application of terahertz time-domain spectroscopy in the recognition of coal–rock interface, including the identification of coal–rock and coal–rock mixtures, as well as the accurate characterization of coal seam thickness. Terahertz detection was used to obtain the optical parameter information of pressed pellets prepared by mixing two different kinds of coal and two kinds of rock. Based on the experiment’s results, a database was established for the identification of coal–rock interfaces for coal mining machines. The terahertz detection was performed on 10 different kinds of sheet anthracite with different thicknesses, and the terahertz spectra of coal seams with different thicknesses were simulated and calculated using simulation software. By comparing the two effective mining thicknesses, parameters can be provided for coal seam mining. The experiment and simulation show that the terahertz time-domain spectroscopy technology has a promising application prospect in the identification of coal–rock interface.

Reflections on good manufacturing practice and quality control system of personalized traditional Chinese medicine preparations

Personalized traditional Chinese medicine(TCM) preparations have entered a stage of rapid development. The key to the healthy development of this industry is to establish a sound manufacturing standard and quality control system. This paper analyzed the characteristics of personalized TCM preparations and drew reference from the quality management standards in the production of commissioned decoctions and oral pastes, on the basis of which the production quality management scheme and cautions for the safe production of personalized TCM preparations was put forward with consideration to various problems that may exist and occur in the production of such preparations. It provided references for formulating the production standards and quality management system of personalized TCM preparations. The production standards and quality control system should develop with the times. In the future, modern technologies such as big data and artificial intelligence should be employed to achieve the automated and intelligent production and establish a sound quality traceability system, online control strategy, and safety management mode of personalized TCM preparations, which will ensure the healthy development of this industry under requirement of good manufacturing practice(GMP).

Research practice and development direction of intelligent manufacturing of presonalized traditional Chinese medicine preparations

In recent years, as people's living standards continue to improve, and the pace of life accelerates dramatically, the demand and quality of traditional Chinese medicine(TCM) services from patients continue to rise. As an essential supplement to the existing forms of TCM application, such as Chinese patent medicine, decoction, and formulated granules, presonalized TCM preparations is facing an increasing market demand. Currently, manual and semi-mechanized production are the primary production ways in presonalized TCM preparations. However, the production process control level is low, and digitalization and informatization need to be improved, which restricts the automated and intelligent development of presonalized TCM preparations. Presonalized TCM preparations faces a significant opportunity and challenge in integrating with intelligent manufacturing through research and development of intelligent equipment and core technology. This paper overviews the connotation and characteristics of intelligent manufacturing and summarizes the application of intelligent manufacturing technologies such as "Internet of things" "big data", and "artificial intelligence" in the TCM industry. Based on the innovative research and development model of "intelligent classification of TCM materials, intelligent decision making of prescription and process, and online control and intelligent production" of presonalized TCM preparations, the research practice and achievements from our research group in the field of intelligent manufacturing of presonalized TCM preparations are introduced. Ultimately, the paper proposes the direction for developing intelligent manufacturing of presonalized TCM preparations, which will provide a reference for the research and application of automation and intelligence of presonalized TCM preparations.

Exploring the Antecedents of Artificial Intelligence Products’ Usage. The Case of Business Students

Exploring the Antecedents of Artificial Intelligence Products’ Usage. The Case of Business Students

The visual content of neural networks as a return to the third-order simulacrum

This article is part of a larger study of design as a cultural phenomenon. In this part of the study, the author examines the process that is currently taking place in the semiotic structure of design, associated with the active introduction of neural networks into the creation of visual content. Artificial intelligence products stylistically take the design away from the fourth-order simulacrum (from the flat design style) and return the design to using the third-order simulacrum as the main iconic form.The object of the research is the transformation of the semiotic design system. The subject of the study is a return to the third–order simulacrum in modern design. The purpose of the study is to show and try to explain how in modern design there is a return to the use of a third-order simulacrum. The research method is a semiotic analysis of modern design based on the methodology of R. Barth's semiotic analysis. The study is also based on J. Baudrillard's theory of semiosis in hyperreality and three orders of simulacra. The author sees the philosophical justification of the art of neural networks in the concept of flat ontologies. The study of the semiotic structure of design allows us to see that the logic of design development carries this phenomenon through successive stages of semiosis associated with a decrease in meaning and a diminution of being. In its development, the design consistently uses first index signs, a second-order simulacrum, then a third-order simulacrum as the main sign form. Nowadays, the main iconic form in design has become a fourth-order simulacrum. Next, design had to either end as a profession and phenomenon, or move into a new cultural paradigm that was not related to simulation. However, unexpected transformations have begun to occur in design, due to the active involvement of non–human agents - neural networks – in the creation of visual content. Neural network products are a typical example of a third-order simulacrum. Thanks to the use of neural networks, modern design finally acquires the vector of transhumanism and closes in simulation.

Design of Data Monitoring System for Intelligent Production Line System Based on Digital Twin

<p>This article establishes a digital twin model for automated production lines. By establishing a more complete digital model, it achieves comprehensive reproduction of the production line, and then extracts and maps data from various production modules in the digital twin to ensure real-time data. In the software development phase, data analysis is conducted on the production and processing process to achieve prediction and management of production. Finally, based on the established digital twin, online simulation virtual debugging can be achieved when flexibly changing production goals, thereby saving production cycles and costs. Therefore, the digital twin platform established in this article has practical significance.</p> <p> </p>

O Problema dos Pensadores Artificiais: Um Debate Metafísico

The possibility of artificial intelligence is usually discussed by philosophers as a problem about artificial thought: can an artificial system think? The production of intelligence in an artificial system would, however, give rise to an intelligent artificial being: an artificial thinker. As such, there is another, less explored way of discussing the possibility of artificial intelligence: can there be an artificial thinker? This is the problem of artificial thinkers (Olson 2018). In this essay, I discuss this problem. To do so, first, I present the problem of artificial thinkers and discuss how best to interpret it (section §1). Then, I discuss the three main views on the metaphysical nature of artificial thinkers: the hardware view, according to which artificial thinkers are intelligent artificial systems; the software view, according to which artificial thinkers are types or copies of programs for artificial intelligence; and finally, the CPU perspective, according to which artificial thinkers are intelligent spatial parts of artificial systems (section §2). All of these views have problems regarding their conception of the persistence conditions of artificial thinkers. Even so, I conclude, artificial thinkers are best conceived as functional artificial systems.

Application of Electronic Information Engineering Technology in Intelligent Communication

As a representative of advanced science and technology, communication intelligence technology has developed steadily in the past few years. At present, the wide application of electronic information engineering technology in electric power industry has a very important impact on the development and construction of electric power communication network. Electronic engineering technology provides a good solution to solve the problem of unstable power supply in China's power system, which not only promotes the development of the power industry, but also plays an important role in the maintenance of China's power system. In recent years, with the steady development of artificial intelligence technology, its application is also increasing, and it has gained more and more widespread social attention. Nowadays, with the continuous development of Internet technology and electronic computers, people's cognition of artificial intelligence technology and artificial intelligence products has slowly changed. The continuous progress of science and technology has made artificial intelligence walk in the front of today's scientific and technological development, and has attracted high attention and application from all walks of life. As the main communication technology produced in today's society, electronic engineering technology provides great convenience for People's Daily life. Electronic engineering technology is constantly innovating and breaking through on the basis of existing, and developing in the direction of intelligence.

Who are the publics engaging in AI?

Given the importance of public engagement in governments' adoption of artificial intelligence systems, artificial intelligence researchers and practitioners spend little time reflecting on who those publics are. Classifying publics affects assumptions and affordances attributed to the publics' ability to contribute to policy or knowledge production. Further complicating definitions are the publics' role in artificial intelligence production and optimization. Our structured analysis of the corpus used a mixed method, where algorithmic generation of search terms allowed us to examine approximately 2500 articles and provided the foundation to conduct an extensive systematic literature review of approximately 100 documents. Results show the multiplicity of ways publics are framed, by examining and revealing the different semantic nuances, affordances, political and expertise lenses, and, finally, a lack of definitions. We conclude that categorizing publics represents an act of power, politics, and truth-seeking in artificial intelligence.

Flat-Silk-Cocoon-Based Wearable Flexible Piezoresistive Sensor and Its Performance.

Flexible sensors are becoming the focus of research because they are very vital for intelligent products, real-time data monitoring, and recording. The flat silk cocoon (FSC), as a special form of cocoon, has all the advantages of silk, which is an excellent biomass carbon-based material and a good choice for preparing flexible sensors. In this work, a flexible piezoresistive sensor was successfully prepared by encapsulating carbonized flat silk cocoons (CFSCs) using an elastic matrix polydimethylsiloxane (PDMS). The sensing performance of the material is 0.01 kPa-1, and the monitoring range can reach 680.57 kPa. It is proved that the sensor can detect human motion and has excellent durability (>800 cycles). In addition, a sensor array for a keyboard based on CFSCs was explored. The sensor has a low production cost and a simple preparation process, and it is sustainable and environmentally friendly. Thus, it may have potential applications in wearable devices and human-computer interactions.