Intelligent Production Research Articles

Viewpoint: Hybrid Intelligence Supports Application Development for Diabetes Lifestyle Management

Type II diabetes is a complex health condition requiring patients to closely and continuously collaborate with healthcare professionals and other caretakers on lifestyle changes. While intelligent products have tremendous potential to support such Diabetes Lifestyle Management (DLM), existing products are typically conceived from a technology-centered perspective that insufficiently acknowledges the degree to which collaboration and inclusion of stakeholders is required. In this article, we argue that the emergent design philosophy of Hybrid Intelligence (HI) forms a suitable alternative lens for research and development. In particular, we (1) highlight a series of pragmatic challenges for effective AI-based DLM support based on results from an expert focus group, and (2) argue for HI’s potential to address these by outlining relevant research trajectories.

Dynamics of human-machine task allocation in intelligent production processes: A case study

Efficiency and effectiveness in advanced production systems depend on judicious task allocation between humans and machine entities. Despite growing academic interest in human–machine task allocation, theoretical research that can effectively guide practice remains lacking. This paper develops a dynamic human–machine task allocation framework aimed at addressing this gap through the identification and evaluation of task complexity and economic benefits influencing task allocation. We conduct a case study on human–machine task allocation during the transition from manual to intelligent production, utilizing actual task data from both manual and intelligent production lines for the same products. This case study provides practical support for our theoretical framework and yields additional insights into human–machine task allocation. Key findings include: (1) Technological progress has demonstrated significant replacement and reinstatement effects, promoting machines to replace humans in performing more complex tasks while generating new manual tasks related to smart devices. (2) The core considerations for human–machine task allocation are task complexity and task intensity. Although technological progress has facilitated the automation of highly complex tasks, the final allocation is ultimately determined by the economic benefits associated with task intensity. (3) The production pattern in the electronics manufacturing industry has evolved from a traditional manual-led approach to a human–machine collaboration pattern in intelligent production lines. Machines focus on production, while human workers handle setup tasks. This transformation allows intelligent production lines to combine the high productivity of machines with the flexibility of humans.

An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

Industry 4.0 integrates the intelligent networking of machines and processes through advanced information and communication technologies (ICTs). Despite advancements, small mechanical manufacturing enterprises face significant challenges transitioning to ICT-supported Industry 4.0 models due to a lack of technical expertise and infrastructure. These enterprises commonly encounter variable production volumes, differing priorities in customer orders, and diverse production capacities across low-, medium-, and high-level outputs. Frequent issues with machine health, glitches, and major breakdowns further complicate optimizing production scheduling. This paper presents a novel production management approach that harnesses bio-inspired methods alongside Internet of Things (IoT) technology to address these challenges. This comprehensive approach integrates the real-time monitoring and intelligent production order distribution, leveraging advanced LoRa wireless communication technology. The system ensures efficient and concurrent data acquisition from multiple sensors, facilitating accurate and prompt capture, transmission, and storage of machine status data. The experimental results demonstrate significant improvements in data collection time and system responsiveness, enabling the timely detection and resolution of machine failures. Additionally, an enhanced genetic algorithm dynamically allocates tasks based on machine status, effectively reducing production completion time and machine idle time. Case studies in a screw manufacturing facility validate the practical applicability and effectiveness of the proposed system. The seamless integration of the scheduling algorithm with the real-time monitoring subsystem ensures a coordinated and efficient production process, ultimately enhancing productivity and resource utilization. The proposed system’s robustness and efficiency highlight its potential to revolutionize production management in small-scale manufacturing settings.

Application scenario design and prospect of generative artificial intelligence (AI) in intelligent manufacturing and supply chain of traditional Chinese medicine

Intelligent manufacturing technologies, including databases, mathematical modeling, and information systems have played a significant role in process control, production management, and supply chain management in traditional Chinese medicine(TCM) industry. However, their ability to process and utilize unstructured data, such as research and development reports, batch production records, quality inspection records, and supplier documents, is relatively weak. For text, images, language, and other unstructured data, generative artificial intelligence(AI) technology has shown strong potential for development in extracting information, extracting knowledge, semantic retrieval, and content generation. Generative AI is expected to provide a feasible set of tools for the utilization of unstructured data resources in the TCM industry. Based on years of research and industrial application experience in TCM intelligent manufacturing technology, this study reviewed the current situation of intelligent manufacturing in TCM and the utilization of unstructured data, analyzed the application value of generative AI in the TCM manufacturing process and supply chain, summarized four typical application scenarios, including intelligent pharmaceutical knowledge base/knowledge graph, intelligent on-the-job trai-ning, intelligent production quality control, and intelligent supply chain. Furthermore, this study also explained the data collection and processing, business process design, application potential, and value of each scenario based on industry demands. Finally, based on the integration of generative AI and TCM industrial models, the study proposed a preliminary concept of a smart industrial brain for TCM, aiming to provide a reference for the application of AI technology in the field of TCM manufacturing.

The Evolving Technological Framework and Emerging Trends in Electrical Intelligence within Nuclear Power Facilities

This paper thoroughly explores the feasibility of integrating a variety of intelligent electrical equipment and smart maintenance technologies within nuclear power plants to enhance the currently limited level of intelligence of these systems and better support operational and maintenance tasks. Initially, this paper outlines the demands and challenges of intelligent electrical systems in nuclear power plants, highlighting the current state of development of intelligent electrical systems, including new applications of artificial intelligence and big data technologies in power grid companies, such as intelligent defect recognition through image recognition, intelligence-assisted inspections, and intelligent production commands. This paper then provides a detailed introduction to the architecture of intelligent electrical equipment, encompassing the smart electrical equipment layer, the smart control system layer, and the cloud platform layer. It discusses the intelligentization of medium- and low-voltage electrical equipment, such as smart circuit breakers, smart switchgear, and low-voltage distribution systems, emphasizing the importance of intelligentization in improving the safety, reliability, and maintenance efficiency of medium- and low-voltage distribution equipment in nuclear power plants. Furthermore, this paper addresses issues in the intelligentization of nuclear power plant electrical systems, such as information silos, the inefficiency of traditional manual inspection processes, and the lack of comprehensive intelligent design and evaluation standards, proposing corresponding solutions. Additionally, this paper presents the trends in intelligent operation and maintenance technology and applications, including primary and secondary fusion technology, intelligent patrol system architecture, intelligent inspection based on non-destructive testing, and a comprehensive solution based on inspection robots. The application of these technologies aids in achieving automated inspection, real-time monitoring, and the intelligent diagnosis of electrical equipment in nuclear power plants. Finally, this paper proposes basic principles for the development of intelligent electrical systems in nuclear power plants, including intelligent architecture, the evolutionary path, and phased goals and key technologies. It emphasizes the gradual transition from automation to digitization and then to intelligentization and presents a specific implementation plan for the intelligentization of the electrical systems in nuclear power plants. This paper concludes with a summary of short-term and long-term goals for improving the performance of nuclear power plant electrical systems through intelligent technologies and prospects for the application of intelligent technologies in the operation and maintenance of nuclear power plants in the future.

The Divine Blueprint: An In-Deth Analysis of the Quran’s Mathematical Architecture -Revealing the Golden Ratio, Solar Year Length, Universal GravitationalConstant, Speedof Light, Planck Constant, and theSignificance of the Numbers 28 and 114

There is a strong belief that Allah Almighty could reveal His existence by encoding the laws of nature and their fundamental constants in the Quran with impeccable precision. However, the mathematical frameworks and patterns embedded within the Quranic text to reveal scientific laws and constants may contain minor deviations in numerical values to empower personal exploration, accommodate diverse interpretations, and strengthen faith. This theory is based on the fact that the Quran is the word of Allah Almighty. Consequently, one would anticipate finding evidence suggesting a divine origin. Many scholars and researchers have suggested that this evidence can be found in the mathematical structure of the Quran through patterns, numerical codes, and other mathematical features within the text that are too complex to be simply attributed to human authorship. Therefore, it is believed that the Quran contains within its structure a mathematical encoding of all physical laws and constants. Verses like 20.98, which state, "He encompasses all things in knowledge," are quoted as evidence. According to this perspective, the Quran is assumed to incorporate mathematical miracles, indicating a potential correspondence between its text and the underlying order of the universe. This theory reveals many mathematical frameworks illustrating how the Quran embeds scientific knowledge. These include the identification of frequent occurrences of the golden ratio within the Quranic text and the manifestation of the golden ratio in the human body, alongside the determination of key celestial parameters such as the length of the solar year. Furthermore, this theory uncovers a numerical link between the Quran and fundamental scientific constants, notably the universal gravitational constant, the speed of light, and the Planck constant. Moreover, this theory utilizes numerical analysis to offer a compelling rationale for why the numbers 28 and 114 are used to represent the total number of Arabic letters and chapters in the Quran, respectively. Finally, this theory highlights the Quran's remarkable mathematical architecture, offering indisputable evidence of its divine composition. We behold a mathematical marvel of cosmic scale, a phenomenon defying human intelligence. Indeed, the Quranis a product of a super intelligence exceeding our comprehension.

Research on Copyright Protection and Attribution of Artificial Intelligence Products - Focusing on Reviewing Cases of Major Foreign AI Products -

Research on Copyright Protection and Attribution of Artificial Intelligence Products - Focusing on Reviewing Cases of Major Foreign AI Products -

확장된 통합기술수용이론을 활용한 ChatGPT 서비스에 관한 중국 소비자 행동 연구

In this study, we focused on empilically analyzing the factors that influence consumer intention to use and purchase behavior at the introduction and diffusion stages of the innovative technology, ChatGPT Plus, using the Extended Unified Theory of Acceptance and Use of Technology (UTAUT). Furthermore, we examined the moderating roles of education and monthly income in specific relationships. To summarize the research results, firstly, among the technology acceptance factors affecting Chinese consumers' use of the innovative ChatGPT Plus paid service, the performance expectancy, effort expectancy, facilitating conditions proposed in the basic UTAUT, and hedonic motivation, price value, and content quality proposed in the extended UTAUT were found to significantly influence the intention to purchase the ChatGPT Plus paid service. On the contrary, it was found that social influence and usage habits do not significantly influence the intention to purchase ChatGPT Plus paid service. Secondly, among the technology acceptance factors that influence the use of ChatGPT Plus paid service, performance expectancy, hedonic motivation, and content quality showed a significant impact on purchase intention through the moderating effect of education. Thirdly, in this study, the purchase intention of ChatGPT Plus paid service was confirmed to have a positive effect on purchase behavior. Through these research results, artificial intelligence-related companies can respond more effectively to consumers' intention to use and purchase behavior, and on this basis, they can improve the quality of artificial intelligence products and GPT Plus services.

Implementing multi-attribute utility theory in service recovery: An operational management perspective on online retailing

How corporate decision-makers allocate resources to address the major issues of service recovery is critical. This article, from the perspective of operations management, integrates Multi-Attribute Utility theory into the field of service recovery. Through the use of online review data from smart home products, the PCRA-Multi-Attribute Utility model is proposed. Based on the model, this paper presents a prioritization strategy for service recovery and reveals that the utility of intelligent products is primarily influenced by service attitude. Furthermore, we discovered that manager's response strategies should pay more attention to recovery related to vital attributes. Strategy optimization can be achieved by adjusting the response length of these key attributes.

Research on Talent Cultivation of Local Product Design Professionals Based on Cooperative Education

The booming growth of creative industries has resulted in a substantial rise in the need for talented individuals in innovative product design. A multitude of institutions and universities possess open-product students majoring in design. Nevertheless, due to the alterations in consumer demand, traditional product design urgently needs reform due to monotonous functions and serious homogenisation. In recent years, the cultural and creative sectors have been actively seeking innovative and intelligent products since they have become a new trend in the consumer market. Moreover, universities' current talent training model for cultivating such needs still needs improvement. Drawing from the current condition of talent development in product design and integrating the pedagogical knowledge gained recently, this paper presents a novel idea of local product design professional talent cultivation founded on the principles of cooperative education.

Adaptive optimization federated learning enabled digital twins in industrial IoT

The Industrial Internet of Things (IIoT) plays a pivotal role in steering enterprises towards comprehensive digital transformation and fostering intelligent production, which serves as a critical pillar of Industry 4.0. Digital twin (DT) emerges as a highly promising technology, enabling the digital transformation of the IIoT by seamlessly bridging physical systems with digital spaces. However, the overall service quality of the IIoT is severely impacted by the resource-limited devices and the massive, heterogeneous and sensitive data in the IIoT. As an innovative distributed machine learning paradigm, federated learning (FL) inherently possesses advantages in handling private and heterogeneous data. In this paper, we propose a novel framework integrating FL with DT-enabled IIoT, termed FDEI, which combines the merits of both to improve service quality while maintaining trustworthiness. To enhance the modeling efficiency, we develop FedOA, an adaptive optimization FL method that dynamically adjusts the local update coefficient and model compression rate in resource-limited IIoT scenarios, to construct the FDEI model. Specifically, leveraging the interdependence between the two variables, we conduct a theoretical analysis of the model convergence rate and derive the associated convergence bounds. Building upon the theoretical analysis, we further propose a joint adaptive adjustment strategy by optimizing the two variables across various clients to minimize runtime differences and accelerate the convergence rate. Numerical results demonstrate that our proposed approach achieves an approximate 68% improvement in convergence speed and a reduction of approximately 66% in traffic consumption compared to the benchmarks (e.g., FedAvg, AFL, and CSFL).

Comparative Evaluation of the Application Effectiveness of Intelligent Production Optimization Methods in Offshore Oil Reservoirs

The development of offshore oil fields confronts challenges associated with high water cut and low displacement efficiency. Reservoir injection-production optimization stands out as an effective means to reduce costs and enhance efficiency in offshore oilfield development. The process of optimizing injection and production in offshore oil reservoirs involves designing strategies for a large number of wells and optimization time steps, constituting a large-scale, complex, and costly optimization computation problem. In recent years, with the rapid advancements in big data and artificial intelligence technologies, sophisticated evolutionary computation methods have found widespread application in reservoir injection-production optimization problems. However, the abundance of intelligent optimization algorithms raises the question of how to choose a method suitable for the complex optimization background of offshore oilfield injection-production optimization. This paper provides a detailed overview of the application of an existing differential evolution algorithm (DE), conventional surrogate-assisted evolutionary algorithm (CSAEA), and global–local surrogate-assisted differential evolution (GLSADE) in the context of practical offshore oilfield injection-production optimization problems. A comprehensive comparison of their performance differences is presented. The study concludes that the global–local surrogate-assisted evolutionary algorithm is the most suitable method for addressing the current challenges in offshore oilfield injection-production optimization.

Zero-defect manufacturing in the textile industry: a review of current advances and challenges

With the Fourth Industrial Revolution (Industry 4.0) and advances in digital technology, zero-defect manufacturing (ZDM) has become a transformative and attractive concept that has the potential to reshape the manufacturing landscape. In this structured literature review, recent developments in ZDM in the textile industry from 2004 to 2023 are examined, with a focus on detection, repair, prediction, and prevention. Through bibliometrics analysis and evaluation of the current situation of ZDM technology, four main shortcomings are highlighted, to be specific, limitations in automated defect detection, incomplete artificial intelligence (AI)-based repair strategies, restricted predictive research, and focused prevention mechanism. Meanwhile, open challenges that require urgent attention are explored, that is systematic ZDM strategy integration, data management complexity, and demand for flexible ZDM frameworks. To address these shortcomings and challenges, three further prospectives are proposed, including addressing research imbalance, vision for an integrated ZDM system, and evolutionary predictive models. These prospectives aim to advance the field and drive the holistic development of ZDM technologies in the textile industry by promoting a more intelligent production strategy with higher quality and less waste.

Omni-directional attitude detection of advanced hydraulic support relative to roadway based on visual measurement principle

In order to address the challenge of detecting the attitude of advanced hydraulic support in a harsh mine environment, this study proposes a binocular vision technology that accurately measures the omni-directional attitude of advanced hydraulic support relative to the roadway in real-time. The first step involves constructing a visual measurement system and optimizing the software development platform based on the actual motion conditions. Next, an innovative algorithm for space point reconstruction and omni-directional attitude is proposed. Finally, an indoor ground experimental platform is built to demonstrate the feasibility and effectiveness of the method by comparing it with high-precision experiments. The results show that the average absolute deviation of the measured attitude angle is within 0.4° and the average absolute deviation of the measured distance is within 6 mm, which meets the accuracy requirements of the fully mechanized mining equipment under the mine. This method has high measurement accuracy and sufficient visibility, which has important practical significance for ensuring the support efficiency of advanced hydraulic support and the intelligent production of working faces.

Determination of Artificial Intelligence Anxiety Status of Nursing Students: Cross-Sectional-Descriptive Study

Aim: The study aimed to determine the anxiety of nursing students about the emergence and use of artificial intelligence products. Material and Method: The data of this descriptive and cross-sectional study were collected between 02.01.2023 and 15.04.2023. The sample of the research consisted of 243 students. The data collection tool included an introductory information form and the Artificial Intelligence Anxiety Scale. T-test, and one-way ANOVA test were used to analyze the data. Results: 64.6% of the students had heard of artificial intelligence-supported devices used in healthcare, 54.7% thought that artificial intelligence applications were useful in ensuring patient safety, and 54.7% thought that the system would reduce the risk of making medical errors. The mean total score of the scale was 46.25 ± 9.66. There was a statistically significant relationship between thinking that artificial intelligence should be a course in education and thinking that artificial intelligence would be indispensable in surgical applications and the artificial intelligence anxiety scale (p

CPSM: A Dynamic Simulation Model for Cucumber Productivity in Solar Greenhouse Based on the Principle of Effective Accumulated Temperature

The Cucumber Productivity Simulation Model (CPSM) was developed to precisely predict the dynamic process of cucumber productivity in a solar greenhouse. This research conducted a variety of sowing experiments and collected data on cucumber productivity and meteorological conditions from 2013 to 2015 and 2018 to 2020. Employing the principles of least squares, the relationship between cucumber productivity indicators and effective accumulated temperature (EAT) was fitted, determining key crop parameters and constructing the CPSM. Validation of the model was conducted using independent experimental data, evaluating its simulation accuracy. The results indicate that (1) CPSM can dynamically and meticulously simulate the formation process of different productivity indicators in cucumber. Normalized root mean square errors (NRMSE) ranged from 0.44% to 19.64%, and mean relative errors (MRE) ranged from 0.31% to 17.23% across different productivity indicator models. The models for organ water content, maximum root length, specific leaf area, and organ fresh weight distribution index demonstrated high simulation accuracy, while others showed relatively high accuracy. (2) Simulation accuracy varied with indicators and varieties. 19 indicators (34.55%) exhibited high simulation accuracy and 30 indicators (54.55%) showed relatively high accuracy. The JY35 variety (10.44 ± 8.49%) outperformed the JS206 variety (13.44 ± 8.50%) in terms of simulation accuracy. The JY35 variety had 39 superior productivity indicators (70.91%) while the JS206 variety had sixteen (29.09%). CPSM utilizes easily accessible temperature data as its input, allowing for precise and detailed simulation of productivity indicators for cucumber production in solar greenhouses. This research lays a theoretical foundation and provides technical support for guiding intelligent production management, efficient utilization of agricultural resources, and climate change productivity assessment in solar greenhouse cucumber production.

CAC-YOLOv8: real-time bearing defect detection based on channel attenuation and expanded receptive field strategy

Bearing defect detection plays a crucial role in the intelligent production of chemical transmission equipment, where timely identification and handling of defective bearings are essential. However, in practical large-scale industrial production, product surface defects are often complex, diverse, and exhibit significant variations in appearance, posing severe challenges to the discriminative ability and detection efficiency of bearing defect detection algorithms. This paper proposes a real-time bearing surface defect detection algorithm, CAC-YOLOv8, which designs the Channel Attenuation Network (CAN) and Compound Pooling Pyramid Spatial Pyramid Pooling Fast (CPPSPPF) structure. Specifically, the model introduces the Channel Attenuation Network to achieve parallel feature extraction, deep feature processing, and feature fusion under different channel numbers, capturing critical features related to bearing defects and thereby improving the inference speed. Subsequently, based on the concept of overlapped receptive fields, a CPPSPPF structure is constructed, utilizing multiple iterations of max-pooling operations with smaller pooling kernel sizes to prevent information loss while expanding the receptive field, thereby strengthening the capturing ability of features at different scales. The experimental results indicate that the proposed CAC-YOLOv8 bearing surface defect detection algorithm, compared to the YOLOv8 model, achieved a 0.3% improvement in mAP@0.5, reduced model size by 14.4%, and enhanced model inference speed by 33.3%. This enables the CAC-YOLOv8 model to significantly improve the real-time performance of bearing defect detection while maintaining high-precision detection. The performance in practical industrial detection demonstrates that the proposed approach has achieved outstanding results in both speed and accuracy.

Small-batch product quality prediction using a novel discrete Choquet fuzzy grey model with complex interaction information

With manufacturing focused on multi-variety and small-batch intelligent production, the challenge is to guarantee and ensure good product quality. This paper proposes a novel small-batch product quality prediction approach considering complex interaction information. In this paper, first, to handle the complex nonlinear correlations among the manufacturing process variables, a kernel principal components analysis is used to extract the major features from the high-dimensional data. Second, we combine the discrete multivariate grey model and generalized discrete Choquet fuzzy integral to propose a discrete Choquet fuzzy grey model so as to capture the complex interaction information among the process variables. Next, an adjoint sensitivity analysis is applied to characterize the changes in the process variables on the predicted product quality index. We conduct an experiment on six semiconductor products made in China to validate the proposed model against a library of nine other methods. Our model yields an average reduction of 11.02% on MAPE, 20.47% on MSE, 13.56% on STD, and an average increase of 46.37% on EVS for six types of products. Our results inform that the proposed model is suitable for predicting manufacturing product quality when the process variables interact significantly, and we can prioritize the process variables for remedial action accordingly.

Design of an Arduino-based intelligent logistics cart

With the achievement of the first centenary goal, we will strive for another 15 years on this basis, aiming to achieve socialist modernization by 2035. The concept of intelligent production is receiving increasing attention, with the logistics industry being a representative sector. Currently, traditional logistics operations are unable to meet the high requirements of modern society due to problems such as high labor costs, poor safety, and low efficiency. In order to change the current situation of the traditional logistics industry, more intelligent equipment is needed to replace labor-intensive manual handling work. In this era, an Arduino-controlled intelligent logistics cart emerges as a development trend. This paper designs and develops an Arduino-controlled intelligent logistics cart, taking the Yunnan Province University Student Engineering Training Comprehensive Ability Competition as an application scenario. It achieves functions such as automatic tracking, intelligent recognition of block colors, automatic scanning of QR codes to obtain tasks, and all-round grasping of objects. The design of this intelligent logistics cart involves hardware selection, system design, and structural design.

Differences in the Definition of an Author under Copyright Laws of the United Kingdom, the United States, and Germany in the Context of Artificial Intelligence

In modern copyright law, which began with Queen Anne s Law, the author plays a very important role as the subject of the work. As such, identifying the author s identity is a matter of priority. ??As science and technology advances, granting author status to anyone has been discussed under copyright law since Burrow-Giles Lithographic Co. v. Sarony in 1884, and the development of artificial intelligence technology is again an issue of legal issues concerning the confirmation of author status of artificial intelligence products. Though it was thought that artificial intelligence products should be protected by law, the problem is that only creations expressing human thoughts or emotions can be protected under the current copyright law. In addition, only human beings can be recognized as authors in precedents and theories. ??This paper introduces the author problem of the distant, recent issue of artificial intelligence products and review the author s concept through content on the author s concept of Anglo-American and continental law countries.