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1.
Big data is increasingly available in all areas of manufacturing and operations, which presents an opportunity for better decision making and discovery of the next generation of innovative technologies. Recently, there have been substantial developments in the field of patent analytics, which describes the science of analysing large amounts of patent information to discover trends. We define Intellectual Property Analytics (IPA) as the data science of analysing large amount of IP information, to discover relationships, trends and patterns for decision making. In this paper, we contribute to the ongoing discussion on the use of intellectual property analytics methods, i.e artificial intelligence methods, machine learning and deep learning approaches, to analyse intellectual property data. This literature review follows a narrative approach with search strategy, where we present the state-of-the-art in intellectual property analytics by reviewing 57 recent articles. The bibliographic information of the articles are analysed, followed by a discussion of the articles divided in four main categories: knowledge management, technology management, economic value, and extraction and effective management of information. We hope research scholars and industrial users, may find this review helpful when searching for the latest research efforts pertaining to intellectual property analytics. 相似文献
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Recent advances in artificial intelligence, computer science, communication, sensing and actuation technologies have resulted in the development of several novel intelligent systems. At the same time, the emergence of nanogenerators has opened a new research avenue with the overarching goal of developing self-powered sensing systems. The concepts of self-powered sensing, based on nanogenerators and intelligent systems can be fused together to open a new area of interdisciplinary research. In this article, we aim to show how these two emerging technologies have been combined to develop self-powered intelligent sensing systems. We first focus on the main keywords in the area of nanogenerators. Keyword co-occurrence network graphs are generated based on the most used keywords in the area of nanogenerators to select key concepts that are directly connected to the concept of intelligent systems. Thus, a detailed review is provided on different intelligent self-powered sensing systems based on nanogenerators. We also discuss the challenges presented by combining intelligent systems and self-powered sensing. As most of intelligent devices rely on machine learning techniques, a comprehensive section is allocated to this topic to focus on its applications in nanogenerator-based devices. 相似文献
3.
Analysing historical patterns of artificial intelligence (AI) adoption can inform decisions about AI capability uplift, but research to date has provided a limited view of AI adoption across different fields of research. In this study we examine worldwide adoption of AI technology within 333 fields of research during 1960–2021. We do this by using bibliometric analysis with 137 million peer-reviewed publications captured in The Lens database. We define AI using a list of 214 phrases developed by expert working groups at the Organisation for Economic Cooperation and Development (OECD). We found that 3.1 million of the 137 million peer-reviewed research publications during the entire period were AI-related, with a surge in AI adoption across practically all research fields (physical science, natural science, life science, social science and the arts and humanities) in recent years. The diffusion of AI beyond computer science was early, rapid and widespread. In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to cover over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. We note AI has experienced boom-bust cycles historically; the AI “springs” and “winters”. We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained. 相似文献
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《工程（英文）》2019,5(6):1010-1016
Safe, efficient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitations in practice. The burgeoning era of big data is influencing the process industries tremendously, providing unprecedented opportunities to achieve smart manufacturing. This kind of manufacturing requires machines to not only be capable of relieving humans from intensive physical work, but also be effective in taking on intellectual labor and even producing innovations on their own. To attain this goal, data analytics and machine learning are indispensable. In this paper, we review recent advances in data analytics and machine learning applied to the monitoring, control, and optimization of industrial processes, paying particular attention to the interpretability and functionality of machine learning models. By analyzing the gap between practical requirements and the current research status, promising future research directions are identified. 相似文献
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Artificial intelligence (AI) technologies and their fields of application are among the most debated developments of recent times. Although being widely discussed academically, publicly and in policy debates, certain aspects of their research, development and application are completely ignored, namely the impact AI has on animals. Animals are affected by the research on and development of this technology since it partially relies on animal testing. In addition, AI is also being applied to improve monitoring and marketing of animals in an agricultural context. We argue that it is insufficient to exclude these aspects from debates around AI. In addition to the surveillance-applications on animals, which can be evaluated as impacting them negatively, AI applications, from which individual animals can benefit, do exist. These can primarily be found in nature and wildlife conservation, as we point out at the end of the paper. By providing an overview on how these technologies are applied to animals and how this affects them, this paper aims to fill a previously existing research gap. 相似文献
6.
The number of academic papers in the area of Artificial Intelligence (AI) and its applications across business and management domains has risen significantly in the last decade, and that rise has been followed by an increase in the number of systematic literature reviews.The aim of this study is to provide an overview of existing systematic reviews in this growing area of research and to synthesise the findings related to drivers, barriers and social implications of the AI adoption in business and management.The methodology used for this tertiary study is based on Kitchenham and Charter's guidelines [14], resulting in a selection of 30 reviews published between 2005 and 2019 which are reporting results of 2021 primary studies.These reviews cover the AI adoption across various business sectors (healthcare, information technology, energy, agriculture, apparel industry, engineering, smart cities, tourism and transport), management and business functions (HR, customer services, supply chain, health and safety, project management, decision-support, systems management and technology adoption).While the drivers for the AI adoption in these areas are mainly economic, the barriers are related to the technical aspects (e.g. availability of data, reusability of models) as well as the social considerations such as, increased dependence on non-humans, job security, lack of knowledge, safety, trust and lack of multiple stakeholders’perspectives.Very few reviews outside of the healthcare management domain consider human, organisational and wider societal factors of the AI adoption.In addition to increased focus on social implications of AI, the reviews are recommending more rigorous evaluation, increased use of hybrid solutions (AI and non-AI) and multidisciplinary approach to AI design and evaluation.Furthermore, this study found that there is a lack of systematic reviews in some of the early AI adoption sectors such as financial industry and retail. 相似文献
7.
With the growing number of applications of artificial intelligence such as autonomous cars or smart industrial equipment, the inaccuracy of utilized machine learning algorithms could lead to catastrophic outcomes. Human-in-the-loop computing combines human and machine intelligence resulting in a hybrid intelligence of complementary strengths. Whereas machines are unbeatable in logic and computation speed, humans are contributing with their creative and dynamic minds. Hybrid intelligent systems are necessary to achieve high accuracy and reliability of machine learning algorithms. In a design science research project with a Swedish manufacturing company, this paper presents an application of human-in-the-loop computing to make operational processes more efficient. While conceptualizing a Smart Power Distribution for electric industrial equipment, this research presents a set of principles to design machine-learning algorithms for hybrid intelligence. From being AI-ready as an organization to clearly focusing on the customer benefits of a hybrid intelligent system, designers need to build and strengthen the trust in the human-AI relationship to make future applications successful and reliable. With the growing trends of technological advancements and incorporation of artificial intelligence in more and more applications, the alliance of humans and machines have become even more crucial. 相似文献
8.
The article describes the evaluation criterion for similarity search in the patent search systems suggested by the authors. The use of artificial intelligence methods, artificial neural networks and machine learning for improving similarity search is described. The results of the comparison based on the proposed criteria for similarity search in Google Patent system, Yandex Patents system and the Russian patent office retrieval system PatSearch are given. 相似文献
9.
This short communication describes the background, objectives, and publications of World Patent Information's special issue on Artificial Intelligence for Intellectual Property (AI for IP). The report serves as the editorial for the WPI's special issue on AI for IP. We look forward to receiving future contributions in research articles, literature/book reviews, conference reports and short communications in the subject areas. 相似文献
10.
Yu-Tong Yang Zhong-Yuan Qiu Zhen Zheng Liang-Xi Pu Ding-Ding Chen Jiang Zheng Rui-Jie Zhang Bo Zhang Shi-Yao Huang 《先进制造进展(英文版)》2024,12(3):591-602
High-pressure die casting (HPDC) is one of the most popular mass production processes in the automotive industry owing to its capability for part consolidation. However, the nonuniform distribution of mechanical properties in large-sized HPDC products adds complexity to part property evaluation. Therefore, a methodology for property prediction must be developed. Material characterization, simulation technologies, and artificial intelligence (AI) algorithms were employed. Firstly, an image recognition technique was employed to construct a temperature-microstructure characteristic model for a typical HPDC Al7Si0.2Mg alloy. Moreover, a porosity/microstructure-mechanical property model was established using a machine learning method based on the finite element method and representative volume element model results. Additionally, the computational results of the casting simulation software were mapped with the porosity/microstructure-mechanical property model, allowing accurate prediction of the property distribution of the HPDC Al-Si alloy. The AI-enabled property distribution model developed in this study is expected to serve as a foundation for intelligent HPDC part design platforms in the automotive industry.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00485-1 相似文献
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《工程（英文）》2019,5(6):995-1002
Smart manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process-safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging information technologies such as artificial intelligence (AI) are quite promising as a means of overcoming these difficulties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety; ② knowledge-based reasoning for process safety; ③ accurate fusion of heterogeneous data from various sources; and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context. 相似文献
13.
《工程（英文）》2020,6(3):291-301
Artificial intelligence (AI) has been developing rapidly in recent years in terms of software algorithms, hardware implementation, and applications in a vast number of areas. In this review, we summarize the latest developments of applications of AI in biomedicine, including disease diagnostics, living assistance, biomedical information processing, and biomedical research. The aim of this review is to keep track of new scientific accomplishments, to understand the availability of technologies, to appreciate the tremendous potential of AI in biomedicine, and to provide researchers in related fields with inspiration. It can be asserted that, just like AI itself, the application of AI in biomedicine is still in its early stage. New progress and breakthroughs will continue to push the frontier and widen the scope of AI application, and fast developments are envisioned in the near future. Two case studies are provided to illustrate the prediction of epileptic seizure occurrences and the filling of a dysfunctional urinary bladder. 相似文献
14.
《工程（英文）》2017,3(5):608-615
The Made in China 2025 initiative will require full automation in all sectors, from customers to production. This will result in great challenges to manufacturing systems in all sectors. In the future of manufacturing, all devices and systems should have sensing and basic intelligence capabilities for control and adaptation. In this study, after discussing multiscale dynamics of the modern manufacturing system, a five-layer functional structure is proposed for uncertainties processing. Multiscale dynamics include: multi-time scale, space-time scale, and multi-level dynamics. Control action will differ at different scales, with more design being required at both fast and slow time scales. More quantitative action is required in low-level operations, while more qualitative action is needed regarding high-level supervision. Intelligent manufacturing systems should have the capabilities of flexibility, adaptability, and intelligence. These capabilities will require the control action to be distributed and integrated with different approaches, including smart sensing, optimal design, and intelligent learning. Finally, a typical jet dispensing system is taken as a real-world example for multiscale modeling and control. 相似文献
15.
In an age when data is regarded as the most essential commodity, organizations are racing to use it for better decision making. The quality of the patent portfolio is an important indicator of technological innovation in an organization and its analysis can reveal several indicators linked to the growth of a company. The advancement of machine learning along with the access to large amounts of patent data has led to a paradigm shift from traditional patent data analysis methodologies to novel approaches. A lot of research has been done in this direction for analysing data on patent citations, patent text, IPC class etc. However, much less has been explored regarding the forecast of patent grant duration and its significance for decision making with an even lower focus on data collected from developing countries. This work is built upon our existing study on patent grant duration prediction by devising a novel methodology of encoding the data using a combination of augmented one-hot encoding and label-encoding. Thereafter, methodologies such as Outlier Detection have been applied to this data to yield an improved result vis-à-vis our baseline results. In addition, we identify some of the important factors which impact the decision on grant duration of patent applications using the raw data from the Indian Patent Office. 相似文献
16.
Strategies for Development of Process Control Models for Hot Metal Desulfurization: Conventional and AI Techniques 总被引：2，自引：0，他引：2
Brahma Deo R. K. Lingamaneni Arghya Dey R. Boom 《Materials and Manufacturing Processes》2005,20(3):407-419
Process control models can be developed by using both conventional and AI approaches. The conventional approaches include regression models and process kinetic models, and the artificial intelligence (AI) approaches are based on artificial neural nets (ANN), genetic algorithm (GA), and fuzzy rule-based expert systems FRBES. Plant data on hot metal desulfurization, carried out by injecting calcium carbide, is analyzed to test and tune different types and combinations of models and then evaluate their relative performance. Although the control models based on process fundamentals provide a fillip to the new ideas for technological developments and improvements, the combination of conventional and AI approaches may be a better option for process control on the shop floor. It is advisable to first develop and test all models and then decide about the best strategy of using them. 相似文献
17.
This study explores the modernization of road-based technologies for the enhancement of mobility while also implementing safer transportation. Mobility plays a critical role in everyday life on a micro and a macro scale combined. Modernization in mobility would enable establishment of a sustainable, digitalized and informed society. The inclusion of AI/ML to enhance road environment, curbing driver distraction, adopting electric vehicles, and integrating low power computing units in vehicular networks are among the potential recommendations for strengthening the evolving digital road architecture. The current ecosystem surrounding road safety and mobility can be boosted even further upon integrating products of modern technology into the classical elements of transportation. Modern technologies are classified and perceptually investigated by realizing the current challenges and proposing seamless potential extensions to the existing infrastructure from each domain. Techno-administrative concepts like the regulation of individual risk profiles for achieving a safer road environment are addressed. 相似文献
18.
The Internet of Things (IoT) has been transformed almost all fields of life, but its impact on the healthcare sector has been notable. Various IoT-based sensors are used in the healthcare sector and offer quality and safe care to patients. This work presents a deep learning-based automated patient discomfort detection system in which patients’ discomfort is non-invasively detected. To do this, the overhead view patients’ data set has been recorded. For testing and evaluation purposes, we investigate the power of deep learning by choosing a Convolution Neural Network (CNN) based model. The model uses confidence maps and detects 18 different key points at various locations of the body of the patient. Applying association rules and part affinity fields, the detected key points are later converted into six main body organs. Furthermore, the distance of subsequent key points is measured using coordinates information. Finally, distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions. The accuracy of the proposed system is assessed on various test sequences. The experimental outcomes reveal the worth of the proposed system’ by obtaining a True Positive Rate of 98% with a 2% False Positive Rate. 相似文献
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Rahul J. Patil 《国际生产研究杂志》2013,51(21):6009-6027
One of the common and important problems in production scheduling is to quote an attractive but attainable due date for an arriving customer order. Among a wide variety of prediction methods proposed to improve due date quotation (DDQ) accuracy, artificial neural networks (ANN) are considered the most effective because of their flexible non-linear and interaction effects modelling capability. In spite of this growing use of ANNs in a DDQ context, ANNs have several intrinsic shortcomings such as instability, bias and variance problems that undermine their accuracy. In this paper, we develop an enhanced ANN-based DDQ model using machine learning, evolutionary and metaheuristics learning concepts. Computational experiments suggest that the proposed model outperforms the conventional ANN-based DDQ method under different shop environments and different training data sizes. 相似文献