Development of a maturity model for technology intelligence

Purpose – The aim of this paper is to promote the authors' understanding of technology intelligence by responding to two questions: What is technology intelligence? How is it accomplished? Design/methodology/approach – To respond to the two main questions about technology intelligence in this paper, the authors have studied relevant academic, peer-reviewed journals and books using the literature databases of Google Scholar, Sciencedirect, Inderscience, Wiley and Emerald Insight. They have used few selection criteria to choose papers and books for inclusion in their study. Findings – Enhancing the authors' understanding of the technology intelligence concept by responding to the two main questions (What is technology intelligence? How is it accomplished?), classifying the main studies in the field of technology intelligence and several practical and theoretical implications are the findings in this paper. Practical implications – A number of practical implications related to technology intelligence structure, process, methods, tools and players are suggested to managers of organizations and companies to improve their technology-related planning processes and decision-making. Originality/value – Despite the considerable level of consensus on the necessity of precise scanning and monitoring technological changes and trends, there is still limited understanding of the technology intelligence concept. This paper intends to enhance the authors' understanding of technology intelligence by responding to two questions: What is technology intelligence? And how is it accomplished?

With the rapid development of the Internet of Things and intelligent technology, the application of Radio Frequency Identification (RFID) technology in training room management is becoming increasingly widespread. An efficient and accurate RFID system can significantly improve the management efficiency and resource utilization of the training room, thereby improving teaching quality and reducing management costs. Although RFID technology has many advantages, there are still some problems in practical applications, such as label collision and recognition of unknown labels. These issues not only affect the performance of the system but may also cause interference with actual teaching and management. This study proposes a grouping-based bit arbitration query tree algorithm and anti-collision technology to solve label collisions and reduce label recognition time in the technology. A new unknown label recognition algorithm is also proposed to improve the recognition efficiency and accuracy of identifying new unknown labels. Related experiments have shown that the recognition accuracy of the algorithm designed this time is 95.86%. Compared with other algorithms, the number of idle time slots is the smallest. When the number of queries is 1000, the algorithm has 1842 queries, and the communication complexity is the best. When the number of unknown tags is 10,000, the actual accuracy rate is 95.642%. Compared with traditional recognition algorithms, the new unknown label recognition algorithm has a smaller frame length in the same label proportion and good recognition performance. On a theoretical level, the research content on RFID technology helps to improve and develop the basic theories of the Internet of Things and intelligent recognition technology and provides solutions and application technologies for equipment management and IoT applications in training rooms. On a practical level, the research results can provide specific guidance for the management of training rooms, help solve equipment management and safety maintenance problems in practical applications, and improve the management efficiency of training rooms.

In this era of rapid development of global science and technology, artificial intelligence and industrial technology are converging at an unprecedented speed, driving social progress and industrial upgrading. In order to deeply discuss the latest development in these two and relevant fields, share research results, and promote academic exchanges and cooperation, we are honored to have organized the 2024 4th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA 2024). This proceedings is the crystallization of this event, which brings together the wisdom and insights of experts, scholars, engineers and technicians from all over the world. AIITA 2024 was grandly held in Guangzhou, China from April 12th to 14th, 2024 via hybrid form, attracting over 200 participants from all over the world. The conference aimed to provide a platform for experts and scholars, engineers and technicians, researchers and developers in the fields of artificial intelligence and industrial engineering to share scientific research results and cutting-edge technologies, understand academic development trends, broaden research ideas, and strengthen academic research and discussion. During the conference, the participants had an in-depth discussion on a number of topics related to the application of artificial intelligence and industrial technology, and jointly explored the new direction of future development. In the keynote speech part, latest research results and cutting-edge technologies in related fields including Graph Neural Networks: Models and Applications in Pattern Recognition (by Prof. Bo Jiang, Anhui University, China), Distributed GPU-accelerated Evolutionary Computation (by Dr. Ran Cheng, Southern University of Science and Technology, China), Quantum Model Learning (by Prof. Daowen Qiu, Sun Yat-sen University, China), Face Image Editing from the Perspective of Conditional Generative (by Prof. Si Wu, South China University of Technology, China), Cooperative Global Robust Output Regulation for Nonlinear Multi-Agent Systems with Switching Networks (by Prof. Wei Liu, South China University of Technology, China) were discussed. At the same time, we have set up a number of presentations and panel discussion sessions, in which participants gained more opportunities to have in-depth discussions and exchanges on specific issues. This conference calls for papers on a wide range of topics, covering all aspects of artificial intelligence and industrial technology applications. We received hundreds of submissions from all over the world, and after strict evaluation and screening, we finally included some excellent papers in this proceedings. These papers not only have high academic value and practical significance, but also represent the latest research dynamics and trends in the fields of artificial intelligence and industrial technology applications. Finally, we would like to thank all the experts, scholars, engineers and staff who participated in the AIITA 2024 for their support and contribution. We believe that with the joint efforts of all of us, the research and application in the fields of artificial intelligence and industrial technology applications will achieve more brilliant achievements. The Committee of AIITA 2024 List of Committee Member is available in this Pdf.

This paper first summarizes the basic concept and development status of steel structure building and full life cycle management, and then discusses the application practice of intelligent construction technology in steel structure building. This paper expounds the construction goal, principle, content and strategy of the intelligent construction technology system of the whole life cycle, and analyzes the implementation path and evaluation method of the system. Finally, the research results are summarized, and the development prospects and challenges of the whole life cycle intelligent construction technology are discussed.

Starting from the actual business needs of power grid production, operation, and management, extract common key technologies of artificial intelligence, build an electric power artificial intelligence platform, provide sample labeling and management, model training, and model reasoning for business personnel, model developers, and application developers, and support power inspection, scheduling, safety supervision, marketing and other businesses through online service invocation. This paper studies the core issues of task scheduling and resource management of the electric power artificial intelligence platform. To begin with, it introduces its capabilities and overall architecture and analyzes its service objects, task types and processing processes. Then container technology and Kubernetes are employed to achieve resource-elastic management for multiple heterogeneous resources; a task scheduling method is put forward based on load balancing; last but not least, the functional components, deployment and application of the artificial intelligence platform are presented. By building the basic support capabilities of electric power artificial intelligence, reducing the threshold of artificial intelligence model training, gathering high-quality samples and high-quality models, unified model services are provided for business applications, thus accelerating the implementation of artificial intelligence applications and promoting the digital transformation and intelligent upgrade of the power grid.

With the rapid development of deep learning technology, intelligent recognition technology, centered around convolutional neural networks (CNNs), has been widely applied in scenarios such as healthcare, image recognition, and autonomous driving. However, the training and inference processes of intelligent recognition models, especially deep CNNs, require massive computational and memory access efforts, posing severe performance and energy efficiency challenges to traditional computing architectures (such as CPUs and GPUs). As intelligent recognition technology becomes increasingly practical and widespread across society, performance requirements for CNNs are steadily increasing. This has significantly driven research into specialized artificial intelligence chips, enabling architectural innovation and optimization of intelligent recognition models.This article will review the early development and current status of intelligent recognition technology, the composition and evolution of convolutional neural networks (CNNs), which play a core role in recognition technology, and how AI chips optimize and enhance CNN performance.In summary, AI chips are specialized accelerators for intelligent recognition computing, and their development is mutually reinforcing with advances in deep learning algorithms. As model complexity continues to grow, AI chips designed for emerging architectures, supporting higher energy efficiency and hardware-software co-design, will continue to be a core driving force behind the advancement of intelligent recognition technology.

Companies are finding it increasingly difficult to keep abreast of the latest technology developments and trends. Technology intelligence provides an organisation with the capability to capture and deliver information in order to develop an awareness of technology threats and opportunities. A conceptual model has been developed to support the establishment and operation of technology intelligence systems. The model consists of three tiers: (a) a framework level, (b) a system level, and (c) a process level. The 'framework' level maps the information requirements and knowledge gaps of the decision-makers to the business intelligence activities of an organisation. The 'system' level provides a mechanism to both tailor and configure a system architecture and its operational modes (mine, trawl, target, scan) to the actual intelligence needs. The 'process' level consists of an operating cycle for running a technology intelligence system. The cycle is composed of six phases, namely: coordinate, search, filter, analyse, document and disseminate.

How have the technology intelligence approaches of large technology‐intensive companies changed in the last 30 years? This paper aims to answer this question through case study research in 26 leading European and North American companies in the pharmaceutical, telecommunication equipment and automobile/machinery industries. The empirical results show that technology intelligence approaches have closely followed the changes in R&D organization, project selection and technology planning. Three generations of technology intelligence management can be distinguished. Furthermore, the contingency factors for the location of technology intelligence within technology intensive companies are identified.

This paper proposes a novel adaptive path tracking and stability control strategy for autonomous vehicles based on intelligent tires technology that is introduced to estimate the tire-road contact states based on the in-tire sensor signals. Firstly, a tire finite element model was established based on ABAQUS software. On this basis, the Spearman correlation analysis method was used to acquire the optimal sensitive positions of the in-tire acceleration sensors. The feature values of the inputs of the tire-road contact states estimation model were determined through the PLS-VIP analysis method. Then, the tire slip angle and lateral force estimation models whose inputs are the in-tire sensor signals was constructed using the LSTM neural network. Subsequently, an adaptive MPC path tracking control strategy in which the tire cornering stiffness is calculated in real-time by the intelligent tires model was designed. Finally, an adaptive NFTSM lateral stability control strategy which uses the tire lateral force estimated by the intelligent tires model was designed to determine the direct yaw moment, and an efficient and concise algorithm was then designed to achieve torque distribution. The simulation results indicate that the proposed control strategy provides better path tracking accuracy and stability control effect in emergency lane change and high-speed evasive maneuvers under different road conditions.

In order to meet with the requirements of equipment network integration, flexible customized assembly, lean logistics management and control of assembly process, an intelligent manufacturing model of sophisticated equipment assembly workshop is proposed in this paper. By modeling the requirement of intelligent manufacturing mode in sophisticated equipment assembly workshop, an intelligent manufacturing technology architecture system and its operation mode are built, which include six layers as user layer, core functional layer, system integration layer, intelligent perception layer, resource layer and basic support layer. The key technologies such as the intelligent perception technology of assembly resources by using intelligent information interaction device based on IPv6 and the abnormal early warning method of critical assembly process based on BP neural network are studied. Finally, the analysis is illustrated in the case of a realistic application from a local manufacturing enterprise to highlight the proposal’s feasibility and applicability by applying the proposed intelligent manufacturing model.

In the 1960s, the researchers of Harbin Institute of Technology (HIT) attempted to do relevant research on natural language processing. With more than 40-year’s effort, HIT has already established three research laboratories for Chinese information processing, i.e. the Machine Intelligence and Translation Laboratory (MI&T Lab), the Intelligent Technology and Natural Language Processing Laboratory (ITNLP) and the Information Retrieval Laboratory (IR-Lab). At present, it has a well-balanced research team of over 200 persons, and the research interests have extended to language processing, machine translation, text retrieval and other fields. Harbin Institute of Technology has accumulated a batch of key techniques and data resources, won many prizes in the technical evaluations at home and abroad. Harbin Institute of Technology has become one of the most important natural language processing bases for teaching and scientific research in China now. This paper gives an introduction to the achievements on NLP in HIT.

PurposeThe purpose of this paper is to review existing smart manufacturing (SM) maturity models' dimensions and maturity levels to assess their applicability and drawbacks. There are many maturity models available but many of them have not been validated or do not provide a useful guide or tool for applications. This gap creates the need for a review of the existing maturity model's applicability.Design/methodology/approachNineteen peer-reviewed maturity models related to “Digital Transformation,” “Industry 4.0” or “Smart Manufacturing” were selected based on a systematic literature review and five consulting firm models were selected based on the author's industry knowledge. The chosen models were analyzed to determine 10 categories of dimensions. Then they are assessed on a 1–5 scale for how applicable they are in the 10 categories of dimensions.FindingsThe five “consulting firm” models have a first-mover advantage, are more widely used in industry and are more applicable, but some require payment, and they lack published details and validation. The 19 “peer reviewed” models are not as widely used, lack awareness in the industry and are not as easy to apply because of no web tool for self-assessment, but they are improving. The categories defined to characterize the models and facilitate comparisons for users include “Information Technology (IT) and Cyber-Physical System (CPS) and Data,” “Strategy and Organization,” “Supply Chain and Logistics,” “Products and Services,” “Culture and Employees,” “Technology and Capabilities,” “Customer and Market,” “Cybersecurity and Risk,” “Leadership and Management” and “Governance and Compliance.” The analyzed maturity models were particularly weak in the areas of cybersecurity, leadership and governance.Practical implicationsResearchers and practitioners can use this review with consideration of their specific needs to determine if a maturity model is applicable or if a new model needs to be developed. The review can also aid in the development of maturity models through the discussion of each of the dimension categories.Originality/valueCompared to existing reviews of SM maturity models, this research determines comprehensive dimension categories and focuses on applicability and drawbacks.

Organizing medical examinations for patients with minimal time losses is the most difficult task for an outpatient clinic when creating a New model of a medical organization providing primary health care. Since during medical examinations it is necessary to perform a whole range of works associated with certain sets of actions, the authors developed an information system with technologies of artificial intelligence, which was tested during periodic medical examinations. The purpose of the study is to develop and test an information system with technologies of artificial intelligence that allows balancing the capabilities of a medical organization and the needs of the patient when undergoing periodic medical examinations. Materials and methods. The tool for solving this problem was an artificial intelligence platform based on the principle of decision making by controlling the positions of attractors in the state space of the Hopfield neural network, which was built into the medical information system using HL7 FHIR. Results. The result of the work was the “Smart Clinic” project, which provides for the creation of an information system with technologies of artificial intelligence for routing patients during periodic medical examinations. The information system provided dynamic routing of patients in real time with the construction of the shortest trajectory and minimal movement across the floors of the building, based on current information about the workload of specialists and the duration of each stage of the examination. Conclusions. The use of an artificial intelligence information system in the outpatient clinic made it possible to reduce the average duration of a patient’s trajectory during a periodic medical examination by 45%, the average waiting time for an appointment – from 7 minutes to 3 minutes, and the total time for undergoing a preventive examination – from 200 to 150 minutes. This made it possible to increase the number of periodic medical examinations per year by 1.5 times, and the level of patient satisfaction reached 95%.

In change management, there emerges the problem of making timely and relevant managerial decisions. During the entire process of change, it is necessary to make decisions that ensure the success of changes, which is rather difficult in conditions of insufficient or, otherwise, abundant information and limited resources. The purpose of the study is to explore the possibilities and present the advantages to using intelligent information technologies in change management. The objectives of the study are to identify the features of decisions made during the period of changes; to identify the factors affecting the implementation of information analytical activities, and to review digital technologies that can accelerate decision making and improve its quality in change management. The results of the study have been obtained on the basis of an analysis of trends in the development of the IT market, the data on the introduction of advanced information technologies into company management for the period of 2020-2021, and an expert survey. Taking into account the formulated requirements for managerial decisions during the period of changes, a methodology for using intelligent information technologies in change management has been developed. Within the framework of the proposed methodology, there is established the relationship between the tasks of change management and information support. There is conducted a review of the digital technologies that can be used in managerial decision-making when implementing changes. The results of the study can be used in the development of an information system model that provides support for managerial decision-making during changes in the company.

In this paper, we consider the results of research on some approaches to decision-making in irrigated agriculture - Intelligent Systems. The traditional resources for increasing the efficiency of agriculture have practically dried up. Achieving a qualitatively new level of agricultural development is possible only with the use of modern digital and intelligent technologies, because of traditional resources for increasing efficiency do not lead to a result. Global climate change and the need for agriculture in risky farming areas necessitate decision support systems for effective governance and sustainable agricultural development. The rapid development of data science and artificial intelligence has led to a new stage in the development of decision support systems - intellectualization. We have proposed to put the Kohonen neural network (Self-organizing map) as the basis for an intelligent decision support system for agro-climatic resources. At the same time, the use of modern software makes it possible to implement complex mathematical models, while new tools for creating intelligent systems such as remote sensing, cloud technologies, big data processing systems and machine learning allow more accurate management decisions at all levels of agricultural production.KeywordsIntellectualizationDecision supportIrrigated agriculture