Advanced Intelligent Technology Research Articles

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications.

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Research on the Application of Task-based Teaching Empowered by Digital Intelligence in College English Teaching

With the rapid development of technology and information technology, it has brought new opportunities and challenges to college English teaching. The task-based teaching method, combined with digitalization tools, provides a more authentic and interactive learning environment for teachers and students. This study aims to facilitate teachers to efficiently implement teaching tasks and improve students’ comprehensive English application abilities by utilizing advanced intelligent technology to implement task-based teaching. Research has found that digital technology empowers task-based teaching, effectively promoting students’ self-directed and collaborative learning, and increasing their interest and participation in learning. This study provides practical experience and theoretical basis for the reform of college English teaching.

An analysis of the applications of mechatronics in intelligent manufacturing

With the continuous development of the social economy, countries increasingly pay more attention to mechatronics and put forward various relevant policies for the actual situation of mechatronics to promote mechatronics and achieve sustainable development. However, from the current actual situation of mechatronics, due to the influence of various external factors, its degree of development cannot meet the requirements of modern social development; therefore, the application of advanced intelligent technology to mechatronics design is inevitable. Based on this, this paper expounds the intelligent manufacturing technology and mechatronics system overview as the basis for analysing the actual situation of the development of modern mechanical and electrical equipment digital design, then analyses the advantages of intelligent technology from different aspects and applies them to the daily mechatronics design to bring huge economic benefits to enterprises, and concludes that the development level of social productivity in the future is closely related to the integration level of mechatronics and intelligent manufacturing.

Trends and Prospects of Port Digital Transformation

In the field of shipping, an increasing number of ports are transitioning from traditional manual container terminals to automated and intelligent container terminals. Automated container terminals are a new generation of ports that are based on modern infrastructure and powered by advanced information technology and port transportation. This article attaches importance to introducing the research background of automation and intelligence in container terminals and explains the application of advanced intelligent technologies and automation machinery. Next, it analyzes the current status of automation in container terminals, using four automated ports: TraPac Port, Long Beach Port in the United States, Nansha Port, and Tianjin Port in China. Finally, it discusses the challenges faced by automation and intelligence in container terminals and highlights the need for government support in terms of economy and policy.

Simulation of the Impact of Key Variables on Smart Supply Chains Efficiency in Iran’s Healthcare Industry

Background: The smartification of supply chains, which enables organizations to stay informed about crises in a timely manner and make appropriate decisions in response to the resulting pressures, has consistently been a crucial factor in the realm of organizational transformation. The healthcare industry in Iran faces more challenges and crises than other industries, especially due to its vital role in public health prevention and care.
 Objectives: According to the reasons described, this research aims to simulate and improve the efficiency of the healthcare supply chain when confronted with crises.
 Methods: In this study, the efficiency of the healthcare supply chain was simulated using the system dynamics approach and Vensim DSS.
 Results: The results indicated that healthcare supply chain efficiency in Iran is unsatisfactory and may confront challenges during crises. Therefore, this study places particular emphasis on examining scenarios for improving the current situation, which stems from the consensus of experts and stakeholders in this field. In the presented scenarios, a 2% improvement in the utilization of advanced intelligent technologies and a 5% improvement in intelligent inventory management were observed. Notably, the combined effect of these two scenarios led to an overall enhancement in the average efficiency of the healthcare smart supply chain. These improvements can increase the average efficiency levels within the pharmaceutical manufacturing segment up to 1.3%, 5.8%, and 7.7% in each of the aforementioned scenarios.
 Conclusions: It can be asserted that although the advancement of smart technologies and intelligent inventory management individually contribute to enhancing the efficiency of the healthcare supply chain in Iran, combining these changes can provide the groundwork for further increasing efficiency up to 7.7%.

Actuation Mechanisms and Applications for Soft Robots: A Comprehensive Review

Soft robots, which exhibit distinguishing features in terms of compliance, adaptability, and safety, have been expansively adopted in various niche applications. For soft robots, innovative actuators have been designed based on smart materials enabling the robots to perform flexible and versatile functions, whereas extra spaces and accessories to accommodate motors and power devices have been eliminated to achieve structural optimisation. Herein, different types of actuation mechanisms for soft robots are summarised to reflect the state-of-the-art research and applications. Major characteristics of the actuation mechanisms are updated. Design methodologies of the actuation mechanisms are discussed in detail. Furthermore, their advantages, disadvantages, and application potential are compared and summarised. In the end, based on our knowledge and understanding, new thoughts and recommendations to further develop the actuation mechanisms are put forward. This review is useful to support the conclusion that, through incorporating actuation mechanisms and advanced intelligent technologies, soft robots tend to create disruptive innovations in applications.

INTELLECTUAL TECHNOLOGIES IN THE MANAGEMENT OF OIL FIELDS

The goal of this effort is to create new strategies and enhance the control over oil-producing wells and an oilfield using artificial intelligence techniques. The advancement of knowledge necessitates the implementation of intelligent technologies with the objective of creating a unified scientific and technological oilfield space. Methods for optimizing operation modes for complex technological systems; expert knowledge-based decision-support systems; unified requirements for intelligent system interfaces and modules, as well as means to represent and store knowledge, including distributed knowledge base architectures; and mathematical and software support are among these. Advanced intelligent technologies and tools built on them may be used to modify goal-attainment procedures. The key characteristics of the applicable artificial intelligence tools that are necessary for implementation at all levels are detailed, along with a suggested three-tiered system of intelligent oil field control that assures optimization of oil output and the operation of equipment. The idea and the suggestions provided in the study can serve as a springboard for more investigation and the construction of actual intelligent wells and intelligent oilfields. Keywords: intelligent oilfield, oil, production, artificial intelligence

Smart Triboelectric Nanogenerators Based on Stimulus-Response Materials: From Intelligent Applications to Self-Powered Systems.

Smart responsive materials can react to external stimuli via a reversible mechanism and can be directly combined with a triboelectric nanogenerator (TENG) to deliver various intelligent applications, such as sensors, actuators, robots, artificial muscles, and controlled drug delivery. Not only that, mechanical energy in the reversible response of innovative materials can be scavenged and transformed into decipherable electrical signals. Because of the high dependence of amplitude and frequency on environmental stimuli, self-powered intelligent systems may be thus built and present an immediate response to stress, electrical current, temperature, magnetic field, or even chemical compounds. This review summarizes the recent research progress of smart TENGs based on stimulus-response materials. After briefly introducing the working principle of TENG, we discuss the implementation of smart materials in TENGs with a classification of several sub-groups: shape-memory alloy, piezoelectric materials, magneto-rheological, and electro-rheological materials. While we focus on their design strategy and function collaboration, applications in robots, clinical treatment, and sensors are described in detail to show the versatility and promising future of smart TNEGs. In the end, challenges and outlooks in this field are highlighted, with an aim to promote the integration of varied advanced intelligent technologies into compact, diverse functional packages in a self-powered mode.

In Situ Sea Cucumber Detection across Multiple Underwater Scenes Based on Convolutional Neural Networks and Image Enhancements.

Recently, rapidly developing artificial intelligence and computer vision techniques have provided technical solutions to promote production efficiency and reduce labor costs in aquaculture and marine resource surveys. Traditional manual surveys are being replaced by advanced intelligent technologies. However, underwater object detection and recognition are suffering from the image distortion and degradation issues. In this work, automatic monitoring of sea cucumber in natural conditions is implemented based on a state-of-the-art object detector, YOLOv7. To depress the image distortion and degradation issues, image enhancement methods are adopted to improve the accuracy and stability of sea cucumber detection across multiple underwater scenes. Five well-known image enhancement methods are employed to improve the detection performance of sea cucumber by YOLOv7 and YOLOv5. The effectiveness of these image enhancement methods is evaluated by experiments. Non-local image dehazing (NLD) was the most effective in sea cucumber detection from multiple underwater scenes for both YOLOv7 and YOLOv5. The best average precision (AP) of sea cucumber detection was 0.940, achieved by YOLOv7 with NLD. With NLD enhancement, the APs of YOLOv7 and YOLOv5 were increased by 1.1% and 1.6%, respectively. The best AP was 2.8% higher than YOLOv5 without image enhancement. Moreover, the real-time ability of YOLOv7 was examined and its average prediction time was 4.3 ms. Experimental results demonstrated that the proposed method can be applied to marine organism surveying by underwater mobile platforms or automatic analysis of underwater videos.

The Vicious Circles of Skill Erosion: A Case Study of Cognitive Automation

Cognitive automation powered by advanced intelligent technologies is increasingly enabling organizations to automate more of their knowledge work tasks. Although this often offers higher efficiency and lower costs, cognitive automation exacerbates the erosion of human skill and expertise in automated tasks. Accepting the erosion of obsolete skills is necessary to reap the benefits of technology—however, the erosion of essential human expertise is problematic if workers remain accountable for tasks for which they lack sufficient understanding, rendering them incapable of responding if the automation fails. Though the phenomenon is widely acknowledged, the dynamics behind such undesired skill erosion are poorly understood. Thus, taking the perspective of sociotechnical systems, we conducted a case study of an accounting firm that had experienced skill erosion over a number of years due to reliance on their software’s automated functions. We synthesized our findings using causal loop modeling based on system dynamics. The resulting dynamic model explains skill erosion via an interplay between humans’ automation reliance, complacency, and mindful conduction. It shows how increasing reliance on automation fosters complacency at both individual and organizational levels, weakening workers’ mindfulness across three work task facets (activity awareness, competence maintenance, and output assessment), resulting in skill erosion. Such skill erosion may remain obscure, acknowledged by neither workers nor managers. We conclude by discussing the implications for theory and practice and identifying directions for future research.

Evolution from Medical Imaging to Visualized Medicine.

The discovery of X-ray in 1895 and the first X-ray image of Mrs. Röntgen's hand opened up a new era of radiology and the research of medical imaging. The evolution of traditional medical imaging has been lasting for over 100years, serving the detection, diagnosis, and treatments of human diseases with a clear view of the anatomy information. In late 1990s, the concept of molecular imaging was proposed as the science and technology of molecular biology and bio-engineering rapidly developed, and it directly gave birth to the emergence of precision medicine for clinical lesion-targeted treatments against various cancers and cardiocerebrovascular diseases. Physiological and pathological changes in live bodies from zebrafish to human beings can be imaged to ensure an efficient image-guided therapy. Nowadays, the philosophy of medical and molecular imaging has been a powerful tool and indispensable modality for doctors to make their decisions and give patients reliable advices. With the ever-emerging developments of advanced intelligent technologies such as flexible sensors, medical meta-data analysis, brain sciences, surgical robots, VR/AR, etc., modern medicine has been evolving from traditional medical and molecular imaging to visualized medicine, which has created novel accessible approaches along with cutting-edge techniques for the revolutionized diagnostic and therapeutic paradigms. In this context, the history and milestones from medical imaging to visualized medicine will be elucidated. And in particular, representative visualized medicine advances including its application to COVID-19 epidemics will be discussed in order to look for its important contributions and a future perspective to modern medicine.

Development of an edge-cloud collaboration framework for fission battery management system

As a “plug-and-play” nuclear system, the technology of fission battery enables nuclear reactor systems to function as batteries. Undoubtedly, advanced intelligent technologies have to be adopted to realize the excellent attributes of fission batteries, i.e., economic, standardized, installed, unattended, and reliable, and its commercial applications. This article proposed the edge-cloud collaboration technology for fission batteries to satisfy its intelligent requirements. A detailed framework for a fission battery management system has been developed. To develop such a management system, four types of concrete transmission information for the edge-cloud collaboration (i.e. resource and data, AI technologies, application and services, safety and strategies) are identified. The edge and cloud designs are also discussed in detail, including their hardware and software. A functional framework of edge-cloud collaboration network platform for fission battery management system is developed preliminarily. The framework is based on a three-layer structure, i.e., perception layer, technical layer and application layer. Although no detailed algorithm or verification is introduced (since the edge-cloud collaboration framework has been widely used in some industry fields but not in nuclear engineering), as primarily work for the future detailed study of fission battery, it is necessary and extremely important.

Design of the Artificial Intelligence Vocal System for Music Education by Using Speech Recognition Simulation.

At present, computer technology is not limited to the network but to many aspects of spread out, including artificial intelligence technology, signal processing technology, speech recognition technology, and so on which have been better developed, and music teaching has also obtained certain achievements. If the detected speech data have no other clutter in the speech, then the endpoint detection can achieve ideal results. The music education system includes the development of four dimensions: WEB terminal, mobile terminal, web interface, and database. Using the powerful functions of computers, such as computing functions and processing functions, combined with advanced intelligent technology and related equipment, the intelligent learning model based on computer technology is established. It can be applied in college teaching, combined with the actual needs of current college music teaching, corresponding to the curriculum and teaching plan issued by the school, combined with the design of online learning system and reasonable use of computer technology to build a vocal music education system. The intelligent system can improve the requirements of vocal music education management level, teaching organization ability, and teaching platform operation ability.

Advanced Engineering Materials for Enhancing Thermal Management and Thermal Safety of Lithium-Ion Batteries: A Review

Lithium-ion batteries (LiBs) are the key power source for electric vehicles (EVs). Battery thermal management system (BTMS) is essential to ensure safety and extend service life of LIBs. This paper reviews the various refrigeration materials used in the BTMS in EVs, including liquid coolant, phase change material (PCM). The thermal properties of these refrigerant materials are summarized and the innovative ways to improve the cooling efficiency of the BTMS are analyzed. The various ways to enhance the battery’s thermal performance by modifying the materials of the electrode, separator, and electrolyte are also reviewed. Finally, the research prospect in area of BTMS is summarized. This review will inspire new BTMS design and further improvement in battery safety and performance with the aid of advanced intelligent technologies.

Advanced Intelligent Technologies in Energy Forecasting and Economical Applications

Advanced Intelligent Technologies in Energy Forecasting and Economical Applications

Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning

When facing various pressures, human beings will have different degrees of bad psychological emotions, especially depression and anxiety. How to effectively obtain psychological data signals and use advanced intelligent technology to identify and make decisions is a research hotspot in psychology and computer science. Therefore, a personal emotional tendency analysis method based on brain functional imaging and deep learning is proposed. Firstly, the EEG forward model is established according to functional magnetic resonance imaging (fMRI), and the transfer matrix from the signal source at the cerebral cortex to the head surface electrode is obtained. Therefore, the activation results of fMRI emotional experiment can be mapped to the three-layer head model to obtain the EEG topographic map reflecting the degree of emotional correlation. Then, combining data enhancement (Mixup) with three-dimensional convolutional neural network (3D-CNN), an emotion-related EEG topographic map classification method based on M-3DCNN is proposed. Mixup is used to generate virtual data, the original data and virtual data are used to train the network together, the number of training samples is expanded, the overfitting phenomenon of 3D-CNN is alleviated, and 3D-CNN is used for feature extraction and classification. Experimental data analysis shows that, compared with traditional methods, the proposed method can retain emotion related EEG signals to a greater extent and obtain a higher accuracy of emotion five classifications under the same feature dimension.

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

Purpose: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, antisaturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional-integral-derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

Research on Video Quality Diagnosis Technology Based on Artificial Intelligence and Internet of Things

The remote video diagnosis system based on the Internet of Things is based on the Internet of Things and integrates advanced intelligent technology. To better promote a harmonious society, constructing a video surveillance system is accelerating in our country. Many enterprises and government agencies have invested much money to build video surveillance systems. The quality of video images is an important index to evaluate the video surveillance system. However, as the number of cameras continues to increase, the monitoring time continues to extend. In the face of many cameras, it is not realistic to rely on human eyes to diagnose video‐solely quality. Besides, due to human eyes’ subjectivity, there will be some deviation in diagnosis through human eyes, and these factors bring new challenges to system maintenance. Therefore, relying on artificial intelligence technology and digital image processing technology, the intelligent diagnosis system of monitoring video quality is born using the computer’s efficient mathematical operation ability. Based on artificial intelligence, this paper focuses on studying video quality diagnosis technology and establishes a video quality diagnosis system for video definition detection and noise detection. This article takes the artificial intelligence algorithm in the diagnosis of video quality effect. Compared with the improved algorithm, the improved video quality diagnosis algorithm has excellent improvement and can well finish video quality inspection work. The accuracy of the improved definition evaluation function for the definition detection of surveillance video and noise detection is as high as 95.56%.

Attribute-Based Encryption With Parallel Outsourced Decryption for Edge Intelligent IoV

Edge intelligence is an emerging concept referring to processes in which data are collected and analyzed and insights are delivered close to where the data are captured in a network using a selection of advanced intelligent technologies. As a promising solution to solve the problems of insufficient computing capacity and transmission latency, the edge intelligence-empowered Internet of Vehicles (IoV) is being widely investigated in both academia and industry. However, data sharing security in edge intelligent IoV is a challenge that should be solved with priority. Although attribute-based encryption (ABE) is capable of addressing this challenge, many time-consuming modular exponential operations and bilinear pair operations as well as serial computing cause ABE to have a slow decryption speed. Consequently, it cannot address the response time requirement of edge intelligent IoV. Given this problem, an ABE model with parallel outsourced decryption for edge intelligent IoV, called ABEM-POD , is proposed. It includes a generic parallel outsourced decryption method for ABE based on Spark and MapReduce. This method is applicable to all ABE schemes with a tree access structure and can be applied to edge intelligent IoV. Any ABE scheme based on the proposed model not only supports parallel outsourced decryption but also has the same security as the original scheme. In this paper, ABEM-POD has been applied to three representative ABE schemes, and the experiments show that the proposed ABEM-POD is efficient and easy to use. This approach can significantly improve the speed of outsourced decryption to address the response time requirement for edge intelligent IoV.

A Research of Electrical Engineering and Automation-based Intelligent Technology Application

As a safety-security field, the transportation field always encourages and embraces advanced intelligent technologies to reduce risks. Recently, automated driving has received extensive attention from researchers and industries, as it is expected to improve the flow of traffic, to significantly reduce drivers’ errors and thusly increase safety. Though many efforts have put on automated driving, many challenges remain in achieving fully autonomous, resulting in partially automated driving. The role of drivers in a partially automated car is monitoring and taking over driving in specific conditions, which is significantly different from the manual cars. The novel role of drivers induces new human factors issues and challenges to maintain traffic safety. This study tries to investigate these human factors issues involved in the human and automated driving interactions and to propose a framework of electrical engineering and automation-based intelligent technology application to mitigate risks. Specifically, trust, attention, situational awareness, and alarm fatigue were identified as significant human factors issues in automated driving. An electrical engineering and automation-based intelligent framework for monitoring cognitive states and generate warnings was proposed.