Intelligent Instrument Research Articles

Environmental-Persistent Free Radicals Prediction by Neural Networks

Air pollution is recognized as a critical threat to human health. The link between the inhalation of fine particulate matter (PM) in combination with toxic gases and the resulting health effects is well documented. The health damages were proven to be interrelated with the free radicals caused by air pollution. Free radicals cause a destructive chain reaction by taking an electron from a stable molecule and converting it into an unstable one. Environmentally persistent free radicals (EPFRs) were not considered until the last ten years as an important part of the air pollution composition. EPFRs are a special class of free radicals with a long half-life, contributing to the development of aging-related diseases in living organisms, as well as harmful oxidative effects in lung tissue. The work is grounded on database of the routinely measured air quality parameters (air pollutants О3, CO, NO2, SO2, PM2.5) and corresponding EPFR data to develop a model for EPFR prediction. An intelligent instrument is used to perceive the pollution information and, on this basis, EPFR values without direct measurement were calculated. Applying the above approach, an average deviation of 0.73 was accomplished.

Analysis of Measurement Accuracy and Reliability of Intelligent Instrumentation in Process Control

Abstract Intelligent instrumentation plays a crucial role in industrial applications, with its measurement accuracy and reliability directly influencing operational efficiency. This article presents the integration of the HART communication protocol to enhance data transmission within smart instruments, focusing on internal structure and process control optimization. A HART smart instrument multiplexer system is established, utilizing an analog PID control algorithm for data regulation and optimal control techniques for automatic parameter adjustment. Additionally, the three-parameter Weibull distribution is employed to assess the reliability of smart meters, deriving distribution characteristics and estimating parameters through failure rate analysis. The performance of the designed HART smart meter multiplexer system is thoroughly evaluated, including control performance, reliability, channel function, and measurement accuracy. Notably, the system demonstrates a minimal overshoot in analog PID control at 0.911%, with a steady-state error range of -5°C to 4°C. Furthermore, the implementation of this system significantly reduces the risk of hazardous failures by 41.91%. In conditions of 350°C temperature and 1.2MPa pressure, the system accurately measures superheated steam flow with a relative error of only 0.05%. By optimizing the settings of smart instruments, this system not only enhances measurement accuracy in industrial production but also improves reliability, supporting diverse applications in the chemical industry.

New technique based on artificial intelligence and custom-made instrumentation for scoliosis surgical treatment

New technique based on artificial intelligence and custom-made instrumentation for scoliosis surgical treatment

Intelligence as an instrument of state power and its use in international politics

The article makes an attempt to theoretically substantiate why intelligence is an independent instrument of state power, its common and distinctive features with other instruments of power. The paper also provides historical examples of the use of intelligence by various states to influence the behaviour of other states, as well as various approaches used therein.Some features of the use of the intelligence instrument of power by states in the conditions of the existence of bipolar and multipolar political systems are also highlighted in the article. It also draws attention to the incentives for the choice of intelligence as an alternative to other instruments of power in the context of the limiting impact of global collective security systems, in particular the United Nations.

Computer Tomography as an Artificial Intelligence Instrument—the Survey of Approach and Results of V.L. Arlazarov’s Scientific School

Computer Tomography as an Artificial Intelligence Instrument—the Survey of Approach and Results of V.L. Arlazarov’s Scientific School

A dataset of stained tongue fur images of TCM

Tongue diagnosis is a distinctive diagnostic technique for traditional Chinese medicine (TCM). It plays an important role as a fundamental basis for diagnosing diseases and syndromes in TCM, focusing on tongue texture and tongue fur. Tongue fur mainly includes tongue fur texture and tongue fur color. Especially latter is an important indicator to identify the nature of the disease. In clinical practice, tongue fur can easily be influenced by medications and dietary intake, resulting in color illusion and misdiagnosis. This dataset records two types of tongue images of TCM, namely stained tongue fur and non-stained tongue fur, with an average of about 1,000 tongue images for each type. The stained tongue fur data include the color change images of the tongue fur captured at intervals of 5 minutes, 10 minutes, and 30 minutes after consuming food that causes staining in health conditions of different populations. The colors are can be primarily categorized into three categories: white, yellow, and gray/black, with yellow being the most prevalent. Non-stained tongue fur data include pathological tongue fur images of different populations, falling into white, yellow, and gray/black. This dataset can serve as a reliable classification basis for the research in the field of tongue diagnosis and effectively solve the problem of tongue fur artifacts. Moreover, it can provide valuable data to support the advancement of more intelligent tongue diagnosis instruments.

Signal drift modeling and analysis of an intelligent instrument using a multivariate adaptive regression splines technique

Signal drift is an important failure mode of intelligent instruments under the combined effect of complex loading conditions and usually exhibits strongly nonlinear characteristics with a high degree of scatter. To solve this problem, this paper applies a multivariate adaptive regression splines (MARS) model to flexibly fit the nonlinear and highly inconsistent signal drifts of intelligent instruments. To investigate the pressure signal drift characteristics of different test transmitters, a unified parameter extraction method is proposed that uses a four-loop recursive algorithm to obtain consistent MARS model parameters. Through an application case study on five intelligent pressure transmitters tested at two temperatures, it is verified that the proposed unified parameter extraction method works perfectly for the fitting and analysis of pressure signal drift of these transmitters. Furthermore, it has advantages in providing quantitative interpretation of signal drift scatter, with potential practical application to investigation and comparison of drift fault curves of instruments that exhibit significant inconsistency.

Water level recognition based on strong edge and sparse constraints

Abstract This paper takes the intelligent water level recognition instrument of Qingming Shanghe Park in Kaifeng as the experimental object, introduces the algorithm of strong edge and sparse constraint into the intelligent water level recognition instrument, and compares the recognition effect of the intelligent water level recognition instrument before and after the introduction of strong edge and sparse constraint algorithms. The results showed that the clarity value was approximately 10% higher, and the recognition speed was also significantly improved. The improvement of recognition speed can effectively promote the work efficiency of the whole method. Strong edges and sparse constraints can effectively improve the accuracy of water level identification, provide scientific and effective data and information for subsequent water resource management, and meet the needs of water resource managers to effectively grasp the law of water level. This can provide technical support for identification methods in other fields, and the ultimate goal is to promote the protection and management of water resources and reduce the harm of natural disasters on people.

Use of real-time immersive digital training and educational technologies to improve patient safety during the processing of reusable medical devices: Quo Vadis?

Hospital acquired infections stemming from contaminated reusable medical devices are of increasing concern. This issue is exaggerated with the introduction of complex medical devices like endoscopes and robotic instrumentation. Although medical device manufacturers validate their cleaning instructions for use, evidence in the literature demonstrates that effective device processing is not being performed consistently within sterile processing departments in clinical settings. The result is increased risks to patient safety. As a solution to this problem, focused one-on-one training increases compliance to the medical device manufacturer's processing instruction. However, often this is not a practical solution for the volume of healthcare staff responsible for device processing activities. This constitutes the first paper to address the blended use of educational and digital technologies to address these challenges and as a result inform safety and sustainability for the medical device sector. Cognitive learning theory is an evidence-based framework for learning. It supports the use of immersive educational experiences using emerging extended reality technologies (e.g., virtual or augmented reality) to increase learning comprehension. The delivery of educational content via these technologies provides an innovative option for repeatable leaning and training outcomes. The motivation is to decrease patient risk of contaminated reusable medical devices. The proposed approach while primary motivated by safety can also enhance sustainability and efficiency enabled by artificial intelligence and robotic instrumentation.

ChatGPT and AI in the Universities: An Introduction to the Near Future

The paper aims to present the theoretical and methodological foundations for developing and implementing artificial intelligence (AI) instruments in higher education within the HumanCentered AI framework and considers ChatGPT as a specific case. It is oriented toward critically analyzing scholarly literature from various disciplines to support a-disciplinary approach to utilizing AI research in higher education. The authors begin by outlining the foundations of the research, then by analyzing the implications of the “lessons” of the COVID-19 pandemic for the development of higher education in the age of AI. This is followed by an analysis of technologies such as ChatGPT that have the potential to radically change higher education in the near future. After that, the authors move on to general remarks about the prospects and challenges of using AI technologies in higher education. The relevance of the research is characterized by the analysis of the latest developments in the field of AI and their possible impact on higher education. The main results of the paper are characteristics of areas in which AI technologies can be implemented in higher education – accreditation and licensing of universities, student recruitment, teaching and learning – as well as areas in which AI technologies should not be used. In conclusion, two hypotheses are formulated, and three general and five specific conclusions about the use of AI technologies in higher education are made.

A Highly Integrated 3D MEMS Force Sensing Module With Variable Sensitivity for Robotic‐Assisted Minimally Invasive Surgery

AbstractIn robotic‐assisted minimally invasive surgery (RMIS), non‐sentient surgical instruments make it impossible for surgeons to perceive operational force during the procedure. To facilitate surgeons with force telepresence during surgery, a highly integrated MEMS‐based piezoresistive 3D force sensing module, which is composed of a MEMS‐based piezoresistive sensor chip, an encapsulation cap with miniature pyramids, and a top elastic layer, is demonstrated. This innovative combined construction allows for rapid replacement of elastic layers with different thicknesses and different Young's modulus, so as to realize an adjustable sensitivity and measurement range for different surgeries. By replacing the elastic layer, the same amount of change in resistance can be achieved for external force of 3 and 10 N in the Z‐axis; the sensitivity in X‐ and Y‐axes can be increased by a maximum of eight and seven times, respectively. Meanwhile, miniature size enables it to be integrated into various surgical instruments’ tip. Experimental demonstrations involving palpation simulated nodule detection of kidney, ex vivo puncture, and threading forces estimation of tissue‐mimicking are conducted to validate the effectiveness of adjusting sensitivity and range. This sensing module is potentially a promising solution for low‐cost and versatility to surgical instruments, which can facilitate unification of force‐sensing/intelligent surgical instruments.

Diagnostics of psychological flexibility and the ability to cope with the inevitable changes among psychology students.

The study concerns the question of how much the emotional competence and the ability to cope with problems in one's own personality increase in the process of professional psychological education, for which students of different years of study were tested. The aim of this study is to deeply diagnose various components of psychological flexibility and the ability to cope with unexpected events among psychology students. The study involved 30 students from 1 to 4 years of university grade level participated into study, divided into 4 equal groups from. Based on testing various aspects of psychological flexibility, emotional intelligence test (EQ test), Emotion Regulation Questionnaire (ERQ) and D.V. Lyusin emotional intelligence instrument (Emin) were used; it was tested using Student's t-test and Kruskal-Wallis H-test to evaluate differences between three or more samples simultaneously. As a result of the study, the significant differences between all groups of participants and between the assessment of individual factors of psychological flexibility in different groups was accepted. Each of the groups demonstrated its specific characteristics of the relationship between emotional competence and coping with stress. Comparison of the results of students from different years of study showed that psychological education does not have a significant effect on emotional intelligence as an indicator of emotional flexibility, but positively affects the development of coping with stress, although in predominantly passive forms. The practical application of the research lies in improving psychology students' learning; the research results can be used as methods for determining psychological flexibility factors that require development in study groups.

Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems

Intelligent instruments are common components in industrial machinery, and fault diagnosis in IoT systems requires the handling of real-time sensor data and expert knowledge. IoT sensors cannot collect data for the diagnosis of all fault types in a specific instrument, and long-distance data transfer introduces additional uncertainties. However, because industrial equipment has complex fault causes and performances, it is typically difficult or expensive to obtain exact fault probabilities. Therefore, in this study, we proposed an innovative failure detection and diagnosis model for intelligent instruments in an IoT system using a Bayesian network, with a focus on handling uncertainties in expert knowledge and IoT monitoring information. The model addresses the challenge of complex fault causes and performances in industrial equipment, which make the obtainment of exact fault probabilities difficult or expensive. The trapezoidal intuitionistic fuzzy number (TrIFN)-based entropy method was applied in order to aggregate expert knowledge to generate priority probability, and the Leaky-OR gate was used to calculate CPT. The effectiveness of the proposed strategy was demonstrated through its application to an intelligent pressure transmitter (IPT) using the GeNIe software.

Creating non-discriminatory Artificial Intelligence systems: balancing the tensions between code granularity and the general nature of legal rules

Over the past decade, concern has grown regarding the risks generated by the use of artificial intelligence systems. One of the main problems associated with the use of these systems is the harm they have been proven to cause to the fundamental right to equality and non-discrimination. In this context, it is vital that we examine existing and proposed regulatory instruments that aim to address this particular issue, especially taking into consideration the difficulties of applying the abstract nature that typically characterises legal instruments and, in particular, the equality and non-discrimination legal framework, to the specific instructions that are needed when coding an artificial intelligence instrument that aims to be non-discriminatory. This paper focuses on examining how article 10 of the new EU Artificial Intelligence Act proposal may be the starting point for a new form of regulation that adapts to the needs of algorithmic systems.

Preface

With great, successful, and prestigious history of ICETAC, the 2022 3rd International Conference on Electrical Technology and Automatic Control (ICETAC 2022) not only sets a solid foundation but also has the potential to be even greater than ever before. The conference took place in Macau, China during December 2nd-4th, 2022 in the form of virtual conference.ICETAC 2022 provided an international cooperation and exchange platform for experts and scholars in related fields, and promoted the scientific and academic information interchange among all the participants and the industrialization cooperation of academic achievements in Electrical Technology and Automatic Control. It is not only an exceptional academic conference, but also a great platform to discover new perspective, and inspire new thinking.During the conference, we had about 100 participations both as speakers and participants, including researchers, industrial experts, professionals, etc. To make this conference on schedule, we had a three-part plenary including a keynote speech delivered by three resources persons from different parts of China and other countries. All the keynote speakers have delivered expertise lectures by imparting the latest findings from their respective research domains. Among them, Assoc. Prof. Lisu Yu from Nanchang University, China addressed a keynote speech on A Novel Multiple Access Scheme for Visible Light Communication Networks in 6G. He provided an overview and outlook on the application of sparse code multiple access (SCMA) for 6G wireless communication systems, which is an emerging disruptive non-orthogonal multiple access (NOMA) scheme for the enabling of massive connectivity and proposed to apply SCMA to a massively distributed access system whose architecture is based on fiber-based visible light communication, ultra-dense networks, and NOMA. Under this framework, he considered the interactions between optical fronthauls and wireless access links, and he also outlined a number of promising directions associated with SCMA. It was a wonderful opportunity for all the participants to interact with the experts and specialists to get their advice or suggestions.The proceedings of ICETAC 2022 contain quantities of excellent papers grouped by topics. All submissions were performed double blind review to check their quality of content, level of innovation, significance, originality and legibility, covering but not limited to the following topics: Electric Vehicle Technology, Intelligent Control System, Automation and Instrumentation, Motors and Electrical Appliances, Fault Detection, etc.We would like to express our gratitude to all the participants for their fruitful work and excellent contribution to ICETAC 2022. Especially, we appreciate the enormous work of the editorial board and reviewers in the preparation of this volume. Also, our special thanks and appreciation go to the Journal of Physics: Conference Series. Hopefully, all participants and other interested readers can benefit scientifically from the proceedings and find it rewarding in the process.The Committee of ICETAC 2022List of Committee member is available in this Pdf.

Fault logic circuit design of switching power supply based on soft reference circuit module

The basic idea of the circuit fault diagnosis method based on fault logic is to establish a computer expert database system first, connect the computer to the GPIB interface card, and then connect each intelligent instrument through the GPIB standard bus. Through the connection of the tested object adapter and the tested object, a circuit fault expert diagnosis and test platform are formed. Based on the traditional fault simulation model, this paper improves the fault logic circuit of switching power supply, designs the system model of fault simulation, deals with the logic structure and circuit hierarchy respectively, and produces the final output through the logic gate circuit by comparing the results of the fault logic circuit and the fault-free logic circuit. The designed switching power supply fault logic circuit works in the switching state, which has the characteristics of low loss and high energy conversion efficiency. Through the simulation analysis, the output is connected to the comparator, which plays the role of the whole wave. The simulation results are in line with the parameter indicators. The functional test results of the chip show that the function of the module is basically normal, which has practical application value and good portability and can be used as a reference for similar electrical designs.

Electro-mechano responsive elastomers with self-tunable conductivity and stiffness.

Materials with programmable conductivity and stiffness offer new design opportunities for next-generation engineered systems in soft robotics and electronic devices. However, existing approaches fail to harness variable electrical and mechanical properties synergistically and lack the ability to self-respond to environmental changes. We report an electro-mechano responsive Field's metal hybrid elastomer exhibiting variable and tunable conductivity, strain sensitivity, and stiffness. By synergistically harnessing these properties, we demonstrate two applications with over an order of magnitude performance improvement compared to state-of-the-art, including a self-triggered multiaxis compliance compensator for robotic manipulators, and a resettable, highly compact, and fast current-limiting fuse with an adjustable fusing current. We envisage that the extraordinary electromechanical properties of our hybrid elastomer will bring substantial advancements in resilient robotic systems, intelligent instruments, and flexible electronics.

Guest Editorial Special Section on Signal Processing and Machine Learning in Intelligent Instrumentation, IEEE Open Journal of Instrumentation and Measurement

Guest Editorial Special Section on Signal Processing and Machine Learning in Intelligent Instrumentation, IEEE Open Journal of Instrumentation and Measurement

Experiment Teaching Reform of Electronic Measurement and Intelligent Instrument Based on Emerging Engineering Education

In view of the electronic measurement and intelligent instrument of experiment teaching problems, students’ innovative practice ability is vigorously promoted through the establishment of the laboratory, development experimental projects, rich teaching methods, multiple experiment assessment system, practice course education, construction of double type on teachers based on Emerging Engineering Education. It can provide reference for electronic information class other professional courses of Emerging Engineering Education.

Application of ChatGPT in multilingual medical education: How does ChatGPT fare in 2023's Iranian residency entrance examination

BackgroundChatGPT is a large language model (LLM) artificial intelligence instrument trained on massive amounts of text data extracted from the internet and/or user input. In the present article, we aim to apply the latest version of ChatGPT to the Iranian Medical Residency Examination. MethodsThe Iranian Medical Residency Examination is composed of 200 multichoice questions covering all domains of medicine. We used ChatGPT to translate questions into English, French, and Spanish. We fed the questions as multiple-choice questions and allowed ChatGPT to provide comprehensive answers and justifications for its choices. ResultsChatGPT was able to answer 161 (81.3% = 161/198) questions correctly when the Persian language was used. When the questions were translated into English, French, and Spanish, ChatGPT answered six, one, and five additional questions correctly, respectively. When comparing the different languages, there was no significant difference in the functioning of ChatGPT in different languages using either the McNemar test or the Binomial test. ConclusionChatGPT can deliver above-average performance in the Iranian Medical Residency Examination, demonstrating its potential for using language models in medicine.