Machine Interaction Research Articles

An information flow field model for human–machine interaction

PurposeAbout 70% of all aircraft accidents are caused by human–machine interaction, thus identifying and quantifying performance shaping factors is a significant challenge in the study of human reliability. An information flow field model of human–machine interaction is put forward to help better pinpoint the factors influencing performance and to make up for the lack of a model of information flow and feedback processes in the aircraft cockpit. To enhance the efficacy of the human–machine interaction, this paper aims to examine the important coupling factors in the system using the findings of the simulation.Design/methodology/approachThe performance-shaping factors were retrieved from the model, which was created to thoroughly describe the information flow. The coupling degree between the performance shaping factors was calculated, and simulation and sensitivity analysis are based on system dynamics.FindingsThe results show that the efficacy of human–computer interaction is significantly influenced by individual important factors and coupling factors. To decrease the frequency of accidents after seven hours, attention should be paid to these factors.Originality/valueThe novelty of this work lies in proposing a theoretical model of cockpit information flow and using system dynamics to analyse the effect of the factors in the human–machine loop on human–machine efficacy.

Frequency-encoded eye tracking smart contact lens for human–machine interaction

Eye tracking techniques enable high-efficient, natural, and effortless human-machine interaction by detecting users’ eye movements and decoding their attention and intentions. Here, a miniature, imperceptible, and biocompatible smart contact lens is proposed for in situ eye tracking and wireless eye-machine interaction. Employing the frequency encoding strategy, the chip-free and battery-free lens successes in detecting eye movement and closure. Using a time-sequential eye tracking algorithm, the lens has a great angular accuracy of <0.5°, which is even less than the vision range of central fovea. Multiple eye-machine interaction applications, such as eye-drawing, Gluttonous Snake game, web interaction, pan-tilt-zoom camera control, and robot vehicle control, are demonstrated on the eye movement model and in vivo rabbit. Furthermore, comprehensive biocompatibility tests are implemented, demonstrating low cytotoxicity and low eye irritation. Thus, the contact lens is expected to enrich approaches of eye tracking techniques and promote the development of human-machine interaction technology.

Magnetorheological Fluid-Based Haptic Feedback Damper

Damping involves the various frictional and other obstructive effects that attenuate free vibration. For a long time, people have mainly used it to make various dampers to reduce mechanical vibration and consume kinetic energy. It is widely used in fields such as aerospace, automotive, and consumer electronics. These dampers mainly act on mechanical structures. In recent years, with the rapid development of novel human–machine interaction methods and force/tactile feedback technology, the damper has begun to act on people, such as when a person interacts with a robot and their force is applied to a structure with damping. This type of damper requires variable damping, and the amount of variation is controlled by the magnitude of human action. This study used magnetorheological fluid (MRF) instead of traditional damping fluids, such as silicone oil, sesame oil, and mechanical oil. Magnetorheological fluid is a controllable fluid with magnetorheological effects, and its viscosity (hardness) can be changed by changing the nearby magnetic field. This study took the design of variable damping keyboard buttons with haptic feedback as an example to study the electromagnetic and dynamic models of variable dampers based on magnetorheological fluids. The experimental setup was designed and used to verify the haptic effectiveness of the scheme, which can be applied to the development of other haptic dampers that require variable damping.

Imperceptible liquid metal based tattoo for Human-Machine interface on hairy skin

Biopotential signals recorded by skin surface electrodes reflect the state of the body and corresponding organ functions. These signals are commonly utilized in clinical diagnosis, health monitoring, sports training, and human–computer interaction. However, the utilization of conventional electrodes on hairy skin causes a number of issues, including suboptimal skin compliance, obstructed sweat secretion and impaired tactile perception, making it difficult to obtain long-term and high-fidelity biopotential recordings. Here, we propose an imperceptible spray-on electrode tattoo (SE-tattoo) with sweat-permeability, high adhesiveness, high conductivity, and fast curing ability (<20 s). The water-based conductive ink is sprayed directly on the hairy skin to form a conformable conductive tattoo that neither affects nor is affected by the skin hair. We demonstrate it for heart health monitoring, sports training, electro-tactile display and close-loop teleoperation control. The SE-tattoo offers a reliable and versatile solution to biopotential recordings and serves as a promising platform for bidirectional human–machine interaction.One-Sentence Summary: Imperceptible sweat-permeable SE-tattoo enables high-fidelity biopotential recordings for bidirectional human–machine interaction.

Real-Time Emotion Recognition System Using Facial Expressions

This paper describes an emotion detection system based on real-time detection using image processing with human-friendly machine interaction. Facial detection has been around for decades. Taking a step ahead, Human expressions displayed by face and felt by the brain, captured via video, electric signal, or image form can be approximated. To recognize emotions via images or videos is a difficult task for the human eye and challenging for machines thus detection of emotion by a machine requires many image processing techniques for feature extraction. This paper proposes a system that has two main processes such as face detection and Facial expression recognition (FER). This research focuses on an experimental study on identifying facial emotions. The flow for an emotion detection system includes the image acquisition, preprocessing of an image, Face detection, feature extraction, and classification. To identify such emotions, the emotion detection system uses KNN Classifier for image classification, and Haar cascade algorithm an Object Detection Algorithm to identify faces in an image or a real-time video. This system works by taking live images from the webcam. The objective of this research is to produce an automatic facial emotion detection system to identify different emotions based on these experiments the system could identify several people that are sad, surprised, and happy, in fear, are angry, disgust etc.

Perceived Ease Of Use and User Experience Using Chat GPT

This research analyzes the impact of Perceived Ease Of Use, Performance Expectations, Effort Expectations, Social Influence, and Facilitating Conditions communication on the Interaction Efficiency of communication interactions with machines. Analysis of these factors is important because they have a direct influence on communication interactions with GPT Chat. Understanding the impact will drive improvements and improve the overall user experience. The theoretical framework in this research is the Unified Theory of Technology Acceptance and Use (UTAUT) and Human Machine Interaction (HCI). Data was collected through a questionnaire with a Likert scale and tested on 100 respondents using the Purposive Sampling method on GPT Chat users. The results of the research show that five variables, namely Effort Expectancy, Social Influence, User Experience, Performance Expectancy, and Customer Satisfaction have a positive and significant influence on user experience, Interaction Efficiency, and Interaction Efficiency. Meanwhile, the other two variables are Facilitation Conditions and Perceived Ease of Use. , has no significant effect on user communication. The results of the determination analysis show that Perceived Ease of Use and the UTAUT Model contribute 25.8% to User Experience, while User Experience contributes 6.2% to Interaction Efficiency. This research improves the Chat and Interaction Efficiency user experience. It contributes to the field of technology acceptance and communication of how humans interact with machines.

Cleanroom Dashboard System for Time-Reduction Checking of Disk Tester Availability

With the vast amount of data being processed and reviewed in production and manufacturing industries nowadays, it takes a lot of time and effort to keep track of their performance metrics. Developing an automation system to improve the overall system performance and reduce man-machine interactions is necessary. Prior to the work, a computer disk drive manufacturing facility checked its fixed-tester availability manually, which made the company unable to optimize its man and machine hours as much as possible. This work proposes a system that displays all the critical information in a dashboard system to address the problem while enabling access to data history logs and further analytic insights. Moreover, the system enables readily accessible fixed-disk tester availability. The outcomes indicate significant improvements, especially in delay reductions, necessary for optimizing man and machine interactions in a fixed-disk testing facility at a leading computer disk drive manufacturer and data storage company at Laguna Technopark.

PEGgel‐Based Multimodal Soft Sensors Capable of Decoupling Thermal and Mechanical Stimuli

AbstractInspired by human skin, soft sensors that are capable of sensing various external signals have been an active area of research. However, the vast majority of the developed soft sensors still lag far behind human skin in terms of multimodal sensing capability. Herein, this work reports on a multimodal soft sensor that can decouple thermal and mechanical signals. The sensor is made of poly(ethylene glycol) gel (PEGgel) containing ionic liquid, which is stretchable and transparent and shows decent strain and temperature sensing performances. Importantly, analysis of the ion relaxation dynamics shows that the charge relaxation time and capacitance change of the sensor are independently sensitive to the variations of temperature and mechanical deformation, respectively. By measuring the charge relaxation time and capacitance, the thermal and mechanical signals detected by the PEGgel sensor can be nicely decoupled without signal interference. Such a multimodal soft sensor may serve as a starting point for the development of lifelike soft sensors for high‐tech applications in a variety of fields such as soft robotics, the internet of things, and human–machine interaction.

A Mechanoluminescent ZnS:Cu/PDMS and Biocompatible Piezoelectric Silk Fibroin/PDMS Hybrid Sensor for Self‐Powered Sensing and Artificial Intelligence Control

AbstractMechanoluminescence (ML) is luminescence induced due to mechanical stress, providing intuitive responses to strain‐related events. Piezoelectricity is the conversion of mechanical strain into electrical signals, offering a quantitative measurement of force/deformation. Combining ML and piezoelectricity within a single device provides a comprehensive understanding of mechanical events, providing qualitative and quantitative information about strain‐related phenomena. A ZnS:Cu/polydimethylsiloxane (PDMS) composite and a biocompatible silk fibroin/PDMS composite are prepared to generate ML and electrical signals, respectively. An innovative method for obtaining powder from silk fabrics is employed. The microstructure and composition of silk fibroin powder are also examined via X‐ray diffraction and Fourier Transform Infrared (FTIR) spectroscopy. Mechanical stimuli such as pressure, stretching, twisting, bending, vibration, and rubbing are applied to the device to demonstrate optical and electrical responses. Under pressure, a voltage of 3.82 V and an output current of 201.6 nA are generated at a force of 1 N. Furthermore, a handwritten test is conducted to qualitatively visualize letters based on ML effects and explore the feasibility of using artificial intelligence to classify voltage signals generated during writing into their corresponding letters. This biocompatible, dual‐modal self‐powered sensor demonstrates broad applicability in wearable technology, biomechanics, human–machine interaction, security, and energy harvesting.

A Smart Manufacturing Process for Textile Industry Automation under Uncertainties

Most textile manufacturing companies in the world heavily rely on manual labor, particularly in the fabric inspection section, especially for cotton fabric. Establishing smart manufacturing systems like industrial automation in the textile industry for cotton fabric inspection is important for error-free inspection. The proposed make-to-order (MTO) inventory model focuses on the strategic development of a supply chain network under fuzzy uncertainty. The distinctiveness of this research lies in integrating a methodology that involves human and machine interaction, along with allocating resources to investment in smart manufacturing. This article presents a case study of the Jagatjit Cotton Textiles (JCT) manufacturing company in Punjab, India, as an example to validate the model and check the performance of SMT in the fabric inspection process in cotton TC mills. This paper contributes by developing four distinct textile supply chain models with industrial automation under triangular and trapezoidal fuzzy demand. A numerical analysis is conducted to verify the effectiveness of installing automated fabric inspection machines in the cotton plant. This article proposes an iterative solution algorithm (KDPMG) to obtain the global optimum for the proposed model. A comparative study of the proposed algorithm, KDPMG, and the genetic algorithm (GA) is presented in this study to verify the credibility of the obtained results. It is observed that KDPMG provides more appropriate solutions to the problem compared to the GA. Moreover, the computational time of KDPMG is significantly less than that of the GA. The rigorous analysis reveals that maximum profit can be achieved under trapezoidal fuzzy demand with fully automated fabric inspection technology. Using a triangular fuzzy demand pattern, the model with fully automated smart manufacturing achieves an 8.62% higher profit compared to a traditional system. Similarly, in the case of a trapezoidal fuzzy demand pattern, the adoption of automation in cotton plants can achieve an 8.69% higher profit. Hence, the implementation of smart manufacturing systems in the mending section of the cotton textile industry proves to be more profitable compared to the traditional inspection process.

Effect of Face Masks on Automatic Speech Recognition Accuracy for Mandarin

Automatic speech recognition (ASR) has been widely used to realize daily human–machine interactions. Face masks have become everyday wear in our post-pandemic life, and speech through masks may have impaired the ASR. This study explored the effects of different kinds of face masks (e.g., surgical mask, KN95 mask, and cloth mask) on the Mandarin word accuracy of two ASR systems with or without noises. A mouth simulator was used to play speech audio with or without wearing a mask. Acoustic signals were recorded at distances of 0.2 m and 0.6 m. Recordings were mixed with two noises at a signal-to-noise ratio of +3 dB: restaurant noise and speech-shaped noise. Results showed that masks did not affect ASR accuracy without noise. Under noises, masks did not significantly influence ASR accuracy at 0.2 m but had significant effects at 0.6 m. The activated-carbon mask had the most significant impact on ASR accuracy at 0.6 m, reducing the accuracy by 18.5 percentage points compared to that without a mask, whereas the cloth mask had the least effect on ASR accuracy at 0.6 m, reducing the accuracy by 0.9 percentage points. The acoustic attenuation of masks on the high-frequency band at around 3.15 kHz of the speech signal attributed to the effects of masks on ASR accuracy. When training ASR models, it may be important to consider mask robustness.

A Comparative Study on the Operational Efficiency of Joystick and Touch Screen Control Methods for Sugarcane Harvesters

Reducing cumbersome mechanical control components is one of the trends of modern agricultural machinery towards a high degree of automation. Whether the control components of sugarcane harvesters can gradually be replaced by central control screens, similar to car cabins, is an unresolved question. At the level of human machine interaction, this involves comparing the efficiency between joystick and touch screen control. This paper conducts a simulated experiment to compare and study the efficiency and user experience of joystick and touch screen control in adjusting the topper and base cutter heights of sugarcane harvesters, aiming to provide a reasonable basis for the design of control interfaces in sugarcane harvester cabins. The electromyographic signals, experiment duration, and subjective cognitive evaluations of participants in both the topper and base cutter groups were analyzed. The results showed that the efficiency, learnability, and ease of use of different control methods varied under different operational tasks. For the topper that corresponds to the operating behavior and height transformation in real time, joystick control demonstrated superior ease of use and operational efficiency compared to touch screen control, with weaker learnability. There was no discernible difference in muscle activation levels between the two control methods. Consequently, joystick control is deemed more suitable for the height adjustment of the topper. Regarding the base cutter with non-real-time mapping of operating behavior and height changes, no significant disparity in ease of use and learnability was observed between the two control methods. Touch screen control yielded lower muscle activation levels and garners higher overall subjective cognitive scale ratings. Thus, touch screen control is considered more suitable for base cutter height adjustment. Lastly, the paper proposes the optimal combination of software and hardware for control components in sugarcane harvester cabins, and provides an objective and multidimensional experimental analysis method for future research on similar human machine interaction interfaces.

Understanding Forearm Muscle Activity during Everyday Common Grasps: Insights for Rehabilitation, Prosthetic Control, and Human–Machine Interaction

The specific role of forearm muscles in the development of activities of daily living (ADL) remains unknown. Consequently, studying forearm muscle activity during the most commonly used grasps in ADL would yield valuable insights for hand function evaluation, rehabilitation, and advancements in prosthetic control. In this study, forearm muscle activity was analyzed in 22 healthy subjects, examining seven representative forearm areas during the performance of seven types of grasps at 50% of maximum effort. A Scheirer–Ray–Hare test revealed significant differences for grasp, spot, and their interaction (α &lt; 0.05), but not for repetition (and its interactions). Specific significant differences between grasps were found in specific spots by means of Bonferroni post hoc analyses, ensuring the possibility to discriminate between grasps, which is key to identifying the person’s intention to perform a particular grasp. The median values ranged from 4.4% to 32.8%, depending on the spot and grasp, with small 95% confidence intervals (0.5% to 5.5%). Cylindrical grasp requires the highest muscle activity among all spots, while lateral pinch demands the least. The findings elucidate the contribution, coordination, and function of each muscle in relation to each grasp, with implications for rehabilitation, prosthetics, and telerobotic and teleoperation systems.

Ultra-stretchable, high conductive, fatigue resistance, and self-healing strain sensor based on mussel-inspired adhesive hydrogel for human motion monitoring

Conductive hydrogels have been widely applied in various electronics, such as artificial skin, flexible devices, and implantable bioelectronics. However, it is still a challenge to develop high-performance hydrogel with high conductivity without compromising the toughness, stretchability, self-healing, and adhesion properties. Herein, inspired by the mussel-adhesion mechanism, an adhesive hydrogel that simultaneously achieves high conductivity (27.0 S·m−1), ultra stretchability (strain > 3700%), and strong toughness (1930 kJ·m−3) was developed by incorporating polydopamine (PDA)/sodium caseinate (SC) cross-linked network into the poly(acrylamide-co-acrylic acid) (P(AM-co-AA))/Al3+ network. Due to the abundant metal coordination and hydrogen bonds in the network, the obtained hydrogel displayed rapid self-healing capability (healing efficiency, HE > 97%) and excellent fatigue resistance. Moreover, the introduction of PDA-SC cross-linked network endowed the hydrogels with robust adhesion (36.5 kPa for pigskin) and high conductivity. The conductive hydrogel was assembled into a strain sensor, which demonstrated prominent sensing performance with a wide detection range (∼2500%), high sensitivity (gauge factor (GF) of 23.7), fast response time and reliable repeatability. The hydrogel strain sensor can be attached on human body to distinguish and monitor both large and subtle movement signals. The developed conductive hydrogel strain sensor is expected to have promising applications in wearable devices, human–machine interaction, and electrical skin, and to extend for use in other portable and wearable energy related devices with multifunction.

How to Keep Balance between Interaction and Automation? Toward User Overall Positive Experience of IoT-Based Smart Home Design

Advances in Internet of Things (IoT) technologies have had a profound impact on the experiential tasks of the smart home, particularly automated tasks supported by sensors, embedded systems, and communication devices that are capable of autonomously performing a range of tasks previously performed by humans. However, the improvement in the level of smart home automation has not necessarily promoted more meaningful, relaxing, and positive experiences for users. This is largely due to the fact that it often overlooks the meaningful involvement of humans in the process. This paper proposes a conceptual design model for a smart home through two studies that aim to maintain the benefits of automation while ensuring positive experiences with user engagement tasks. Study 1 involved qualitative data using semi-structured interviews to understand what tasks lead to positive user experiences (P-UX) during the use of the smart home and what kind of factors influence these positive experiences. Study 2, using an online questionnaire, quantitatively investigated the varying impact of these factors on positive experiences in both automation and human–machine interactions. The results of both studies show that there are nine factors that influence positive user experience in the smart home, with instrumentality, convenience, and flexibility playing an important role in the positive experience of automation, aesthetics, immersion, association, and memory, having a greater effect on the positive experience of user engagement in interactions, and customization and emotion contributing to both paradigms. In future smart home design, this model will help designers rationally allocate tasks between automation and human–machine interaction as a way to enhance the overall positive user experience.

Parameter study and identification of DEM modeling for varied sand moisture content based on bulldozing experiment

The discrete element method (DEM) has been widely used to simulate varied sand particles interacting with earthmoving machines. However, past research using DEM barely addressed accurate verification and validation for different sand moisture content. Therefore, the main purpose of this paper is to reveal the range of key parameters of an adhesive force model used in the DEM simulation corresponding to the specific sand moisture content. We considered the bulldozing phenomenon to be typical earthmoving work and performed the bulldozing experiments under different sand moisture levels to investigate the bulldozing force variations. Subsequently, the DEM simulation with an adhesive force model calculated the bulldozing force corresponding to the experimental results. The values for two adhesive parameters, i.e., a scaling magnitude and the maximum adhesive distance between particles, were adjusted such that the maximum bulldozing force calculated in the DEM coincides with that of the experiments under different moisture contents. The experimental verification of the DEM revealed the relationship curves between these two key parameters corresponding to the different moisture content. The relationship obtained in this paper implies that the DEM simulation carefully adjusting the adhesive force parameters can reproduce machine interaction on moist sand environments accurately.

Revolutionizing flexible Electronics: Integrating liquid metal DIW 3D printing by bimolecular interpenetrating network

Ultra-flexible liquid metal (LM) composites have significant potential in various applications, including soft robotics, wearable electronics, and human–machine interactions. This burgeoning field necessitates mass production at a critical scale alongside highly accurate and fully automated technology. In this study, a direct ink-writing (DIW) 3D all-printing strategy was developed, integrating cellulose nanofibrils (CNF), water-based polyurethane (WPU), and LM to fabricate high-performance LM-based electronic films, circuits, and diverse 2D or 3D structures. The stable ternary interface system was investigated, and bimolecular interpenetrating network system of CNF/WPU significantly enhanced the flexibility, reducing LM damage, mitigation, and leakage. Moreover, the systematic control of the DIW all-printing process facilitated high-resolution and excellent printability. The super-flexibility and precise electrical conductivity were demonstrated by investigating bending deformation and recyclable electrical signals. Electronics based on ultra-flexible LM, with a high LM content (78.0%), exhibited an accurate electrical response to bending deformation, extraordinary flexibility, and recyclable durability (up to 500 cycles). Notably, the all-printed system facilitates the complete automation, complex structuring, and precise moulding of various appliances. DIW 3D all-printing of LM has the potential to create complex conductive architectures for programmable and multi-material LM-based electronics, meeting high-precision, large-scale, and automated production requirements.

Safety evaluation and prediction of takeover performance in automated driving considering drivers’ cognitive load: A driving simulator study

Automated vehicles alleviate the need for driver attention and control. However, a takeover request (TOR) to the driver remains essential for emergencies and safety–critical scenarios beyond automation's capability. Thus, accessing drivers’ safety performance in various TOR scenarios is crucial for conditionally automated driving (SAE L3). However, TOR safety performance is seldom examined concerning drivers' cognitive load, despite its presumed relevance to TOR scenarios and human–machine interaction. Moreover, the adequacy of the time window preceding a TOR, critical for TOR safety prediction, remains inadequately explored. This study aims to assess safety performance across diverse TOR scenarios in Level 3 conditional automation, incorporating drivers’ cognitive load, and predict TOR safety by considering the time window's impact. A driving simulator experiment gathered eye movement and driving behavior data from 37 recruited participants. Participants were instructed to take control of the vehicle from automated driving within a time budget (TB) of 3 s or 7 s in obstacle avoidance (OA) or lane keeping (LK) scenarios while engaging in non-driving-related tasks (NDRTs). Participants’ subjective cognitive load in the different TOR scenarios was scaled using NASA-TLX. Furthermore, safe TOR performance was predicted utilizing Convolutional Neural Networks (CNNs) across different time window sizes preceding a TOR. The results indicate that: 1) cognitive loads in takeover scenarios ranked from highest to lowest as TB = 3s_OA, TB = 3s_LK, TB = 7s_OA, TB = 7s_LK; and the cognitive loads of the NDRTs ranked from highest to lowest as mistake finding, texting, chatting, monitoring; 2) the takeover safety performance in the four scenarios from lowest to highest was TB = 3s_OA, TB = 3s_LK, TB = 7s_OA, TB = 7s_LK; likewise, the takeover safety performance during the four NDRTs ranked from lowest to highest as mistake finding, monitoring, texting, chatting; 3) the time window size before the TORs significantly affected the prediction performance of the model. A 30-second window was recommended as optimal for predicting takeover safety using the CNN model, achieving an average F1 score of 0.8120 and 81.98 % accuracy. This study's findings enhance our comprehension of driving behavior characteristics during TOR and offer valuable insights for detecting driver states in conditionally automated driving contexts.

Capacitive BaTiO3-PDMS hand-gesture sensor: Insights into sensing mechanisms and signal classification with machine learning

Flexible sensors have gained extensive interest because of their versatile applications in healthcare, robotics, and wearable devices. This study introduces a capacitive sensor utilizing barium titanate oxide (BaTiO3)-polydimethylsiloxane (PDMS) for bending sensing and addresses crucial performance parameters including sensitivity, repeatability, response time, and durability. The sensor exhibited a notable capacitance change of 42.85% in conjunction with fast (1 s) responses and recovery times, and minimal hysteresis (<2%). Its reliable performance across varying bending rates and durability through extensive cyclic tests underscore its applicability in real-world scenarios. Importantly, the sensor's capabilities were enhanced by integrating machine learning (ML), achieving an impressive accuracy of 97.11% in recognizing hand-sign language gestures. Furthermore, finite element analysis was employed to validate the correlation between the increase in compression-induced packing density and capacitance enhancement. This holistic integration of advanced materials, computational simulations, and ML not only extends the boundaries of sensor technology but also holds promise for revolutionizing human–machine interactions, aiding speech-impaired individuals, and enriching virtual reality experiences. This study represents a pivotal advancement in the field of flexible sensors and the unlocking of new dimensions of their applications.

Distributed virtual simulation experimental software for high-power electric traction system of 600 km/h high-speed maglev train

The operational speed of high-speed maglev trains has reached a groundbreaking 600 km/h, facilitating faster intercity transportation. However, the design and verification of corresponding high-power electric traction systems face significant challenges. To address these issues, this study develops a distributed virtual simulation experimental software for the high-power electric traction system of a 600 km/h high-speed maglev train. The software first establishes virtual digital models for the high-speed maglev train, the high-speed maglev guideways, and the high-power electric traction converters to simulate the high-speed maglev line operation scenarios. Next, it constructs a client–server simulation software architecture, designs a human–machine interaction platform and an electric traction mathematical module, and devises an information exchange method to enable real-time tracking and display for the operating conditions of high-power electric traction systems. Lastly, by integrating electric traction control strategies, fault analysis and optimization design modules, the software effectively designs and verifies the optimal electric traction systems of high-speed maglev trains, and promptly identifies the potential faults in long stator linear synchronous motors and high-power electric traction converters within the electric traction systems. Engineering applications demonstrate that the software can significantly enhance the design and virtual simulation efficiency for the high-power electric traction systems of high-speed maglev trains, thus meeting critical engineering application needs.