Internal Thread Defect Generation Algorithm and Detection System Based on Generative Adversarial Networks and You Only Look Once.

In the field of industrial inspection, accurate detection of thread quality is crucial for ensuring mechanical performance. Existing machine-vision-based methods for internal thread defect detection often face challenges in efficient detection and sufficient model training samples due to the influence of mechanical geometric features. This paper introduces a novel image acquisition structure, proposes a data augmentation algorithm based on Generative Adversarial Networks (GANs) to effectively construct high-quality training sets, and employs a YOLO algorithm to achieve internal thread defect detection. Through multi-metric evaluation and comparison with external threads, high-similarity internal thread image generation is achieved. The detection accuracy for internal and external threads reached 94.27% and 93.92%, respectively, effectively detecting internal thread defects.

How the Degree of Anthropomorphism of Human-like Robots Affects Users' Perceptual and Emotional Processing: Evidence from an EEG Study.

Anthropomorphized robots are increasingly integrated into human social life, playing vital roles across various fields. This study aimed to elucidate the neural dynamics underlying users' perceptual and emotional responses to robots with varying levels of anthropomorphism. We investigated event-related potentials (ERPs) and event-related spectral perturbations (ERSPs) elicited while participants viewed, perceived, and rated the affection of robots with low (L-AR), medium (M-AR), and high (H-AR) levels of anthropomorphism. EEG data were recorded from 42 participants. Results revealed that H-AR induced a more negative N1 and increased frontal theta power, but decreased P2 in early time windows. Conversely, M-AR and L-AR elicited larger P2 compared to H-AR. In later time windows, M-AR generated greater late positive potential (LPP) and enhanced parietal-occipital theta oscillations than H-AR and L-AR. These findings suggest distinct neural processing phases: early feature detection and selective attention allocation, followed by later affective appraisal. Early detection of facial form and animacy, with P2 reflecting higher-order visual processing, appeared to correlate with anthropomorphism levels. This research advances the understanding of emotional processing in anthropomorphic robot design and provides valuable insights for robot designers and manufacturers regarding emotional and feature design, evaluation, and promotion of anthropomorphic robots.

An Investigation into the Effects of Correlated Color Temperature and Illuminance of Urban Motor Vehicle Road Lighting on Driver Alertness.

Current international optical science research focuses on the non-visual effects of lighting on human cognition, mood, and biological rhythms to enhance overall well-being. Nocturnal roadway lighting, in particular, has a substantial impact on drivers' physiological and psychological states, influencing behavior and safety. This study investigates the non-visual effects of correlated color temperature (CCT: 3000K vs. 4000K vs. 5000K) and illuminance levels (20 lx vs. 30 lx) of urban motor vehicle road lighting on driver alertness during various driving tasks. Conducted between 19:00 and 20:30, the experiments utilized a human-vehicle-light simulation platform. EEG (ß waves), reaction time, and subjective evaluations using the Karolinska Sleepiness Scale (KSS) were measured. The results indicated that the interaction between CCT and illuminance, as well as between CCT and task type, significantly influenced driver alertness. However, no significant effect of CCT and illuminance on reaction time was observed. The findings suggest that higher illuminance (30 lx) combined with medium CCT (4000K) effectively reduces reaction time. This investigation enriches related research, provides valuable reference for future studies, and enhances understanding of the mechanisms of lighting's influence on driver alertness. Moreover, the findings have significant implications for optimizing the design of urban road lighting.

Data modeling analysis of GFRP tubular filled concrete column based on small sample deep meta learning method.

The meta-learning method proposed in this paper addresses the issue of small-sample regression in the application of engineering data analysis, which is a highly promising direction for research. By integrating traditional regression models with optimization-based data augmentation from meta-learning, the proposed deep neural network demonstrates excellent performance in optimizing glass fiber reinforced plastic (GFRP) for wrapping concrete short columns. When compared with traditional regression models, such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Radial Basis Function Neural Networks (RBFNN), the meta-learning method proposed here performs better in modeling small data samples. The success of this approach illustrates the potential of deep learning in dealing with limited amounts of data, offering new opportunities in the field of material data analysis.

Internal thread defect detection system based on multi-vision.

In the realm of industrial inspection, the precise assessment of internal thread quality is crucial for ensuring mechanical integrity and safety. However, challenges such as limited internal space, inadequate lighting, and complex geometry significantly hinder high-precision inspection. In this study, we propose an innovative automated internal thread detection scheme based on machine vision, aimed at addressing the time-consuming and inefficient issues of traditional manual inspection methods. Compared with other existing technologies, this research significantly improves the speed of internal thread image acquisition through the optimization of lighting and image capturing devices. To effectively tackle the challenge of image stitching for complex thread textures, an internal thread image stitching technique based on a cylindrical model is proposed, generating a full-view thread image. The use of the YOLOv8 model for precise defect localization in threads enhances the accuracy and efficiency of detection. This system provides an efficient and intuitive artificial intelligence solution for detecting surface defects on geometric bodies in confined spaces.

Selecting the Appropriate Speed for Rotational Elements in Human-Machine Interfaces: A Quantitative Study.

The motion of rotation, which served as a dynamic symbol within human-computer interfaces, has garnered extensive attention in interface and graphic design. This study aimed to establish speed benchmarks for interface design by exploring visual system preferences for the perception of both simple and complex rotating icons within the velocity range of 5-1800 degrees per second. The research conducted two experiments with 12 participants to examine the observers' just noticeable difference in speed (JNDS) and perceived speed for rotational icons. Experiment one measured the JNDS over eight-speed levels using a constant stimulus method, achieving a range of 14.9-29%. Building on this, experiment two proposed a sequence of speeds within the given range and used a rating scale method to assess observers ' subjective perception of the speed series' rapidity. The findings indicated that speed increases impacted the ability to differentiate between speeds; key points for categorizing low, medium, and high speeds were identified at 10, 180, and 720 degrees/s, respectively. Shape complexity was found to modulate the visual system's perception of actual speed, such that at rotation speeds above 180 degrees/s, complex icons appeared to rotate faster than simpler ones. Most importantly, the study applied quantitative methods and metrology to interface design, offering a more scientific approach to the design workflow.

Study on design optimization of GFRP tubular column composite structure based on machine learning method.

Circular reinforced concrete wound glass fiber reinforced polymer (GFRP) columns and reinforced concrete filled GFRP columns are extensively utilized in civil engineering practice. Various factors influence the performance of these two types of GFRP columns, thereby impacting the whole project. Therefore, it is highly significant to establish the prediction models for ultimate displacement and ultimate bearing capacity to optimize the design of the two types of GFRP columns. In this study, based on the experiments conducted under different conditions on the two kinds of GFRP columns, automatic machine learning along with four other commonly used machine learning methods were employed for modeling to analyze how the column parameters (cross section shape, concrete strength, height of GFRP column, wound GFRP wall thickness, inner diameter of wound GFRP column) affect their performance. The differences in performance among these five machine learning methods were analyzed after modeling. Subsequently, we obtained the variation patterns in ultimate displacement and ultimate bearing capacity of the columns influenced by each parameter by testing the data using the optimal model. Based on these findings, the optimal design schemes for the two types of GFRP columns are proposed. The contribution of this paper is three-fold. First, AutoML sheds light on the automatic prediction of ultimate displacement and ultimate bearing capacity of GFRP column. Second, in this paper, two optimal design schemes of GFRP columns are proposed. Third, for AEC industrial practitioners, the whole process is automatic, accurate and less reliant on data expertise and the optimization design scheme proposed in the article is relatively scientific.

Image Entropy-Based Interface Evaluation Method for Nuclear Power Plants.

The digital interface is crucial for nuclear plant operators, influencing their decision-making significantly. However, evaluations of these interfaces often overlook users' decision-making performance; lack established standards, typically occurring after the design phase; and are unsuitable for large-scale assessments. Recognizing the vital role of interface information, this paper built on our previous research and proposed a method tailored for nuclear power plant interfaces, utilizing image entropy to evaluate the impact of information on decision-making. A comparative analysis with an experimental evaluation method empirically validated the effectiveness of the proposed method. This research offers a unique decision-making-centric method to interface evaluation, providing a standardized, adaptable framework for various design phases and enabling extensive and rapid evaluations.

Research on Guidance Methods of Digital Twin Robotic Arms Based on User Interaction Experience Quantification.

The evolution of the manufacturing sector coupled with advancements in digital twin technology has precipitated the extensive integration of digital twin robotic arms within the industrial domain. Notwithstanding this trend, there exists a paucity of studies examining the interaction of these robotic arms in virtual reality (VR) contexts from the user's standpoint. This paper delves into the virtual interaction of digital twin robotic arms by concentrating on effective guidance methodologies for the input of their target motion trajectories. Such a focus is pivotal to optimize input precision and efficiency, thus contributing to research on the virtual interaction interfaces of these robotic arms. During empirical evaluations, metrics related to human-machine interaction, such as objective operational efficiency, precision, and subjective workload, were meticulously quantified. Moreover, the influence of disparate guidance methods on the interaction experience of digital twin robotic arms and their corresponding scenarios was investigated. Consequent findings offer pivotal insights regarding the efficacy of these guidance methods across various scenarios, thereby serving as an invaluable guide for future endeavors aiming to bolster interactive experiences in devices akin to digital twin robotic arms.

Classification of User Emotional Experiences on B2C Websites Utilizing Infrared Thermal Imaging.

The acquisition of physiological signals for analyzing emotional experiences has been intrusive, and potentially yields inaccurate results. This study employed infrared thermal images (IRTIs), a noninvasive technique, to classify user emotional experiences while interacting with business-to-consumer (B2C) websites. By manipulating the usability and aesthetics of B2C websites, the facial thermal images of 24 participants were captured as they engaged with the different websites. Machine learning techniques were leveraged to classify their emotional experiences, with participants' self-assessments serving as the ground truth. The findings revealed significant fluctuations in emotional valence, while the participants' arousal levels remained consistent, enabling the categorization of emotional experiences into positive and negative states. The support vector machine (SVM) model performed well in distinguishing between baseline and emotional experiences. Furthermore, this study identified key regions of interest (ROIs) and effective classification features in machine learning. These findings not only established a significant connection between user emotional experiences and IRTIs but also broadened the research perspective on the utility of IRTIs in the field of emotion analysis.

Influence of nuclear power plant interface complexity on user decision-making: an ERP study.

ABSTRACUser decision-making concerning critical operations is very important to nuclear power plant (NPP) safety. The NPP interface is the main information source that guides decision-making; thus, a good interface design is essential. Among the interface design factors such as interface complexity, layout and colour, interface complexity (the amount of information in the interface) has the greatest impact on NPP operator decision-making. This paper used the event-related potential (ERP) to evaluate the impact of interface complexity on user decision-making and found interface complexity has a specific range suitable for decision-making. Based on this important finding, a fast and economical method of evaluating NPP interfaces in all design phases was proposed. This method compensates for the shortcomings of traditional methods, such as heuristic evaluation and experimental evaluation, which are inconvenient for evaluating interfaces in initial design phase; it can also be applied to interfaces with similar features in other industrial fields. Practitioner summary: Evaluation of the impact of NPP interface complexity on user decision-making through an ERP experiment revealed a specific range of interface complexity that facilitates user decision-making. Based on this finding, a new, fast and inexpensive interface evaluation method was proposed. Abbreviations: NPP: nuclear power plant, it is a thermal power station in which the heat source is a nuclear reactor; ERP: event-related potential, it is the measured brain response that is the direct result of a specific cognitive, or motor event.

ERP Study of Mine Management System Warning Interface under Fatigue.

Due to the large volume of monitoring data in mines, concentrating on and reviewing the data for a long period of time will easily cause fatigue. To study the influence of different visual codes of early-warning interfaces on the response of individuals who are fatigued, the changes in the subjective fatigue and corresponding frequency waves are compared before and after a fatigue-inducing task, as well as using event-related potential to study the behavioral data and EEG signals of subjects who participated in an oddball task on an early-warning interface. The results showed that all 14 subjects became fatigued after the fatigue-inducing task, and the amplitude of P200 when text is used in a fatigued state was the largest, with the longest latency. The subjects showed a slower reaction time and a reduced accuracy rate, thus indicating that in designing a warning interface, when text rather than color is used as a visual code, the operating load will be larger, mental load is increased, and attention resources are consumed. The experimental results provide the basis for the design and evaluation of early-warning interfaces of mine management systems.

Enhancing User Experience of Eye-Controlled Systems: Design Recommendations on the Optimal Size, Distance and Shape of Interactive Components from the Perspective of Peripheral Vision.

For special populations with motor impairments, eye-controlled interaction may be the only way for them to communicate with the outside world. Because of the dominance of vision in the motor mechanism, eye-controlled interaction has high usability and important research value. During eye-controlled interaction, the visual channel needs to receive information from the graphical user interface (GUI) and transmit the user's eye-controlled instructions, which overburdens the visual channel and reduces the efficiency of eye-controlled interaction. This study presents an ergonomic experiment to study how to design interactive GUI components in an eye-controlled user interface. The experiments were conducted based on the shape, size, and distance (from the object to the center of the screen) of the visual interactive components. The experiment comprised three parts: (1) the pre-experiment determined the evaluation index and selected the icon material; (2) the formal experiment was a three-factor within-subjects experiment, which included a search task using participants' peripheral vision; and (3) after the experiment, subjective evaluations were conducted using a questionnaire. The results showed that the shape, size, and distance of the interactive object significantly affected the reaction time, and the size factor significantly affected the movement time of the eye-controlled interaction. Finally, combined with the results of the subjective evaluation, we concluded that the recommended sizes of the interactive components were 2.889°, 3.389°, and 3.889°, and the recommended distances were 5.966° and 8.609°. Additionally, designers should utilize components with simple concrete shapes as much as possible to improve user recognition efficiency. Our study provides enlightening recommendations on how to design components in eye-controlled interactive interfaces, and has great guiding significance for building design standards of the eye-controlled systems.

Research on Discrete Semantics in Continuous Hand Joint Movement Based on Perception and Expression.

Continuous movements of the hand contain discrete expressions of meaning, forming a variety of semantic gestures. For example, it is generally considered that the bending of the finger includes three semantic states of bending, half bending, and straightening. However, there is still no research on the number of semantic states that can be conveyed by each movement primitive of the hand, especially the interval of each semantic state and the representative movement angle. To clarify these issues, we conducted experiments of perception and expression. Experiments 1 and 2 focused on perceivable semantic levels and boundaries of different motion primitive units from the perspective of visual semantic perception. Experiment 3 verified and optimized the segmentation results obtained above and further determined the typical motion values of each semantic state. Furthermore, in Experiment 4, the empirical application of the above semantic state segmentation was illustrated by using Leap Motion as an example. We ended up with the discrete gesture semantic expression space both in the real world and Leap Motion Digital World, containing the clearly defined number of semantic states of each hand motion primitive unit and boundaries and typical motion angle values of each state. Construction of this quantitative semantic expression will play a role in guiding and advancing research in the fields of gesture coding, gesture recognition, and gesture design.

Which is better for concept visualization? Shape or spirit similarity: Evidence from event-related potentials.

Shape and spirit similarity are two kinds of common artistic modes in concept visualization. The adoption depends on the designers' subjective preference and judgment, which may cause potential risks for semantic communication. This article used pairs of real image-concrete word as the roots, and contrasted four kinds of multimodal mappings such as shape similarity-concrete concept, shape similarity-abstract concept, spirit similarity-concrete concept, and spirit similarity-abstract concept to compare the matching difference through the S1(picture)-S2(word) paradigm. The behavioral results showed that shape similarity had advantages in both matching rate and reaction time over spirit similarity, but the difference was more significant to the concrete word than to the abstract word. The ERPs showed that the N1, P2, and N400 components had alike effects with the behavioral results, but the mappings of spirit similarity-concrete concept elicited the largest positivity of P600, suggesting the complicated mechanisms of semantic integration and concreteness effect in the multimodal mappings. This study proves that the concrete concept should be visualized according to its appearance, not the most striking feature or function; but the visulization of abstract concept shows less difference after a concreteness transition.

Improving Eye-Computer Interaction Interface Design: Ergonomic Investigations of the Optimum Target Size and Gaze-triggering Dwell Time.

Interactive feedback of interface elements and low level of spatial accuracy are two main key points for the interaction research in the Eye-computer interaction system. This study tried to solve these two problems from the perspective of human-computer interactions and ergonomics. Two experiments were conducted to explore the optimum target size and gaze-triggering dwell time of the eye-computer interaction (ECI) system. Experimental Series 1 was used as the pre-experiment to identify the size that has a greater task completion rate. Experimental Series 2 was used as the main experiment to investigate the optimum gaze-triggering dwell time by using a comprehensive evaluation of the task completion rate, reaction time, and NASA-TLX (Task Load Index). In Experimental Series 1, the optimal element size was determined to be 256 × 256p x 2. The conclusion of Experimental Series 2 was that when the dwell time is set to 600 ms, the efficiency of the interface is the highest, and the task load of subjects is minimal as well. Finally, the results of Experiment Series 1 and 2 have positive effects on improving the usability of the interface. The optimal control size and the optimal dwell time obtained from the experiments have certain reference and application value for interface design and software development of the ECI system.

Effects of Users' Familiarity With the Objects Depicted in Icons on the Cognitive Performance of Icon Identification.

This study investigated the effects of users' familiarity with the objects depicted in icons on the cognitive performance of icon identification. First, without knowing the specific semantic information of icons, 20 participants were required to search for target icons among visually similar distractors for 3-hour-long training sessions across 1 week, during which their familiarity with different icons was manipulated by differential exposure frequencies. Half of the icons were presented 10 times more often than the other half. Subsequently, participants' abilities to recall corresponding semantic information when cued with associated target icons were tested after they had learned all the icons. The results showed that, in both the visual search task and the semantic information recall task, participants performed significantly better when the icons were more familiar. Importantly, the effects of icon complexity in the visual search task diminished as participants became familiar with the icons, and the beneficial effects of familiarity in the semantic information recall task were larger when the icons were complex. These findings have practical implications for icon design. When creating new icons for time critical user interfaces, icons should be kept as simple as possible and employ familiar, commonly used, graphics.