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Conventional image-based process control schemes require distinct, detectable features (e.g., edges or textures). In the absence of such features, current methods fail or require additional preprocessing. They are also prone to false detections from random noise, dirt, or scratches on the surface. In addition, they often focus on pixel intensity differences, making them less effective for subtle shifts or uniform surfaces. Analyzing speckles generated by coherent illumination (e.g., lasers) has been proven superior in situations where the surface has subtle or no features. Components with smooth surfaces (e.g., gears) lack distinct image features, making traditional methods ineffective. Laser speckle image provides unique dynamic and surface-sensitive insights that are not attainable with conventional imaging. The robustness of speckles to lighting and contamination makes speckle analysis even better suited for industrial environments. In addition, speckle patterns allow for fine-grained analysis of specific regions. This study introduces speckle pattern analysis for control charting and process control. The developed scheme is also capable of simultaneously detecting shifts that occur in different parts of the speckle pattern. It has two main steps. In the first step, we develop an EMWA control chart based on the maximum absolute Fourier magnitude spectrum differences of the current image (speckle pattern) and the reference speckle pattern. In the second step, which is performed when the chart signals, we use a p value analysis to identify the shifted areas of the image. Our scheme relies on dividing the image into equal-sized grids, analyzing them individually, and then aggregating the results. We use a gear's case for our numerical analyses and conduct simulation runs under different shift sizes and gridding scenarios. Finally, we illustrate how the developed scheme can be used for online process monitoring.

Speckle metrology is a powerful optical sensing tool for non-destructive testing (NDT) and advanced surface characterization, enabling ultra-precise measurements of surface deformations and displacements. These capabilities are critical for material analysis and surface assessment in sensing-driven applications. However, traditional correlation methods often struggle to balance resolution and robustness, particularly when simultaneously measuring both small- and large-scale deformations in noisy, high-frequency data environments. In this paper, we present an adaptive resolution approach for speckle displacement measurement that combines grid-based phase correlation with statistical refinement for enhanced accuracy and resolution.

Unlike traditional phase correlation techniques that rely on global correlation, our method introduces a flexible grid-based framework with localized correlation and dynamic overlap adjustments. To improve measurement performance, we developed an optimization technique that uses the median absolute deviation of residuals between reference and deformed images, enabling the algorithm to automatically adapt grid sizes based on local deformation characteristics. This allows it to handle both small- and large-scale deformations simultaneously and effectively. The approach resulted in a relative error reduction of up to 14 % compared to the best of the results obtained using a manually fixed grid size.

The proposed sensing methodology is validated through a series of numerical simulations and experimental studies, including controlled deformations with a micrometer translation stage and random speckle displacements on water-sprayed surfaces. Results demonstrate that our method can accurately detect both known and unknown deformations with high accuracy and precision, outperforming traditional techniques in terms of adaptability and robustness, particularly for surface deformation analysis.

In engineering education, where hands-on problem-solving and technical proficiency especially in physics-based learning are critical, the role of artificial intelligence (AI) tools like ChatGPT remains debated; whether AI serves as a breakthrough innovation or presents new challenges. This study seeks to bridge that gap by examining the impact of ChatGPT on mechanical engineering students in a project-based course. It explores how students used AI tools to understand key concepts, support group collaboration, and improve coding and writing tasks. Using survey data from first-year students encouraged to integrate AI into their coursework, the research provides insights into the ethical and educational implications of AI in engineering education, considering both its benefits and challenges. The findings indicate that while ChatGPT was widely utilized for coding tasks such as MATLAB programming and enhancing conceptual understanding, its impact on group collaboration was modest. Ethical concerns, including the temptation to misuse AI, highlight the need for structured guidelines to ensure responsible AI usage. The study also identifies the necessity of verifying AI-generated outputs, as AI tools may produce inaccurate or misleading information, particularly in technical problem-solving. This paper offers recommendations for optimizing AI-assisted learning, fostering critical thinking, and adapting assessment practices to balance AI's educational benefits with academic integrity. These insights aim to guide educators and policymakers in effectively integrating AI into engineering and physics education while addressing its challenges to create a productive learning environment.

This Letter introduces a novel, to the best of our knowledge, method for assessing fruit quality using white-light biospeckle displacement analysis. The primary aim is to demonstrate that speckle displacement behavior differs between healthy and decaying regions, offering a unique means of gauging bioactivity levels, a feature that has not been explored in previous biospeckle research. By examining speckle displacement vectors over time, significant differences were observed in the displacement patterns between healthy and decaying regions within the fruit tissue. Clustering techniques based on the size and direction of these vectors enabled sharp discrimination between these tissue states. The results indicate that analyzing biospeckle displacement behavior over the observation period provides valuable insights into dynamic processes within the fruit, facilitating a non-invasive evaluation of its quality.

This paper proposes a computationally light adaptivefiltering approach, normalized least mean squares (NLMS), to model the nonlinearity caused by the current transformer (CT) iron core saturation. A simplified CT model was used to generate adataset considering four different nonlinear iron core magnetic characteristics. The preliminary results show satisfactory results in the cases where the CT iron core nonlinearity is within certain limits.

The paper investigates the application of machine learning (ML) for voltage sag source localization (VSSL) in electrical power systems. To overcome feature-selection challenges for traditional ML methods and provide more meaningful sequential features for deep learning methods, the paper proposes three time-sample-based feature forms, and evaluates an existing feature form. The effectiveness of these feature forms is assessed using k-means clustering with k = 2 referred to as downstream and upstream classes, according to the direction of voltage sag origins. Through extensive voltage sag simulations, including noises in a regional electrical power network, k-means identifies a sequence involving the multiplication of positive-sequence current magnitude with the sine of its angle as the most prominent feature form. The study develops further traditional ML methods such as decision trees (DT), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), an ensemble learning (EL), and a designed one-dimensional convolutional neural network (1D-CNN). The results found that the combination of 1D-CNN or SVM with the most prominent feature achieved the highest accuracies of 99.37% and 99.13%, respectively, with acceptable/fast prediction times, enhancing VSSL. The exceptional performance of the CNN was also approved by field measurements in a real power network. However, selecting the best ML methods for deployment requires a trade-off between accuracy and real-time implementation requirements. The research findings benefit network operators, large factory owners, and renewable energy park producers. They enable preventive maintenance, reduce equipment downtime/damage in industry and electrical power systems, mitigate financial losses, and facilitate the assignment of power-quality penalties to responsible parties.

This paper addresses the lack of consideration of short time scales, below one hour, such as sub-15-min and sub-1-hr, in grid codes for frequency quality analysis. These time scales are becoming increasingly important due to the flexible market-based operation of power systems as well as the rising penetration of renewable energy sources and battery energy storage systems. For this, firstly, a set of frequency-quality indices is considered, complementing established statistical indices commonly used in power-quality standards. These indices provide valuable insights for quantifying variations, events, fluctuations, and outliers specific to the discussed time scales. Among all the implemented indices, the proposed indices are based on over/under frequency events (6 indices), fast frequency rise/drop events (6 indices), and summation of positive and negative peaks (1 index), of which the 5 with the lowest thresholds are identified as the most dominant. Secondly, k-means and k-medoids clustering methods in a learning scheme are employed to identify typical patterns within the discussed time windows, in which the number of clusters is determined based on prior knowledge linked to reality. In order to clarify the frequency variations and patterns, three frequency case studies are analyzed: case 1 (sub-15-min scale, 10-s values, 6 months), case 2 (sub-1-hr scale, 10-s values, 6 months), and case 3 (sub-1-hr, 3-min values, the year 2021). Results obtained from the indices and learning methods demonstrate a full picture of the information within the windows. The maximum value of the highest frequency value minus the lowest one over the windows is about 0.35 Hz for cases 1 and 2 and 0.25 Hz for case 3. Over-frequency values (with a typical 0.1% threshold) slightly dominates under-frequency values in cases 1 and 2, while the opposite is observed in case 3. Medium fluctuations occur in 35% of windows for cases 1 and 2 and 41% for case 3. Outlier values are detected using the quartile method in 70% of windows for case 2, surpassing the other two cases. About six or seven typical patterns are also extracted using the presented learning scheme, revealing the frequency trends within the short time windows. The proposed approaches offer a simpler alternative than tracking frequency single values and also capture more comprehensive information than existing approaches that analyze the aggregated frequency values at the end of the specific time windows without considering the frequency trends. In this way, the network operators have the possibility to monitor the frequency quality and trends within short time scales using the most dominant indices and typical patterns.

Understanding the influence of teaching culture (tradition) within academic departments is crucial for new teachers navigating the complex landscape of higher education. This paper investigates the possible impact of the department’s teaching culture on the pedagogical approaches of new teachers, forming their teaching style, concentrating on insights gathered from interviews with experienced colleagues in a Swedish university department with a focus on engineering education. By exploring the department’s teaching traditions and identifying potential challenges faced by new teachers, this study offers valuable insights into enhancing teaching styles and fostering student engagement. Drawing upon both experiential knowledge and insights from pedagogic literature and courses, the authors provide practical strategies to overcome obstacles and promote operative teaching practices. Ultimately, the outcomes of this study aim to empower new teachers to create enriching learning environments that promote student motivation, engagement, and overall academic success, aligning with the findings of existing literature on pedagogy and student learning outcomes.