Optical/thermal analysis methodology for a space-qualifiable RTP furnace

A methodology to predict the coupled optical/thermal performance of a reflective cavity heating system was developed and a laboratory test to verify the method was carried out. The procedure was utilized to design a rapid thermal processing (RTP) furnace for the Robot-Operated Material Processing in Space (ROMPS) Program which is a planned STS HH-G canister experiment involving robotics and material processing in microgravity. The laboratory test employed a tungsten-halogen reflector/lamp to heat thin, p-type silicon wafers. Measurements instrumentation consisted of 5-mil Pt/Pt-Rh thermocouples and an optical pyrometer. The predicted results, utilizing an optical ray-tracing program and a lumped-capacitance thermal analyzer, showed good agreement with the measured data for temperatures exceeding 1300 C

Editorial

As a PhD student at the University of Leicester, I was the first editor-in-chief of FRONTIER for its launch in 2014 and I’m delighted to have been asked to write the editorial for this issue

Editorial

Welcome to FRONTIER, the new University of Leicester research magazine run by postgraduates for postgraduate

ForestMonkey: Toolkit for Reasoning with AI-based Defect Detection and Classification Models

Artificial intelligence (AI) reasoning and explainable AI (XAI) tasks have gained popularity recently, enabling users to explain the predictions or decision processes of AI models. This paper introduces Forest Monkey (FM), a toolkit designed to reason the outputs of any AI-based defect detection and/or classification model with data explainability. Implemented as a Python package, FM takes input in the form of dataset folder paths (including original images, ground truth labels, and predicted labels) and provides a set of charts and a text file to illustrate the reasoning results and suggest possible improvements. The FM toolkit consists of processes such as feature extraction from predictions to reasoning targets, feature extraction from images to defect characteristics, and a decision tree-based AI-Reasoner. Additionally, this paper investigates the time performance of the FM toolkit when applied to four AI models with different datasets. Lastly, a tutorial is provided to guide users in performing reasoning tasks using the FM toolkit.Comment: 6 pages, 5 figures, accepted in 2023 IEEE symposium series on computational intelligence (SSCI

The use of technology to build digital communities

In today’s digital world social media has the potential to strengthen and broaden communities. It can provide a bridge between on and off-line communication, helping to strengthen links between children, parents and practitioners as they engage in shared activities (Carter Olson, 2016). Online communities can serve as an audience for sharing children’s digital products and experiences with a wider audience, and, as Magos et.al. (2013) suggest, international interactions can build intercultural understandings. When technology is used to build links between children across the world it can act as both a window and a mirror for young children: a ‘window’ to help them understand each other’s cultures and a ‘mirror’ to help develop their own identity (Cox and Galda, 1990). This chapter addresses the following themes: • the challenges and benefits of using online communities to enhance children’s learning; • how practitioners might use digital media in their daily interactions with children and as part of their own professional networking activities; • the role of technology in establishing links between children’s homes and early years settings. The chapter offers examples of these themes in practice through a detailed case study of how The Leicestershire Early Years Writing Project used technology to build communities of practice among practitioners, parents and children

Efficient Retrieval of Images with Irregular Patterns using Morphological Image Analysis: Applications to Industrial and Healthcare datasets

Image retrieval is the process of searching and retrieving images from a database based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or medical images by extracting features from the images, such as deep features, colour-based features, shape-based features and local features. This has applications across a spectrum of industries, including fault inspection, disease diagnosis, and maintenance prediction. This paper proposes an image retrieval framework to search for images containing similar irregular patterns by extracting a set of morphological features (DefChars) from images; the datasets employed in this paper contain wind turbine blade images with defects, chest computerised tomography scans with COVID-19 infection, heatsink images with defects, and lake ice images. The proposed framework was evaluated with different feature extraction methods (DefChars, resized raw image, local binary pattern, and scale-invariant feature transforms) and distance metrics to determine the most efficient parameters in terms of retrieval performance across datasets. The retrieval results show that the proposed framework using the DefChars and the Manhattan distance metric achieves a mean average precision of 80% and a low standard deviation of 0.09 across classes of irregular patterns, outperforming alternative feature-metric combinations across all datasets. Furthermore, the low standard deviation between each class highlights DefChars' capability for a reliable image retrieval task, even in the presence of class imbalances or small-sized datasets.Comment: 35 pages, 5 figures, 19 tables (17 tables in appendix), submitted to Special Issue: Advances and Challenges in Multimodal Machine Learning 2nd Edition, Journal of Imaging, MDP

The Effects of Task Difficulty and Magnitude of Reward on Mental Defectives in Level of Aspiration Tests

The purpose of this study is to investigate the effects of task difficulty and magnitude of reinforcement upon the performance of mentally retarded institutionalized individuals in a level of aspiration situation

Morphological Image Analysis and Feature Extraction for Reasoning with AI-based Defect Detection and Classification Models

As the use of artificial intelligent (AI) models becomes more prevalent in industries such as engineering and manufacturing, it is essential that these models provide transparent reasoning behind their predictions. This paper proposes the AI-Reasoner, which extracts the morphological characteristics of defects (DefChars) from images and utilises decision trees to reason with the DefChar values. Thereafter, the AI-Reasoner exports visualisations (i.e. charts) and textual explanations to provide insights into outputs made by masked-based defect detection and classification models. It also provides effective mitigation strategies to enhance data pre-processing and overall model performance. The AI-Reasoner was tested on explaining the outputs of an IE Mask R-CNN model using a set of 366 images containing defects. The results demonstrated its effectiveness in explaining the IE Mask R-CNN model's predictions. Overall, the proposed AI-Reasoner provides a solution for improving the performance of AI models in industrial applications that require defect analysis.Comment: 8 pages, 3 figures, 5 tables; submitted to 2023 IEEE symposium series on computational intelligence (SSCI

Development and Testing of a Variable Conductance Thermal Acquisition, Transport, and Switching System

This paper describes the development and testing of a scalable thermal control architecture for instruments, subsystems, or systems that must operate in severe space environments with wide variations in sink temperature. The architecture is comprised by linking one or more hot-side variable conductance heat pipes (VCHPs) in series with one or more cold-side loop heat pipes (LHPs). The VCHPs provide wide area heat acquisition, limited distance thermal transport, modest against gravity pumping, concentrated LHP startup heating, and high switching ratio variable conductance operation. The LHPs provide localized heat acquisition, long distance thermal transport, significant against gravity pumping, and high switching ratio variable conductance operation. Combining two variable conductance devices in series ensures very high switching ratio isolation from severe environments like the Earth's moon, where each lunar day spans 15 Earth days (270 K sink, with a surface-shielded/space viewing radiator) and each lunar night spans 15 Earth days (80-100 K radiative sink, depending on location). The single VCHP-single LHP system described herein was developed to maintain thermal control of International Lunar Network (ILN) anchor node lander electronics, but it is also applicable to other variable heat rejection space missions in severe environments. The LHPVCHP system utilizes a stainless steel wire mesh wick ammonia VCHP, a Teflon wick propylene LHP, a pair of one-third square meter high radiators (one capillary-pumped horizontal radiator and a second gravity-fed vertical radiator), a half-meter of transport distance, and a wick-bearing co-located flow regulator (CLFR) to allow operation with a hot (deactivated) radiator. The VCHP was designed with a small reservoir formed by extending the length of its stainless steel heat pipe tubing. The system was able to provide end-to-end switching ratios of 300-500 during thermal vacuum testing at ATK, including 3-5 W/K ON conductance and 0.01 W/K OFF conductance. The test results described herein also include an in-depth analysis of VCHP condenser performance to explain VCHP switching operation in detail. Future multi-VCHP/multi-LHP thermal management system concepts that provide scalability to higher powers/longer transport lengths are also discussed in the paper