Affective colormap design for accurate visual comprehension in industrial tomography

The design of colormaps can help tomography operators obtain accurate visual compre-hension, thereby assisting safety-critical decisions. The research presented here is about deploying colormaps that promote the best affective responses for industrial microwave tomography (MWT). To answer the two research questions related to our study, we firstly conducted a quantitative analysis of 11 frequently-used colormaps on a segmentation task. Secondly, we presented the same colormaps within a crowdsourced study comprising two parts to verify the quantitative outcomes. The first part encoded affective responses from participants into a prevailing four-quadrant valence–arousal grid; the second part recorded participant ratings towards the accuracy of each colormap on MWT segmentation. We concluded that three colormaps are the best suited in the context of MWT tasks. We also found that the colormaps triggering emotions in the positive–exciting quadrant can facilitate more accurate visual comprehension than other affect-related quadrants. A synthetic colormap design guideline was consequently proposed

The Magic of Vision: Understanding What Happens in the Process

How important is the human vision? Simply speaking, it is central for domain\ua0related users to understand a design, a framework, a process, or an application\ua0in terms of human-centered cognition. This thesis focuses on facilitating visual\ua0comprehension for users working with specific industrial processes characterized\ua0by tomography. The thesis illustrates work that was done during the past two\ua0years within three application areas: real-time condition monitoring, tomographic\ua0image segmentation, and affective colormap design, featuring four research papers\ua0of which three published and one under review.The first paper provides effective deep learning algorithms accompanied by\ua0comparative studies to support real-time condition monitoring for a specialized\ua0microwave drying process for porous foams being taken place in a confined chamber.\ua0The tools provided give its users a capability to gain visually-based insights\ua0and understanding for specific processes. We verify that our state-of-the-art\ua0deep learning techniques based on infrared (IR) images significantly benefit condition\ua0monitoring, providing an increase in fault finding accuracy over conventional\ua0methods. Nevertheless, we note that transfer learning and deep residual network\ua0techniques do not yield increased performance over normal convolutional neural\ua0networks in our case.After a drying process, there will be some outputted images which are reconstructed\ua0by sensor data, such as microwave tomography (MWT) sensor. Hence,\ua0how to make users visually judge the success of the process by referring to the\ua0outputted MWT images becomes the core task. The second paper proposes an\ua0automatic segmentation algorithm named MWTS-KM to visualize the desired low\ua0moisture areas of the foam used in the whole process on the MWT images, effectively\ua0enhance users\u27understanding of tomographic image data. We also prove its\ua0performance is superior to two other preeminent methods through a comparative\ua0study.To better boost human comprehension among the reconstructed MWT image,\ua0a colormap deisgn research based on the same segmentation task as in the second\ua0paper is fully elaborated in the third and the fourth papers. A quantitative\ua0evaluation implemented in the third paper shows that different colormaps can\ua0influence the task accuracy in MWT related analytics, and that schemes autumn,\ua0virids, and parula can provide the best performance. As the full extension of\ua0the third paper, the fourth paper introduces a systematic crowdsourced study,\ua0verifying our prior hypothesis that the colormaps triggering affect in the positiveexciting\ua0quadrant in the valence-arousal model are able to facilitate more precise\ua0visual comprehension in the context of MWT than the other three quadrants.\ua0Interestingly, we also discover the counter-finding that colormaps resulting in\ua0affect in the negative-calm quadrant are undesirable. A synthetic colormap design\ua0guideline is brought up to benefit domain related users.In the end, we re-emphasize the importance of making humans beneficial in every\ua0context. Also, we start walking down the future path of focusing on humancentered\ua0machine learning(HCML), which is an emerging subfield of computer\ua0science which combines theexpertise of data-driven ML with the domain knowledge\ua0of HCI. This novel interdisciplinary research field is being explored to support\ua0developing the real-time industrial decision-support system

Augmented Reality with Industrial Process Tomography: To Support Complex Data Analysis in 3D Space

Today, in-situ analyzing and monitoring are imperative for ensuring successful and healthy industrial processes in confined environments. With the rapid development of digitization, augmented reality (AR) has been utilized for letting people immersively interact with the necessary information. However, there are still knowledge gaps between AR technique and domain users pertaining to effective analysis of complex data. Hence, new solutions empowering domain users would benefit the whole industry. In this study, we report an initial prototype supporting complex data visualization and analysis in entire 3D surroundings within industrial process tomography (IPT). Microsoft HoloLens 2 is equipped for users to interact with the 3D information characterizing the workflow of the industrial process with high immersion. Our work distinctly improves the performance compared to existing solutions, pointing the way towards how AR should be deployed and developed more efficiently for aiding IPT systems

Supporting visualization analysis in industrial process tomography by using augmented reality—A case study of an industrial microwave drying system

Industrial process tomography (IPT) based process control is an advisable approach in industrial heating processes for improving system efficiency and quality. When using it, appropriate dataflow pipelines and visualizations are key for domain users to implement precise data acquisition and analysis. In this article, we propose a complete data processing and visualizing workflow regarding a specific case—microwave tomography (MWT) controlled industrial microwave drying system. Furthermore, we present the up-to-date augmented reality (AR) technique to support the corresponding data visualization and on-site analysis. As a pioneering study of using AR to benefit IPT systems, the proposed AR module provides straightforward and comprehensible visualizations pertaining to the process data to the related users. Inside the dataflow of the case, a time reversal imaging algorithm, a post-imaging segmentation, and a volumetric visualization module are included. For the time reversal algorithm, we exhaustively introduce each step for MWT image reconstruction and then present the simulated results. For the post-imaging segmentation, an automatic tomographic segmentation algorithm is utilized to reveal the significant information contained in the reconstructed images. For volumetric visualization, the 3D generated information is displayed. Finally, the proposed AR system is integrated with the on-going process data, including reconstructed, segmented, and volumetric images, which are used for facilitating interactive on-site data analysis for domain users. The central part of the AR system is implemented by a mobile app that is currently supported on iOS/Android platforms

Playing with Data: An Augmented Reality Approach to Interact with Visualizations of Industrial Process Tomography

Industrial process tomography (IPT) is a specialized imaging technique widely used in industrial scenarios for process supervision and control. Today, augmented/mixed reality (AR/MR) is increasingly being adopted in many industrial occasions, even though there is still an obvious gap when it comes to IPT. To bridge this gap, we propose the first systematic AR approach using optical see-through (OST) head mounted displays (HMDs) with comparative evaluation for domain users towards IPT visualization analysis. The proof-of-concept was demonstrated by a within-subject user study (n=20) with counterbalancing design. Both qualitative and quantitative measurements were investigated. The results showed that our AR approach outperformed conventional settings for IPT data visualization analysis in bringing higher understandability, reduced task completion time, lower error rates for domain tasks, increased usability with enhanced user experience, and a better recommendation level. We summarize the findings and suggest future research directions for benefiting IPT users with AR/MR

Monitoring and Visualization of Crystallization Processes Using Electrical Resistance Tomography: CaCO3 and Sucrose Crystallization Case Studies

In the current research work, electrical resistance tomography (ERT) was employed for monitoring and visualization of crystallization processes. A first-of-its-kind MATLAB-based interactive GUI application "ERT-Vis" is presented. Two case studies involving varied crystallization methods were undertaken. The experiments were designed and performed involving calcium carbonate reactive (precipitative) crystallization for the high conductivity solution-solute media, and the cooling crystallization of sucrose representing the lower conductivity solution-solute combination. The software successfully provided key insights regarding the process in both crystallization systems. It could detect and separate the solid concentration distributions in the low as well as high conductivity solutions using the visual analytics tools provided. The performance and utility of the software were studied using a software evaluation case study involving domain experts. Participant feedback indicated that ERT-Vis software helps by reconstructing images instantaneously, interactively visualizing, and evaluating the output of the crystallization process monitoring data

Affective Colormap Design for Accurate Visual Comprehension in Industrial Tomography

The design of colormaps can help tomography operators obtain accurate visual comprehension, thereby assisting safety-critical decisions. The research presented here is about deploying colormaps that promote the best affective responses for industrial microwave tomography (MWT). To answer the two research questions related to our study, we firstly conducted a quantitative analysis of 11 frequently-used colormaps on a segmentation task. Secondly, we presented the same colormaps within a crowdsourced study comprising two parts to verify the quantitative outcomes. The first part encoded affective responses from participants into a prevailing four-quadrant valence–arousal grid; the second part recorded participant ratings towards the accuracy of each colormap on MWT segmentation. We concluded that three colormaps are the best suited in the context of MWT tasks. We also found that the colormaps triggering emotions in the positive–exciting quadrant can facilitate more accurate visual comprehension than other affect-related quadrants. A synthetic colormap design guideline was consequently proposed