ABSTRACT
Human face related digital technologies have been widely applied in various fields including face recognition based biometrics, facial landmarks based face deformation for gaming, facial reconstruction for those who are disfigured from an accident in the medical field and others. Such technologies typically rely on the information of a full, uncovered face and their performance would suffer varying degrees of deterioration according to the level of facial occlusion exhibited. 2D face recovery from occluded faces has therefore become an important research area as it is both crucial and desirable to attain full facial information before it is used in downstream tasks. In this paper, we address the problem of 2D face recovery from facial-mask occlusions, a pertinent issue that is widely observed in situations such as the Covid-19 pandemic. In recent trends, most researches are carried out through deep learning techniques to recover masked faces. The whole process consists of two tasks which are image segmentation and image inpainting. As U-Net is a typical deep learning model for image segmentation, but it also helpful in image inpainting and image colorization, so it has been frequently used in solving face recovery problems. To further explore the capability of U-Net and its variants for face recovery from masked faces, we propose to conduct a comparative study on several U-Net based models on a synthetic dataset that was generated based on public face datasets and mask generator. Results showed that Resnet U-Net and VGG16 U-Net had performed better in face recovery among the six different U-Net based models. © 2022 IEEE.
ABSTRACT
With the advent of industrial revolution (IR) 4.0, the process of digitalization is occurring rapidly in all sectors of industry and business. For instance, companies are making their operations smarter by adopting digital technologies such as big data analytics, industrial internet of things, etc. to gain valuable insights from their existing business processes. IR 4.0 technologies serve as an enabler to empower the next wave of business growth by providing efficient delivery of products and services in a timely and cost-effective manner. Such a scenario has resulted in the rising demand of workforce that are not only field competent but also possess the essential digital skills in order to cope with the fast-changing needs of the industry. From the educational perspective, the aforementioned phenomenon has led to the pressing need to nurture digital competency for both educators and students, which is further accelerated due to the global pandemic of coronavirus disease. In this chapter, we wish to highlight the importance of digital competency in ensuring a sustainable, future-proof technical education in coping with the needs of future workplace. In particular, we wish to focus on how professional digital competency can be developed through the adoption of open educational resources (OER) and open-source software (OSS). The status quo of the digitalization shift in both industry and academia will be discussed in the introduction section, followed by a critical discussion on the recent progress pertaining digital competency, OER, and OSS. By proposing a framework and suggestions on how to bridge the gap among digital competency, OER and OSS shall be presented with recommendations for future works. © 2022 selection and editorial matter, Muhammad Mujtaba Asad, Fahad Sherwani, Razali Bin Hassan, and Prathamesh Churi;individual chapters, the contributors.
ABSTRACT
In the past year, the global COVID-19 pandemic has caused an unprecedented impact on our lives. In manufacturing, strict lock-down due to the pandemic have also disrupted the business operations of manufacturing companies. From literature, there are limited studies that explore the impact of the pandemic towards manufacturing related companies in various aspects. This study attempts to answer what constitute the main challenge for these companies and the associated response strategies to overcome the challenge. A survey was conducted at a manufacturing trade exhibition that involved in-depth interviews with representatives from 22 manufacturing related companies. The outcomes of this study reveal that declining sales and marketing activities, difficulties in performing business meeting and lock-down measures are the top three challenges. Main response strategies for these companies are the adoption of ICT tools for virtual business processes, with proactive business change and diversification measures such as automation. The results of this study indicate the importance of automation and digitalization towards resilience and adaptability of manufacturing related companies against the impacts of the pandemic. © 2021 IEEE.
ABSTRACT
This article presents a software-implemented 3-dimensional simulated analysis of a 4-tire test room and the 6-tire test room. The results of the average performance through the simulated analysis of 100 iterations were obtained. The simulation showed the temperature distribution in the test rooms. This objective of this study was to assess the efficiency of the start-up process in each test room and to find the most efficient setup. A promising improvement would be to install the heaters at the bottom of the room under the drums instead of at the ceiling. The results of the simulation will be compared to the data of temperature logging of the tire test rooms once there is availability upon the lifting of the Covid pandemic lockdown restrictions. © 2021 Institute of Physics Publishing. All rights reserved.
ABSTRACT
Study objective: We determine the percentage of diagnosed and undiagnosed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among a sample of US emergency department (ED) health care personnel before July 2020. Methods: This was a cross-sectional analysis of ED health care personnel in 20 geographically diverse university-affiliated EDs from May 13, to July 8, 2020, including case counts of prior laboratory-confirmed coronavirus disease 2019 (COVID-19) diagnoses among all ED health care personnel, and then point-in -time serology (with confirmatory testing) and reverse transcriptase-polymerase chain reaction testing in a sample of volunteers without a previous COVID-19 diagnosis. Health care staff were categorized as clinical (physicians, advanced practice providers, and nurses) and nonclinical (clerks, social workers, and case managers). Previously undiagnosed infection was based on positive SARS-CoV-2 serology or reverse transcriptase-polymerase chain reaction result among health care personnel without prior diagnosis. Results: Diagnosed COVID-19 occurred in 2.8% of health care personnel (193/6,788), and the prevalence was similar for nonclinical and clinical staff (3.8% versus 2.7%;odds ratio 1.5;95% confidence interval 0.7 to 3.2). Among 1,606 health care personnel without previously diagnosed COVID-19, 29 (1.8%) had evidence of current or past SARS-CoV-2 infection. Most (62%;18/29) who were seropositive did not think they had been infected, 76% (19/25) recalled COVID-19-compatible symptoms, and 89% (17/19) continued to work while symptomatic. Accounting for both diagnosed and undiagnosed infections, 4.6% (95% confidence interval 2.8% to 7.5%) of ED health care personnel were estimated to have been infected with SARS-CoV-2, with 38% of those infections undiagnosed. Conclusion: In late spring and early summer 2020, the estimated prevalence of severe acute respiratory syndrome coronavirus 2 infection was 4.6%, and greater than one third of infections were undiagnosed. Undiagnosed SARS-CoV-2 infection may pose substantial risk for transmission to other staff and patients.