Automation technologies and their impact on employment: A review, synthesis and future research agenda

Filippi, Emilia; Bannò, Mariasole; Trento, Sandro

doi:10.1016/j.techfore.2023.122448

Cited by 33 publications

(20 citation statements)

References 70 publications

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“…Coffman et al (2021) found evidence of discriminatory behaviour in their research, as employers show a discernible bias, favouring equally competent male candidates over their female counterparts when making hiring decisions. In contrast, Filippi et al (2023) studied the potential risk of automation technologies replacing human workers in Europe. Their findings show that the gender gap in the risk of substitution favours women, meaning that women are slightly less likely to be replaced by automation compared to men.…”

Section: E Discriminatory Behaviormentioning

confidence: 99%

Exploring the Multifaceted Challenges of Women in Engineering: A Comprehensive Literature Review

Dabić,

Posinković,

Maley

et al. 2024

IEEE Trans. Eng. Manage.

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The present research delves into the complex dynamics of gender equality, highlighting women's experiences within the engineering sector. Drawing from a literature spanning from 2005 to 2023, we gathered insights from 108 pertinent articles on the topic. Our results show that a substantial portion of research underscore the persistent biases and barriers women encounter in engineering. Through our analysis, we unveiled four dominant themes: 'The Impact of Sex Differences on Productivity', 'Gender Digital Divide', 'Discriminatory Behaviour', and 'Women and Performance'. Applying the glass ceiling theory as analytical framework, we discern a prevailing neglect towards women's challenges in the engineering field. Our findings accentuate the necessity for innovative policy interventions. To this end, we introduce a comprehensive policy model tailored to champion robust gender equity initiatives within engineering field.

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Section: E Discriminatory Behaviormentioning

confidence: 99%

Exploring the Multifaceted Challenges of Women in Engineering: A Comprehensive Literature Review

Dabić,

Posinković,

Maley

et al. 2024

IEEE Trans. Eng. Manage.

View full text Add to dashboard Cite

show abstract

“…The automation of image acquisition in NDT digital radiography [11,16,17] is only a piece of the puzzle. To ensure that proper decision-making regarding the reliability of a tested part is achieved, it is essential to assess the acquired images for quality in accordance with operational NDT Standards, identify relevant indications (flaws), and evaluate if the detected flaws constitute a defect or not (see ASTM E1316-17a, Standard Terminology for Nondestructive Examinations) [2].…”

Section: Automated Defect Recognition (Adr) In Digital Ndt Radiographymentioning

confidence: 99%

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters - An Experimental Study

Hena¹,

Wei²,

Ibarra-Castanedo³

et al. 2023

Preprint

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In response to the growing inspection demand exerted by process automation in component manufacturing, Non-destructive testing (NDT) continues to explore automated approaches that utilize deep learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep learning models. This study investigates the influence of two image quality parameters, namely Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), on the performance of U-net deep learning segmentation model. Input images were acquired with varying combinations of exposure factors such as kilovoltage, milli-ampere, and exposure time, which altered the resultant quality. The data was sorted into 5 different datasets according to their measured SNR and CNR values. The deep learning model was trained 5 distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection over Union (IoU) metric of 0.9594 on test data of the same category but drops to 0.5875 when tested on lower CNR test data. The result in this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters, to enhance the performance of deep learning segmentation models in NDT radiography applications.

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“…With the increasing proliferation and adoption of process automation in the manufacturing industry, high manufacturing throughput of components is achievable [8][9][10][11]. The need to inspect these fabricated components places a huge demand on NDT technicians [12], who traditionally employ trained skills and experience to manipulate X-ray acquisition setups, and visually assess and evaluate NDT radiography images.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters—An Experimental Study

Hena

Wei

Ibarra-Castanedo

et al. 2023

Sensors

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In response to the growing inspection demand exerted by process automation in component manufacturing, non-destructive testing (NDT) continues to explore automated approaches that utilize deep-learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep-learning models. This study investigated the influence of two image-quality parameters, namely signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), on the performance of a U-net deep-learning semantic segmentation model. Input images were acquired with varying combinations of exposure factors, such as kilovoltage, milli-ampere, and exposure time, which altered the resultant radiographic image quality. The data were sorted into five different datasets according to their measured SNR and CNR values. The deep-learning model was trained five distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection-over-union (IoU) metric of 0.9594 on test data of the same category but dropped to 0.5875 when tested on lower CNR test data. The result of this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters in order to enhance the performance of deep-learning segmentation models for NDT digital X-ray radiography applications.

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Automation technologies and their impact on employment: A review, synthesis and future research agenda

Cited by 33 publications

References 70 publications

Exploring the Multifaceted Challenges of Women in Engineering: A Comprehensive Literature Review

Exploring the Multifaceted Challenges of Women in Engineering: A Comprehensive Literature Review

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters - An Experimental Study

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters—An Experimental Study

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