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1 – 10 of over 1000Xiaoliang Qian, Heqing Zhang, Cunxiang Yang, Yuanyuan Wu, Zhendong He, Qing-E Wu and Huanlong Zhang
This paper aims to improve the generalization capability of feature extraction scheme by introducing a micro-cracks detection method based on self-learning features. Micro-cracks…
Abstract
Purpose
This paper aims to improve the generalization capability of feature extraction scheme by introducing a micro-cracks detection method based on self-learning features. Micro-cracks detection of multicrystalline solar cell surface based on machine vision is fast, economical, intelligent and easier for on-line detection. However, the generalization capability of feature extraction scheme adopted by existed methods is limited, which has become an obstacle for further improving the detection accuracy.
Design/methodology/approach
A novel micro-cracks detection method based on self-learning features and low-rank matrix recovery is proposed in this paper. First, the input image is preprocessed to suppress the noises and remove the busbars and fingers. Second, a self-learning feature extraction scheme in which the feature extraction templates are changed along with the input image is introduced. Third, the low-rank matrix recovery is applied to the decomposition of self-learning feature matrix for obtaining the preliminary detection result. Fourth, the preliminary detection result is optimized by incorporating the superpixel segmentation. Finally, the optimized result is further fine-tuned by morphological postprocessing.
Findings
Comprehensive evaluations are implemented on a data set which includes 120 testing images and corresponding human-annotated ground truth. Specifically, subjective evaluations show that the shape of detected micro-cracks is similar to the ground truth, and objective evaluations demonstrate that the proposed method has a high detection accuracy.
Originality/value
First, a self-learning feature extraction method which has good generalization capability is proposed. Second, the low-rank matrix recovery is combined with superpixel segmentation for locating the defective regions.
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Mohammad Hossein Jarrahi, Sarah Kenyon, Ashley Brown, Chelsea Donahue and Chris Wicher
The purpose of this paper is to present a framework that captures the strategic value of artificial intelligence (AI) systems. Although AI has become a crucial component of…
Abstract
Purpose
The purpose of this paper is to present a framework that captures the strategic value of artificial intelligence (AI) systems. Although AI has become a crucial component of digital transformation efforts tied to organizational strategy, many firms struggle to derive strategic value from emerging AI systems.
Design/methodology/approach
The analytical framework in this paper is based on a learning-centered approach. Specifically, by building on the knowledge-based perspective, this paper elaborates on how AI can contribute to organizational learning to create a competitive advantage in knowledge-intensive contexts.
Findings
This paper argues that the power of AI as a strategic resource lies in its self-learning capacities. Such learning capacities are only realized in partnership with humans through mutual learning. This paper formulates the concept of artificial capital and the ways artificial and human capital can together drive routinization and strategic learning processes that connect internal and external environments of the organization.
Originality/value
This is a timely contribution as many organizations are considering adopting AI technologies for strategic purposes. This paper translates the proposed framework into several practical implications for managing and developing AI to meet strategic business goals.
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Yangkun Wang, Feng Zhang, Shiwen Zhang and Guang Yang
A multi-load available, response reliable and product-friendly method is in urgent need to diagnose the signs of incipient arcing. This paper aims to propose a novel algorithm…
Abstract
Purpose
A multi-load available, response reliable and product-friendly method is in urgent need to diagnose the signs of incipient arcing. This paper aims to propose a novel algorithm that originates the application of correlativity analysis of wavelet high-frequency component in state discrimination and further in arcing detection.
Design/methodology/approach
The proposed method calculates the correlation coefficient between the extraction by wavelet transform of arcing series current and that of normal, compares it with a predefined threshold and outputs a trip signal when eight qualified arcing half cycles within a period of 0.5 s are detected.
Findings
Typical appliances are selected in laboratory for arc detection to test the method which carries on independently of impedance type. The algorithm could be optimized to identify arcing for different kinds of loads, including resistive, inductive, capacitive and switching power supply loads, with a same correlation coefficient threshold.
Practical implications
The arithmetic operations of the method are addition and multiplication, which contribute to efficient data computation and transmission for micro-processor to undertake. The reference optimal sampling rate recommended for the algorithm helps to reduce the processed data volume and shows its promising prospect for portable product development.
Originality/value
This proposed correlativity analysis of wavelet transform component algorithm could classify the tested signal into two categories, which benefits the discrimination of normal and fault states in condition monitoring. Laboratory tests prove that it works effectively in arc detection for the commonly used impedance types of loads and needs no offline self-learning or training of samples.
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ELIZABETH ORNA and GRAHAM STEVENS
The client and the Problem The Information Services Division of the British Tourist Authority is responsible for dealing with over 1.5 million inquiries a year, through about 80…
Abstract
The client and the Problem The Information Services Division of the British Tourist Authority is responsible for dealing with over 1.5 million inquiries a year, through about 80 information staff in the offices which it maintains in 25 countries. Some of the staff are British, others are nationals of the countries where the offices are situated. While some are on the permanent staff of the BTA, others are recruited for short periods and work for BTA for times ranging from a season to a few years. Methods of recruitment of local staff vary from country to country, according to local legislation; in some countries it is possible to advertise, interview and recruit in the same way as would be done in the United Kingdom, but in others this is not possible, and BTA's recruitment depends on those who take the initiative of offering themselves for employment.
K.A.J.M. Kuruppuarachchi and K.O.L.C Karunanayake
The purpose of this paper is to identify socio-economic/demographic characteristics and to evaluate the knowledge on different open distance learning (ODL) concepts of BSc…
Abstract
Purpose
The purpose of this paper is to identify socio-economic/demographic characteristics and to evaluate the knowledge on different open distance learning (ODL) concepts of BSc undergraduates of The Open University of Sri Lanka (OUSL) at first registration.
Design/methodology/approach
The surveying technique was adapted with a piloted structured questionnaire consisting of two components. The structured component was used to evaluate personal, socio-economic and demographic data. The open ended component evaluated the student’s perception on ODL concepts. The questionnaire was randomly adapted to 456 (35 percent Colombo Regional Centre (CRC) registrants) prospective BSc undergraduates at first registration time at the CRC in 2014. Data collected from the structured component were frequency tabulated and cross-tabulated with the SPSS computer software. Responses of the open ended part were examined, categorized and the frequency percentages of each response category were calculated.
Findings
The structured component recognized that the majority of BSc undergraduates of the OUSL represent employed (53 percent), late adolescents (92 percent below age 27) who reside in rural or semi-urban areas (75 percent). They belong mostly to the lower middle class and 69 percent are from families which have a monthly family income below SLR30,000/(USD208). Answers of the open ended component on ODL concepts recognized that, prior knowledge on ODL concepts were developed by most BSc undergraduates. Approximately 50 percent of respondents perceived OUSL as an institute which facilitates working people by conducting part time-based or distance mode education with self-learning features. In total, 56.9 percent students perceived the role of an ODL teacher correctly as a facilitator or a guide. The educational process was perceived correctly as an ODL system by 52 percent, while the remainder also identified the system to be a more self-study and student centered flexible learning system. However, the role of a BSc student at OUSL was recognized as self-independent learners by only 36.7 percent and the majority had no clear perception of the role they have to play as an ODL student. Hence, more attention should be paid to make students recognize the role they have to play in an ODL system in order to succeed at OUSL.
Originality/value
Although research has been carried out periodically on the process of ODL education system at OUSL, on the graduate (output) and dropouts, etc., not many have focused on the nature of input such as characteristic features of first registrant and their prior knowledge on ODL. As the output invariably depends on the input and the process, this type of survey is timely and novel.
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Machine vision technology has carried connotations of improved productivity since inception. Industrial‐proofing this concept has proven difficult until recent years. The concept…
Abstract
Machine vision technology has carried connotations of improved productivity since inception. Industrial‐proofing this concept has proven difficult until recent years. The concept has now begun to show paybacks in the original projections and will only be enhanced in future designs.
Brad McKenna, Wenjie Cai and Hyunsun Yoon
Research into older adults' use of social media remains limited. Driven by increasing digitalisation in China, the authors focus on Chinese older adults (aged 60–75)’ use of…
Abstract
Purpose
Research into older adults' use of social media remains limited. Driven by increasing digitalisation in China, the authors focus on Chinese older adults (aged 60–75)’ use of WeChat.
Design/methodology/approach
This study used a qualitative interpretive approach and interviewed Chinese older adults to uncover their social practices of WeChat use in everyday life.
Findings
By using social practice theory (SPT), the paper unfolds Chinese older adults' social practices of WeChat use in everyday life and reveals how they adopt and resist the drastic changes in Chinese society.
Originality/value
The study contributes to new understandings of SPT from technology use by emphasising the dynamic characteristics of its three elements. The authors synthesise both adoptions and resistance in SPT and highlight the importance of understanding three elements interdependently within specific contexts, which are conditioned by structure and agency.
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Xiaoliang Qian, Jing Li, Jianwei Zhang, Wenhao Zhang, Weichao Yue, Qing-E Wu, Huanlong Zhang, Yuanyuan Wu and Wei Wang
An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which…
Abstract
Purpose
An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which have strong generalization and data representation ability at the same time is still an open problem for machine vision-based methods.
Design/methodology/approach
A micro-crack detection method based on adaptive deep features and visual saliency is proposed in this paper. The proposed method can adaptively extract deep features from the input image without any supervised training. Furthermore, considering the fact that micro-cracks can obviously attract visual attention when people look at the solar cell’s surface, the visual saliency is also introduced for the micro-crack detection.
Findings
Comprehensive evaluations are implemented on two existing data sets, where subjective experimental results show that most of the micro-cracks can be detected, and the objective experimental results show that the method proposed in this study has better performance in detecting precision.
Originality/value
First, an adaptive deep features extraction scheme without any supervised training is proposed for micro-crack detection. Second, the visual saliency is introduced for micro-crack detection.
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G. Stylios, J. Fan, J.O. Sotomi and R. Deacon
Introduces a new concept which has been developed to combat the problems of sewability, named “the Sewability Integrated Environment”. It aims at knitted as well as woven…
Abstract
Introduces a new concept which has been developed to combat the problems of sewability, named “the Sewability Integrated Environment”. It aims at knitted as well as woven materials and incorporates automated objective measurement systems to achieve automatic prediction of seam pucker, and sewing damage, on‐line optimum sewing conditions and the necessary amount of fabric property correction in fabric Finishing. Describes and discusses these systems and puts forward arguments and new ideas for future research in advance garment manufacture.
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Di Wu, Lei Wu, Alexis Palmer, Dr Kinshuk and Peng Zhou
Interaction content is created during online learning interaction for the exchanged information to convey experience and share knowledge. Prior studies have mainly focused on the…
Abstract
Purpose
Interaction content is created during online learning interaction for the exchanged information to convey experience and share knowledge. Prior studies have mainly focused on the quantity of online learning interaction content (OLIC) from the perspective of types or frequency, resulting in a limited analysis of the quality of OLIC. Domain concepts as the highest form of interaction are shown as entities or things that are particularly relevant to the educational domain of an online course. The purpose of this paper is to explore a new method to evaluate the quality of OLIC using domain concepts.
Design/methodology/approach
This paper proposes a novel approach to automatically evaluate the quality of OLIC regarding relevance, completeness and usefulness. A sample of OLIC corpus is classified and evaluated based on domain concepts and textual features.
Findings
Experimental results show that random forest classifiers not only outperform logistic regression and support vector machines but also their performance is improved by considering the quality dimensions of relevance and completeness. In addition, domain concepts contribute to improving the performance of evaluating OLIC.
Research limitations/implications
This paper adopts a limited sample to train the classification models. It has great benefits in monitoring students’ knowledge performance, supporting teachers’ decision-making and even enhancing the efficiency of school management.
Originality/value
This study extends the research of domain concepts in quality evaluation, especially in the online learning domain. It also has great potential for other domains.
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