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Article
Publication date: 16 August 2019

Shuangshuang Liu and Xiaoling Li

Conventional image super-resolution reconstruction by the conventional deep learning architectures suffers from the problems of hard training and gradient disappearing. In…

Abstract

Purpose

Conventional image super-resolution reconstruction by the conventional deep learning architectures suffers from the problems of hard training and gradient disappearing. In order to solve such problems, the purpose of this paper is to propose a novel image super-resolution algorithm based on improved generative adversarial networks (GANs) with Wasserstein distance and gradient penalty.

Design/methodology/approach

The proposed algorithm first introduces the conventional GANs architecture, the Wasserstein distance and the gradient penalty for the task of image super-resolution reconstruction (SRWGANs-GP). In addition, a novel perceptual loss function is designed for the SRWGANs-GP to meet the task of image super-resolution reconstruction. The content loss is extracted from the deep model’s feature maps, and such features are introduced to calculate mean square error (MSE) for the loss calculation of generators.

Findings

To validate the effectiveness and feasibility of the proposed algorithm, a lot of compared experiments are applied on three common data sets, i.e. Set5, Set14 and BSD100. Experimental results have shown that the proposed SRWGANs-GP architecture has a stable error gradient and iteratively convergence. Compared with the baseline deep models, the proposed GANs models have a significant improvement on performance and efficiency for image super-resolution reconstruction. The MSE calculated by the deep model’s feature maps gives more advantages for constructing contour and texture.

Originality/value

Compared with the state-of-the-art algorithms, the proposed algorithm obtains a better performance on image super-resolution and better reconstruction results on contour and texture.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 12 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 10 August 2021

Zi-yan Yu and Tian-jian Luo

Clothing patterns play a dominant role in costume design and have become an important link in the perception of costume art. Conventional clothing patterns design relies…

Abstract

Purpose

Clothing patterns play a dominant role in costume design and have become an important link in the perception of costume art. Conventional clothing patterns design relies on experienced designers. Although the quality of clothing patterns is very high on conventional design, the input time and output amount ratio is relative low for conventional design. In order to break through the bottleneck of conventional clothing patterns design, this paper proposes a novel way based on generative adversarial network (GAN) model for automatic clothing patterns generation, which not only reduces the dependence of experienced designer, but also improve the input-output ratio.

Design/methodology/approach

In view of the fact that clothing patterns have high requirements for global artistic perception and local texture details, this paper improves the conventional GAN model from two aspects: a multi-scales discriminators strategy is introduced to deal with the local texture details; and the self-attention mechanism is introduced to improve the global artistic perception. Therefore, the improved GAN called multi-scales self-attention improved generative adversarial network (MS-SA-GAN) model, which is used for high resolution clothing patterns generation.

Findings

To verify the feasibility and effectiveness of the proposed MS-SA-GAN model, a crawler is designed to acquire standard clothing patterns dataset from Baidu pictures, and a comparative experiment is conducted on our designed clothing patterns dataset. In experiments, we have adjusted different parameters of the proposed MS-SA-GAN model, and compared the global artistic perception and local texture details of the generated clothing patterns.

Originality/value

Experimental results have shown that the clothing patterns generated by the proposed MS-SA-GAN model are superior to the conventional algorithms in some local texture detail indexes. In addition, a group of clothing design professionals is invited to evaluate the global artistic perception through a valence-arousal scale. The scale results have shown that the proposed MS-SA-GAN model achieves a better global art perception.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 14 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 25 June 2020

Minghua Wei and Feng Lin

Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this…

Abstract

Purpose

Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this paper proposes an EEG signals classification method based on multi-dimensional fusion features.

Design/methodology/approach

First, the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals. Then, the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks (3DCNNs) model. Finally, the spatial-frequency features are incorporated to the bidirectional gated recurrent units (Bi-GRUs) models to extract the spatial-frequency-sequential multi-dimensional fusion features for recognition of brain's sensorimotor region activated task.

Findings

In the comparative experiments, the data sets of motor imagery (MI)/action observation (AO)/action execution (AE) tasks are selected to test the classification performance and robustness of the proposed algorithm. In addition, the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.

Originality/value

The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks, so as to achieve more stable classification performance in dealing with AO/MI/AE tasks, and has the best robustness on EEG signals of different subjects.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Book part
Publication date: 10 August 2017

Magdalena Nowicka-Franczak

Public acts of self-criticism in Eastern Europe – a genre cultivated and extorted by the communist parties – did not disappear with the end of communism. In the young…

Abstract

Public acts of self-criticism in Eastern Europe – a genre cultivated and extorted by the communist parties – did not disappear with the end of communism. In the young democracies of the region self-criticism has become an attempt to diagnose society’s ‘backward’ character and to develop ‘self-correction’ scenarios in order to participate in the Western modernising discourse. On the one hand, conservative and right-wing elites suppose that public acts of self-criticism (performed by politicians, artists or scholars) can endow the vetting procedures of the ancien régime with a sense of social catharsis and retroactive justice. On the other hand, liberal and left-wing intellectuals subject themselves to collective self-reckoning, not only with their choices made in the transition period, but also with the memory of WWII, in order to shape a civil society free of anti-Semitism and intolerance. An analysis based on the discourse-historical approach in critical discourse analysis, Reinhart Koselleck’s historical semantics and Michel Foucault’s notion of discourse, and carried out on the text corpus of selected acts of self-criticism in Poland, aims to diagnose the role these acts had in shaping public discourse on the troublesome past.

Details

National Identity and Europe in Times of Crisis
Type: Book
ISBN: 978-1-78714-514-6

Keywords

Article
Publication date: 1 February 2008

John McCormick and Kerry Barnett

The purpose of this paper was to posit and test hypotheses concerned with relationships between teachers' demographics, locus of control and career stages.

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Abstract

Purpose

The purpose of this paper was to posit and test hypotheses concerned with relationships between teachers' demographics, locus of control and career stages.

Design/methodology/approach

A sample consisting of 416 Australian non‐executive high school teachers was gathered from 40 randomly selected high schools. Multilevel regression analysis reflecting the nested nature of the sample of teachers within schools, and allowing for testing for school effects, was employed.

Findings

The paper finds that significant gender and years of teaching experience differences were identified for a number of career stages. There were positive relationships between years of teaching experience and later career stages. A number of multilevel models relating locus of control and demographic variables to career stage were developed and are reported.

Originality/value

The paper shows that teachers' generalized beliefs about personal control may be related to career stages and school practices should nurture beliefs in personal control, rather than dependence on powerful others in the school setting.

Details

Journal of Educational Administration, vol. 46 no. 1
Type: Research Article
ISSN: 0957-8234

Keywords

Article
Publication date: 27 July 2021

Papangkorn Pidchayathanakorn and Siriporn Supratid

A major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance…

Abstract

Purpose

A major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).

Design/methodology/approach

Here, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.

Findings

Implicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.

Research limitations/implications

A future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.

Practical implications

This paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.

Originality/value

In most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.

Article
Publication date: 7 September 2010

Linda M. Cohen

The purpose of this paper is to highlight how a commonly‐overlooked resource – physical assets – can be used to advantage as both a tactical and strategic tool during

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Abstract

Purpose

The purpose of this paper is to highlight how a commonly‐overlooked resource – physical assets – can be used to advantage as both a tactical and strategic tool during mergers and acquisitions (M&A). It aims to present an overview of strategies for managers to consider when faced with M&A – both for deterring and defending against unwanted acquirer attention, and for managing M&A post‐transaction.

Design/methodology/approach

Integrating findings from different research streams (e.g. financial, management, geography and real estate), and drawing on interviews and recent M&A reports, the paper distills physical asset strategies into a general overview and a two‐stage framework.

Findings

Firms' physical assets can play a significant role in driving, defending and managing M&A. By affecting both financial and organizational outcomes, it is shown how physical assets are a powerful strategic resource within the manager's toolkit. Deter‐and‐defend strategies reduce M&A vulnerability and defend against hostile raiders; Managing M&A strategies improve post‐M&A revenue generation, efficiency gains and increased organizational effectiveness.

Practical implications

For managers facing M&A, this paper highlights a range of strategic options which are often overlooked in M&A research. Beyond M&A, many of these strategies can also be used by any firm facing financial and performance pressures.

Originality/value

The paper highlights a category of M&A strategies that can have a significant impact on M&A outcomes, but is often underplayed in general management and strategy research. It elaborates on a range of strategy options. Also, by integrating findings from diverse research streams, this paper offers a broadened perspective of M&A strategies.

Details

Journal of Business Strategy, vol. 31 no. 6
Type: Research Article
ISSN: 0275-6668

Keywords

Article
Publication date: 22 September 2021

A. Prakash, A. Shyam Joseph, R. Shanmugasundaram and C.S. Ravichandran

This paper aims to propose a machine learning approach-based power theft detection using Garra Rufa Fish (GRF) optimization. Here, the analyzing of power theft is an…

Abstract

Purpose

This paper aims to propose a machine learning approach-based power theft detection using Garra Rufa Fish (GRF) optimization. Here, the analyzing of power theft is an important part to reduce the financial loss and protect the electricity from fraudulent users.

Design/methodology/approach

In this section, a new method is implemented to reduce the power theft in transmission lines and utility grids. The detection of power theft using smart meter with reliable manner can be achieved by the help of GRF algorithm.

Findings

The loss of power due to non-technical loss is small by using this proposed algorithm. It provides some benefits like increased predicting capacity, less complexity, high speed and high reliable output. The result is analyzed using MATLAB/Simulink platform. The result is compared with an existing method. According to the comparison result, the proposed method provides the good performance than existing method.

Originality/value

The proposed method gives good results of comparison than those of the other techniques and has an ability to overcome the associated problems.

Details

Journal of Engineering, Design and Technology , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1726-0531

Keywords

Content available
Article
Publication date: 1 March 2006

Craig Henry

125

Abstract

Details

Strategy & Leadership, vol. 34 no. 2
Type: Research Article
ISSN: 1087-8572

Book part
Publication date: 14 July 2006

Jamie Morgan

The purpose of this paper is to explain how the current “crisis” in the UK pension system arose. I argue that it is a result of a combination of changes in government…

Abstract

The purpose of this paper is to explain how the current “crisis” in the UK pension system arose. I argue that it is a result of a combination of changes in government policy and basic instabilities always inherent in the financial system. Policy changes increased the vulnerability of the pension system to those instabilities. The background to these changes and also the frame of reference in terms of which the “crisis” itself is now phrased is broadly neoliberal. Its theoretical roots are in ideas of the efficiency of free markets. Its policy roots are expressed in a series of similar neoliberal policy tendencies in other capitalist states. I further argue that neoliberal solutions to the pension crisis simply offer more of the very matters that created the problems in the first place. Moreover, the very terms of debate, based in markets, financialisation of saving and individualisation of risk, disguise a more basic debate about providing a living retirement income for all. This is a debate that New Labour is simply not prepared to constructively engage with in any concrete fashion.

Details

The Hidden History of 9-11-2001
Type: Book
ISBN: 978-1-84950-408-9

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