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Article

Li Xiaoling

In order to improve the weak recognition accuracy and robustness of the classification algorithm for brain-computer interface (BCI), this paper proposed a novel…

Abstract

Purpose

In order to improve the weak recognition accuracy and robustness of the classification algorithm for brain-computer interface (BCI), this paper proposed a novel classification algorithm for motor imagery based on temporal and spatial characteristics extracted by using convolutional neural networks (TS-CNN) model.

Design/methodology/approach

According to the proposed algorithm, a five-layer neural network model was constructed to classify the electroencephalogram (EEG) signals. Firstly, the author designed a motor imagery-based BCI experiment, and four subjects were recruited to participate in the experiment for the recording of EEG signals. Then, after the EEG signals were preprocessed, the temporal and spatial characteristics of EEG signals were extracted by longitudinal convolutional kernel and transverse convolutional kernels, respectively. Finally, the classification of motor imagery was completed by using two fully connected layers.

Findings

To validate the classification performance and efficiency of the proposed algorithm, the comparative experiments with the state-of-the-arts algorithms are applied to validate the proposed algorithm. Experimental results have shown that the proposed TS-CNN model has the best performance and efficiency in the classification of motor imagery, reflecting on the introduced accuracy, precision, recall, ROC curve and F-score indexes.

Originality/value

The proposed TS-CNN model accurately recognized the EEG signals for different tasks of motor imagery, and provided theoretical basis and technical support for the application of BCI control system in the field of rehabilitation exoskeleton.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

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Article

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

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Article

Xiaoling Li and Shuang shuang Liu

For the large-scale power grid monitoring system equipment, its working environment is increasingly complex and the probability of fault or failure of the monitoring…

Abstract

Purpose

For the large-scale power grid monitoring system equipment, its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing. This paper proposes a fault classification algorithm based on Gaussian mixture model (GMM), which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.

Design/methodology/approach

The algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy (MYT) decomposition under each normal distribution in the GMM. Then, the weight value of GMM is used to calculate weighted classification value of fault detection and separation, and by comparing the actual control limits with the classification result of GMM, the fault classification results are obtained.

Findings

The experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.

Originality/value

The proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

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Article

Wenjuan Shen and Xiaoling Li

recent years, facial expression recognition has been widely used in human machine interaction, clinical medicine and safe driving. However, there is a limitation that…

Abstract

Purpose

recent years, facial expression recognition has been widely used in human machine interaction, clinical medicine and safe driving. However, there is a limitation that conventional recurrent neural networks can only learn the time-series characteristics of expressions based on one-way propagation information.

Design/methodology/approach

To solve such limitation, this paper proposes a novel model based on bidirectional gated recurrent unit networks (Bi-GRUs) with two-way propagations, and the theory of identity mapping residuals is adopted to effectively prevent the problem of gradient disappearance caused by the depth of the introduced network. Since the Inception-V3 network model for spatial feature extraction has too many parameters, it is prone to overfitting during training. This paper proposes a novel facial expression recognition model to add two reduction modules to reduce parameters, so as to obtain an Inception-W network with better generalization.

Findings

Finally, the proposed model is pretrained to determine the best settings and selections. Then, the pretrained model is experimented on two facial expression data sets of CK+ and Oulu- CASIA, and the recognition performance and efficiency are compared with the existing methods. The highest recognition rate is 99.6%, which shows that the method has good recognition accuracy in a certain range.

Originality/value

By using the proposed model for the applications of facial expression, the high recognition accuracy and robust recognition results with lower time consumption will help to build more sophisticated applications in real world.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 4
Type: Research Article
ISSN: 1756-378X

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Article

Xiaoling Li, Yufang Wang, Liping Fu and Meirong Xu

The purpose of this paper is to explore why the university library should become the incubation center of research innovation literacy (RIL) competency. It states how the…

Abstract

Purpose

The purpose of this paper is to explore why the university library should become the incubation center of research innovation literacy (RIL) competency. It states how the library develops RIL instruction and how the library impels university students to improve their independent knowledge innovation competence.

Design/methodology/approach

This paper reviews some of the research papers on information literacy (IL) instruction. Based on background analysis, it enumerates and states the methods of the effective practice of the library in RIL competency instruction.

Findings

The university library possesses the information resources and advanced web technology, owns the experience in IL instruction, and has an advantage in terms of information instruction and research environment. The university library has the ability and responsibility to practice RIL instruction.

Originality/value

This paper provides a point of view that the university library can develop the RIL instruction based on IL. The university library can play not only an important role in general courses of the university, but can also sufficiently expand the function of librarians.

Details

The Electronic Library, vol. 27 no. 4
Type: Research Article
ISSN: 0264-0473

Keywords

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Article

Xiaoling Li, Xingyao Ren and Xu Zheng

This paper aimed to analyze the short- and long-term effects of the breadth and depth of seller competition on the performance of platform companies, and investigated the…

Abstract

Purpose

This paper aimed to analyze the short- and long-term effects of the breadth and depth of seller competition on the performance of platform companies, and investigated the underlying mechanisms of customers’ two-sided marketing tactics on the structure of the competition between sellers.

Design/methodology/approach

A longitudinal research design was adopted by gathering daily market objective data on e-commerce platforms for 250 days, and the dynamic evolution effects was analyzed by using a vector autoregression model which compared the differences between the short- and long-term effectiveness of different customer relationship management (CRM) strategies.

Findings

The breadth of competition amongst sellers improves the performance of platforms, whilst the depth of competition among sellers has a positive effect on the short-term performance. However, it has a negative effect on the long-term performance of their platforms. In both the short and long terms, advertising tactics that attract new buyers contribute more to increases in the breadth of seller competition than those that attract existing buyers do. Subsidies for new sellers decrease the depth of seller competition more than those for old sellers.

Research limitations/implications

Further research could be undertaken to investigate the validity of marketing tactics other than advertising tactics, and thus expand the time windows of the available data.

Practical implications

It is imperative for platform companies to implement effective control over seller competition to balance the interests of the sellers and of themselves.

Originality/value

The dyadic paradigm of CRM research has been extended by considering the perspective of the electronic platform company, how the tactics of exploitation and exploration of two-sided customers impact upon seller competitive structures have been delved into and why new customers have a unique value to platform companies has been identified.

Details

Nankai Business Review International, vol. 6 no. 2
Type: Research Article
ISSN: 2040-8749

Keywords

Content available
Article

Zhao-Peng Li, Li Yang, Si-Rui Li and Xiaoling Yuan

China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with…

Abstract

Purpose

China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various major challenges. One of the most important challenges is its impact on the social and economic development of arid and semi-arid regions. By simulating the carbon price trends under different economic development and energy consumption levels, this study aims to help the government can plan ahead to formulate various countermeasures to promote the integration of arid and semi-arid regions into the national carbon market.

Design/methodology/approach

To achieve this goal, this paper builds a back propagation neural network model, takes the third phase of the European Union Emissions Trading System (EU ETS) as the research object and uses the mean impact value method to screen out the important driving variables of European Union Allowance (EUA) price, including economic development (Stoxx600, Stoxx50, FTSE, CAC40 and DAX), black energy (coal and Brent), clean energy (gas, PV Crystalox Solar and Nordex) and carbon price alternatives Certification Emission Reduction (CER). Finally, this paper sets up six scenarios by combining the above variables to simulate the impact of different economic development and energy consumption levels on carbon price trends.

Findings

Under the control of the unchanged CER price level, economic development, black energy and clean energy development will all have a certain impact on the EUA price trends. When economic development, black energy consumption and clean energy development are on the rise, the EUA price level will increase. When the three types of variables show a downward trend, except for the sluggish development of clean energy, which will cause the EUA price to rise sharply, the EUA price trend will also decline accordingly in the remaining scenarios.

Originality/value

On the one hand, this paper incorporates driving factors of carbon price into the construction of carbon price prediction system, which not only has higher prediction accuracy but also can simulate the long-term price trend. On the other hand, this paper uses scenario simulation to show the size, direction and duration of the impact of economic development, black energy consumption and clean energy development on carbon prices in a more intuitive way.

Details

International Journal of Climate Change Strategies and Management, vol. 12 no. 5
Type: Research Article
ISSN: 1756-8692

Keywords

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Article

Dwi Suhartanto, Marwansyah Marwansyah, Muhammad Muflih, Moh Farid Najib and Irgiana Faturohman

The purpose of this paper is to integrate the Quality–Loyalty Model and the Religiosity–Loyalty Model to assess loyalty formation toward Halal food.

Abstract

Purpose

The purpose of this paper is to integrate the Quality–Loyalty Model and the Religiosity–Loyalty Model to assess loyalty formation toward Halal food.

Design/methodology/approach

Data from 320 respondents were collected in Bandung Indonesia through a survey. A partial least-square modeling was employed to evaluate the association between food quality, religiosity, perceived value, satisfaction and loyalty.

Findings

This study discloses that the two integrated loyalty models are fit, indicating that incorporating these loyalty models provides a better comprehension of loyalty toward Halal food. Further, this study confirms the importance of both food quality and religiosity in determining loyalty.

Practical implications

This research offers an important finding for Halal food managers to develop customer loyalty through food quality and religiosity. This research recommends that Halal food managers, besides obtaining Halal certification, need to constantly innovate and adopt world food-quality standards to deal with customers’ constantly changing demands.

Originality/value

This research is the first that integrates the Quality–Loyalty Model and the Religiosity–Loyalty Model to get a better understanding of loyalty formation toward Halal food.

Details

British Food Journal, vol. 122 no. 1
Type: Research Article
ISSN: 0007-070X

Keywords

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Article

Xiaoling Wang, Yingrui Hou, Guoqing Wang, Mudong Hao and Hao Li

The purpose of this paper is to illustrate the dependence of flexible-electronics properties on the metal conductor parameters, such as the width, thickness, connection…

Abstract

Purpose

The purpose of this paper is to illustrate the dependence of flexible-electronics properties on the metal conductor parameters, such as the width, thickness, connection length and inner meander radius of the conductor.

Design/methodology/approach

This paper uses the finite element method to simulate flexible electronics with a copper conductor attached to polyimide substrate under tension, by using different parameters of the conductor.

Findings

By careful variation of copper conductor parameters, the authors obtain an optimized structure that can undergo large deformations with small stress concentrations, lending convenience for packaging.

Originality/value

The authors have developed an optimization method for selecting metal conductor parameters in flexible electronics.

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Article

Andy Wei Hao, Xin Liu, Michael Hu and Xiaoling Guo

The paper examines the cultural differences in consumers' evaluations of vertical brand extensions.

Abstract

Purpose

The paper examines the cultural differences in consumers' evaluations of vertical brand extensions.

Design/methodology/approach

A 2 (extension types: upward, downward) × 2 (nationality: USA, China) × 2 (ownership: owner, non-owner) between-subjects design with thinking styles as a covariate was employed to test consumers' evaluations of vertical brand extensions. A total of 228 subjects from the US and 194 from China participated in the two experimental studies.

Findings

The paper finds that consumers prefer downward extensions to upward extensions. Furthermore, Chinese consumers have even more favorable evaluations of downward extension products than do American consumers. In addition, analytic thinkers exhibit a stronger ownership effect than holistic thinkers.

Originality/value

The research contributes to the understanding of culture differences in vertical brand extension evaluations.

Details

Cross Cultural & Strategic Management, vol. 27 no. 2
Type: Research Article
ISSN: 2059-5794

Keywords

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