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Article
Publication date: 26 July 2023

Bing Peng-Loong Wong, M. Abu Saleh, Raechel Johns and Ravi Chinta

Despite the important role that exploitation plays in innovation and new product development (NPD), research on the relative impact of internal organisational stocks of existing…

162

Abstract

Purpose

Despite the important role that exploitation plays in innovation and new product development (NPD), research on the relative impact of internal organisational stocks of existing knowledge on subsequent exploitation is largely absent. In particular, there is lack of clarity within the extant literature regarding the associations between organisational exploitation and, respectively, the distal-proximal technological experience and radical-incremental innovative experience generated by multiproduct firms. Thus, this study seeks to further enhance researchers’ theoretical understanding on the relationship between organisational exploitation and internal knowledge stocks categorised along two dimensions of organisational experience accumulated by multiproduct firms that have not previously been considered jointly.

Design/methodology/approach

This paper pursues a focussed literature review approach and applies the underlying theory of exploitation to develop a theory explaining the possible relationships between organisational exploitation and internal knowledge stocks.

Findings

Based on the theory of exploitation, this paper proposes a new direction in studying the various internal knowledge stocks and their respective impact on subsequent organisational exploitation.

Practical implications

The proposed research direction suggests an emerging framework of possible relationships between exploitative new radical products development in firms, and respectively, proximal and distal technological experience, and radical and incremental innovative experience, accumulated in multiproduct firms. This novel framework can guide further research on this topic.

Originality/value

To fill a research gap regarding the possible relationships between subsequent exploitative endeavours and two dimensions of organisational experience that have been traditionally associated with the exploration-exploitation construct, this paper proposes and develops a novel typology of knowledge stocks categorised along two dimensions of organisational experience accumulated by multiproduct firms that have not previously been considered jointly in the literature.

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Article
Publication date: 16 October 2009

Junguo Wang, Jianzhong Zhou and Bing Peng

The purpose of this paper is to detect the periodic signal under strong noise background, and estimate its amplitude/phase.

1035

Abstract

Purpose

The purpose of this paper is to detect the periodic signal under strong noise background, and estimate its amplitude/phase.

Design/methodology/approach

Melnikov method is adopted as calculating the threshold value when chaos occurs, and the detected signal is taken as a system parameter. The system's output state is changed if the parameter has a slight change near the threshold. Meantime, the phase of system's output is recognized to judge whether the output state changes, and the signal parameter is estimated according to the necessary condition.

Findings

A small periodic signal in noise can be detected by Duffing oscillator via a transition from chaotic motion to periodic motion.

Research limitations/implications

The paper shows how to calculate the amplitude/phase in low signal‐to‐noise ratios.

Practical implications

The Duffing system is sensitive to the weak periodic signal and has definite immunity to noise, so it is easy to construct a system composed of many oscillators that could process complex signals, even though the environmental noise is intense.

Originality/value

This paper presents a nonlinear method for detecting and extracting the weak signal.

Details

Kybernetes, vol. 38 no. 10
Type: Research Article
ISSN: 0368-492X

Keywords

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Article
Publication date: 10 August 2010

Wang Junguo, Zhou Jianzhong and Peng Bing

The purpose of this paper is to improve forecasting accuracy for short‐term load series.

339

Abstract

Purpose

The purpose of this paper is to improve forecasting accuracy for short‐term load series.

Design/methodology/approach

A forecasting method based on chaotic time series and optimal diagonal recurrent neural networks (DRNN) is presented. The input of the DRNN is determined by the embedding dimension of the reconstructed phase space, and adaptive dynamic back propagation (DBP) algorithm is used to train the network. The connection weights of the DRNN are optimized via modified genetic algorithms, and the best results of optimization are regarded as initial weights for the network. The new method is applied to predict the actual short‐term load according to its chaotic characteristics, and the forecasting results also validate the feasibility.

Findings

For the chaos time series, the hybrid neural genetic method based on phase space reconstruction can carry out the short‐term prediction with the higher accuracy.

Research limitations/implications

The proposed method is not suited to medium and long‐term load forecasting.

Practical implications

The accuracy of the load forecasting is important to the economic and secure operation of power systems; also, the neural genetic method can improve forecasting accuracy.

Originality/value

This paper will help overcome the defects of traditional neural network and make short‐term load forecasting more accurate and fast.

Details

Kybernetes, vol. 39 no. 8
Type: Research Article
ISSN: 0368-492X

Keywords

Available. Open Access. Open Access
Article
Publication date: 7 May 2019

Yanan Wang, Jianqiang Li, Sun Hongbo, Yuan Li, Faheem Akhtar and Azhar Imran

Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of…

1772

Abstract

Purpose

Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and to study it scientifically, we often have to make a set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to gain some understanding of how the corresponding system behaves, and the quality of these understandings essentially depends on the credibility of given assumptions or models, known as VV&A (verification, validation and accreditation). The main purpose of this paper is to present an in-depth theoretical review and analysis for the application of VV&A in large-scale simulations.

Design/methodology/approach

After summarizing the VV&A of related research studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations (such as crowd network simulations).

Findings

The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations (such as crowd network simulations).

Originality/value

This paper will help researchers to provide support of a recommendation for formulating VV&A in large-scale simulations (such as crowd network simulations).

Details

International Journal of Crowd Science, vol. 3 no. 1
Type: Research Article
ISSN: 2398-7294

Keywords

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Article
Publication date: 21 November 2023

Zhenhua Quan, Wenjie Qian and Jianhua Mao

The purpose of this article is to explore the relationship between the attributes of Olympic mascots and their impact on sponsorship effectiveness. Based on a multiattribute model…

377

Abstract

Purpose

The purpose of this article is to explore the relationship between the attributes of Olympic mascots and their impact on sponsorship effectiveness. Based on a multiattribute model and the introduction of engagement theory and the meaning transfer model, this article uses the 2022 Beijing Winter Olympics mascot “Bing Dwen Dwen” as the research object to empirically analyze the effects and mechanisms of the mascot's attributes on preference, event engagement, sponsorship enterprise trust and sponsorship enterprise attitude, ultimately constructing a sponsorship effectiveness model.

Design/methodology/approach

The survey method was used to examine 238 respondents' emotions and attitudes towards companies participating in sponsoring Olympic mascots.

Findings

The study found that the main attributes of the mascot include visual and emotional factors, both of which have a positive impact on preference, with emotional factors having a greater influence than visual factors. Visual and emotional factors indirectly affect engagement through preference. Preference and engagement play a completely mediating role in the effect of mascot attributes on sponsorship enterprise trust and sponsorship enterprise attitude.

Practical implications

This study provides practical recommendations for managers to achieve marketing success in sports sponsorship through mascots.

Originality/value

This paper provides a measurement tool for the study of mascot attributes and important support for subsequent research in sponsorship marketing.

Details

Asia Pacific Journal of Marketing and Logistics, vol. 36 no. 4
Type: Research Article
ISSN: 1355-5855

Keywords

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Article
Publication date: 26 June 2019

Hui Wang, Jingsong Peng, Bing Zhao, Xin-Dong Zhang, Jie Yu, Yuan Li and Mao-Min Wang

Near-net-shaped processes of jet engine blade have better performance in both reducing the material waste during production and improving work reliability in service, while the…

397

Abstract

Purpose

Near-net-shaped processes of jet engine blade have better performance in both reducing the material waste during production and improving work reliability in service, while the geometric features of blade, both sculptured surface and thin-walled shape, make the precise machining of blade challenging and difficult owing to its dynamics behaviors under complex clamping and machining loads. This paper aims to present a fundamental approach on modeling and performance analysis of the blade–fixture system.

Design/methodology/approach

A computerized framework on the complex blade–fixture dynamic behavior has been developed. Theoretical mechanic analysis on blade fixturing and machining is proposed with an especial emphasis on the boundary conditions of the blade–fixture system. Then the finite element analysis (FEA) method is used to simulate the variation trend of preloads, stiffness and blade distortion. The strong influence of parameters of workpiece–fixture configuration on blade distortion and machining error is investigated.

Findings

With a case of real jet engine blade machining, the experimental results and theoretical predictions suggest good agreement on their variation tendency. The loaded pressure of clamps has a critical influence on the total stiff performance of the blade–fixture system, and the profile error of the blade contributes much to the inconsistency in geometric dimension and surface integrity of blades’ machining. In the end, the results also validate the effectiveness of this methodology to predict and improve the performance of the blade–fixture configuration design.

Originality/value

The adaptive machining of near-net-shaped jet engine blade is a new high-performance manufacturing technology in aerospace production. This study provides a fundamental methodology for the performance analysis of blade-fixture system, to clear the variation law of blade distortion during preloading and machining, which will contribute to minimize the machining error and improve productivity.

Details

Assembly Automation, vol. 39 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 12 July 2023

Zhifeng Lin, Wei Zhang, Jiawei Li, Jing Yang, Bing Han and Peng Xie

As a common form of failure in industry, corrosion causes huge economic losses. At present, with the development of computational techniques, artificial intelligence (AI) is…

363

Abstract

Purpose

As a common form of failure in industry, corrosion causes huge economic losses. At present, with the development of computational techniques, artificial intelligence (AI) is playing a more and more important role in the field of scientific research. This paper aims to review the application of AI in corrosion protection research.

Design/methodology/approach

In this paper, the role of AI in corrosion protection is systematically described in terms of anticorrosion materials and methods, corrosion image recognition and corrosion life prediction.

Findings

With efficient and in-depth data processing methods, AI can rapidly advance the research process in terms of anticorrosion materials and methods, corrosion image recognition and corrosion life prediction and save on costs.

Originality/value

This paper summarizes the application of AI in corrosion protection research and provides the basis for corrosion engineers to quickly and comprehensively understand the role of AI and improve production processes.

Details

Anti-Corrosion Methods and Materials, vol. 70 no. 5
Type: Research Article
ISSN: 0003-5599

Keywords

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Article
Publication date: 30 August 2020

Xiangyou Shen, Bing Pan, Tao Hu, Kaijun Chen, Lin Qiao and Jinyue Zhu

Online review bias research has predominantly focused on self-selection biases on the user’s side. By collecting online reviews from multiple platforms and examining their biases…

473

Abstract

Purpose

Online review bias research has predominantly focused on self-selection biases on the user’s side. By collecting online reviews from multiple platforms and examining their biases in the unique digital environment of “Chinanet,” this paper aims to shed new light on the multiple sources of biases embedded in online reviews and potential interactions among users, technical platforms and the broader social–cultural norms.

Design/methodology/approach

In the first study, online restaurant reviews were collected from Dianping.com, one of China's largest review platforms. Their distribution and underlying biases were examined via comparisons with offline reviews collected from on-site surveys. In the second study, user and platform ratings were collected from three additional major online review platforms – Koubei, Meituan and Ele.me – and compared for possible indications of biases in platform's review aggregation.

Findings

The results revealed a distinct exponential-curved distribution of Chinese users’ online reviews, suggesting a deviation from previous findings based on Western user data. The lack of online “moaning” on Chinese review platforms points to the social–cultural complexity of Chinese consumer behavior and online environment that goes beyond self-selection at the individual user level. The results also documented a prevalent usage of customized aggregation methods by review service providers in China, implicating an additional layer of biases introduced by technical platforms.

Originality/value

Using an online–offline design and multi-platform data sets, this paper elucidates online review biases among Chinese users, the world's largest and understudied (in terms of review biases) online user group. The results provide insights into the unique social–cultural cyber norm in China's digital environment and bring to light the multilayered nature of online review biases at the intersection of users, platforms and culture.

Details

Journal of Hospitality and Tourism Insights, vol. 4 no. 1
Type: Research Article
ISSN: 2514-9792

Keywords

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Article
Publication date: 1 February 2013

Manoj Kumar, Parboti Shankar Mukherjee and Nirendra Mohan Misra

The dependency on human expertise for analysis and interpretation is the main reason for wear debris analysis not being used in industry to its full potential and becoming one of…

2540

Abstract

Purpose

The dependency on human expertise for analysis and interpretation is the main reason for wear debris analysis not being used in industry to its full potential and becoming one of the most powerful machine condition monitoring strategies. The dependency on human expertise makes the interpretation and result subjective in nature, costly and time consuming. The purpose of this paper is to review work being done to develop an automatic, reliable and objective wear particle classification system as a solution to the above problem. At the same time it also aims to discuss some common off line test methods being practiced for wear debris analysis.

Design/methodology/approach

Computer image analysis is a solution for some of the problems associated with the conventional techniques. First it is tried to efficiently describe the characteristics of computer images of different types of wear debris using a few numerical parameters. Then using some Artificial Intelligence tools, the wear particle classification system can be developed.

Findings

Many shape, size and surface parameters are discussed in the paper. Out of these, nine numerical parameters are selected to describe and distinguish six common type of wear debris. Once the type of debris is identified, the mode of wear and hence the machine condition can be assessed.

Practical implications

The present process of fault and condition monitoring of an equipment by wear debris analysis involves human judgment of debris formations. A set‐up standard for comparison of debris will enable the maintenance team to diagnose faults in a comparatively better way.

Originality/value

The aim of this paper is to discuss the difficulties in identifying wear particles and finding out the exact health of equipment, which, due to its subjective nature, is influenced by human errors. An objective method with certain standards for classification of wear particles compatible with an artificial intelligence system will yield some flawless results of wear debris analysis, which has not been attempted in the past as per available literature.

Details

Industrial Lubrication and Tribology, vol. 65 no. 1
Type: Research Article
ISSN: 0036-8792

Keywords

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Article
Publication date: 18 July 2018

Bing Hua, Zhiwen Zhang, Yunhua Wu and Zhiming Chen

The geomagnetic field vector is a function of the satellite’s position. The position and speed of the satellite can be determined by comparing the geomagnetic field vector…

155

Abstract

Purpose

The geomagnetic field vector is a function of the satellite’s position. The position and speed of the satellite can be determined by comparing the geomagnetic field vector measured by on board three-axis magnetometer with the standard value of the international geomagnetic field. The geomagnetic model has the disadvantages of uncertainty, low precision and long-term variability. Therefore, accuracy of autonomous navigation using the magnetometer is low. The purpose of this paper is to use the geomagnetic and sunlight information fusion algorithm to improve the orbit accuracy.

Design/methodology/approach

In this paper, an autonomous navigation method for low earth orbit satellite is studied by fusing geomagnetic and solar energy information. The algorithm selects the cosine value of the angle between the solar light vector and the geomagnetic vector, and the geomagnetic field intensity as observation. The Adaptive Unscented Kalman Filter (AUKF) filter is used to estimate the speed and position of the satellite, and the simulation research is carried out. This paper also made the same study using the UKF filter for comparison with the AUKF filter.

Findings

The algorithm of adding the sun direction vector information improves the positioning accuracy compared with the simple geomagnetic navigation, and the convergence and stability of the filter are better. The navigation error does not accumulate with time and has engineering application value. It also can be seen that AUKF filtering accuracy is better than UKF filtering accuracy.

Research limitations/implications

Geomagnetic navigation is greatly affected by the accuracy of magnetometer. This paper does not consider the spacecraft’s environmental interference with magnetic sensors.

Practical implications

Magnetometers and solar sensors are common sensors for micro-satellites. Near-Earth satellite orbit has abundant geomagnetic field resources. Therefore, the algorithm will have higher engineering significance in the practical application of low orbit micro-satellites orbit determination.

Originality/value

This paper introduces a satellite autonomous navigation algorithm. The AUKF geomagnetic filter algorithm using sunlight information can obviously improve the navigation accuracy and meet the basic requirements of low orbit small satellite orbit determination.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 11 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

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