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Article
Publication date: 6 November 2017

Zhifei Yang, Zhiye Du, Jiangjun Ruan, Shuo Jin, Guodong Huang, Qixiang Lian and Yifan Liao

The purpose of this paper is numerical calculation of total electric field in oil-paper insulation. Now, there is no effective method to consider the influence of space charges…

160

Abstract

Purpose

The purpose of this paper is numerical calculation of total electric field in oil-paper insulation. Now, there is no effective method to consider the influence of space charges when calculating the total electric field distribution in the main insulation system of the valve-side winding of an ultra-high-voltage direct current converter transformer.

Design/methodology/approach

To calculate the total electric field in an oil-paper insulation system, a new simulation method in single-layer oil-paper insulation based on the transient upstream finite element method (TUFEM) is proposed, in which the time variable is considered. The TUFEM is used to calculate the total electric field in an oil-paper insulation system by considering the move law of space charges. The simulation method is verified by comparing the simulation results to the test data. The move law of space charges and distribution characteristics of the electric field under difference voltage values in single-layer oil-paper insulation were presented.

Findings

The results show that the TUFEM has an excellent accuracy compared with the test data. When carrier mobility is a constant, the time to reach the steady state is inversely correlated with the initial electric field intensity, and the distortion rate of the internal total electric field is positively correlated with the initial electric field intensity.

Originality/value

This paper provides an exploratory research on the simulation of space charge transport phenomenon in oil-paper and has guiding significance to the design of oil-paper insulation.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 36 no. 6
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 1 December 2001

Shuming Cai, Ngai Weng Chan, Hsiang‐te Kung and Pin‐Shuo Liu

This study examines the causes of flood disasters in Jianghan Plain, China and provides practical solutions to mitigate them. Results from this study indicate that both historical…

1777

Abstract

This study examines the causes of flood disasters in Jianghan Plain, China and provides practical solutions to mitigate them. Results from this study indicate that both historical archives and more recent recorded data point to an increasing frequency in flood disasters since 1961. Furthermore, damage and losses from flood disasters have also increased significantly in the region. By analyzing the physical geographic factors and human activities, this study found that the main causative factors contributing to increasing flood disasters are landform/topography, climate elements, reduced drainage capacity of rivers in contrast to increased flood discharge, and human activities. Finally, the study examines various practical solutions to mitigate flood disasters in the Jianghan Plain.

Details

Disaster Prevention and Management: An International Journal, vol. 10 no. 5
Type: Research Article
ISSN: 0965-3562

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Article
Publication date: 13 February 2024

Wenzhen Yang, Shuo Shan, Mengting Jin, Yu Liu, Yang Zhang and Dongya Li

This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.

174

Abstract

Purpose

This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.

Design/methodology/approach

The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach.

Findings

Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%.

Originality/value

The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.

Details

Robotic Intelligence and Automation, vol. 44 no. 1
Type: Research Article
ISSN: 2754-6969

Keywords

Available. Open Access. Open Access
Article
Publication date: 6 October 2023

Xiaomei Jiang, Shuo Wang, Wenjian Liu and Yun Yang

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these…

659

Abstract

Purpose

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

Design/methodology/approach

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

Findings

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

Originality/value

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

Details

Journal of Electronic Business & Digital Economics, vol. 2 no. 2
Type: Research Article
ISSN: 2754-4214

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Article
Publication date: 26 January 2023

Yuting Rong, Shan Liu, Shuo Yan, Wei Wayne Huang and Yanxia Chen

Lenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns…

562

Abstract

Purpose

Lenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns with risk limitations or lowering risks with expected returns for P2P lenders.

Design/methodology/approach

This paper used data from a leading online P2P lending platform in America. First, the authors constructed a logistic regression-based credit scoring model and a linear regression-based profit scoring model to predict the default probabilities and profitability of loans. Second, based on the predictions of loan risk and loan return, the authors constructed linear programming model to form the optimal loan portfolio for lenders.

Findings

The research results show that compared to a logistic regression-based credit scoring method, the proposed new framework could make more returns for lenders with risks unchanged. Furthermore, compared to a linear regression-based profit scoring method, the proposed new framework could lower risks for lenders without lowering returns. In addition, comparisons with advanced machine learning techniques further validate its superiority.

Originality/value

Unlike previous studies that focus solely on predicting the default probability or profitability of loans, this study considers loan allocation in online P2P lending as an optimization research problem using a new framework based upon modern portfolio theory (MPT). This study may contribute theoretically to the extension of MPT in the specific context of online P2P lending and benefit lenders and platforms to develop more efficient investment tools.

Details

Industrial Management & Data Systems, vol. 123 no. 3
Type: Research Article
ISSN: 0263-5577

Keywords

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Article
Publication date: 1 October 2020

Kim-Shyan Fam, Shuo She and Djavlonbek Kadirov

98

Abstract

Details

Asia Pacific Journal of Marketing and Logistics, vol. 32 no. 7
Type: Research Article
ISSN: 1355-5855

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Article
Publication date: 14 January 2025

Taiye Luo, Juanjuan Qu and Shuo Cheng

Enhancing total factor productivity through digital transformation is a crucial pathway for the high-quality development of manufacturing enterprises. This research aims to…

45

Abstract

Purpose

Enhancing total factor productivity through digital transformation is a crucial pathway for the high-quality development of manufacturing enterprises. This research aims to investigate the impact mechanisms of manufacturing enterprises’ total factor productivity in the context of digital transformation.

Design/methodology/approach

Using the data from 536 Chinese listed manufacturing enterprises from 2018 to 2021, this research divides digital transformation into two dimensions (i.e. digital transformation breadth and digital transformation depth) and examines their impacts on total factor productivity as well as the mediation effects of innovation capability and reconfiguration capacity.

Findings

It is found that digital transformation breadth, digital transformation depth and their interaction can positively affect manufacturing enterprises’ total factor productivity. The innovation capability and reconfiguration capacity of manufacturing enterprises act as mediators between digital transformation breadth and total factor productivity, as well as between digital transformation depth and total factor productivity.

Originality/value

This study is one of the first attempts to investigate the impact mechanisms of manufacturing enterprises’ total factor productivity from the perspective of digital transformation breadth and depth.

Details

Industrial Management & Data Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0263-5577

Keywords

Available. Open Access. Open Access
Article
Publication date: 22 November 2023

En-Ze Rui, Guang-Zhi Zeng, Yi-Qing Ni, Zheng-Wei Chen and Shuo Hao

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural…

1745

Abstract

Purpose

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

Design/methodology/approach

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Findings

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

Originality/value

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 34 no. 1
Type: Research Article
ISSN: 0961-5539

Keywords

Available. Content available
Article
Publication date: 8 June 2012

620

Abstract

Details

Chinese Management Studies, vol. 6 no. 2
Type: Research Article
ISSN: 1750-614X

Available. Open Access. Open Access
Article
Publication date: 18 April 2023

Wenzhen Yang, Johan K. Crone, Claus R. Lønkjær, Macarena Mendez Ribo, Shuo Shan, Flavia Dalia Frumosu, Dimitrios Papageorgiou, Yu Liu, Lazaros Nalpantidis and Yang Zhang

This study aims to present a vision-guided robotic system design for application in vat photopolymerization additive manufacturing (AM), enabling vat photopolymerization AM hybrid…

763

Abstract

Purpose

This study aims to present a vision-guided robotic system design for application in vat photopolymerization additive manufacturing (AM), enabling vat photopolymerization AM hybrid with injection molding process.

Design/methodology/approach

In the system, a robot equipped with a camera and a custom-made gripper as well as driven by a visual servoing (VS) controller is expected to perceive objective, handle variation, connect multi-process steps in soft tooling process and realize automation of vat photopolymerization AM. Meanwhile, the vat photopolymerization AM printer is customized in both hardware and software to interact with the robotic system.

Findings

By ArUco marker-based vision-guided robotic system, the printing platform can be manipulated in arbitrary initial position quickly and robustly, which constitutes the first step in exploring automation of vat photopolymerization AM hybrid with soft tooling process.

Originality/value

The vision-guided robotic system monitors and controls vat photopolymerization AM process, which has potential for vat photopolymerization AM hybrid with other mass production methods, for instance, injection molding.

Details

Journal of Intelligent Manufacturing and Special Equipment, vol. 4 no. 2
Type: Research Article
ISSN: 2633-6596

Keywords

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