Search results

1 – 10 of over 4000
Article
Publication date: 20 February 2020

Haiming Liang, Xiao Zhang, Fang Fang and Xi Chen

The aim of this paper is to propose an optimization method for determining the emergency action, in which the compatibility between emergency alternatives and the…

Abstract

Purpose

The aim of this paper is to propose an optimization method for determining the emergency action, in which the compatibility between emergency alternatives and the collaborative relationship between departments are considered.

Design/methodology/approach

The individual emergency cost and individual emergency effect of each emergency alternative are calculated. And the collaborative emergency cost and collaborative emergency effect associated with a pair of emergency alternatives are calculated. Then, a bi-objective programming model maximizing the total emergency effect and minimizing the total emergency cost is constructed. A novel nondominated sorting genetic algorithm II (NNSGA II) is designed to solve the constructed model, subsequently. Finally, an example is given to illustrate the use of the proposed method, and the performance of NNSGA II is evaluated through a simulation experiment.

Findings

This paper proposes an effective method to manage complex emergency events that requires the coordinations of multiple departments. Also, this paper provides a new algorithm to determine an appropriate emergency action that performs well in managing both the emergency cost and emergency effect.

Originality/value

The findings contribute to the current methods in the field of emergency management. The method is used for dealing with the individual information of emergency alternatives and the collaborative information associated with a pair of alternatives.

Article
Publication date: 5 March 2018

Xi Chen, Yanfeng Chen, Bo Zhang, Dongyuan Qiu and Zi Li

This study aims to predict the unstable period-1 orbit (UPO-1) of DC–DC converters and find analytical expressions to describe it.

Abstract

Purpose

This study aims to predict the unstable period-1 orbit (UPO-1) of DC–DC converters and find analytical expressions to describe it.

Design/methodology/approach

Nonlinear dynamical phenomena of a peak–current–mode controlled direct current–direct current (DC–DC) Boost converter are discussed briefly first. Then fast fourier transform (FFT) analysis of state variables under different dynamic states is provided, and the characteristic of the harmonic content in different states is summarized. Following these, a scientific hypothesis on the harmonic content of the UPO-1 is presented, and the Equivalent Small Parameter method is adopted then, thus analytic-form expressions of the UPO-1 can be derived.

Findings

According to results of theoretical analysis, numerical simulations and experiments, this paper illustrates that, like stable period-1 orbit, the UPO-1 is also made up of the DC component and harmonics with integer times of switching frequency.

Originality/value

This work provides an unreported approach for extracting the UPO-1 of DC–DC converters, which is mainly based on predicting the harmonic structure information of the orbit. According to experimental parts of the work, it shows that the stabilizer can be designed easier by using the proposed method. Additionally, from a broader perspective, the results could also have implications in a wide class of forced oscillation systems.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 37 no. 2
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 24 August 2021

Yue Xu, Qingcong Wu, Bai Chen and Xi Chen

For the robot-assisted upper limb rehabilitation training process of the elderly with damaged neuromuscular channels and hemiplegic patients, bioelectric signals are added…

Abstract

Purpose

For the robot-assisted upper limb rehabilitation training process of the elderly with damaged neuromuscular channels and hemiplegic patients, bioelectric signals are added to transform the traditional passive training mode into the active training mode.

Design/methodology/approach

This paper mainly builds a steady-state visual stimulation interface, an electroencephalography (EEG) signal processing platform and an exoskeleton robot verification platform. The target flashing stimulation blocks provide visual stimulation at the specified position according to the specified frequency and stimulate EEG signals of different frequency bands. The EEG signal-processing platform constructed in this paper removes the noise by using Butterworth band-pass filtering and common average reference filtering on the obtained signals. Further, the features are extracted to identify the volunteer’s active movement intention through the canonical correlation analysis (CCA) method. The classification results are transmitted to the upper limb exoskeleton robot control system, combined with the position and posture of the exoskeleton robot to control the joint motion of robot.

Findings

Through a large number of experimental studies, the average accuracy of offline recognition of motion intention recognition can reach 86.1%. The control strategy with a three-instruction judgment method reduces the average execution error rate of the entire control system to 6.75%. Online experiments verify the feasibility of the steady-state visual evoked potentials (SSVEP)-based rehabilitation system.

Originality/value

An EEG signal analysis method based on SSVEP is integrated into the control of an upper limb exoskeleton robot, transforming the traditional passive training mode into the active training mode. The device used to record EEG is of very low cost, which has the potential to promote the rehabilitation system for further widely applications.

Details

Industrial Robot: the international journal of robotics research and application, vol. 49 no. 1
Type: Research Article
ISSN: 0143-991X

Keywords

Content available
Article
Publication date: 28 January 2020

Ruibin Geng, Shichao Wang, Xi Chen, Danyang Song and Jie Yu

With the popularity of social media and, recently, live streaming, internet celebrity endorsements have become a prevalent approach to content marketing for e-commerce…

12344

Abstract

Purpose

With the popularity of social media and, recently, live streaming, internet celebrity endorsements have become a prevalent approach to content marketing for e-commerce sellers. Despite the widespread use of social media and online communities, empirical studies investigating the economic value of user-generated content (UGC) and marketer-generated content (MGC) still lag behind. The purpose of this paper is to contribute both theoretically and practically to capture both first-order effects and second-order effects of internet celebrity endorsements on marketing outcomes in an e-commerce context.

Design/methodology/approach

This study conducts a cross-sectional regression to evaluate the economic value of internet celebrity endorsement, and a panel vector autoregressive model is adopted to examine the relationship between celebrities’ and consumers’ content marketing behaviors and e-commerce sales performance. The authors also adopt look-ahead propensity-score matching technique to correct for selection bias.

Findings

The empirical results show that the content generation efforts of marketers and the interaction behaviors between marketers and consumers will significantly influence the e-commerce sales, which refers to the first-order effects of internet celebrity endorsement. Moreover, interactions within the fan community exert second-order effects of content marketing on sales performance.

Originality/value

This paper provides new insights for e-commerce retailers to evaluate the economic values of internet celebrity endorsement, a new content marketing practice in e-commerce platform.

Details

Industrial Management & Data Systems, vol. 120 no. 3
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 24 March 2022

Yi Huang and Xi Chen

This paper aims to characterize the relationship between the interelectrode capacitance (C) of metal-oxide-semiconductor field-effect transistors (MOSFETs) and the applied…

Abstract

Purpose

This paper aims to characterize the relationship between the interelectrode capacitance (C) of metal-oxide-semiconductor field-effect transistors (MOSFETs) and the applied bias voltage (V) by a fractional-order equivalent model.

Design/methodology/approach

A Riemann–Liouville-type fractional-order equivalent model is proposed for the CV characteristic of MOSFETs, which is based on the mathematical relationship between fractional calculus and the semiconductor physical model for the interelectrode capacitance of metal oxide semiconductor structure. The CV characteristic data of an N-channel MOSFET are obtained by Silvaco TCAD simulation. A differential evolution-based offline scheme is exploited for the parameter identification of the proposed model.

Findings

According to the results of theoretical analysis, mathematical derivation, simulation and comparison, this paper illustrates that, along with the variation of bias voltage applied, the interelectrode capacitance (C) of MOSFETs performs a fractional-order characteristic.

Originality/value

This work uncovers the fractional-order characteristic of MOSFETs’ interelectrode capacitance. By the proposed model, the influence of doping concentration on the gate leakage parasitic capacitance of MOSFETs can be revealed. In the pre-defined doping concentration range, the relative error of the proposed model is less than 5% for the description of CV characteristics of metal-oxide-semiconductor field-effect transistors (MOSFETs). Compared to some existing models, the proposed model has advantages in both model accuracy and model complexity, and the variation of model parameters can directly reflect the relationship between the characteristics of MOSFETs and the doping concentration of materials. Accordingly, the proposed model can be used for the microcosmic mechanism analysis of MOSFETs. The results of the analysis produce evidence for the widespread existence of fractional-order characteristics in the physical world.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 41 no. 5
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 1 October 2021

Xi Chen, Youheng Fu, Fanrong Kong, Runsheng Li, Yu Xiao, Jiannan Hu and Haiou Zhang

The major problem that limits the widespread use of WAAM technology is the forming quality. However, most of the current research focuses on post-process detections that…

Abstract

Purpose

The major problem that limits the widespread use of WAAM technology is the forming quality. However, most of the current research focuses on post-process detections that are time-consuming, expensive and destructive. This paper aims to achieve the on-line detection and classification of the common defects, including hump, deposition collapse, deviation, internal pore and surface slag inclusion.

Design/methodology/approach

This paper proposes an in-process multi-feature data fusion nondestructive testing method based on the temperature field of the WAAM process. A thermal imager is used to collect the temperature data of the deposition layer in real-time. Efficient processing methods are proposed in this paper, such as the temperature stack algorithm, width extraction algorithm and a classification model based on a residual neural network. Some features closely related to the forming quality were extracted, containing the profile image and width curve of the deposition layer and abnormal temperature features in longitudinal and cross-sections. These features are used to achieve the detection and classification of defects.

Findings

Thermal non-destructive testing is a potentially superior technology for in-process detection in the industrial field. Based on the temperature field, extracting the most relevant features of the defect information is crucial. This paper pushes current infrared (IR) monitoring methods toward real-time detection and proposes an in-process multi-feature data fusion non-destructive testing method based on the temperature field of the WAAM process.

Originality/value

In this paper, the single-layer and multi-layer WAAM samples are preset with various defects, such as hump, deposition collapse, deviation, pore and slag inclusion. A multi-feature nondestructive testing methodology is proposed to realize the in-process detection and classification of the defects. A temperature stack algorithm is proposed, which improves the detection accuracy of profile change and solves the problem of uneven temperature from arc striking to arc extinguishing. The combination of residual neural network greatly improves the accuracy and efficiency of detection.

Details

Rapid Prototyping Journal, vol. 28 no. 3
Type: Research Article
ISSN: 1355-2546

Keywords

Article
Publication date: 21 July 2020

Shuang Zhang, Song Xi Chen and Lei Lu

With the presence of pricing errors, the authors consider statistical inference on the variance risk premium (VRP) and the associated implied variance, constructed from…

Abstract

Purpose

With the presence of pricing errors, the authors consider statistical inference on the variance risk premium (VRP) and the associated implied variance, constructed from the option prices and the historic returns.

Design/methodology/approach

The authors propose a nonparametric kernel smoothing approach that removes the adverse effects of pricing errors and leads to consistent estimation for both the implied variance and the VRP. The asymptotic distributions of the proposed VRP estimator are developed under three asymptotic regimes regarding the relative sample sizes between the option data and historic return data.

Findings

This study reveals that existing methods for estimating the implied variance are adversely affected by pricing errors in the option prices, which causes the estimators for VRP statistically inconsistent. By analyzing the S&P 500 option and return data, it demonstrates that, compared with other implied variance and VRP estimators, the proposed implied variance and VRP estimators are more significant variables in explaining variations in the excess S&P 500 returns, and the proposed VRP estimates have the smallest out-of-sample forecasting root mean squared error.

Research limitations/implications

This study contributes to the estimation of the implied variance and the VRP and helps in the predictions of future realized variance and equity premium.

Originality/value

This study is the first to propose consistent estimations for the implied variance and the VRP with the presence of option pricing errors.

Details

China Finance Review International, vol. 11 no. 1
Type: Research Article
ISSN: 2044-1398

Keywords

Article
Publication date: 14 June 2022

Sheng Zhang, Peng Lan, Hai-Chao Li, Chen-Xi Tong and Daichao Sheng

Prediction of excess pore water pressure and estimation of soil parameters are the two key interests for consolidation problems, which can be mathematically quantified by…

Abstract

Purpose

Prediction of excess pore water pressure and estimation of soil parameters are the two key interests for consolidation problems, which can be mathematically quantified by a set of partial differential equations (PDEs). Generally, there are challenges in solving these two issues using traditional numerical algorithms, while the conventional data-driven methods require massive data sets for training and exhibit negative generalization potential. This paper aims to employ the physics-informed neural networks (PINNs) for solving both the forward and inverse problems.

Design/methodology/approach

A typical consolidation problem with continuous drainage boundary conditions is firstly considered. The PINNs, analytical, and finite difference method (FDM) solutions are compared for the forward problem, and the estimation of the interface parameters involved in the problem is discussed for the inverse problem. Furthermore, the authors also explore the effects of hyperparameters and noisy data on the performance of forward and inverse problems, respectively. Finally, the PINNs method is applied to the more complex consolidation problems.

Findings

The overall results indicate the excellent performance of the PINNs method in solving consolidation problems with various drainage conditions. The PINNs can provide new ideas with a broad application prospect to solve PDEs in the field of geotechnical engineering, and also exhibit a certain degree of noise resistance for estimating the soil parameters.

Originality/value

This study presents the potential application of PINNs for the consolidation of soils. Such a machine learning algorithm helps to obtain remarkably accurate solutions and reliable parameter estimations with fewer and average-quality data, which is beneficial in engineering practice.

Details

Engineering Computations, vol. 39 no. 7
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 17 May 2022

Lin Li, Xi Chen and Tie Zhang

Many metal workpieces have the characteristics of less texture, symmetry and reflectivity, which presents a challenge to existing pose estimation methods. The purpose of…

Abstract

Purpose

Many metal workpieces have the characteristics of less texture, symmetry and reflectivity, which presents a challenge to existing pose estimation methods. The purpose of this paper is to propose a pose estimation method for grasping metal workpieces by industrial robots.

Design/methodology/approach

Dual-hypothesis robust point matching registration network (RPM-Net) is proposed to estimate pose from point cloud. The proposed method uses the Point Cloud Library (PCL) to segment workpiece point cloud from scenes and a trained-well robust point matching registration network to estimate pose through dual-hypothesis point cloud registration.

Findings

In the experiment section, an experimental platform is built, which contains a six-axis industrial robot, a binocular structured-light sensor. A data set that contains three subsets is set up on the experimental platform. After training with the emulation data set, the dual-hypothesis RPM-Net is tested on the experimental data set, and the success rates of the three real data sets are 94.0%, 92.0% and 96.0%, respectively.

Originality/value

The contributions are as follows: first, dual-hypothesis RPM-Net is proposed which can realize the pose estimation of discrete and less-textured metal workpieces from point cloud, and second, a method of making training data sets is proposed using only CAD models with the visualization algorithm of the PCL.

Details

Industrial Robot: the international journal of robotics research and application, vol. 49 no. 6
Type: Research Article
ISSN: 0143-991X

Keywords

Open Access
Article
Publication date: 30 November 2021

Xi Chen and Hag-Min Kim

The psychic distance often hinders the interaction between cross-border e-commerce (CBEC) and consumers. This paper aims to discuss the issues of psychic distance of…

Abstract

Purpose

The psychic distance often hinders the interaction between cross-border e-commerce (CBEC) and consumers. This paper aims to discuss the issues of psychic distance of consumers in the CBEC. In addition, it attempts to explain the factors that affect psychic distance from three dimensions of culture, economy and politics and the two different shopping behaviors caused by psychic distance.

Design/methodology/approach

This research incorporates both theoretical and empirical studies. In this study, 249 validated questionnaires were selected from 300 Chinese CBEC consumers by snowball sampling, and the relationship between variables was tested using structural equation model (SEM). This was done through online research, and it is ensured that the data obtained are first-hand information.

Findings

The paper suggests the theoretical model operationalizing CBEC psychic distance and the empirical analysis results show that all the six influencing factors have a positive impact on the psychic distance of consumers. Logistics infrastructure barriers in the economic dimension are confirmed as the major influencing factor, and the significance of the political dimension is relatively small. Based on consumers' uncertainty of various kinds of information, psychic distance subconsciously causes consumers to deviate in the cross-shopping process.

Originality/value

Currently, research on e-commerce mainly focuses on saving trade costs and improving consumer welfare, while research on the internal impact of CBEC on consumers is insufficient. Psychic distance is a new concept in the field of cultural and social research. The originality of this paper is that the concept of psychic distance has been extended from overseas invested enterprises to research with CBEC consumers as the selected object. The obstacles of CBEC have been widely studied, but few are related to the closeness of consumers, or the inner feelings of consumers are ignored. In the context of CBEC, this paper lists the actual external factors and potential threats that may affect consumers' consumption concerns of CBEC from three dimensions. The real emotions of consumers in the face of these difficulties indirectly affect the purchase satisfaction and reduce the purchase desire. Consumer psychic distance is a real phenomenon in cross-border shopping, and it is almost inevitable for these difficulties. On the premise of inevitability, high psychic distance will slow down cross-border shopping in the eyes of consumers.

Details

International Trade, Politics and Development, vol. 5 no. 2
Type: Research Article
ISSN: 2586-3932

Keywords

1 – 10 of over 4000