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1 – 10 of 52
Article
Publication date: 19 June 2017

Jingli Yang, Zhen Sun and Yinsheng Chen

This paper aims to enhance the reliability of self-validating multifunctional sensors.

Abstract

Purpose

This paper aims to enhance the reliability of self-validating multifunctional sensors.

Design/methodology/approach

An effective fault detection, isolation and data recovery (FDIR) strategy by using kernel principal component analysis (KPCA) coupled with gray bootstrap and fault reconstruction methods.

Findings

The proposed FDIR strategy is able to the address fault detection, isolation and data recovery problem of self-validating multifunctional sensors efficiently.

Originality/value

A KPCA-based model which can overcome the limitation of existing linear-based models is used to achieve the fault detection task. By using gray bootstrap method, the position of all faulty sensitive units can be calculated even under the multiple faults situation. A reconstruction-based contribution method is adopted to evaluate the amplitudes of the fault signals, and the fault-free output of the faulty sensitive units can be used to replace the fault output.

Article
Publication date: 20 November 2020

Lydie Myriam Marcelle Amelot, Ushad Subadar Agathee and Yuvraj Sunecher

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The…

Abstract

Purpose

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.

Design/methodology/approach

Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.

Findings

The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.

Research limitations/implications

The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.

Originality/value

This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.

Details

African Journal of Economic and Management Studies, vol. 12 no. 1
Type: Research Article
ISSN: 2040-0705

Keywords

Article
Publication date: 24 November 2017

PengPeng Hu, Taku Komura, Duan Li, Ge Wu and Yueqi Zhong

The purpose of this paper is to present a novel framework of reconstructing the 3D textile model with synthesized texture.

Abstract

Purpose

The purpose of this paper is to present a novel framework of reconstructing the 3D textile model with synthesized texture.

Design/methodology/approach

First, a pipeline of 3D textile reconstruction based on KinectFusion is proposed to obtain a better 3D model. Second, “DeepTextures” method is applied to generate new textures for various three-dimensional textile models.

Findings

Experimental results show that the proposed method can conveniently reconstruct a three-dimensional textile model with synthesized texture.

Originality/value

A novel pipeline is designed to obtain 3D high-quality textile models based on KinectFusion. The accuracy and robustness of KinectFusion are improved via a turntable. To the best of the authors’ knowledge, this is the first paper to explore the synthesized textile texture for the 3D textile model. This is not only simply mapping the texture onto the 3D model, but also exploring the application of artificial intelligence in the field of textile.

Details

International Journal of Clothing Science and Technology, vol. 29 no. 6
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 15 March 2018

PengPeng Hu, Duan Li, Ge Wu, Taku Komura, Dongliang Zhang and Yueqi Zhong

Currently, a common method of reconstructing mannequin is based on the body measurements or body features, which only preserve the body size lacking of the accurate body…

702

Abstract

Purpose

Currently, a common method of reconstructing mannequin is based on the body measurements or body features, which only preserve the body size lacking of the accurate body geometric shape information. However, the same human body measurement does not equal to the same body shape. This may result in an unfit garment for the target human body. The purpose of this paper is to propose a novel scanning-based pipeline to reconstruct the personalized mannequin, which preserves both body size and body shape information.

Design/methodology/approach

The authors first capture the body of a subject via 3D scanning, and a statistical body model is fit to the scanned data. This results in a skinned articulated model of the subject. The scanned body is then adjusted to be pose-symmetric via linear blending skinning. The mannequin part is then extracted. Finally, a slice-based method is proposed to generate a shape-symmetric 3D mannequin.

Findings

A personalized 3D mannequin can be reconstructed from the scanned body. Compared to conventional methods, the method can preserve both the size and shape of the original scanned body. The reconstructed mannequin can be imported directly into the apparel CAD software. The proposed method provides a step for digitizing the apparel manufacturing.

Originality/value

Compared to the conventional methods, the main advantage of the authors’ system is that the authors can preserve both size and geometry of the original scanned body. The main contributions of this paper are as follows: decompose the process of the mannequin reconstruction into pose symmetry and shape symmetry; propose a novel scanning-based pipeline to reconstruct a 3D personalized mannequin; and present a slice-based method for the symmetrization of the 3D mesh.

Details

International Journal of Clothing Science and Technology, vol. 30 no. 2
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 26 September 2008

Li Lijun, Guan Tao, Ren Bo, Yao Xiaowen and Wang Cheng

The purpose of this paper is to propose a novel registration method using Euclidean reconstruction and natural features tracking for AR‐based assembly guidance systems.

Abstract

Purpose

The purpose of this paper is to propose a novel registration method using Euclidean reconstruction and natural features tracking for AR‐based assembly guidance systems.

Design/methodology/approach

The method operates in two steps: offline Euclidean reconstruction and online tracking. Offline stage involves obtaining the structure of scene using Euclidean reconstruction technique. The classification trees are constructed using affine transform for online initialization. In tracking, the classification‐based wide baseline matching strategy and Td,d test are used to get a fast and accurate initialization for the first frame after which a modified optical flow tracker is used to fulfill the task of feature tracking in the real‐time video sequences. The four specified points are transferred to the current image to compute the registration matrix for augmentation.

Findings

Firstly, Euclidean reconstruction was used instead of projective reconstruction to get the projections of predefined features. Compared with the six points needed in projective reconstruction‐based method, this method can run normally even when only four features are successfully tracked. Secondly, an adaptive strategy was proposed to adjust the classification trees using the tracked features in online stage by which one can initialize or reinitialize the system, even with large difference between the first and reference images.

Originality/value

Some indoor and outdoor experiments are provided to validate the performance of the proposed method.

Details

Assembly Automation, vol. 28 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 9 October 2019

Ayman Assem, Sherif Abdelmohsen and Mohamed Ezzeldin

Cities lying within conflict zones have continually faced hardships of both war aftermath and long-term sustainable reconstruction. Challenges have surpassed the typical…

Abstract

Purpose

Cities lying within conflict zones have continually faced hardships of both war aftermath and long-term sustainable reconstruction. Challenges have surpassed the typical question of recovery from post-conflict trauma, preserving urban heritage and iconic elements of the built environment, to face issues of critical decision making, rebuilding effectiveness and funding mechanisms, leading to time-consuming processes that lack adequate consistent long-term management. Some approaches have explored methods of effective long-term city reconstruction management but have not fully developed comprehensive approaches that alleviate the management of such complex processes. The paper aims to discuss these issues.

Design/methodology/approach

The authors devise an approach for the smart management of post-conflict city reconstruction. The authors focus on evaluation, strategic planning, reconstruction projects and implementation. The authors integrate building information modeling and geographic/geospatial information systems in a platform that allows for real-time analysis, reporting, strategic planning and decision making for managing reconstruction operations and projects among involved stakeholders including government agencies, funding organizations, city managers and public participants.

Findings

The approach suggested a smart management system for the reconstruction process of post-conflict cities. Implementing this system was shown to provide a multi-objective solution for post-conflict city reconstruction based on its interlinked modules.

Research limitations/implications

Results may lack generalizability and require testing on several cases to provide rigorous findings for different case studies.

Practical implications

Implications include developing smart management systems for use by city managers and government authorities in post-conflict zones, as well as bottom-up decision making by including participant citizens especially populations in the diaspora.

Originality/value

The approach offers an integrated platform that informs city reconstruction decision makers, allowing for strategic planning tools for efficient planning, monitoring tools for continuous management during and after reconstruction, and effective platforms for communication among all stakeholders.

Details

Archnet-IJAR: International Journal of Architectural Research, vol. 14 no. 2
Type: Research Article
ISSN: 2631-6862

Keywords

Article
Publication date: 14 June 2013

Christian Ivancsits and Min‐Fan Ricky Lee

This paper aims to address three major issues in the development of a vision‐based navigation system for small unmanned aerial vehicles (UAVs) which can be characterized…

1013

Abstract

Purpose

This paper aims to address three major issues in the development of a vision‐based navigation system for small unmanned aerial vehicles (UAVs) which can be characterized as follows: technical constraints, robust image feature matching and an efficient and precise method for visual navigation.

Design/methodology/approach

The authors present and evaluate methods for their solution such as wireless networked control, highly distinctive feature descriptors (HDF) and a visual odometry system.

Findings

Proposed feature descriptors achieve significant improvements in computation time by detaching the explicit scale invariance of the widely used scale invariant feature transform. The feasibility of wireless networked real‐time control for vision‐based navigation is evaluated in terms of latency and data throughput. The visual odometry system uses a single camera to reconstruct the camera path and the structure of the environment, and achieved and error of 1.65 percent w.r.t total path length on a circular trajectory of 9.43 m.

Originality/value

The originality/value lies in the contribution of the presented work to the solution of visual odometry for small unmanned aerial vehicles.

Article
Publication date: 13 December 2019

Yang Li and Xuhua Hu

The purpose of this paper is to solve the problem of information privacy and security of social users. Mobile internet and social network are more and more deeply…

Abstract

Purpose

The purpose of this paper is to solve the problem of information privacy and security of social users. Mobile internet and social network are more and more deeply integrated into people’s daily life, especially under the interaction of the fierce development momentum of the Internet of Things and diversified personalized services, more and more private information of social users is exposed to the network environment actively or unintentionally. In addition, a large amount of social network data not only brings more benefits to network application providers, but also provides motivation for malicious attackers. Therefore, under the social network environment, the research on the privacy protection of user information has great theoretical and practical significance.

Design/methodology/approach

In this study, based on the social network analysis, combined with the attribute reduction idea of rough set theory, the generalized reduction concept based on multi-level rough set from the perspectives of positive region, information entropy and knowledge granularity of rough set theory were proposed. Furthermore, it was traversed on the basis of the hierarchical compatible granularity space of the original information system and the corresponding attribute values are coarsened. The selected test data sets were tested, and the experimental results were analyzed.

Findings

The results showed that the algorithm can guarantee the anonymity requirement of data publishing and improve the effect of classification modeling on anonymous data in social network environment.

Research limitations/implications

In the test and verification of privacy protection algorithm and privacy protection scheme, the efficiency of algorithm and scheme needs to be tested on a larger data scale. However, the data in this study are not enough. In the following research, more data will be used for testing and verification.

Practical implications

In the context of social network, the hierarchical structure of data is introduced into rough set theory as domain knowledge by referring to human granulation cognitive mechanism, and rough set modeling for complex hierarchical data is studied for hierarchical data of decision table. The theoretical research results are applied to hierarchical decision rule mining and k-anonymous privacy protection data mining research, which enriches the connotation of rough set theory and has important theoretical and practical significance for further promoting the application of this theory. In addition, combined the theory of secure multi-party computing and the theory of attribute reduction in rough set, a privacy protection feature selection algorithm for multi-source decision table is proposed, which solves the privacy protection problem of feature selection in distributed environment. It provides a set of effective rough set feature selection method for privacy protection classification mining in distributed environment, which has practical application value for promoting the development of privacy protection data mining.

Originality/value

In this study, the proposed algorithm and scheme can effectively protect the privacy of social network data, ensure the availability of social network graph structure and realize the need of both protection and sharing of user attributes and relational data.

Details

Library Hi Tech, vol. 40 no. 1
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 24 September 2019

Kun Wei, Yong Dai and Bingyin Ren

This paper aims to propose an identification method based on monocular vision for cylindrical parts in cluttered scene, which solves the issue that iterative closest point…

Abstract

Purpose

This paper aims to propose an identification method based on monocular vision for cylindrical parts in cluttered scene, which solves the issue that iterative closest point (ICP) algorithm fails to obtain global optimal solution, as the deviation from scene point cloud to target CAD model is huge in nature.

Design/methodology/approach

The images of the parts are captured at three locations by a camera amounted on a robotic end effector to reconstruct initial scene point cloud. Color signatures of histogram of orientations (C-SHOT) local feature descriptors are extracted from the model and scene point cloud. Random sample consensus (RANSAC) algorithm is used to perform the first initial matching of point sets. Then, the second initial matching is conducted by proposed remote closest point (RCP) algorithm to make the model get close to the scene point cloud. Levenberg Marquardt (LM)-ICP is used to complete fine registration to obtain accurate pose estimation.

Findings

The experimental results in bolt-cluttered scene demonstrate that the accuracy of pose estimation obtained by the proposed method is higher than that obtained by two other methods. The position error is less than 0.92 mm and the orientation error is less than 0.86°. The average recognition rate is 96.67 per cent and the identification time of the single bolt does not exceed 3.5 s.

Practical implications

The presented approach can be applied or integrated into automatic sorting production lines in the factories.

Originality/value

The proposed method improves the efficiency and accuracy of the identification and classification of cylindrical parts using a robotic arm.

Article
Publication date: 10 August 2010

Bahar Durmaz, Stephen Platt and Tan Yigitcanlar

The paper aims to examine the role of creative industries in general and the film industry in particular for place‐making, spatial development, tourism, and the formation…

3400

Abstract

Purpose

The paper aims to examine the role of creative industries in general and the film industry in particular for place‐making, spatial development, tourism, and the formation of creative cities.

Design/methodology/approach

The article reveals the preliminary findings of two case studies from Beyoglu, Istanbul, and Soho, London.

Findings

The research found a relation between place and creativity and the positive contribution to creativity of being in a city center. Among the creative industries, the film industry plays an important role in the economic and spatial development of cities by fostering endogenous creativeness, attracting exogenous talent, and contributing to the formation of places that creative cities require.

Originality/value

The paper raises interesting questions about the importance of place to creativity, also questioning whether creative industries can be a driver for regeneration.

Details

International Journal of Culture, Tourism and Hospitality Research, vol. 4 no. 3
Type: Research Article
ISSN: 1750-6182

Keywords

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