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Article
Publication date: 29 April 2021

Omobolanle Ruth Ogunseiju, Johnson Olayiwola, Abiola Abosede Akanmu and Chukwuma Nnaji

Construction action recognition is essential to efficiently manage productivity, health and safety risks. These can be achieved by tracking and monitoring construction…

Abstract

Purpose

Construction action recognition is essential to efficiently manage productivity, health and safety risks. These can be achieved by tracking and monitoring construction work. This study aims to examine the performance of a variant of deep convolutional neural networks (CNNs) for recognizing actions of construction workers from images of signals of time-series data.

Design/methodology/approach

This paper adopts Inception v1 to classify actions involved in carpentry and painting activities from images of motion data. Augmented time-series data from wearable sensors attached to worker's lower arms are converted to signal images to train an Inception v1 network. Performance of Inception v1 is compared with the highest performing supervised learning classifier, k-nearest neighbor (KNN).

Findings

Results show that the performance of Inception v1 network improved when trained with signal images of the augmented data but at a high computational cost. Inception v1 network and KNN achieved an accuracy of 95.2% and 99.8%, respectively when trained with 50-fold augmented carpentry dataset. The accuracy of Inception v1 and KNN with 10-fold painting augmented dataset is 95.3% and 97.1%, respectively.

Research limitations/implications

Only acceleration data of the lower arm of the two trades were used for action recognition. Each signal image comprises 20 datasets.

Originality/value

Little has been reported on recognizing construction workers' actions from signal images. This study adds value to the existing literature, in particular by providing insights into the extent to which a deep CNN can classify subtasks from patterns in signal images compared to a traditional best performing shallow network.

Details

Smart and Sustainable Built Environment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2046-6099

Keywords

Content available
Article
Publication date: 1 April 2021

Arunit Maity, P. Prakasam and Sarthak Bhargava

Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF…

Abstract

Purpose

Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.

Design/methodology/approach

A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.

Findings

It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.

Originality/value

The generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

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Article
Publication date: 24 May 2021

Arya Panji Pamuncak, Mohammad Reza Salami, Augusta Adha, Bambang Budiono and Irwanda Laory

Structural health monitoring (SHM) has gained significant attention due to its capability in providing support for efficient and optimal bridge maintenance activities…

Abstract

Purpose

Structural health monitoring (SHM) has gained significant attention due to its capability in providing support for efficient and optimal bridge maintenance activities. However, despite the promising potential, the effectiveness of SHM system might be hindered by unprecedented factors that impact the continuity of data collection. This research presents a framework utilising convolutional neural network (CNN) for estimating structural response using environmental variations.

Design/methodology/approach

The CNN framework is validated using monitoring data from the Suramadu bridge monitoring system. Pre-processing is performed to transform the data into data frames, each containing a sequence of data. The data frames are divided into training, validation and testing sets. Both the training and validation sets are employed to train the CNN models while the testing set is utilised for evaluation by calculating error metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). Comparison with other machine learning approaches is performed to investigate the effectiveness of the CNN framework.

Findings

The CNN models are able to learn the trend of cable force sensor measurements with the ranges of MAE between 10.23 kN and 19.82 kN, MAPE between 0.434% and 0.536% and RMSE between 13.38 kN and 25.32 kN. In addition, the investigation discovers that the CNN-based model manages to outperform other machine learning models.

Originality/value

This work investigates, for the first time, how cable stress can be estimated using temperature variations. The study presents the first application of 1-D CNN regressor on data collected from a full-scale bridge. This work also evaluates the comparison between CNN regressor and other techniques, such as artificial neutral network (ANN) and linear regression, in estimating bridge cable stress, which has not been performed previously.

Details

Engineering Computations, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0264-4401

Keywords

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Book part
Publication date: 19 October 2020

Abstract

Details

The Econometrics of Networks
Type: Book
ISBN: 978-1-83867-576-9

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Abstract

Details

Travel Survey Methods
Type: Book
ISBN: 978-0-08-044662-2

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Article
Publication date: 19 October 2010

Usama Abdulazim Mohamed, Galal H. Galal‐Edeen and Adel A. El‐Zoghbi

The previous generations of implemented B2B e‐commerce hub solutions (e‐Marketplaces) did not successfully fulfil the requirements of buyers and suppliers (“Participants”…

Abstract

Purpose

The previous generations of implemented B2B e‐commerce hub solutions (e‐Marketplaces) did not successfully fulfil the requirements of buyers and suppliers (“Participants”) in different business domains to carry out their daily business and online commercial transactions with one another because of their inappropriateness, and lack of flexibility. The limitations of these provided solutions came from a lot of architectural and technological challenges in the provided technical architectures that were used to build these solutions. This research aims to provide a proposed architecture to build integrated B2B e‐Commerce hub solutions. It also aims to make use of bottom‐up/top‐down approaches to building an integrated solution and to resolve the reasons for the failure of previous generations of B2B e‐commerce hubs.

Design/methodology/approach

The research uses the EDI reference model, which is provided by the ISO organization to survey and analyze the challenges of previous generations of B2B e‐Commerce hubs solutions and their architectures. The study develops a proposed solution architecture based on the recent approaches to building IOSs to build a B2B e‐commerce hub solution architecture that can be used to implement vertical B2B e‐commerce hubs (vertical e‐Marketplaces). The paper assesses the capabilities of the proposed solution architecture for building vertical B2B e‐Marketplaces by applying the proposed architecture to the building of a vertical B2B e‐Marketplace for the oil and gas sector in Egypt.

Findings

Previous B2B e‐Commerce hub initiatives failed to extend their products and services to their “Participants”, and required substantial investment and effort from each “Participant” to join such a B2B e‐Commerce hub. The failure of these IOS projects lies in their inability to integrate B2B e‐Commerce networks based on IOS and consequently, they supported very few partners and “Participants”. These IOS approaches did not resolve the existing challenges of B2B e‐Commerce hubs, especially in the realm of interoperability.

Originality/value

The main contribution of the proposed architecture comes from the creation of a clear automatic path between a business requirements layer and a technology layer by combining both Service Oriented Architecture and management requirements in a single framework to provide dynamic products and flexible services. It provides a complete Multi Channel Framework to resolve the interoperability challenges.

Details

Journal of Enterprise Information Management, vol. 23 no. 6
Type: Research Article
ISSN: 1741-0398

Keywords

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Book part
Publication date: 29 February 2008

Abstract

Details

Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

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Article
Publication date: 17 August 2020

Dimitrios Sakkos, Edmond S. L. Ho, Hubert P. H. Shum and Garry Elvin

A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In our pilot study published in, Sakkos:SKIMA…

Abstract

Purpose

A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In our pilot study published in, Sakkos:SKIMA 2019, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly but also features semantic transformations of illumination which enhance the generalisation of the model.

Design/methodology/approach

In our pilot study published in SKIMA 2019, the proposed framework successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask generated randomly. In this paper, we further enhance the data augmentation framework by proposing new variations in image appearance both locally and globally.

Findings

Experimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place.

Originality/value

Such data augmentation allows us to effectively train an illumination-invariant deep learning model for BGS. We further propose a post-processing method that removes noise from the output binary map of segmentation, resulting in a cleaner, more accurate segmentation map that can generalise to multiple scenes of different conditions. We show that it is possible to train deep learning models even with very limited training samples. The source code of the project is made publicly available at https://github.com/dksakkos/illumination_augmentation

Details

Journal of Enterprise Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0398

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Article
Publication date: 5 February 2021

Changro Lee

Prior studies on the application of deep-learning techniques have focused on enhancing computation algorithms. However, the amount of data is also a key element when…

Abstract

Purpose

Prior studies on the application of deep-learning techniques have focused on enhancing computation algorithms. However, the amount of data is also a key element when attempting to achieve a goal using a quantitative approach, which is often underestimated in practice. The problem of sparse sales data is well known in the valuation of commercial properties. This study aims to expand the limited data available to exploit the capability inherent in deep learning techniques.

Design/methodology/approach

The deep learning approach is used. Seoul, the capital of South Korea is selected as a case study area. Second, data augmentation is performed for properties with low trade volume in the market using a variational autoencoder (VAE), which is a generative deep learning technique. Third, the generated samples are added into the original dataset of commercial properties to alleviate data insufficiency. Finally, the accuracy of the price estimation is analyzed for the original and augmented datasets to assess the model performance.

Findings

The results using the sales datasets of commercial properties in Seoul, South Korea as a case study show that the augmented dataset by a VAE consistently shows higher accuracy of price estimation for all 30 trials, and the capabilities inherent in deep learning techniques can be fully exploited, promoting the rapid adoption of artificial intelligence skills in the real estate industry.

Originality/value

Although deep learning-based algorithms are gaining popularity, they are likely to show limited performance when data are insufficient. This study suggests an alternative approach to overcome the lack of data problem in property valuation.

Details

Property Management, vol. 39 no. 3
Type: Research Article
ISSN: 0263-7472

Keywords

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Article
Publication date: 1 February 1999

G. Zavarise and P. Wriggers

The numerical solution of contact problems via the penalty method yields approximate satisfaction of contact constraints. The solution can be improved using augmentation

Abstract

The numerical solution of contact problems via the penalty method yields approximate satisfaction of contact constraints. The solution can be improved using augmentation schemes. However their efficiency is strongly dependent on the value of the penalty parameter and usually results in a poor rate of convergence to the exact solution. In this paper we propose a new method to perform the augmentations. It is based on estimated values of the augmented Lagrangians. At each augmentation the converged state is used to extract some data. Such information updates a database used for the Lagrangian estimation. The prediction is primarily based on the evolution of the constraint violation with respect to the evolution of the contact forces. The proposed method is characterised by a noticeable efficiency in detecting nearly exact contact forces, and by superlinear convergence for the subsequent minimisation of the residual of constraints. Remarkably, the method is relatively insensitive to the penalty parameter. This allows a solution which fulfils the constraints very rapidly, even when using penalty values close to zero.

Details

Engineering Computations, vol. 16 no. 1
Type: Research Article
ISSN: 0264-4401

Keywords

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