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Article

Haiyang Gu, Kaiqi Liu, Xingyi Huang, Quansheng Chen, Yanhui Sun and Chin Ping Tan

Parallel factor analysis (PARAFAC) coupled with support-vector machine (SVM) was carried out to identify and discriminate between the fluorescence spectroscopies of…

Abstract

Purpose

Parallel factor analysis (PARAFAC) coupled with support-vector machine (SVM) was carried out to identify and discriminate between the fluorescence spectroscopies of coconut water brands.

Design/methodology/approach

PARAFAC was applied to reduce three-dimensional data of excitation emission matrix (EEM) to two-dimensional data. SVM was applied to discriminate between six commercial coconut water brands in this study. The three largest variation data from fluorescence spectroscopy were extracted using the PARAFAC method as the input data of SVM classifiers.

Findings

The discrimination results of the six commercial coconut water brands were achieved by three SVM methods (Ga-SVM, PSO-SVM and Grid-SVM). The best classification accuracies were 100.00%, 96.43% and 94.64% for the training set, test set and CV accuracy.

Originality/value

The above results indicate that fluorescence spectroscopy combined with PARAFAC and SVM methods proved to be a simple and rapid detection method for coconut water and perhaps other beverages.

Details

British Food Journal, vol. 122 no. 10
Type: Research Article
ISSN: 0007-070X

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Article

AiHua Zhu, Caozheng Fu, JianWei Yang, Qiang Li, Jiao Zhang, Hongxiao Li and Kaiqi Zhang

This study aims to investigate the effect of time-varying passenger flow on the wheel wear of metro vehicles to provide a more accurate model for predicting wheel wear and…

Abstract

Purpose

This study aims to investigate the effect of time-varying passenger flow on the wheel wear of metro vehicles to provide a more accurate model for predicting wheel wear and a new idea for reducing wheel wear.

Design/methodology/approach

Sectional passage flow data were collected from an operational metro line. A wheel wear simulation based on time-varying passenger flow was performed via the SIMPACK software to obtain the worn wheel profile and wear distribution. The simulation involves the following models: vehicle system dynamics model, wheel-track rolling contact model, wheel wear model and variable load application model. Later, the simulation results were compared with those obtained under the traditional constant load condition and the measured wear data.

Findings

For different distances traveled by the metro vehicle, the simulated wheel profile and wear distribution under the variable load remained closer to the measurements than those obtained under the constant load. As the distance traveled increased, the depth and position of maximum wear and wear growth rate under the variable load tended to approach the corresponding measured values. In contrast, the simulation results under the constant load differed greatly from the measured values. This suggests that the model accuracy under the variable load was significantly improved and the simulation results can offer a more accurate basis for wear prediction.

Practical implications

These results will help to predict wheel wear more accurately and provide a new idea for simulating wheel wear of metro vehicles. At the same time, measures for reducing wheel wear were discussed from the perspective of passenger flow changes.

Originality/value

Existing research on the wheel wear of metro vehicles is mainly based on the constant load condition, which is quite different from the variable load condition where the passenger flow in real vehicles varies over time. A method of simulating wheel wear based on time-varying load is proposed in this paper. The proposed method shows a great improvement in simulation accuracy compared to traditional methods and can provide a more accurate basis for wear prediction and wheel repair.

Details

Industrial Lubrication and Tribology, vol. 71 no. 9
Type: Research Article
ISSN: 0036-8792

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Article

Jinliang Liu, Yanmin Jia, Guanhua Zhang and Jiawei Wang

During service period, due to the overload or other non-load factors, diagonal cracks of the pre-stressed concrete beam are seriously affecting the safety of the bridge…

Abstract

Purpose

During service period, due to the overload or other non-load factors, diagonal cracks of the pre-stressed concrete beam are seriously affecting the safety of the bridge structure. The purpose of this paper is to quickly realize the shear bearing capacity and shear stiffness through maximum width of the diagonal cracks and make correct judgments.

Design/methodology/approach

Through the shear failure test of four test beams, collecting data of diagonal cracks and shear stiffness loss value. According to the deformation curve of the shear stiffness, and combined with the calculation formula of the maximum width of diagonal cracks, the formula for calculating the effective shear stiffness based on the maximum width of diagonal cracks is deduced, then the results are verified by test data. Data regression method is used to establish the effective shear stiffness loss ratio calculation formula, the maximum width of diagonal cracks used as a variable factor, and the accuracy of this formula is verified by comparing the shear failure test results of pre-stressed hollow plates.

Findings

With the increase in width of the diagonal crack, the loss rate of shear stiffness of the concrete beams is initially fast and then becomes slow. The calculation formulae for shear stiffness based on the maximum width of the diagonal cracks were deduced, and the feasibility and accuracy of the formulae were verified by analysis and calculation of shear test data.

Originality/value

A method for quickly determine the shear stiffness loss of structures by using maximum width of the diagonal cracks is established, and using this method, engineers can quickly determine effective shear stiffness loss ratio, without complex calculations. So this method not only ensures the safety of human life, but also saves money.

Details

International Journal of Structural Integrity, vol. 9 no. 4
Type: Research Article
ISSN: 1757-9864

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Article

K. Satya Sujith and G. Sasikala

Object detection models have gained considerable popularity as they aid in lot of applications, like monitoring, video surveillance, etc. Object detection through the…

Abstract

Purpose

Object detection models have gained considerable popularity as they aid in lot of applications, like monitoring, video surveillance, etc. Object detection through the video tracking faces lot of challenges, as most of the videos obtained as the real time stream are affected due to the environmental factors.

Design/methodology/approach

This research develops a system for crowd tracking and crowd behaviour recognition using hybrid tracking model. The input for the proposed crowd tracking system is high density crowd videos containing hundreds of people. The first step is to detect human through visual recognition algorithms. Here, a priori knowledge of location point is given as input to visual recognition algorithm. The visual recognition algorithm identifies the human through the constraints defined within Minimum Bounding Rectangle (MBR). Then, the spatial tracking model based tracks the path of the human object movement in the video frame, and the tracking is carried out by extraction of color histogram and texture features. Also, the temporal tracking model is applied based on NARX neural network model, which is effectively utilized to detect the location of moving objects. Once the path of the person is tracked, the behaviour of every human object is identified using the Optimal Support Vector Machine which is newly developed by combing SVM and optimization algorithm, namely MBSO. The proposed MBSO algorithm is developed through the integration of the existing techniques, like BSA and MBO.

Findings

The dataset for the object tracking is utilized from Tracking in high crowd density dataset. The proposed OSVM classifier has attained improved performance with the values of 0.95 for accuracy.

Originality/value

This paper presents a hybrid high density video tracking model, and the behaviour recognition model. The proposed hybrid tracking model tracks the path of the object in the video through the temporal tracking and spatial tracking. The features train the proposed OSVM classifier based on the weights selected by the proposed MBSO algorithm. The proposed MBSO algorithm can be regarded as the modified version of the BSO algorithm.

Content available
Article

Falah Alsaqre and Osama Almathkour

Classifying moving objects in video sequences has been extensively studied, yet it is still an ongoing problem. In this paper, we propose to solve moving objects…

Abstract

Classifying moving objects in video sequences has been extensively studied, yet it is still an ongoing problem. In this paper, we propose to solve moving objects classification problem via an extended version of two-dimensional principal component analysis (2DPCA), named as category-wise 2DPCA (CW2DPCA). A key component of the CW2DPCA is to independently construct optimal projection matrices from object-specific training datasets and produce category-wise feature spaces, wherein each feature space uniquely captures the invariant characteristics of the underlying intra-category samples. Consequently, on one hand, CW2DPCA enables early separation among the different object categories and, on the other hand, extracts effective discriminative features for representing both training datasets and test objects samples in the classification model, which is a nearest neighbor classifier. For ease of exposition, we consider human/vehicle classification, although the proposed CW2DPCA-based classification framework can be easily generalized to handle multiple objects classification. The experimental results prove the effectiveness of CW2DPCA features in discriminating between humans and vehicles in two publicly available video datasets.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2210-8327

Keywords

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