An embedded system based on DSP platform and PCA-SVM algorithms for rapid beef meat freshness prediction and identification

Arsalane, Assia; Barbri, Noureddine El; Tabyaoui, Abdelmoumen; Klilou, Abdessamad; Rhofir, Karim; Halimi, A.

doi:10.1016/j.compag.2018.07.031

Cited by 44 publications

(14 citation statements)

References 20 publications

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“…A SVM algorithm is implemented on a digital signal processor (DSP) for signal prediction in classication and regression applications in (Zabalza et al, 2012), and despite the high consumption of time in some stages, the SVM implementation has been carried out successfully. Furthermore, the implementation of SVMs in DSPs for image tracking was also seen in (Assia Arsalanea, 2018) and (Xinghong Li, 2017), and both of them show that SVM has higher tracking precision and obtained a high speed of target tracking, which makes it possible to achieve real-time performance.…”

Section: Introductionmentioning

confidence: 90%

A Proposed Methodology for Online Implementation of a Support Vector Machine With Applications in Power Transformers Event Classification

Costa

Souza

Simões

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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The constant evolution of resources in computational processing and machine learning algorithms, combined with the increasing complexity of embedded systems, made the hardware implementation of machine learning models more viable. This paper proposes a methodology for online implementation of a support vector machine classifier through the development of a simple, concise, and easily adapted algorithm for data classification. The system was validated through the development of an application that classifies disturbances in a power transformer, followed by a comparison with the results obtained with the Library for Support Vector Machines (LIBSVM). Besides the very similar results when compared with the LIBSVM, the proposed methodology achieved high overall accuracy and fast classification time.

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Section: Introductionmentioning

confidence: 90%

A Proposed Methodology for Online Implementation of a Support Vector Machine With Applications in Power Transformers Event Classification

Costa

Souza

Simões

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

View full text Add to dashboard Cite

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“…SVM is a supervised learning algorithm for solving classification and regression prediction problems based on statistical learning theory [36], [37]. The main task is finding the smallest subset data for prediction, identifying outliers and derivations in sample space.…”

Section: ) Prediction Model For Quality Indicator Under Cold Storagementioning

confidence: 99%

Evaluation on Frozen Shellfish Quality by Blockchain Based Multi-Sensors Monitoring and SVM Algorithm During Cold Storage

et al. 2020

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IoT-enable monitoring can provide valuable information for the shellfish quality evaluation during cold storage condition. However, IoT based information storage relies on the centralized platform, it is possible to tamper. In this paper, we establish blockchain based multi-sensors (WSN) monitoring system to collect quality parameters and verify captured information for improving transparency and trust during cold storage. The implementation of the K-means and SVM algorithms were used in quality evaluation applications to classify and predict the quality loss of frozen shellfish. The results show blockchain based WSN monitoring can achieve the dynamic indicators continuous monitoring and ensures the data security and reliability. The proportion of the training set and the test set in the allowable deviation range is 88.89% and 87.17%. The root mean square error (RMSE) of training set and test set are 0.1502 and 0.1793 by SVM model. The performance of the K-means and SVM model has higher accuracy than BP model. This paper could help to reduce the risk of food losses and improve quality and safety management of frozen shellfish during cold storage. INDEX TERMS Blockchain, cold storage, frozen shellfish, K-means and SVM, IoT.

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“…Implementing SVM algorithms to real-time systems or embedded systems is also a popular approach. Zhang et al [ 21 ] implemented SVM to detect ventricular fibrillation in real time, Arsalane et al [ 22 ] deployed SVM to an embedded system for meat freshness evaluation based on machine vision. Dey et al [ 23 ] studied performance of SVM classification algorithms running on embedded processors and suggested parallel computations.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring

Mucha

2020

Sensors

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The aim of operational load monitoring is to make predictions about the remaining usability time of structures, which is extremely useful in aerospace industry where in-service life of aircraft structural components can be maximized, taking into account safety. In order to make such predictions, strain sensors are mounted to the structure, from which data are acquired during operational time. This allows to determine how many load cycles has the structure withstood so far. Continuous monitoring of the strain distribution of the whole structure can be complicated due to vicissitude nature of the loads. Sensors should be mounted in places where stress and strain accumulations occur, and due to experiencing variable loads, the number of required sensors may be high. In this work, different machine learning and artificial intelligence algorithms are implemented to predict the current safety factor of the structure in its most stressed point, based on relatively low number of strain measurements. Adaptive neuro-fuzzy inference systems (ANFIS), support-vector machines (SVM) and Gaussian processes for machine learning (GPML) are trained with simulation data, and their effectiveness is measured using data obtained from experiments. The proposed methods are compared to the earlier work where artificial neural networks (ANN) were proven to be efficiently used for reduction of the number of sensors in operational load monitoring processes. A numerical comparison of accuracy and computational time (taking into account possible real-time applications) between all considered methods is provided.

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An embedded system based on DSP platform and PCA-SVM algorithms for rapid beef meat freshness prediction and identification

Cited by 44 publications

References 20 publications

A Proposed Methodology for Online Implementation of a Support Vector Machine With Applications in Power Transformers Event Classification

A Proposed Methodology for Online Implementation of a Support Vector Machine With Applications in Power Transformers Event Classification

Evaluation on Frozen Shellfish Quality by Blockchain Based Multi-Sensors Monitoring and SVM Algorithm During Cold Storage

Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring

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