Embedding metric learning into an extreme learning machine for scene recognition

Wang, Chen; Peng, Guohua; Baets, Bernard De

doi:10.1016/j.eswa.2022.117505

Cited by 18 publications

(5 citation statements)

References 47 publications

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“…The optimal network is obtained by exploring the balance among the complexity of the model and the training error (Ma et al, 2022). The ELM designs a single-layer feedforward network by randomly generating the input weights and biases of the hidden layers (Wang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed

Rajković

Jeromela

Pezo

et al. 2023

Journal of Food Composition and Analysis

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Section: Introductionmentioning

confidence: 99%

Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed

Rajković

Jeromela

Pezo

et al. 2023

Journal of Food Composition and Analysis

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“…By striking a balance between model complexity and training error, the optimal network is achieved [ 46 ]. ELM constructs a single-layer feed-forward network by randomly generating input weights and biases for the hidden layers [ 47 ].…”

Section: Methodsmentioning

confidence: 99%

Sunflower Oil Winterization Using the Cellulose-Based Filtration Aid—Investigation of Oil Quality during Industrial Filtration Probe

Nedić Grujin,

Lužaić,

Pezo

et al. 2023

Foods

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Waxes, phospholipids, free fatty acids, peroxides, aldehydes, soap, trace metals and moisture present in crude sunflower oil have a negative effect on the oil quality and are, therefore, removed during the refining process. Waxes crystallizing at low temperatures are removed during winterization by cooling and filtration. Waxes have poor filtration characteristics and an industrial filtration process must be enhanced by the use of filtration aids, which improve filter cake structure and properties, and consequently prolong the filtration cycle. Today, traditional filtration aids (diatomite, perlite, etc.) being used in the industry are frequently replaced by cellulose-based aids. The aim of this study is to examine the effect of oil filtration assisted by two cellulose-based filtration aids on the chemical parameters (wax, moisture, phospholipids, soaps, and fatty acids), oil transparency, carotenoids, and Fe and Cu content of sunflower oil obtained in an industrial horizontal pressure leaf filter. In order to investigate the mentioned parameters, the following techniques were used: gravimetric (waxes and moisture content), spectrophotometric (phospholipids and carotenoid content and oil transparency), volumetric (soaps and free fatty acids content) as well as inductively coupled plasma mass spectrometry (ICP-MS) for Fe and Cu content. An artificial neural network model (ANN) was employed for the prediction of removal efficiency based on the chemical quality, oil transparency, Fe and Cu content in oils before filtration, as well as filtration aid quantity and filtration time. Cellulose-based filtration aids had multiple beneficial effects; on average, 99.20% of waxes, 74.88% of phospholipids, 100% of soap, 7.99% of carotenoids, 16.39% of Fe and 18.33% of Cu were removed.

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“…For complicated scene images (objects in the mines refer to such ones), metric learning can be very useful to improve the performance of a distance-dependent classifier [106]. Some authors have investigated the obstacles for vision-based obstacle detection for a mobile robot, which primarily includes the detection of obstacles front of the robot within a corridor, and they have proposed algorithms for obstacle detection using image processing techniques [107].…”

Section: Machine Scene Analysis and Scene Understandingmentioning

confidence: 99%

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

Zhironkin

Taran

2023

Energies

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The expansion of end-to-end Industry 4.0 technologies in various industries has caused a technological shock in the mineral resource sector, wherein itsdigital maturity is lower than in the manufacturing sector. As a result of the shock, the productivity and profitability of raw materials extraction has begun to lag behind the industries of its deep processing, which, in the conditions of volatile raw materials markets, can provoke sectoral crises. The diffusion of Industry 4.0 technologies in the mining sector (Mining 4.0) can prevent a technological shock if they are implemented in all segments, including quarrying (Surface Mining 4.0). The Surface Mining 4.0 technological platform would connect the advanced achievements of the Fourth Industrial Revolution (end-to-end digital artificial intelligence technologies, cyber-physical systems and unmanned production with traditional geotechnology) without canceling them, but instead bringing them to a new level of productivity, resource consumption, and environmental friendliness. In the future, the development of Surface Mining 4.0 will provide a response to the technological shock associated with the acceleration of the digital modernization of the mining sector and the increase in labor productivity, which are reducing the operating costs of raw materials extraction. In this regard, the given review is an attempt to analyze the surface mining digital transformation over the course of the diffusion of Industry 4.0 technologies covered in scientific publications. The authors tried to show the core and frontiers of Surface Mining 4.0 development to determine the production, economic, and social effect of replacing humans with digital and cyber-physical systems in the processes of mineral extraction. Particular attention was paid to the review of research on the role of Surface Mining 4.0 in achieving sustainable development goals.

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Embedding metric learning into an extreme learning machine for scene recognition

Cited by 18 publications

References 47 publications

Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed

Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed

Sunflower Oil Winterization Using the Cellulose-Based Filtration Aid—Investigation of Oil Quality during Industrial Filtration Probe

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

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