IoT System for Pellet Proportioning Based on BAS Intelligent Recommendation Model

Yang, Aimin; Zhuansun, Yunxi; Shi, Yan; Liu, Huixiang; Chen, Yongjie; Li, Ruishan

doi:10.1109/tii.2019.2960600

Cited by 27 publications

(14 citation statements)

References 27 publications

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“…CNN model is a kind of biophysical model, which imitates the nervous system of animal vision system, and is designed by using the idea of Receptive field that pictures have high invariance under the conditions of translation, zooming and tilting. It mainly deals with the recognition of two-dimensional images [12]- [15]. CNN's feature extraction process is a process of mapping from shallow features to deep features.…”

Section: A Feature Extraction Methods Of Deep Fusion Of Mineral Phase Based On Cnnmentioning

confidence: 99%

Deep Fusion Feature Extraction and Classification of Pellet Phase

Li¹,

Zhang²,

Yang³

et al. 2020

IEEE Access

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Pellet quality including chemical composition, physical properties and metallurgical performance of three parts, its quality and mineral composition, properties and structure of the pellets has the close relation, studies show that the mineralogical micro structure and distribution of pellets had significant effects on the metallurgical properties, so the analysis and determination of pellets of mineral composition and micro structure is very important to improve the quality of pellets. Paper to pellets micro mineral as the research object, mainly studies the CNN and PCA two kinds of image processing algorithm, in the heart of the traditional model of CNN structure characteristics obtained by convolution PCA dimension reduction at a time, will be the main features of PCA to extract into the depth of the CNN learning, realize the mineral phase of shallow and deep information of the image to do effective fusion, in a larger extent, reflects the mineralogical characteristics, thus more intuitive response metallurgical properties. The location and alkalinity of the ore phase were identified by the extracted deep fusion feature, and the results were compared with those of the traditional CNN algorithm. It was found that the accuracy of location and alkalinity recognition of PCA and CNN coupling algorithm was 93.82% and 91.26%, respectively, which were higher than the accuracy of traditional CNN algorithm 92.73% and 88.93%, which verified the accuracy of PCA and CNN coupling model and its applicability in mineral phase recognition.

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Section: A Feature Extraction Methods Of Deep Fusion Of Mineral Phase Based On Cnnmentioning

confidence: 99%

Deep Fusion Feature Extraction and Classification of Pellet Phase

Li¹,

Zhang²,

Yang³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Based on the active CPS network system, the user management, node monitoring, information processing and other functions of active CPS nodes are designed and realized, and then a middleware of Internet of things for a specific application is established [16]. The application middleware of Internet of things mainly realizes the unification of various data formats and the integration of information, and builds a service support platform on this basis to provide open interfaces for various services in the application layer for the development of applications by the third party [17].…”

Section: Related Work a Application Of Iot In Medical Fieldmentioning

confidence: 99%

Artificial Intelligence Recommendation System of Cancer Rehabilitation Scheme Based on IoT Technology

Han

et al. 2020

IEEE Access

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“…It presents a state of incomprehensibility and unpredictability with its own strategy. Some algorithms are difficult to converge due to the influence, but when the number of robots increases, the variance of strategy gradient algorithm will increase [28]- [30] Of course, some researchers used the model-based strategy method to predict the next state of the environment by modeling the environment and robot, using the learning strategy in deep learning, or based on certain assumptions and communication with each other. However, it is not applicable or too expensive for many scenarios.…”

Section: Robot Formation Control Based On Internet Of Things Technmentioning

confidence: 99%

Robot Formation Control Based on Internet of Things Technology Platform

et al. 2020

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The cooperative control technology of robot formation can sense all kinds of external environment in real time. It is a multi-functional control and management system including visual recognition, task management execution and distribution, behavior decision-making and so on. It can easily adapt to all kinds of harsh environment. In order to meet the efficient response requirements of robot formation control, a real-time transmission system of robot cooperative motion control is built based on the Internet of things platform, which collects and feeds back the trajectory of multiple robots. Through particle swarm optimization deep learning algorithm, more accurate identification, prediction and guidance of the robot's next action. Finally, the simulation of robot formation motion is established by MATLAB software, which verifies the feasibility of particle swarm optimization deep learning neural network algorithm under the Internet of things technology. Compared with the traditional robot formation control method, the optimized control method has faster convergence speed, smaller error and more accurate position, which provides method guidance for the accuracy and efficiency of robot formation control technology. INDEX TERMS Robot, formation cooperative control, Internet of things, particle swarm optimization, deep learning.

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IoT System for Pellet Proportioning Based on BAS Intelligent Recommendation Model

Cited by 27 publications

References 27 publications

Deep Fusion Feature Extraction and Classification of Pellet Phase

Deep Fusion Feature Extraction and Classification of Pellet Phase

Artificial Intelligence Recommendation System of Cancer Rehabilitation Scheme Based on IoT Technology

Robot Formation Control Based on Internet of Things Technology Platform

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