Real-time Neural Networks Implementation Proposal for Microcontrollers

Guimarães, Caio José Borba Vilar; Fernandes, Marcelo A. C.

doi:10.3390/electronics9101597

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…Jose et al discuss a strategy for the implementation of an artificial neural network algorithm on microcontrollers that is suitable for IoT and Machine-to-Machine (M2M) applications [48].…”

Section: Dineva Et Al Propose a New Methodology For Multi-label Classification At The Diagnosis Of Multiple Faults Occurring In Electricamentioning

confidence: 99%

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

2021

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The paper presents an analysis and summary of the current research state concerning the application of machine learning and fuzzy logic for solving problems in electronics. The investigated domain is conceptualized with aim the achievements, trending topics and future research directions to be outlined. The applied research methodology includes a bibliographic approach in combination with a detailed examination of 66 selected papers. The findings reveal the gradually increasing interest over the last 10 years in the machine learning and fuzzy logic techniques for modeling, implementing and improving different hardware-based intelligent systems.

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“…Jose et al discuss a strategy for the implementation of an artificial neural network algorithm on microcontrollers that is suitable for IoT and Machine-to-Machine (M2M) applications [48].…”

Section: Dineva Et Al Propose a New Methodology For Multi-label Classification At The Diagnosis Of Multiple Faults Occurring In Electricamentioning

confidence: 99%

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

2021

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“…Regarding the hardware implementation for AI codes on microcontrollers, the work in [ 18 ] highlighted this issue based on backpropagation training. They confirmed through this implementation the required minimum time for specific application-based microcontrollers.…”

Section: Related Workmentioning

confidence: 99%

Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller

Hussein

Mohammed

Galal

et al. 2022

Sensors

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The Internet of Things (IoT) era is mainly dependent on the word “Smart”, such as smart cities, smart homes, and smart cars. This aspect can be achieved through the merging of machine learning algorithms with IoT computing models. By adding the Artificial Intelligence (AI) algorithms to IoT, the result is the Cognitive IoT (CIoT). In the automotive industry, many researchers worked on self-diagnosis systems using deep learning, but most of them performed this process on the cloud due to the hardware limitations of the end-devices, and the devices obtain the decision via the cloud servers. Others worked with simple traditional algorithms of machine learning to solve these limitations of the processing capabilities of the end-devices. In this paper, a self-diagnosis smart device is introduced with fast responses and little overhead using the Multi-Layer Perceptron Neural Network (MLP-NN) as a deep learning technique. The MLP-NN learning stage is performed using a Tensorflow framework to generate an MLP model’s parameters. Then, the MLP-NN model is implemented using these model’s parameters on a low cost end-device such as ARM Cortex-M Series architecture. After implementing the MLP-NN model, the IoT implementation is built to publish the decision results. With the proposed implemented method for the smart device, the output decision based on sensors values can be taken by the IoT node itself without returning to the cloud. For comparison, another solution is proposed for the cloud-based architecture, where the MLP-NN model is implemented on Cloud. The results clarify a successful implemented MLP-NN model for little capabilities end-devices, where the smart device solution has a lower traffic and latency than the cloud-based solution.

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“…It was decided to use MLP due to its high efficiency in relevant problems of damage classification [29,[33][34][35]. In addition, the implementation possibilities of the perceptron or similar neural structures on microcontrollers have been repeatedly presented in the literature, which shows the possibilities of using the detector in industrial practice [36][37][38]. The paper also presents the possibilities of damage classification (indication of one of the three analyzed failure types) with the use of MLP; fault classification is based on the same input vector as the fault detector, excluding the regenerative mode, and the classifier recognizes the same three types of damage.…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis of the Current Sensor Fault Detection Mechanism Based on Neural Networks in the PMSM Drive System

Jankowska

Dybkowski

2023

Electronics

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In this paper, a current sensor fault detection mechanism based on multilayer perceptron (MLP) in a permanent magnet synchronous motor (PMSM) drive system is presented. The solution for the PMSM was previously described and tested only in simulation studies. The described application allows the detection of basic faults (lack of signal, gain error, signal noise) in current sensors and the indication of the phase (A or B) in which the fault occurred. The work is focused on the analysis of the fault detector but also presents the possibilities of their classification. The work mainly presents experimental research for different values of speed during the load and regenerative mode. In addition to the study of various operating conditions of the drive system, the detector efficiency was also verified for three neural structures with a different number of neurons in the hidden layers. The work also presents simulation tests (in Matlab Simulink software) for the additional conditions of the drive system for the same neural structures as in the experimental studies. The results obtained during offline and online faults detection with the use of the DS1103 controller are presented.

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Real-time Neural Networks Implementation Proposal for Microcontrollers

Cited by 10 publications

References 22 publications

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller

Experimental Analysis of the Current Sensor Fault Detection Mechanism Based on Neural Networks in the PMSM Drive System

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