A Visual Sensing Concept for Robustly Classifying House Types through a Convolutional Neural Network Architecture Involving a Multi-Channel Features Extraction

Tavakkoli, Vahid; Mohsenzadegan, Kabeh; Kyamakya, Kyandoghere

doi:10.3390/s20195672

Cited by 5 publications

(3 citation statements)

References 39 publications

(43 reference statements)

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“…The training model is used in accordance with the state of the object of identification because CNN has several layers implemented at the training stage. The modern CNN discovered by LeChun has seven layer structures (not including the input layer) namely LeNet-5 which has the following structures C1, S2, C3, S4, C5, F6 output [29]. Example of case studies in image recognition on CNN have three stages, namely the input, CNN, and output stages [20].…”

Section: F Convolutional Neural Network (Cnn) Deep Learningmentioning

confidence: 99%

Sound Classification for Javanese Eagle Based on Improved Mel-Frequency Cepstral Coefficients and Deep Convolutional Neural Network

Permana,

Rahman

2024

IJACSA

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The Javanese Eagle is a rare and protected animal in Indonesia. These animals only live in a few species and are threatened with extinction. These birds need to be bred to avoid extinction. One form of communication between the Javanese eagles and each other is the sound of their tweets. These tweets can be studied and classified to conserve endangered animals. This study will classify the sound of the Javanese Eagles for the benefit of animal conservation. Data in the form of voice tweets will be classified. This classification uses algorithms from improved MFCC (Mel-Frequency Cepstral Coefficients) and Deep Convolutional Neural Network. The result of this study was to classify the sound of the Javanese Eagle from the lack of food or drink, the normal tweets state of the bird, and to find out the Javanese Eagle in finding a partner. This research has been carried out by comparing the CNN architecture with AlexNet and VGGNet models and various combinations of training, validation, and test data. The best model dataset underwent division into 80% for training, 10% for validation, and 10% for testing. Training and testing of both IMFCC and VGGNet models occurred using the same dataset. During training, VGGNet achieved 100% accuracy, while testing yielded 99%. ROC Curve: 'Normal' AUC 0.996, 'Looking for Partner' AUC 1.000, 'Looking for Food' AUC 0.996. This study aids Javanese Eagle conservation, crucial for preventing extinction at conservation sites.

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Section: F Convolutional Neural Network (Cnn) Deep Learningmentioning

confidence: 99%

Sound Classification for Javanese Eagle Based on Improved Mel-Frequency Cepstral Coefficients and Deep Convolutional Neural Network

Permana,

Rahman

2024

IJACSA

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“…The first modern CNN used by Tavakkoli et al [29] has a 7-layer structure (excluding the input layer), namely LeNet-5, which has the following structures C1, S2, C3, S4, C5 and F6 output. In addition, Zhang et al [27] reported that several layers in a CNN model are to be considered for better performance.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A CNN Prediction Method for Belt Grinding Tool Wear in a Polishing Process Utilizing 3-Axes Force and Vibration Data

et al. 2021

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This paper presents a tool wear monitoring methodology on the abrasive belt grinding process using vibration and force signatures on a convolutional neural network (CNN). A belt tool typically has a random orientation of abrasive grains and grit size variation for coarse or fine material removal. Degradation of the belt condition is a critical phenomenon that affects the workpiece quality during grinding. This work focuses on the identifation and the study of force and vibrational signals taken from sensors along an axis or combination of axes that carry important information of the contact conditions, i.e., belt wear. Three axes of the two sensors are aligned and labelled as X-axis (parallel to the direction of the tool during the abrasive process), Y-axis (perpendicular to the direction of the tool during the abrasive process) and Z-axis (parallel to the direction of the tool during the retract movement). The grinding process was performed using a customized abrasive belt grinder attached to a multi-axis robot on a mild-steel workpiece. The vibration and force signals along three axes (X, Y and Z) were acquired for four discrete sequential belt wear conditions: brand-new, 5-min cycle time, 15-min cycle time, and worn-out. The raw signals that correspond to the sensor measurement along the different axes were used to supervisedly train a 10-Layer CNN architecture to distinguish the belt wear states. Different possible combinations within the three axes of the sensors (X, Y, Z, XY, XZ, YZ and XYZ) were fed as inputs to the CNN model to sort the axis (or combination of axes) in the order of distinct representation of the belt wear state. The CNN classification results revealed that the combination of the XZ-axes and YZ-axes of the accelerometer sensor provides more accurate predictions than other combinations, indicating that the information from the Z-axis of the accelerometer is significant compared to the other two axes. In addition, the CNN accuracy of the XY-axes combination of dynamometer outperformed that of other combinations.

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“…In [ 1 ], the authors propose a sensing concept for reliably classifying different types of houses. For this challenging endeavour, they propose/introduce a novel convolutional neural network architecture involving multi-channel features extraction.…”

Section: Visual Sensingmentioning

confidence: 99%

Document-Image Related Visual Sensors and Machine Learning Techniques

Kyamakya

Mosa

Machot

et al. 2021

Sensors

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A Visual Sensing Concept for Robustly Classifying House Types through a Convolutional Neural Network Architecture Involving a Multi-Channel Features Extraction

Cited by 5 publications

References 39 publications

Sound Classification for Javanese Eagle Based on Improved Mel-Frequency Cepstral Coefficients and Deep Convolutional Neural Network

Sound Classification for Javanese Eagle Based on Improved Mel-Frequency Cepstral Coefficients and Deep Convolutional Neural Network

A CNN Prediction Method for Belt Grinding Tool Wear in a Polishing Process Utilizing 3-Axes Force and Vibration Data

Document-Image Related Visual Sensors and Machine Learning Techniques

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