A Fault Detection and Data Reconciliation Algorithm in Technical Processes with the Help of Haar Wavelets Packets

Mercorelli, Paolo

doi:10.3390/a10010013

Cited by 19 publications

(13 citation statements)

References 14 publications

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“…As discussed in [ 53 ], the image data that are corrupted by various noises impact the resulting performance of the proposed neural network. For this reason, the noisy image data are recovered with the pre-processing step (using various filters).…”

Section: Methodsmentioning

confidence: 99%

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

Cucor

Petrov

Kamencay

et al. 2022

Sensors

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In this paper, we present an assessment framework that can be used to score segments of physical and digital infrastructure based on their features and readiness to expedite the deployment of Connected and Automated Vehicles (CAVs). We discuss the equipment and methodology applied for the collection and analysis of required data to score the infrastructure segments in an automated way. Moreover, we demonstrate how the proposed framework can be applied using data collected on a public transport route in the city of Zilina, Slovakia. We use two types of data to demonstrate the methodology of the assessment-connectivity and positioning data to assess the connectivity and localization performance provided by the infrastructure and image data for road signage detection using a Convolutional Neural Network (CNN). The core of the research is a dataset that can be used for further research work. We collected and analyzed data in two settings—an urban and suburban area. Despite the fact that the connectivity and positioning data were collected in different days and times, we found highly underserved areas along the investigated route. The main problem from the point of view of communication in the investigated area is the latency, which is an issue associated with infrastructure segments mainly located at intersections with heavy traffic or near various points of interest. The low accuracy of localization has been observed mainly in dense areas with large buildings and trees, which decrease the number of visible localization satellites. To address the problem of automated assessment of the traffic sign recognition precision, we proposed a CNN that achieved 99.7% precision.

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Section: Methodsmentioning

confidence: 99%

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

Cucor

Petrov

Kamencay

et al. 2022

Sensors

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“…With the development of computer vision technology, computer computing power, and various algorithms of artificial intelligence (AI) [1,2], machine learning [3,4], and modern digital and deep learning [5], many crop pest detection and recognition methods have been presented [6]. Martineau et al [7] investigated forty-four studies on this topic, including a lot of methods of image capture, feature extraction, and classification and tested datasets, and generally discussed the questions that might still remain unsolved.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Convolution-Capsule Network for Crop Insect Pest Recognition

Zhang

et al. 2022

Electronics

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Accurate crop insect pest identification in fields is useful to control pests and beneficial to agricultural yield and quality. However, it is a difficult and challenging problem due to the crop insect pests being small with various sizes, postures, shapes, and disorganized backgrounds. Multi-scale convolution-capsule network (MSCCN) is constructed for crop insect pest identification. It consists of a multi-scale convolution module, capsule network (CapsNet) module, and SoftMax classification module. Multi-scale convolution is used to extract the multi-scale discriminative features, CapsNet is employed to encode the hierarchical structure of the size-variant insect pests in the crop images, and Softmax is adopted for insect pest identification. MSCCN combines the advantages of convolutional neural network (CNN), CapsNet, and multi-scale CNN, and can learn multi-scale robust features from pest images of different shapes and sizes for pest recognition and identify various morphed pests. Experimental results on the crop pest image dataset show that this method has a good recognition rate of 91.4%.

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“…Theoretically, fault diagnosis aims at detecting and identifying any type of potential abnormalities and faults [10,11]. Numerous artificial intelligence techniques and statistical learning methods have been widely used in fault diagnosis, such as k-nearest neighbor (k-NN) algorithms [12], Bayesian classifier [13], support vector machine (SVM) [14], and deep learning approaches [15].…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Approach for Electromechanical Actuators with Simulating Model under Small Experimental Data Sample Condition

et al. 2022

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Electromechanical actuators (EMAs) have shown a high efficiency in flight surface control with the development of more electric aircraft. In order to identify the abnormalities and potential failures of EMA, a methodology for fault diagnosis is developed. A simulating model of EMA is first built to perform different working states. Based on the modeling of EMA, the corresponding faults are then simulated to re-generate the fault data. Afterwards, a gated recurrent unit (GRU) and co-attention-based fault diagnosis approach is proposed to classify the working states of EMA. Experiments are conducted and a satisfying classification accuracy on simulated data is obtained. Furthermore, fault diagnosis on an actual working system is performed. The experimental results demonstrate that the proposed method has a high efficiency.

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A Fault Detection and Data Reconciliation Algorithm in Technical Processes with the Help of Haar Wavelets Packets

Cited by 19 publications

References 14 publications

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

Multi-Scale Convolution-Capsule Network for Crop Insect Pest Recognition

A Fault Diagnosis Approach for Electromechanical Actuators with Simulating Model under Small Experimental Data Sample Condition

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