Current Techniques for Breath Sound Analysis

Hadjileontiadis, Leontios J.; Moussavi, Zahra

doi:10.1007/978-3-319-71824-8_9

Cited by 8 publications

(7 citation statements)

References 130 publications

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“…The last layer of the network is a fully connected layer, each neuron from the max-pooling layer is thus connected to each of the exemplary output neurons that represent specific classes, e.g., manufacturing technologies. When training a CNN, the weights of each layer as well as the number and numeric structure of the filters are adapted to achieve the best classification results [9,10]. Socalled full-stack AI-platforms offer pre-trained architectures to support the implementation of CNNs through the benefits of transfer learning [11].…”

Section: Fundamentalsmentioning

confidence: 99%

Automated geometric analysis of metallic components through picture recognition models for manufacturing technology assessments

Buechler

Schulz

Zaeh

2022

Prod. Eng. Res. Devel.

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The selection and interaction of various manufacturing technologies are key difficulties in product development and production processes. A component’s geometry is one of the most important factors to consider when choosing the best technology. This article presents a method for an automated geometry analysis of metallic components. The goal is to analyze manufacturing technology alternatives regarding their capability to create required geometries. It also aims at short computing times since the outcome of this geometric analysis supplements a part screening methodology for the selection of the most suitable manufacturing technology for each component. To achieve a successful classification, artificial intelligence (AI) approaches are trained with images of the components that are labeled with suitable manufacturing technologies. The AI models hence learn how components of different manufacturing technologies look like and which characteristics they embody. To support the classification model, object recognition models are tested to automatically extract component features such as holes, coinages, or profile compositions. After training and comparing different AI approaches, the best performers are selected and implemented to analyze unseen image data of upcoming projects. In summary, this article’s research unifies existing AI approaches for image analyses with the field of production technology and product development. It provides a general methodology for applying image classification and object detection approaches in development processes of metallic components.

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Section: Fundamentalsmentioning

confidence: 99%

Automated geometric analysis of metallic components through picture recognition models for manufacturing technology assessments

Buechler

Schulz

Zaeh

2022

Prod. Eng. Res. Devel.

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“…The prevailing OCs are randomly defined, and then OCs are calculated by using consecutive applications of SWF. The aligned OCs are subtracted from the input data of the rest of the elements Finally, if the residual input data do not include the oscillation mode of adequate energy, the SWD algorithm is finished [15].…”

Section: Pre-processing Based On Swarm Decomposition Techniquementioning

confidence: 99%

Swarm Decomposition Technique Based Hybrid Model for Very Short-Term Solar PV Power Generation Forecast

Dokur

2020

ELEKTRON ELEKTROTECH

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Accurate predictions of solar photovoltaic (PV) power generation at different time horizons are essential for reliable operation of energy management systems. The output power of a PV power plant is dependent on non-linear and intermittent environmental factors, such as solar irradiance, wind speed, relative humidity, etc. Intermittency and randomness of solar PV power effect precision of estimation. To address the challenge, this paper presents a Swarm Decomposition Technique (SWD) based hybrid model as a novel approach for very short-term (15 min) solar PV power generation forecast. The original contribution of the study is to investigate use of SWD for solar data forecast. The solar PV power generation data with hourly resolution obtained from the field (grid connected, 857.08 kWp Akgul Solar PV Power Plant in Turkey) are used to develop and validate the forecast model. Specifically, the analysis showed that the hybrid model with SWD technique provides highly accurate predictions in cloudy periods.

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“…On the other hand, ARS are abnormal sounds that are overlayed on normal respiratory sounds [ 10 ]. ARS can be categorized into two main types: continuous and discontinuous [ 12 ]. The nomenclature recognized by the European Respiratory Society Task Force on Respiratory Sounds [ 13 ] is that continuous ARS are called wheezes, and discontinuous ARS are called crackles, which will be followed in this study.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Adventitious Respiratory Sounds: A (Un)Solved Problem?

Rocha

Pessoa

Marques

et al. 2020

Sensors

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(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we studied the influence of event duration on automatic ARS classification, namely, how the creation of the Other class (negative class) affected the classifiers’ performance. (2) Methods: We conducted a set of experiments where we varied the durations of the other events on three tasks: crackle vs. wheeze vs. other (3 Class); crackle vs. other (2 Class Crackles); and wheeze vs. other (2 Class Wheezes). Four classifiers (linear discriminant analysis, support vector machines, boosted trees, and convolutional neural networks) were evaluated on those tasks using an open access respiratory sound database. (3) Results: While on the 3 Class task with fixed durations, the best classifier achieved an accuracy of 96.9%, the same classifier reached an accuracy of 81.8% on the more realistic 3 Class task with variable durations. (4) Conclusion: These results demonstrate the importance of experimental design on the assessment of the performance of automatic ARS classification algorithms. Furthermore, they also indicate, unlike what is stated in the literature, that the automatic classification of ARS is not a solved problem, as the algorithms’ performance decreases substantially under complex evaluation scenarios.

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Current Techniques for Breath Sound Analysis

Cited by 8 publications

References 130 publications

Automated geometric analysis of metallic components through picture recognition models for manufacturing technology assessments

Automated geometric analysis of metallic components through picture recognition models for manufacturing technology assessments

Swarm Decomposition Technique Based Hybrid Model for Very Short-Term Solar PV Power Generation Forecast

Automatic Classification of Adventitious Respiratory Sounds: A (Un)Solved Problem?

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