Wyniki wyszukiwania - Biblioteka Nauki

1

Comparison of Lithuanian and Polish Consonant Phonemes Based on Acoustic Analysis – Preliminary Results

100%

Korvel G. , Kurasova O. , Kostek B.

Archives of Acoustics

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2019

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tom Vol. 44, No. 4

693--707

EN

The goal of this research is to find a set of acoustic parameters that are related to differences between Polish and Lithuanian language consonants. In order to identify these differences, an acoustic analysis is performed, and the phoneme sounds are described as the vectors of acoustic parameters. Parameters known from the speech domain as well as those from the music information retrieval area are employed. These parameters are time- and frequency-domain descriptors. English language as an auxiliary language is used in the experiments. In the first part of the experiments, an analysis of Lithuanian and Polish language samples is carried out, features are extracted, and the most discriminating ones are determined. In the second part of the experiments, automatic classification of Lithuanian/English, Polish/English, and Lithuanian/Polish phonemes is performed.

2

A foot joint and muscle force assessment of the running stance phase whilst wearing normal shoes and bionic shoes

100%

Zhou H. , Xu D. , Quan W. , Ugbolue U. C. , Sculthorpe N. F. , Baker J. S. , Gu Y.

Acta of Bioengineering and Biomechanics

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2022

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tom Vol. 24, no. 1

191--202

EN

The aim of this study was to investigate the differences in ankle joint parameters of biomechanics changes between the normal shoes (NS) and the bionic shoes (BS) during the running stance phases. Methods: A total of 40 Chinese male runners from Ningbo University were recruited for this study (age: 22.3 ± 3.01 years; height: 174.67 ± 7.11 cm; body weight (BW): 66.83 ± 9.91 kg). The participants were asked to perform a running task. Statistical parametric mapping (SPM) analysis was used to investigate any differences between NS and BS during the running stance phases. The principal component analysis (PCA) and support vector machine (SVM) were used to further explore the differences of the muscle force between the BS and NS. Results: Significant differences ( p < 0.05) were found in the first metatarsophalangeal joint (MPJ1), ground reaction force (GRF), ankle joint and around muscle forces. Furthermore, the accuracy of SVM model in identifying the gait muscle force between BS and NS reached 100%, which proved that the BS had a very large impact on the gait muscle force compared with NS. Conclusions: We found that BS may be better suited to the human condition than other unstable shoes, or even NS. In addition, our results suggest that BS play an important role in reducing ankle injuries during running by increasing muscle participation in unstable conditions while better restoring the most primitive instability of foot condition that humans have.

3

Machine learning methods for optimal compatibility of materials in ecodesign

88%

Rojek I. , Dostatni E.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2020

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tom Vol. 68, nr 2

199--206

EN

Machine learning (ML) methods facilitate automated data mining. The authors compare the effectiveness of selected ML methods (RBF networks, Kohonen networks, and random forest) as modelling tools supporting the selection of materials in ecodesign. Applied in the design process, ML methods help benefit from the knowledge, experience and creativity of designers stored in historical data in databases. Implemented into a decision support system, the knowledge can be utilized – in the case under analysis – in the process of design of environmentally friendly products. The study was initiated with an analysis of input data for the selection of materials. The input data, specified in cooperation with designers, include both technological and environmental parameters which guarantee the desired compatibility of materials. Next, models were developed using selected ML methods. The models were assessed and implemented into an expert system. The authors show which models best fit their purpose and why. Models supporting the selection of materials, connections and disassembly methods help boost the recycling properties of designed products.

4

From conventional to machine learning methods for maritime riskassessment

75%

Rawson A. , Brito M. , Sabeur Z. , Tran-Thanh L.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2021

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tom Vol. 15 No. 4

757--764

EN

Within the last thirty years, the range and complexity of methodologies proposed to assess maritime risk have increased significantly. Techniques such as expert judgement, incident analysis, geometric models, domain analysis and Bayesian Networks amongst many others have become dominant within both the literature and industry. On top of this, advances in machine learning algorithms and big data have opened opportunities for new methods which might overcome some limitations of conventional approaches. Yet, determining the suitability or validity of one technique over another is challenging as it requires a systematic multicriteria approach to compare the inputs, assumptions, methodologies and results of each method. Within this paper, such an approach is proposed and tested within an isolated waterway in order to justify the proposed advantages of a machine learning approach to maritime risk assessment and should serve as inspiration for future work.

5

Wybrane algorytmy uczenia maszynowego w segmentacji obrazu kłaczków osadów ściekowych

75%

Wodołażski A.

Przegląd Elektrotechniczny

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2021

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tom R. 97, nr 8

134--136

PL

W artykule przedstawiono wybrane algorytmy uczenia maszynowego do przetwarzania obrazu mikroskopowego utlenionych kłaczków osadów ściekowych w celu oceny skuteczności monitorowania procesu tlenowej stabilizacji. Przedstawiono i porównano trzy techniki segmentacji algorytmem: k-means, fuzzy c-means oraz progowania Otsu w ocenie skuteczności segmentacji obszarów utlenionych i wykryciu zjawiska spęcznienia lub pienienia się kłaczków osadu ściekowego. Wykorzystane metryki GCE, RI, VI skutecznie porównują zmiany morfologiczne i strukturalne kłaczków poprzez ocenę segmentacji i kwantyfikacji obrazu. Analiza obrazów mikroskopowych przy wykorzystaniu technik uczenia maszynowego zapewniają oszczędność czasu i stanowią alternatywę metod fizyko-chemicznych w ocenie tlenowej stabilizacji osadu ściekowego

EN

The article presents selected machine learning algorithms for processing the microscopic image of oxidized sewage sludge flocs in order to assess the effectiveness of monitoring the oxygen stabilization process. Three techniques of segmentation were presented and compared by algorithm: k-means, fuzzy c-means and Otsu thresholding in assessing segmentation effectiveness of oxidized areas and detecting the swelling or foaming phenomenon of sewage sludge flocs. The GCE, RI, VI metrics has been effectively used and compared for morphological and structural changes of the flocs by assessing the image segmentation and quantification. The analysis of microscopic images using machine learning techniques save time and constitute an alternative to the physico-chemical methods to assessment aerobic stabilization of sewage sludge.

6

Wykorzystanie algorytmów ewolucyjnych do wspomagania prac inżynierskich

75%

Dobrodziej J.

Problemy Eksploatacji

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2000

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tom nr 4

25-34

PL

Omówiono możliwości wykorzystania algorytmów ewolucyjnych do wspomagania prac inżynierskich w wyniku pozyskiwania reguł logicznych na drodze odkrywania wiedzy w zbiorach lub bazach danych. Przedstawiono koncepcję równoległego, hierarchicznego algorytmu ewolucyjnego, przeznaczonego do wyszukiwania reguł logicznych.

EN

The paper present the possibilities of evolutionary algorithms application in engineering work support system. The algorithm was implemented as machine learning method in order to get logical rules in data files or databases. Machine learning is relatively young discipline and it is like that many new, more powerful methods will be developed in the future. The method presented here fall into the general category of inductive concept learning, which constitutes perhaps the most advanced task in machine learning.

7

Machine learning-based seismic response and performance assessment of reinforced concrete buildings

63%

Kazemi F. , Asgarkhani N. , Jankowski R.

Archives of Civil and Mechanical Engineering

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2023

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tom Vol. 23, no. 2

art. no. e94, 2023

EN

Complexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data points of training dataset for developing data-driven techniques, Incremental Dynamic Analyses (IDAs) were performed considering 165 RC MRFs with two-, to twelve-Story elevations having the bay lengths of 5.0 m, 6.1 m, and 7.6 m assuming near-fault seismic excitations. Then, important structural features were considered in datasets to train and test the ML-based prediction models, which were improved with innovative techniques. The results show that improved algorithms have higher R2 values for estimating the Maximum Interstory Drift Ratio (IDRmax), and two improved algorithms of artificial neural networks and extreme gradient boosting can estimate the Median of IDA curves (M-IDAs) of RC MRFs, which can be used to estimate the seismic limit-state capacity and performance assessment of existing or newly constructed RC buildings. To validate the generality and accuracy of the proposed ML-based prediction model, a five-Story RC building with different input features was used, and the results are promising. Therefore, graphical user interface is introduced as user-friendly tool to help researchers in estimating the seismic limit-state capacity of RC buildings, while reducing the computational cost and analytical efforts.

8

Using bayesian networks to model competence of lifeboat coxswains

63%

Billard R. , Smith J. , Masharraf M. , Veitch B.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2020

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tom Vol. 14. no. 3

585--594

EN

The assessment of lifeboat coxswain performance in operational scenarios representing offshore emergencies has been prohibitive due to risk. For this reason, human performance in plausible emergencies is difficult to predict due to the limited data that is available. The advent of lifeboat simulation provides a means to practice in weather conditions representative of an offshore emergency. In this paper, we present a methodology to create probabilistic models to study this new problem space using Bayesian Networks (BNs) to formulate a model of competence. We combine expert input and simulator data to create a BN model of the competence of slow-speed maneuvering (SSM). We demonstrate how the model is improved using data collected in an experiment designed to measure performance of coxswains in an emergency scenario. We illustrate how this model can be used to predict performance and diagnose background information about the student. The methodology demonstrates the use of simulation and probabilistic methods to increase domain awareness where limited data is available. We discuss how the methodology can be applied to improve predictions and adapt training using machine learning.

9

Delamination identification using machine learning methods and Haar wavelets

63%

Feklistova L. , Hein H.

Computer Assisted Methods in Engineering and Science

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2012

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tom Vol. 19, no. 4

351-360

EN

The present paper focuses on the identification of delamination size and location in homogeneous and composite laminates. The modal analysis methods are employ ed to calculate the data patterns. An aggregated approach combining Haar wavelets, support vector mac hines (SVMs) and artificial neural networks (ANNs) is used to solve identification problems. The usabili ty and effectiveness of the proposed technique are tested by several numerical experiments. The advantages of the proposed method lie in the ability to make fast and accurate calculations.