Application of wavelet synchrosqueezed transforms to the analysis of infrasound signals generated by wind turbines.

The issues reported in this article concern the development of methods applied for measurement, processing, and analysis of infrasound signals generated in association with the operation of wind farms. In particular, the discussion involves the results of the analysis using synchrosqueezed wavelet transforms of infrasound noise emitted by a 2 MW wind turbine that have been recorded during its operation in actual conditions. To record infrasound signals, a wireless measurement system was used, consisting of a base station and three synchronized mobile recording stations. To identify the wavelet structures with the highest ratio of energy, the synchrosqueezed wavelet transforms were used, and the courses of six time runs representing instantaneous frequencies were determined. Application of this approach enables the selection of energy-dominant waveforms from the time-frequency images, whose assessment can be performed mainly in terms of qualitative measures. Application of the synchrosqueezed wavelet transform is an effective tool for the purposes of detection and selection in the designated wavelet structures for the recorded infrasound dominant frequencies for which the carried energy ranges have the highest value.

Latest Trends in the Improvement of Measuring Methods and Equipment in the Area of NDT.

The adequate assessment of key apparatus conditions is a hot topic in all branches of industry [...].

Application of Correlation Analysis for Assessment of Infrasound Signals Emission by Wind Turbines.

The study reported in this paper is concerned with areas related to developing methods of measuring, processing and analyzing infrasound noise caused by operation of wind farms. The paper contains the results of the correlation analysis of infrasound signals generated by a wind turbine with a rated capacity of 2 MW recorded by three independent measurement setups comprising identical components and characterized by the same technical parameters. The measurements of infrasound signals utilized a dedicated measurement system called INFRA, which was developed and built by KFB ACOUSTICS Sp. z o.o. In particular, the scope of the paper includes the results of correlation analysis in the time domain, which was carried out using the autocovariance function separately for each of the three measuring setups. Moreover, the courses of the cross-correlation function were calculated separately for each of the potential combinations of infrasound range recorded by the three measuring setups. In the second stage, a correlation analysis of the recorded infrasound signals in the frequency domain was performed, using the coherence function. In the next step, infrasound signals recorded in three setups were subjected to time-frequency transformations. In this part, the waveforms of the scalograms were determined by means of continuous wavelet transform. Wavelet coherence waveforms were calculated in order to determine the level of the correlation of the obtained dependencies in the time-frequency domain. The summary contains the results derived from using correlation analysis methods in the time, frequency and time-frequency domains.

Distribution Transformer Parameters Detection Based on Low-Frequency Noise, Machine Learning Methods, and Evolutionary Algorithm.

The paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. The method, as input data, uses the frequency spectra of sound pressure levels generated during operation by transformers in the real environment. The model also uses the background characteristic to take under consideration the changing working conditions of the transformers. The method searches for frequency intervals and its resolution using both a classic genetic algorithm and particle swarm optimization. The interval selection was verified using five state-of-the-art machine learning algorithms. The research was conducted on 16 different distribution transformers. As a result, a method was proposed that allows the detection of a specific transformer model, its type, and its power with an accuracy greater than 84%, 99%, and 87%, respectively. The proposed optimization process using the genetic algorithm increased the accuracy by up to 5%, at the same time reducing the input data set significantly (from 80% up to 98%). The machine learning algorithms were selected, which were proven efficient for this task.

Classification of Low Frequency Signals Emitted by Power Transformers Using Sensors and Machine Learning Methods.

This paper proposes a method of automatically detecting and classifying low frequency noise generated by power transformers using sensors and dedicated machine learning algorithms. The method applies the frequency spectra of sound pressure levels generated during operation by transformers in a real environment. The spectra frequency interval and its resolution are automatically optimized for the selected machine learning algorithm. Various machine learning algorithms, optimization techniques, and transformer types were researched: two indoor type transformers from Schneider Electric and two overhead type transformers manufactured by ABB. As a result, a method was proposed that provides a way in which inspections of working transformers (from background) and their type can be performed with an accuracy of over 97%, based on the generated low-frequency noise. The application of the proposed preprocessing stage increased the accuracy of this method by 10%. Additionally, machine learning algorithms were selected which offer robust solutions (with the highest accuracy) for noise classification.

[Outcome of operative treatment for supination-external rotation Lauge-Hansen stage IV ankle fractures]. / Ocena wyników leczenia zlaman kostek goleni typu supinacyjno-rotacyjnego IV stopnia wedlug Lauge-Hansena.

INTRODUCTION: Ankle fractures are among the most common musculoskeletal injures. These fractures occur with an overall age- and sex-adjusted incidence rate around 180 per 100 000 person-years. The most frequent mechanism is considered to be supination-external rotation (60 to 80% of all ankle fractures) consisting of pathologic external rotation of the foot initially placed in some degree of supination. According to Lauge-Hansen classification, ankle joint structures are damaged in a sequence where the final, stage IV injuries, represents transverse fracture of the medial malleolus or its equivalent-rupture of the deltoid ligament. AIM OF THE WORK: The aim of this study is to compare the results of two subtypes of supination-external rotation stage IV fractures. MATERIAL AND METHODS: 43 patients treated surgically in 2006 to 2007 at Authors institution because of stage IV supination-external rotation ankle fracture were submitted to retrospective analysis. There were 25 patients with bimalleolar fracture (type 1) and in 18 patients with lateral malleolar fracture with accompanying rupture of the deltoid ligament (type 2). The mean age was 46 years (from 20 to 82 years). Average follow up period was 37 months (from 24 to 46 months). For the evaluation of treatment AOFAS hind-foot score (American Orthopedic Foot and Ankle Society) was used. RESULTS: The mean AOFAS score scale for Type 1 fractures was 85 points and for type 2 was significantly higher and amounted to 91 points (p < 0.05). CONCLUSIONS: Supination-external rotation stage IV ankle fractures with medial malleolar fracture, requires the implementation of additional diagnostic and therapeutic strategies and procedures in order to improve the outcome of results.

[Evaluation of attitude towards euthanasia expressed by first year medical students from Szczecin, Greifswald and Lund medical faculties]. / Ocena podejscia do eutanazji studentów pierwszego roku medycyny w Szczecinie, Greifswaldzie i Lund.

INTRODUCTION: Medical students' attitude towards euthanasia is a very important ethical problem because they may grapple with this question as future doctors. The aim of the study was to compare the attitude to euthanasia in the group of first year medical students from Pomeranian Medical University in Szczecin, Ernst-Moritz-Arndt University Greifswald (Germany) and Lund University (Sweden). MATERIAL AND METHODS: The study is based on anonymous filling out of the questionnaire about euthanasia by first year medical students. 233 students (61%) answered the questionnaire. There were 65 Polish students, 71 German and 97 Swedish ones. In the group of respondents there were 129 (55%) women and 104 (45%) men. The average age was 22.3 years. RESULTS: 82% of questioned German students declared the acceptance of euthanasia and it was a significantly higher percentage than in comparison to 61% of Swedish students (p < 0.007) and 48% Polish ones (p < 0.0001). Poles were more often against euthanasia (29%) in comparison to 12% of Swedes (p < 0.02) and 3% of Germans (p < 0.001). Unnatural support of patient's life was the most often accepted by students clinical situation to use euthanasia. Significantly more Germans than Poles (79% vs 48%; p < 0.005) and Swedes (79% vs 50%; p < 0.02) accepted euthanasia in the group of questioned students declaring themselves as believers. CONCLUSIONS: German students in the highest percentage declared the acceptance of euthanasia and Polish ones--the highest objection. It may be connected with religious beliefs as the element of cultural differences among above three countries. It seems very proper to continue the study among older medical students.