Diplomová práce se zabývá topologickou optimalizací elektrických strojů a reluktančními synchronními stroji spouštěnými za sítě. Práce obsahuje literární rešerši na téma topologické optimalizace elektrických strojů a na téma synchronní reluktanční stroj spouštěný ze sítě. Jsou zde popsány možné způsoby charakterizace optimalizovaného prostoru. Především je rozebrán vliv rozmístění Gaussových funkcí na finální Gaussovu síť. V této práci je vytvořen vyhodnocovací algoritmus pro jednotlivé jedince, který zajišťuje komunikaci mezi Ansys Maxwell a optimalizačním softwarem SyMSpace. Navíc tento algoritmus vede ke zkrácení výpočetní doby počáteční selekcí nevyhovujících jedinců. Dále je provedena topologická optimalizace LSSynRM s využitím normalizované Gaussovy sítě a zhodnocení výsledků.

The master's thesis summarizes the theory of flow measurement by differential pressure sensors, especially by Normalized Orifice, and summarizes theory concerning Averaging Pitot Tube. It is briefly described LabVIEW programming environment and flow measuring track for which the software was developed. The thesis describes creation of concept of program and in particular its final realization. The program provide observing actual events on measuring track, saving data to file, detail analysis of stored data and creation of measurement report. The main objective is to determine the Averaging Pitot Tube coefficient.

The so-called 3σ-rule is a simple and widely used heuristic for outlier detection. This term is a generic term of some statistical hypothesis tests whose test statistics are known as normalized or studentized residuals. The conditions, under which this rule is statistically substantiated, were analyzed, and the extent it applies to geodetic least-squares adjustment was investigated. Then, the efficiency or non-efficiency of this method was analyzed and demonstrated on the example of repeated observations. / Die sogenannte 3σ-Regel ist eine einfache und weit verbreitete Heuristik für die Ausreißererkennung. Sie ist ein Oberbegriff für einige statistische Hypothesentests, deren Teststatistiken als normierte oder studentisierte Verbesserungen bezeichnet werden. Die Bedingungen, unter denen diese Regel statistisch begründet ist, werden analysiert. Es wird untersucht, inwieweit diese Regel auf geodätische Ausgleichungsprobleme anwendbar ist. Die Effizienz oder Nichteffizienz dieser Methode wird analysiert und demonstriert am Beispiel von Wiederholungsmessungen.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18556号 / 工博第3917号 / 新制||工||1602(附属図書館) / 31456 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 田村 正行, 准教授 須﨑 純一, 教授 小池 克明 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

I studied movement characteristics and vegetative resources effects on home range size of beavers at Redstone Arsenal (RSA) in north central Alabama, USA. Beavers were captured and radio tagged from 11 wetlands during winter and spring of 2011. I monitored movements of radio-tagged beavers using radio telemetry from May 2011–April 2012. Beavers moved faster, presumably more favorable to central place foraging, in wetland as they proceeded farther away from the central place, but did not in upland. Additionally, distributions of hourly distances from lodges were bimodal. Home range, core areas, and distance from lodge did not differ between age classes. Home range sizes increased with increasing habitat productivity and resource dispersion, whereas home ranges decreased with temporal variation in resources throughout the year. Quantity and spatial distribution of resources and patterns of foraging behavior influence movements and home ranges of central place foragers.

The problem of whether Landsat 7 data could be used to delineate areas prone to mass movement, particularly debris flows and landslides, was examined using three techniques: change detection in NDVI (Normalized Difference Vegetation Index), change detection in band 5, and the tasseled cap transformation. These techniques were applied to areas that had recently experienced mass movement: Layton, Davis County and Alpine, Spanish Fork Canyon and Santaquin, Utah County. No distinctive spectral characteristics were found with any of these techniques with two possible explanations: 1. That despite improved spatial resolution in Landat 7 over its predecessors and improved digital image processing capabilities, the resolution is still too low to detect these characteristics or 2. That the aspects of a slope that make it prone to mass movement are undetectable at any resolution by remote sensing. Change detection in NDVI examined if areas that remained unchanged (defined as < 5% change) between August 14, 1999 and October 17, 1999 correlated to areas that are prone to mass movement. There was no correlation. Change detection in band 5 was examined between August 14, 1999 and October 17, 1999, October 17, 1999 and May 28, 2000, and August 14, 1999 and May 28, 2000. An interesting result is that the Shurtz Lake and Thistle landslides (Spanish Fork Canyon) showed changes of greater than 30% during August 14, 1999 - October 17, 1999 and October 17, 1999 - May 28, 2000. These changes were limited to these landslides and not seen in abundance in surrounding areas. A similar localization of 30% change was seen in the Cedar Bench landslide (Layton) for the same time periods. There were no other correlations. The tasseled cap ransformation shows areas of dominate greenness, soil brightness or wetness. None of these factors had distinctive patterns in the areas studied when compared to surrounding, mass movement-prone areas so no conclusions can be drawn about the utility of the tasseled cap transformation as it relates to areas of potential mass movement.

According to the World Health Organization, almost all human population (99%) lives in 117 countries with over 6000 cities, where air pollutant concentration exceeds recommended thresholds. The most common, so-called criteria, air pollutants that affect human lives, are particulate matter (PM) and gas-phase (SO2, CO, NO2, O3 and others). Therefore, many countries or regions worldwide have imposed regulations or interventions to reduce these effects. Whenever an intervention occurs, air quality changes due to changes in ambient factors, such as weather characteristics and human activities. One approach for assessing the effects of interventions or events on air quality is through the use of the Weather Normalized Model (WNM). However, current deterministic models struggle to accurately capture the complex, non-linear relationship between pollutant concentrations and their emission sources. Hence, the primary objective of this thesis is to examine the power of machine learning (ML) and deep learning (DL) techniques to develop and improve WNMs. Subsequently, these enhanced WNMs are employed to assess the impact of events on air quality. Furthermore, these ML/DL-based WNMs can serve as valuable tools for conducting exploratory data analysis (EDA) to uncover the correlations between independent variables (meteorological and temporal features) and air pollutant concentrations within the models.  It has been discovered that DL techniques demonstrated their efficiency and high performance in different fields, such as natural language processing, image processing, biology, and environment. Therefore, several appropriate DL architectures (Long Short-Term Memory - LSTM, Recurrent Neural Network - RNN, Bidirectional Recurrent Neural Network - BIRNN, Convolutional Neural Network - CNN, and Gated Recurrent Unit - GRU) were tested to develop the WNMs presented in Paper I. When comparing these DL architectures and Gradient Boosting Machine (GBM), LSTM-based methods (LSTM, BiRNN) have obtained superior results in developing WNMs. The study also showed that our WNMs (DL-based) could capture the correlations between input variables (meteorological and temporal variables) and five criteria contaminants (SO2, CO, NO2, O3 and PM2.5). This is because the SHapley Additive exPlanations (SHAP) library allowed us to discover the significant factors in DL-based WNMs. Additionally, these WNMs were used to assess the air quality changes during COVID-19 lockdown periods in Ecuador. The existing normalized models operate based on the original units of pollutants and are designed for assessing pollutant concentrations under “average” or consistent weather conditions. Predicting pollution peaks presents an even greater challenge because they often lack discernible patterns. To address this, we enhanced the Weather Normalized Models (WNMs) to boost their performance specifically during daily concentration peak conditions. In the second paper, we accomplished this by developing supervised learning techniques, including Ensemble Deep Learning methods, to distinguish between daily peak and non-peak pollutant concentrations. This approach offers flexibility in categorizing pollutant concentrations as either daily concentration peaks or non-daily concentration peaks. However, it is worth noting that this method may introduce potential bias when selecting non-peak values. In the third paper, WNMs are directly applied to daily concentration peaks to predict and analyse the correlations between meteorological, temporal features and daily concentration peaks of air pollutants.

An algorithm, which is able to find out, whether a given digital photo was tampered, and to generate tampering map, which depicts the processed parts of the image, was analyzed in details and implemented. The software was also optimized, deeply tested, the modes giving the best quality were found. The program can be launched on a usual user PC. / В процессе работы был детально разобран и реализован алгоритм поиска признаков постобработки в изображениях. Разработанное приложение было оптимизировано, было проведено его тестирование, были найдены режимы работы приложения с более высокими показателями точности. Реализованное приложение может быть запущено на обычном персональном компьютере. Помимо информации о наличии выявленных признаков постобработки полученное приложение генерирует карту поданного на вход изображения, на которой выделены его участки, возможно подвергнутые постобработке.

<p dir="ltr">The vagus nerve (VN) is the tenth cranial nerve that mediates most of the parasympathetic functions of the autonomic nervous system. The axons of the human VN comprise a mix of unmyelinated and myelinated axons, where ~80% of the axons are unmyelinated C fibers (Havton et al., 2021). Understanding that most VN axons are unmyelinated, there is a need to map the pathways of these axons to and from organs to understand their function(s) and whether C fiber morphology or signaling characteristics yield insights into their functions. Developing a machine learning model that detects and predicts the morphology of VN single fiber action potentials based on select fiber characteristics, e.g., diameter, myelination, and position within the VN, allows us to more readily categorize the nerve fibers with respect to their function(s). Additionally, the features of this machine learning model could help inform peripheral neuromodulation devices that aim to restore, replace, or augment one or more specific functions of the VN that have been lost due to injury, disease, or developmental abnormalities.</p><p dir="ltr">We designed and trained four types of Multi-layer Perceptron Artificial Deep Neural Networks (MLP-ANN) with 10,000 rat abdominal vagal C-fibers simulated via the peripheral neural interface model ViNERS. We analyze the accuracy of each MLP-ANN’s SFAP predictions by conducting normalized cross-correlation and morphology analyses with the ViNERS C-fiber SFAP counterparts. Our results showed that our best MLP predicted over 94% of the C-fiber SFAPs with strong normalized cross-correlation coefficients of 0.7 through 1 with the ViNERS SFAPs. Overall, this novel tool can use a C-fiber’s biophysical characteristics (i.e., fiber diameter size, fiber position on the x/y axis, etc.) to predict C-fiber SFAP morphology.</p>

This report analyzes the difference between discriminative and generative image classifiers when tested on noise. The generative classifier was a maximum-likelihood based classifier using a normalizing flow as the generative model. In this work, a coupling flow such as RealNVP was used. For the discriminative classifier a convolutional network was implemented. A detailed description of how these classifiers were implemented is given in the report. The report shows how this generative classifier outperforms the discriminative classifier when tested on adversarial noise. However, tests are also conducted on salt and pepper noise and Gaussian noise, here the results show that the generative classifier gets outperformed by the discriminative classifier. Tests were also conducted on Gaussian noise once both classifiers had been trained on Gaussian noise, the results from these tests show that the discriminative classifier performs significantly better once trained on Gaussian noise. However, the generative classifier does only show marginal increases in performance and performs worse on clean data once trained on Gaussian noise. / Den här rapporten analyserar skillnaden mellan diskriminativa och generativa modellklasser för bildigenkänning när de testas på brus. Den generativa modellklassen var en maximum-likelihood baserad generativ klassifikationsmodell. Inom detta arbete användes kopplingsflödet RealNVP. För den diskriminativa bildigenkänningsmodellen så implementerades ett faltningsnätverk. En detaljerad beskrivning för hur dessa bildigenkänningsmodeller genomfördes är given i rapporten. Rapporten visar hur den generativa modellklassen överträffar den diskriminativa modellklassen när de testas på adversarialt brus. Testerna utförs emellertid med salt och peppar brus och Gaussiskt brus, för dessa visar resultaten att den generativa modellklassen överträffas av den diskriminativa modellklassen. Den generativa modellklassen visar emellertid endast marginella ökningar i prestanda, och har en sämre prestanda på ren data efter att den tränats på Gaussiskt brus. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm