Procedure Proposal for ULA Category Aircraft Certification

Import 26/06/2013Tato bakalářská práce se zabývá stavbou a certifikací ultralehkých letadel. Celou práci jsem rozdělil do 11 kapitol. První až desátá kapitola se věnuje Letecké Amatérské Asociaci ČR, koncepcím uspořádání letounu, používaným materiálům, jednoduchým výpočtům a legislativní části. Formuláře potvrzující způsobilost ultralehkého letounu jsou uvedeny v přílohách. Jedenáctou kapitolu jsem věnoval letounům pro výcvikové účely nových pilotů, kde jsem stanovil určité požadavky na tyto letouny. Dále jsem zde vybral některé letouny, které splňují mé požadavky na výcvikový letoun.This bachelor thesis deals with the construction and certification of ultralight aircraft. The whole work has been divided into 11 chapters. The first and tenth chapter is devoted to Light Aircraft Association of the Czech Republic, concepts arrangement airplane, used materials, simple calculations and the legislative part. Form certifying the eligibility of ultralight aircraft are listed in the Annexes. The eleventh chapter is dedicated aircraft for the purpose of training new pilots, where I set certain requirements for these aircraft. Furthermore, I have selected some aircraft that meet my requirements for training aircraft.342 - Institut dopravyvelmi dobř

Recognition of Vehicle Class in Image

Cieľom tejto bakalárskej práce je rozpoznať typ vozidla z obrazu pomocou neurónových sietí. Vozidlá sú rozdelené na šesť typov a to konkrétne - osobné auto, malá dodávka, dodávka, nákladné auto, kamión a autobus. Dátová sada bola vlastnoručne zozbieraná z videozáznamov, ktoré zaznamenávajú trajektóriu vozidiel. Následne bol zostrojený anotačný nástroj na anotovanie obrázkov. Na trénovanie sietí boli použité architektúry: VGG16, ResNet50, Xception, InceptionResNet-v2. Výsledkom práce je porovnanie architektúr. Všetky architektúry sa natrénovali a dosiahli výsledok nad 90%.The goal of this bachelor thesis is to recognize the type of vehicle from the image using neural networks. Vehicles are divided into 6 types, namely a car, a small van, a van, a mini truck, a truck and a bus. The data set was picked from videos that record the trajectory of the vehicles. Subsequently, an image annotation tool was built. The following architectures were used for network training: VGG16, ResNet50, Xception, InceptionResNet-v2. The result of the work is a comparison of architectures. All architectures were trained and achieved a result above 90%.

Toxicity of perfluorinated carboxylic acids for aquatic organisms

Toxicity of perfluorinated carboxylic acids with carbon chain C8 to C12 were tested with oligochaeta Tubifex tubifex. Toxicity was evaluated as the exposure time ET50 from onset of damage of the oligochaeta in saturated aqueous solutions. The ET50 fluctuated between 25 and 257 minutes. No statistically significant difference was found among the C8, C9 and C12 acids (ET50 between 143 and 257 minutes with large standard deviation). The acids with carbon chain C10 and C11 induced the effect significantly quicker (25 to 47 minutes). No acute toxicity measured in the three-minute test was observed in any case

Reduced Translocation of Cadmium from Roots Is Associated with Increased Production of Phytochelatins and Their Precursors

Cadmium (Cd) is a non-essential trace element and its environmental concentrations are approaching toxic levels, especially in some agricultural soils. Understanding how and where Cd is stored in plants is important for ensuring food safety. In this study, we examined two plant species that differ in the distribution of Cd among roots and leaves. Lettuce and barley were grown in nutrient solution under two conditions: chronic (4 weeks) exposure to a low, environmentally relevant concentration (1.0 μM) of Cd and acute (1 h) exposure to a high concentration (5.0 mM) of Cd. Seedlings grown in solution containing 1.0 μM CdCl2 did not show symptoms of toxicity and, at this concentration, 77% of the total Cd was translocated to leaves of lettuce, whereas only 24% of the total Cd was translocated to barley leaves. We tested the hypothesis that differential accumulation of Cd in roots and leaves is related to differential concentrations of phytochelatins (PCs), and its precursor peptides. The amounts of PCs and their precursor peptides in the roots and shoots were measured using HPLC. Each of PC2–4 was synthesized in the barley root upon chronic exposure to Cd and did not increase further upon acute exposure. In the case of lettuce, no PCs were detected in the root given either Cd treatment. The high amounts of PCs produced in barley root could have contributed to preferential retention of Cd in barley roots

Semantic Segmentation of Pathologies in Retinal Images

The thesis aimed to segment pathology visible in the retina images, such as exudates, hemorrhages, and microaneurysms. For that, two well known deep neural networks, named U-Net and SegFormer, were trained. To test the performance of the models, one publicly available dataset was used, named IDRiD. Obtained results were reported after analyzing different factors which affected the performance of the models U-Net and Segformer

Semantic Segmentation of Pathologies in Retinal Images

The thesis aimed to segment pathology visible in the retina images, such as exudates, hemorrhages, and microaneurysms. For that, two well known deep neural networks, named U-Net and SegFormer, were trained. To test the performance of the models, one publicly available dataset was used, named IDRiD. Obtained results were reported after analyzing different factors which affected the performance of the models U-Net and Segformer

Recognition of Vehicle Class in Image

The goal of this bachelor thesis is to recognize the type of vehicle from the image using neural networks. Vehicles are divided into 6 types, namely a car, a small van, a van, a mini truck, a truck and a bus. The data set was picked from videos that record the trajectory of the vehicles. Subsequently, an image annotation tool was built. The following architectures were used for network training: VGG16, ResNet50, Xception, InceptionResNet-v2. The result of the work is a comparison of architectures. All architectures were trained and achieved a result above 90%