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Using artificial intelligence to monitor the state of the machine
Popara, Nikola ; Bražina, Jakub (referee) ; Kovář, Jiří (advisor)
This thesis is focus on monitoring state of machine parts that are under the most stress. Type of artificial intelligence used in this work is recurrent neural network and its modifications. Chosen type of neural network was used because of the sequential character of used data. This thesis is solving three problems. In first problem algorithm is trying to determine state of mill tool wear using recurrent neural network. Used method for monitoring state is indirect. Second Problem was focused on detecting fault of a bearing and classifying it to specific category. In third problem RNN is used to predict RUL of monitored bearing.
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Recurrent Neural Networks for Speech Recognition
Nováčik, Tomáš ; Karafiát, Martin (referee) ; Veselý, Karel (advisor)
This master thesis deals with the implementation of various types of recurrent neural networks via programming language lua using torch library. It focuses on finding optimal strategy for training recurrent neural networks and also tries to minimize the duration of the training. Furthermore various types of regularization techniques are investigated and implemented into the recurrent neural network architecture. Implemented recurrent neural networks are compared on the speech recognition task using AMI dataset, where they model the acustic information. Their performance is also compared to standard feedforward neural network. Best results are achieved using BLSTM architecture. The recurrent neural network are also trained via CTC objective function on the TIMIT dataset. Best result is again achieved using BLSTM architecture.
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Image Captioning with Recurrent Neural Networks
Kvita, Jakub ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
Tato práce se zabývá automatickým generovaním popisů obrázků s využitím několika druhů neuronových sítí. Práce je založena na článcích z MS COCO Captioning Challenge 2015 a znakových jazykových modelech, popularizovaných A. Karpathym. Navržený model je kombinací konvoluční a rekurentní neuronové sítě s architekturou kodér--dekodér. Vektor reprezentující zakódovaný obrázek je předáván jazykovému modelu jako hodnoty paměti LSTM vrstev v síti. Práce zkoumá, na jaké úrovni je model s takto jednoduchou architekturou schopen popisovat obrázky a jak si stojí v porovnání s ostatními současnými modely. Jedním ze závěrů práce je, že navržená architektura není dostatečná pro jakýkoli popis obrázků.
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Codec Detection from Speech
Jon, Josef ; Matějka, Pavel (referee) ; Černocký, Jan (advisor)
Tato práce se zabývá detekcí kodeků z komprimovaného řečového signálu. Cílem bylo zjistit, jaké charakteristiky rozlišují jednotlivé kodeky a následně vytvořit prostředí vhodné pro experimenty s různými typy a konfiguracemi klasifikátorů. Použity byly Support vector machines a především neuronové sítě, které byly vytvořeny pomocí nástroje Keras. Hlavním přínosem této práce je experimentální část, ve které je analyzován vliv různých parametrů neuronové sítě. Po nalezení nejvhodnější kombinace parametrů dosáhla síť přesnosti klasifikace přes 98% na testovací sadě obsahující data z 6 kodeků.
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Advanced methods for sleep quality assessment
Doležalová, Anna ; Králík, Martin (referee) ; Ronzhina, Marina (advisor)
This diploma thesis is focused on advanced sleep assessment using deep learning. Metrics for sleep assessment and their use are described here. There are hearth rate and accelerometer data from Apple Watch used for classification. The basis for the classification was a model composed of 1D convolution networks in combination with recurrent neural network. LSTM and GRU were used as recurrent networks. Models were taught to classify into two, three and five phases. At last the resulting methods are compared.
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EEG Classification Model for Emotion Detection Using Python
Vengerová, Veronika ; Zaheer, Muhammad Asad (referee) ; Jawed, Soyiba (advisor)
Táto práca sa zaoberá rozoznávaním emócií z elektroencefalogramu (EEG). Dva modely na binárnu klasifikáciu emócií, kde jeden model klasifikuje neutrálnu emóciu alebo strach a druhý šťastie a smútok. Počas práce boli vyskúšané mnohé rôzne architektúry, pričom najlepšie výsledky boli dosiahnuté modelom pozostávajúcim z dvoch vetiev KNN-LSTM spojenými pred výstupnou vrstvou. Výsledná presnosť bola 87.309% na klasifikáciu šťastia a smútku a 84.865% na klasifikáciu neutrálnej emócie a strachu.
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Detection of objects and tracking the route of movement of traffic participants for the needs of intelligent transport nodes
Vymazal, Tomáš ; Kiac, Martin (referee) ; Burget, Radim (advisor)
The master‘s thesis is focused on the object detection. The aim of this thesis is to desine an experiment to assess the detection models YOLOv5, YOLOR, Scaled-YOLOv4 and EfficientDet and to compare their properties (detection speed, memory requirements, accuracy and certainty of detection). For this purpose a custom data set is created to investigate these parameters. The study shows that the YOLOv5 network is performd as the best solution. Deep SORT is used for object tracking which is important for the subsequent extraction of training data from video footage for object movement prediction. The added value is the design of the prediction algorithm which is based on a polynomial regression model.
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Advanced methods for sleep quality assessment
Doležalová, Anna ; Králík, Martin (referee) ; Ronzhina, Marina (advisor)
This diploma thesis is focused on advanced sleep assessment using deep learning. Metrics for sleep assessment and their use are described here. There are hearth rate and accelerometer data from Apple Watch used for classification. The basis for the classification was a model composed of 1D convolution networks in combination with recurrent neural network. LSTM and GRU were used as recurrent networks. Models were taught to classify into two, three and five phases. At last the resulting methods are compared.
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Gpon Attacks And Errors Classification
Tomašov, Adrián
This paper focuses on various types of attacks and errors in an activation process of Gigabitcapablepassive optical networks. The process sends messages via Physical Layer Operation Administrationand Maintenance header field inside the transmitted frame. An exemplar network communicationis captured by a special hardware-accelerated network interface card capable of processing opticalsignals from passive optical networks. The captured data is filtered of irrelevant parts and messagesand correctly formatted into a suitable shape for a neural network. The filtered data is divided intosmall sequences called time windows and analyzed using a recurrent neural network-based on Gatedrecurrent unit cells. A new neural network model is designed to classify sequences into several categories:additional message, missing message, error inside (noisy) message, and message order error.All of these categories represent a certain type of attack or error. The proposed model can distinguishmessage sequences into these categories with high accuracy resulting in revealing a possible attackeror drift from protocol recommendation.
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