Co-generation is an efficient energy system that generates steam and electricity simultaneously. In ordinary operation, fuel cost accounts for more than 60% of the operational cost. As a result, the boiler efficiency and optimization level of co-generation are both high. To achieve further energy conservation, objectives of this thesis are to find the Profit-maximizing dispatch and efficiency enhancing strategy of the co-generation systems under deregulation. In a coexistent environment of both Bilateral and Poolco-based power market, there are bid-based spot dispatch, and purchases and sales agreement-based contract dispatch. For profit-maximizing dispatch, the steam of boilers, fuels and generation output will be obtained by using the SQP(Sequential Quadratic Programming ) method. In order to improve the boiler efficiency, this thesis utilizes artificial neural networks(ANN) and evolutionary programming(EP) methods to search for the optimal operating conditions of boilers. A co-generation system (back-pressure type and extraction type) is used to illustrate the effectiveness of the proposed method.

Commodity brain-computer interfaces (BCI) are beginning to accompany everything from toys and games to sophisticated health care devices. These contemporary interfaces allow for varying levels of interaction with a computer. Not surprisingly, the more intimately BCIs are integrated into the nervous system, the better the control a user can exert on a system. At one end of the spectrum, implanted systems can enable an individual with full body paralysis to utilize a robot arm and hold hands with their loved ones [28, 62]. On the other end of the spectrum, the untapped potential of commodity devices supporting electroencephalography (EEG) and electromyography (EMG) technologies require innovative approaches and further research. This thesis proposes a modularized software architecture designed to build flexible systems based on input from commodity BCI devices. An exploratory study using a commodity EEG provides concrete assessment of the potential for the modularity of the system to foster innovation and exploration, allowing for a combination of a variety of algorithms for manipulating data and classifying results. Specifically, this study analyzes a pipelined architecture for researchers, starting with the collection of spatio temporal brain data (STBD) from a commodity EEG device and correlating it with intentional behaviour involving keyboard and mouse input. Though classification proves troublesome in the preliminary dataset considered, the architecture demonstrates a unique and flexible combination of a liquid state machine (LSM) and a deep belief network (DBN). Research in methodologies and techniques such as these are required for innovation in BCIs, as commodity devices, processing power, and algorithms continue to improve. Limitations in terms of types of classifiers, their range of expected inputs, discrete versus continuous data, spatial and temporal considerations and alignment with neural networks are also identified. / Graduate / 0317 / 0984 / jasoncummer@gmail.com

This diploma thesis deals with automatic soccer event detection . Its goal is to introduce reader to this issue , discuss possible ways of solution of this task and then implement event detection . This work aims at event recognition using spatio - temporal data of gaming objects . Introduced way of dealing with event detection lies in its converting to sequence labeling task . Then such task is solved using LSTM recurrent neural networks . Lastly , result of sequence labeling is interpreted as detected events . Library for event detection has been created as the output of this work . This library allow user to experiment with different variants how to formulate event detection as sequence labeling task .

This paper uses a back-propagating neural network (BPN) to predict the price movements of major crypto currencies, leveraging technical factors as well as measurements of collective sentiment derived from the micro-blogging network Twitter. Our dataset consists of daily, hourly and minutely price levels for Bitcoin, Ether and Litecoin along with 8 popular technical indicators, as well as all tweets with the currencies' cash tags during respective time periods. Insprired by previous research which suggest that artificial neural networks are superior forecasting models in this setting, we were able to create a system generating automated investment decisions on a daily, hourly and minutely time basis. The study concluded that price trends are indeed predictable, with a correct prediction rate above 50% for all models, and corrensponding profitable trading strategies for all currencies on an hourly basis when neglecting trading fees, buy-sell spreads and order delays. The overall highest predictability is obtained on the hourly trading interval for Bitcoin, yielding an accuracy of 55.74% and a cumulative return of 175.1% between October 16, 2021 and December 31, 2021. / I denna studie används ett bakåtpropagerande neoronnät (BPN) för att förutsäga prisrörelser i större kryptovalutor med hjälp av tekniska faktorer och kvantifiering av kollektivt sentimentet från mikrobloggnätverket Twitter. Vårt dataset består av dagliga, timvisa och minutvisa prisnivåer för Bitcoin, Ether och Litecoin tillsammans med 8 populära tekniska indikatorer, samt alla tweets med valutornas "cash tags" under respektive tidsperiod. Med inspiration från tidigare forskning som hävdar att artificiella nauronnät är överlägsna prognosmodeller i denna typ av analys kunde vi skapa ett system som genererar automatiska investeringsbeslut på daglig, timvis och minutvis basis. Vi hävdar med denna studie att pristrender är förutsägbara för dessa kryptovalutor, med en korrekt förutsägelsefrekvens på över 50% för alla modeller, och med lönsamma handelsstrategier för alla valutor på timbasis när man bortser från handelsavgifter, köp- och säljspreadar och orderfördröjningar. Den högsta förutsägbarheten erhålls på timhandelsintervallet för Bitcoin, vilket ger en nogrannhet på 55,74% och en ackumulerad avkastning på 175,1% mellan den 16 oktober 2021 och den 31 december 2021.

長久以來,人類在萬物中獨具的高智慧特質吸引了無數的哲學家和科學家 投入對其研究,除了醫學的原因之外,由於人腦所具有卓越的辨識系統及學 習能力,為數不少的科學家們相信人腦存在許多最適化系統與設計,因此如 何模仿人類腦神經的組織與運作,一直是很多人努力及夢寐以求的.因此類 神經網路就是依據這些理念而在各研究領域上廣為發展與應用,其中本文 所探討的倒傳遞神經網路模型更是目前類神經網路模型中最具代表性,應 用最廣的模型.而結構性時間數列模型則是將可被觀察的變數分解成趨勢, 季節性,不規則性等不可被觀察項,故其對經濟意義的解釋是相當明當明顯 的,藉由狀態空間模式的轉換,我們將很容易地利用卡門濾器來作估計與預 測.而本文所欲探的重點在於比較有學習機能的倒傳遞神經網及可利用最 新的資訊更新之結構性時間數列何者之預測能利較佳,藉此瞭解二者之一 些特性.

This diploma thesis deals with image super-resolution with conservation of good quality. Firstly, there are described state of the art methods dealing with this problem, as well as principles of neural networks with focus on convolutional ones. Finally, there is described a few models of convolutional neural network for image super-resolution to double size, which have been trained, tested and compared on newly created database with pictures of people.

This master thesis deals with classification of sleep stages on the base of polysomnographic signals. On several signals was performed analysis and feature extraxtion in time domain and in frequency domain as well. For feature extraxtion was used EEG, EOG and EMG signals. For classification was selected classification models K-NN, SVM and artifical neural network. Accuracy of classifation is different depending on used method and spleep stages split. The best results achieved classification among stages Wake, REM, and N3, with neural network usage. In this case the succes was 93,1 %.