Thermodynamic Analysis of CO2 Closed-Cycle Gas Turbine for Marine Applications at Various Pressure Ratios

A thermodynamic analysis of CO2 closed-cycle gas turbine is presented in this paper. Two processes are investigated - base process and the same process upgraded with a heat regenerator. Maximum specific useful work is 159.94 kJ/kg for both observed processes. Involving of heat regenerator inside base CO2 closed-cycle gas turbine process requires attention due to required temperature differences - high pressure ratios cannot be obtained with a high efficiency heat regenerator. Base CO2 closed-cycle gas turbine process did not reach cycle efficiency higher than 25%, while for the upgraded process the cycle efficiency can reach 40% at high pressure ratio and for high regenerator efficiency. Additionally, multilayer perceptron is trained in order to achieve high quality models for estimating specific useful work and efficiency for both, base and upgraded process. As a result, MLP with three hidden layers achieved high values of R2 score

Use of Convolutional Neural Network for Fish Species Classification

Fish population monitoring systems based on underwater video recording are becoming more popular nowadays, however, manual processing and analysis of such data can be time-consuming. Therefore, by utilizing machine learning algorithms, the data can be processed more efficiently. In this research, authors investigate the possibility of convolutional neural network (CNN) implementation for fish species classification. The dataset used in this research consists of four fish species (Plectroglyphidodon dickii, Chromis chrysura, Amphiprion clarkii, and Chaetodon lunulatus), which gives a total of 12859 fish images. For the aforementioned classification algorithm, different combinations of hyperparameters were examined as well as the impact of different activation functions on the classification performance. As a result, the best CNN classification performance was achieved when Identity activation function is applied to hidden layers, RMSprop is used as a solver with a learning rate of 0.001, and a learning rate decay of 1e-5. Accordingly, the proposed CNN model is capable of performing high-quality fish species classifications

Use of Artificial Neural Network for Estimation of Propeller Torque Values in a CODLAG Propulsion System

An artificial neural network (ANN) approach is proposed to the problem of estimating the propeller torques of a frigate using combined diesel, electric and gas (CODLAG) propulsion system. The authors use a multilayer perceptron (MLP) feed-forward ANN trained with data from a dataset which describes the decay state coefficients as outputs and system parameters as inputs – with a goal of determining the propeller torques, removing the decay state coefficients and using the torque values of the starboard and port propellers as outputs. A total of 53760 ANNs are trained – 26880 for each of the propellers, with a total 8960 parameter combinations. The results are evaluated using mean absolute error (MAE) and coefficient of determination (R2). Best results for the starboard propeller are MAE of 2.68 [Nm], and MAE of 2.58 [Nm] for the port propeller with following ANN configurations respectively: 2 hidden layers with 32 neurons and identity activation and 3 hidden layers with 16, 32 and 16 neurons and identity activation function. Both configurations achieve R2 value higher than 0.99

Comparison of conventional and heat balance based energy analyses of steam turbine

This paper presents a comparison of conventional and heat balance based energy analyses of steam turbine. Both analyses are compared by using measured operating parameters from low power steam turbine exploitation. The major disadvantage of conventional steam turbine energy analysis is that extracted energy flow streams are not equal in real (polytropic) and ideal (isentropic) expansion processes, while the heat balance based energy analysis successfully resolved mentioned problem. Heat balance based energy analysis require an increase of steam mass flow rates extracted from the turbine in ideal (isentropic) expansion process to ensure always the same energy flow streams to all steam consumers. Increase in steam mass flow rate extracted through each turbine extraction (heat balance based energy analysis) result with a decrease in energy power losses and with an increase in energy efficiency of whole turbine and all of its cylinders (when compared to conventional analysis). All of the obtained conclusions in this research are valid not only for the analyzed low power steam turbine, but also for any other steam turbine with steam extractions

Frigate Speed Estimation Using CODLAG Propulsion System Parameters and Multilayer Perceptron

Authors present a Multilayer Perceptron (MLP) artificial neural network (ANN) method for the purpose of estimating a speed of a frigate using a combined diesel-electric and gas (CODLAG) propulsion system. Dataset used is publicly available, as condition-based maintenance of naval propulsion plants dataset, out of which GT Compressor decay state coefficient and GT Turbine decay state coefficient are unused, while 15 features are used as input and ship speed is used as dataset output. Data set consists of 11934 data points out of which 8950 (75%) are used as a training set and 2984 (25%) are used as a testing set. 26880 MLPs, with 8960 different parameter combinations are trained using a grid search algorithm, quality of each solution being estimated with coefficient of determination (R2) and mean absolute error (MAE). Results show that a high-quality estimation can be made using an MLP, with best result having an error of just 3.4485x10-5 knots (absolute error of 0.00014% of the range). This result was achieved with a MLP with three hidden layers containing 100 neurons each, logistic activation function, LBFGS solver, constant learning rate of 0.1 and no L2 regularization

Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory

COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on financial movement of Crude Oil price and three U.S. stock indexes: DJI, S&P 500 and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the Stationary Wavelet Transform (SWT) and Bidirectional Long Short-Term Memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM+WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.Comment: 26 pages, 9 figure

UGRADBENI SUSTAVI ZA UČINSKE ISTOSMJERNE PRETVARAČE

Potreba za učinkovitom pretvorbom jedne razine parametara električne energije u drugu svakim je danom sve veća. Iz tog razloga osmišljeni su elektronički učinski pretvarači. Ovaj rad daje pregled na istosmjerni silazni pretvarač (eng. DC/DC buck converter) koji je upravljan pomoću Arduino kompatibilnog mikrokontrolera. Takav pretvarač radi na načelu snižavanja ulaznog napona tako da na svom izlazu daje napon koji ovisi o faktoru opterećenja D koji je upravljan pomoću PWM (eng. pulse – width modulation) signala. U radu se detaljno opisuje postupak projektiranja istosmjernog silaznog pretvarača uz pomoć izračuna i odabira odgovarajućih komponenti, te i sama izrada pretvarača na eksperimentalnoj pločici. Prikazani su dobiveni eksperimentalni rezultati na osciloskopu za isprekidani (eng. discontinuous) i neisprekidani (eng. continuous) način rada te ovisnosti upravljačkog signala generiranog pomoću Arduino kompatibilnog mikrokontrolera na izlazni napon i izlaznu struju pretvarača. Uspoređeni su rezultati za pretvarač bez povratne veze i s njom te objašnjeno zašto je povratna veza od velike važnosti u reguliranim sustavima. Nakon izrade pretvarača na eksperimentalnoj pločici slijedi i izrada na tiskanoj pločici nanošenjem maske vodljivih linija na površinu pločice tehnikom sitotiska te jetkanje pomoću željezovog(III) klorida (FeCl3). Rad se zaključuje lemljenjem elemenata na prethodno jetkanu pločicu i testiranjem istog. Budući da istosmjerni pretvarači postaju sve složeniji (LCD ispis, priključivanje na internet…) postaje smisleno govoriti o ugradbenim sustavima koji nadziru rad pretvarača.The need for high conversion efficiency from one voltage level to another is increasing every day. This paper provides an overview on DC/DC buck converter which is controlled by the Arduino compatible microcontroller. A buck converter steps down voltage from its input to its output which depends on duty cycle D which is controlled by PWM (pulse – width modulation) signal from the Arduino compatible microcontroller. This paper describes the design process of DC/DC buck converter by calculating and selecting the appropriate components, as well as building a circuit on breadboard. The experimental results are shown on oscilloscope for discontinuous and continuous mode, as well as dependency control signal generated with Arduino microcontroler on output voltage and output current. The results were compared for converter without voltage feedback and with added voltage feedback. It has been explained importance of DC/DC power converter regulation. Building a converter on PCB (printed circuit board) starts with applying the mask of conductive lines on the board surface and etching using iron(III) chloride (FeCl3). This work is concluded by soldering the elements on the board and testing it. Since DC/DC converters become more complex (LCD printing, internet connection…) embedded systems has been used more frequently

Application of waveform transformation in algorithms for the detection of bladder cancer

Obradom signala moguća je optimizacija i izlučivanje važnih značajki koje kasnije doprinose boljoj i jednostavnijoj analizi. Ovaj rad orijentiran je na izlučivanje značajki i detekciju karcinoma mokraćnog mjehura iz uzoraka tkiva mokraćnog mjehura koji je snimljen cistoskopijom. Izlučivanje značajki izvedeno je koristeći valićnu transformaciju uz pomoć Haar, Daubechies, Symlet i Biorthogonal valićnih funkcija na prvoj, trećoj i petoj dekompozicijskoj razini. Provođenje valićne transformacije na uzorcima rezultira aproksimacijom te horizontalnim, vertikalnim i dijagonalnim detaljima, gdje se svi detalji odbacuju a aproksimacija se koristi kao ulazni podatak u neuronsku mrežu. Neuronska mreža realizirana je optimalnom arhitekturom za osnovnu klasifikaciju. Ispitani su utjecaji valićnih funkcija i dekompozicijskih razina na točnost i performanse neuronske mreže. Prema dobivenim rezultatima vidljivo je kako uz povećanje dekompozicijske razine povećava se i brzina treniranja dok točnost ostaje približno jednaka. Treniranje neuronske mreže je do 37 puta brže ukoliko se koristi valićna transformacija na pet dekompozicijskih razina u odnosu na uzorke tkiva mokraćnog mjehura koji nisu prethodno obrađeni.With signal processing, it is possible to optimize and extract important features that can later contribute to the better and easier analysis. This paper is focused on feature extraction and bladder cancer detection form bladder tissue samples taken during cystoscopy. Feature extraction was performed using wavelet transform with Haar, Daubechies, Symlet and Biorthogonal wavelet function at the first, third, and fifth decomposition levels. The result of using wavelet transform is an approximation and horizontal, vertical, diagonal details, where all details are discarded and the approximation is used as input to the neural network. The neural network is realized with an optimal architecture for basic classification. The influences of wavelet functions and decomposition levels on the accuracy and performance of the neural network were examined. According to the obtained results, it is evident that with increasing the decomposition level, the training speed also increases while the accuracy remains approximately the same. The neural network training process is up to 37 times faster when using wavelet transform at five decomposition levels compared to bladder tissue samples that are not previously processed

Application of waveform transformation in algorithms for the detection of bladder cancer

Obradom signala moguća je optimizacija i izlučivanje važnih značajki koje kasnije doprinose boljoj i jednostavnijoj analizi. Ovaj rad orijentiran je na izlučivanje značajki i detekciju karcinoma mokraćnog mjehura iz uzoraka tkiva mokraćnog mjehura koji je snimljen cistoskopijom. Izlučivanje značajki izvedeno je koristeći valićnu transformaciju uz pomoć Haar, Daubechies, Symlet i Biorthogonal valićnih funkcija na prvoj, trećoj i petoj dekompozicijskoj razini. Provođenje valićne transformacije na uzorcima rezultira aproksimacijom te horizontalnim, vertikalnim i dijagonalnim detaljima, gdje se svi detalji odbacuju a aproksimacija se koristi kao ulazni podatak u neuronsku mrežu. Neuronska mreža realizirana je optimalnom arhitekturom za osnovnu klasifikaciju. Ispitani su utjecaji valićnih funkcija i dekompozicijskih razina na točnost i performanse neuronske mreže. Prema dobivenim rezultatima vidljivo je kako uz povećanje dekompozicijske razine povećava se i brzina treniranja dok točnost ostaje približno jednaka. Treniranje neuronske mreže je do 37 puta brže ukoliko se koristi valićna transformacija na pet dekompozicijskih razina u odnosu na uzorke tkiva mokraćnog mjehura koji nisu prethodno obrađeni.With signal processing, it is possible to optimize and extract important features that can later contribute to the better and easier analysis. This paper is focused on feature extraction and bladder cancer detection form bladder tissue samples taken during cystoscopy. Feature extraction was performed using wavelet transform with Haar, Daubechies, Symlet and Biorthogonal wavelet function at the first, third, and fifth decomposition levels. The result of using wavelet transform is an approximation and horizontal, vertical, diagonal details, where all details are discarded and the approximation is used as input to the neural network. The neural network is realized with an optimal architecture for basic classification. The influences of wavelet functions and decomposition levels on the accuracy and performance of the neural network were examined. According to the obtained results, it is evident that with increasing the decomposition level, the training speed also increases while the accuracy remains approximately the same. The neural network training process is up to 37 times faster when using wavelet transform at five decomposition levels compared to bladder tissue samples that are not previously processed

UGRADBENI SUSTAVI ZA UČINSKE ISTOSMJERNE PRETVARAČE

Potreba za učinkovitom pretvorbom jedne razine parametara električne energije u drugu svakim je danom sve veća. Iz tog razloga osmišljeni su elektronički učinski pretvarači. Ovaj rad daje pregled na istosmjerni silazni pretvarač (eng. DC/DC buck converter) koji je upravljan pomoću Arduino kompatibilnog mikrokontrolera. Takav pretvarač radi na načelu snižavanja ulaznog napona tako da na svom izlazu daje napon koji ovisi o faktoru opterećenja D koji je upravljan pomoću PWM (eng. pulse – width modulation) signala. U radu se detaljno opisuje postupak projektiranja istosmjernog silaznog pretvarača uz pomoć izračuna i odabira odgovarajućih komponenti, te i sama izrada pretvarača na eksperimentalnoj pločici. Prikazani su dobiveni eksperimentalni rezultati na osciloskopu za isprekidani (eng. discontinuous) i neisprekidani (eng. continuous) način rada te ovisnosti upravljačkog signala generiranog pomoću Arduino kompatibilnog mikrokontrolera na izlazni napon i izlaznu struju pretvarača. Uspoređeni su rezultati za pretvarač bez povratne veze i s njom te objašnjeno zašto je povratna veza od velike važnosti u reguliranim sustavima. Nakon izrade pretvarača na eksperimentalnoj pločici slijedi i izrada na tiskanoj pločici nanošenjem maske vodljivih linija na površinu pločice tehnikom sitotiska te jetkanje pomoću željezovog(III) klorida (FeCl3). Rad se zaključuje lemljenjem elemenata na prethodno jetkanu pločicu i testiranjem istog. Budući da istosmjerni pretvarači postaju sve složeniji (LCD ispis, priključivanje na internet…) postaje smisleno govoriti o ugradbenim sustavima koji nadziru rad pretvarača.The need for high conversion efficiency from one voltage level to another is increasing every day. This paper provides an overview on DC/DC buck converter which is controlled by the Arduino compatible microcontroller. A buck converter steps down voltage from its input to its output which depends on duty cycle D which is controlled by PWM (pulse – width modulation) signal from the Arduino compatible microcontroller. This paper describes the design process of DC/DC buck converter by calculating and selecting the appropriate components, as well as building a circuit on breadboard. The experimental results are shown on oscilloscope for discontinuous and continuous mode, as well as dependency control signal generated with Arduino microcontroler on output voltage and output current. The results were compared for converter without voltage feedback and with added voltage feedback. It has been explained importance of DC/DC power converter regulation. Building a converter on PCB (printed circuit board) starts with applying the mask of conductive lines on the board surface and etching using iron(III) chloride (FeCl3). This work is concluded by soldering the elements on the board and testing it. Since DC/DC converters become more complex (LCD printing, internet connection…) embedded systems has been used more frequently