Intelligent Automation System for Vessels Recognition: Comparison of SIFT and SURF Methods

Nowadays, with the rise of drone and satellite technology, there is a possibility for its application in sea and coastal surveillance. An advantage of this type of application is the automated recognition of marine objects, among which the most important are vessels. This paper presents the principle of vessel recognition based on the extraction of satellite image features of the vessel and the application of a multilayer perceptron (MLP). Dataset used in this research contains the total of 2750 images, where 2112 images are used as training set while the remaining 638 images are used for testing purposes. The SIFT and SURF algorithms were used to extract image features, which were later used as the input vector for MLP.The best results are achieved if a model with four hidden layers is used. These layers are constructed with 32, 128, 32, 128 neurons with ReLU activation function, respectively. Regarding the application of feature extraction, it can be observed that better results are achieved if the SIFT algorithm is used. The ROC AUC value achieved with the combination of SIFT and MLP reaches 0.99

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

The Leakage of Steam Mass Flow Rate through the Gland Seals – Influence on Turbine Produced Power

In this paper is presented an analysis of gland seals operation and their influence on the performance of low power steam turbine with two cylinders and steam reheating, which can be used in marine applications. Performed analysis presents a comparison of steam turbine main operating parameters when gland seals operation is neglected (as usual in the most of the literature) and when steam mass flow rates leaked through all gland seals are taken into consideration. Steam mass flow rate leakage through all gland seals reduces produced power of the whole turbine and both of its cylinders. Operation of gland seal mounted at the inlet in the first cylinder of any steam turbine (cylinder which operates with the steam of the highest pressure) has the most notable influence on the reduction of the whole turbine produced power. Gland seal mounted at the outlet of the last turbine cylinder (cylinder which operates with the steam of the lowest pressure) did not have any influence on the reduction of steam turbine produced power. In any detail analysis of a steam turbine (especially the complex turbine with multiple cylinders), gland seals operation should be considered due to their notable influence on the turbine performance

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

Determining features in algorithms for urinary bladder cancer detection

U prvom dijelu rada bilo je potrebno izlučiti vektor značajki slika karcinoma mokraćnog mjehura pomoću dva algoritma, SIFT (eng. Scale-Invariant Feature Transform) i SURF (eng. Speeded Up Robust Features) algoritma. Rezultati su pokazali da je pomoću SIFT algoritma izlučeno više ključnih točaka što dovodi do drugog dijela rada gdje je potrebno izlučiti značajke iz originalne slike te dvije augumentirane i pronaći značajke koje se podudaraju. Također pri usporedbi vremena potrebnog da se izluče značajke, SURF algoritam je dvostruko brži od SIFT-a. U zadnjem koraku pomoću izlučenih značajki na setu podataka, trenira se umjetna neuronska mreža. Uz optimalnu arhitekturu mreže, veća točnost i manji gubici postigli su se koristeći SIFT algoritam.In the first part of the paper, a feature vector was extracted from bladder cancer images using two algorithms, the SIFT (Scale-Invariant Feature Transform) algorithm and the SURF (Speeded Up Robust Features) algorithm. More keypoints were extracted using the SIFT algorithm, in the second part of the paper it was necessary to extract the features from the original image and the two augmented ones to find features that matched. Also when comparing the time it takes to extract features, the SURF algorithm is twice as fast as SIFT. In the last step, using the extracted features on the data set, the artificial neural network is trained. With optimal network architecture, higher accuracy and lower losses were achieved using the SIFT algorithm

Determining features in algorithms for urinary bladder cancer detection

U prvom dijelu rada bilo je potrebno izlučiti vektor značajki slika karcinoma mokraćnog mjehura pomoću dva algoritma, SIFT (eng. Scale-Invariant Feature Transform) i SURF (eng. Speeded Up Robust Features) algoritma. Rezultati su pokazali da je pomoću SIFT algoritma izlučeno više ključnih točaka što dovodi do drugog dijela rada gdje je potrebno izlučiti značajke iz originalne slike te dvije augumentirane i pronaći značajke koje se podudaraju. Također pri usporedbi vremena potrebnog da se izluče značajke, SURF algoritam je dvostruko brži od SIFT-a. U zadnjem koraku pomoću izlučenih značajki na setu podataka, trenira se umjetna neuronska mreža. Uz optimalnu arhitekturu mreže, veća točnost i manji gubici postigli su se koristeći SIFT algoritam.In the first part of the paper, a feature vector was extracted from bladder cancer images using two algorithms, the SIFT (Scale-Invariant Feature Transform) algorithm and the SURF (Speeded Up Robust Features) algorithm. More keypoints were extracted using the SIFT algorithm, in the second part of the paper it was necessary to extract the features from the original image and the two augmented ones to find features that matched. Also when comparing the time it takes to extract features, the SURF algorithm is twice as fast as SIFT. In the last step, using the extracted features on the data set, the artificial neural network is trained. With optimal network architecture, higher accuracy and lower losses were achieved using the SIFT algorithm

OGRANIČENJA UGRADBENIH SUSTAVA PRIMJENJIVIH U ISTOSMJERNIM PRETVARAČIMA

U ovom radu opisan je način djelovanja istosmjernog silazno-uzlaznog pretvarača (eng. buck-boost converter). Silazno-uzlazni pretvarač izrađen na eksperimentalnoj pločici upravljan je pomoću javno dostupne (eng. open-source) platforme Arduino Nano. Izrađeni pretvarač korišten je za edukacijsku primjenu, za smanjivanje ili povećanje istosmjernog napona na izlazu pretvarača. Opisana su oba načina rada silazno-uzlaznog pretvarača isprekidani (eng. discontinuous) i neisprekidani (eng. continuous). Kada pretvarač radi na granici dvaju režima rada struja prigušnice iL se smanjuje do vrijednosti nula na kraju periode, to nazivamo granični način rada. Prikazane su i izlazne vrijednosti silazno-uzlaznog pretvarača na osciloskopu sa povratnom vezom i bez nje. Kako bi izlazni napon ostao nepromijenjen bez obzira na otpor trošila potrebno je dodati povratnu vezu. Dodavanjem povratne veze prilikom naglog opterećenja izlaza pretvarača dodatnom strujom izlazni napon Uout trenutno se smanjuje, međutim u tom trenutku se generira PWM signal većeg faktora opterećenja D, te se izlazni napon vraća na zadanu vrijednost. Kod istosmjernog silazno-uzlaznog pretvarača s povratnom vezom bitna je brzina odziva mikrokontrolera na promjenu izlaznog napona. Primijećeno je da pretvarač s povratnom vezom ima slabije preformanse kada je na mikrokontroler priključen LCD zaslon.This paper describes the operation mode of the DC/DC buck-boost converter. Buck-boost converter made on the breadboard is controlled by publicly available open-source platform Arduino Nano. Designed converter is used for educational application, for reducing or increasing the DC voltage at the converter output. Both modes of the buck-boost converter are described, discontinuous and continuous. In the case when converter operates on the border of the two modes the coil current decrease to zero at the end of the switching period. This case has been tested by simulation and by measurement on designed circuit. Measured waveforms indicate difference between unregulated and regulated power converter. By increasing the reference voltage, the output voltage increases. For DC/DC buck-boost converter with voltage feedback a speed of response is slower when the LCD display is not connected

OGRANIČENJA UGRADBENIH SUSTAVA PRIMJENJIVIH U ISTOSMJERNIM PRETVARAČIMA

U ovom radu opisan je način djelovanja istosmjernog silazno-uzlaznog pretvarača (eng. buck-boost converter). Silazno-uzlazni pretvarač izrađen na eksperimentalnoj pločici upravljan je pomoću javno dostupne (eng. open-source) platforme Arduino Nano. Izrađeni pretvarač korišten je za edukacijsku primjenu, za smanjivanje ili povećanje istosmjernog napona na izlazu pretvarača. Opisana su oba načina rada silazno-uzlaznog pretvarača isprekidani (eng. discontinuous) i neisprekidani (eng. continuous). Kada pretvarač radi na granici dvaju režima rada struja prigušnice iL se smanjuje do vrijednosti nula na kraju periode, to nazivamo granični način rada. Prikazane su i izlazne vrijednosti silazno-uzlaznog pretvarača na osciloskopu sa povratnom vezom i bez nje. Kako bi izlazni napon ostao nepromijenjen bez obzira na otpor trošila potrebno je dodati povratnu vezu. Dodavanjem povratne veze prilikom naglog opterećenja izlaza pretvarača dodatnom strujom izlazni napon Uout trenutno se smanjuje, međutim u tom trenutku se generira PWM signal većeg faktora opterećenja D, te se izlazni napon vraća na zadanu vrijednost. Kod istosmjernog silazno-uzlaznog pretvarača s povratnom vezom bitna je brzina odziva mikrokontrolera na promjenu izlaznog napona. Primijećeno je da pretvarač s povratnom vezom ima slabije preformanse kada je na mikrokontroler priključen LCD zaslon.This paper describes the operation mode of the DC/DC buck-boost converter. Buck-boost converter made on the breadboard is controlled by publicly available open-source platform Arduino Nano. Designed converter is used for educational application, for reducing or increasing the DC voltage at the converter output. Both modes of the buck-boost converter are described, discontinuous and continuous. In the case when converter operates on the border of the two modes the coil current decrease to zero at the end of the switching period. This case has been tested by simulation and by measurement on designed circuit. Measured waveforms indicate difference between unregulated and regulated power converter. By increasing the reference voltage, the output voltage increases. For DC/DC buck-boost converter with voltage feedback a speed of response is slower when the LCD display is not connected