Coevolution of banks and capital markets in the modern view of effective parts of the financial system architecture

Due to the onset of the last economic crisis, the recent focus of policy makers has been shifted from economic development to the possibility of fast identification of the sources of financial shocks and imbalances in order to quickly solve the extreme stresses that can emerge in the financial system. Therefore, the interdependences of financial institutions and financial leverages as well as the causes of endogenous changes of real interest rates and the differences emerging between interest rates and the rates affecting expenditure are observed more often in this context. The paper emphasizes this problem as well as the problem of the need for a different structure of the financial system and its participants that would contribute to a higher level of economic development. In this context, on the example of data on the Croatian capital market shareholders’ structure and data on the amount of loans to companies in the period from 2006 to 2018, a model was developed via ANFIS. The model indicated the positive and negative impacts of banks and shareholders’ structure on the capital market

Using ANFIS in joint dynamics of monetization, financial development, public debt and unemployment analysis

Purpose: The modern concepts of contemplating joint dynamics of monetary policy effects on economic growth and its indicators require an indirect approach based on empirical research of mainly financial infrastructure, competitiveness of the financial markets and current economic conditions. Meanwhile, the problems of unemployment and the structure of employment within these concepts are most frequently linked with the polarization of the labor market and two important factors, that is, the effects of growth on unemployment and the fact that technological changes affect the changes in salary ranges. Methodology: By using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the set of data from 1995 to 2016, this paper analyzes these issues through a prism of established balances between the labor and financial markets, i.e., the monetization of economy (M1/GDP), financial development (Loans/GDP) and the share of gross government debt in GDP (government gross debt/GDP). Results: The proposed model suggests that the rate of unemployment is conditioned by the financial cycle and monetary policy (M1/GDP, Loans/GDP), as well as the business cycle and fiscal policy (gross d/BDP) and that a controlled and properly directed level of monetization of the economy (M1/BDP) and financial development measured as Loans/GDP can be “sufficient” for economic growth. Conclusion: Waiting in the “monetary union lobby”, i.e., waiting for the ERM II exchange mechanism can last longer than the set deadlines, leading to the need for Croatian economic policy to optimize monetary and fiscal policy measures in order to increase economic growth and reduce unemployment

Stereo Visual Odometry for Indoor Localization of Ship Model

Typically, ships are designed for open sea navigation and thus research of autonomous ships is mostly done for that particular area. This paper explores the possibility of using low-cost sensors for localization inside the small navigation area. The localization system is based on the technology used for developing autonomous cars. The main part of the system is visual odometry using stereo cameras fused with Inertial Measurement Unit (IMU) data coupled with Kalman and particle filters to get decimetre level accuracy inside a basin for different surface conditions. The visual odometry uses cropped frames for stereo cameras and Good features to track algorithm for extracting features to get depths for each feature that is used for estimation of ship model movement. Experimental results showed that the proposed system could localize itself within a decimetre accuracy implying that there is a real possibility for ships in using visual odometry for autonomous navigation on narrow waterways, which can have a significant impact on future transportation

INTELLIGENT ESTIMATION IN DYNAMIC POSITIONING SYSTEMS OF MARINE VESSELS

Sustavi za dinamičko pozicioniranje plovnih objekata koriste se za održavanje njihove pozicije, smjera napredovanja i brzine, održavanje unaprijed definirane putanje gibanja, potpomognuto sidrenje i sl. Da bi se ove operacije uopće mogle provoditi, nužno je, između ostalog, omogućiti i određivanje precizne estimacije niskofrekventne pozicije, smjera napredovanja i brzine plovnog objekta, te estimaciju vjetrovnog i sporopromjenjivog opterećenja koje uzrokuju ostali vanjski poremećaji. U realnim sustavima za pozicioniranje plovnih objekata funkciju observera, tj. estimatora, ima neka od inačica Kalmanovog filtra koji ima već dugu tradiciju u brodskim sustavima upravljanja. U radu su analizirani klasični koncepti na kojima su temeljeni postojeći sustavi za dinamičko pozicioniranje te su istražene značajke dinamičkog pozicioniranja plovnih objekata s teoretske i praktične strane, posebno u dijelu koji se odnosi na problematiku filtriranja, identifikacije, estimacije i predikcije. Uočene su brojne prednosti, ali i nedostaci postojećih rješenja koji se mogu otkloniti primjenom novijih računalnih tehnologija kao što su algoritmi strojnog učenja i računalne inteligencije. Iz navedenih razloga, predložene su i konstruirane strukture statičkih, dinamičkih i hibridnih inteligentnih identifikatora i estimatora za potrebe identifikacije i estimacije u sustavima za dinamičko pozicioniranje. Od posebnog značaja su predloženi hibridni sustavi inteligentnih identifikatora i estimatora s proširenim Kalmanovim filtrom te inteligentni identifikatori za fuziju senzorskih informacija i rekonstrukciju signala u prekidu. Predloženi inteligentni identifikatori i estimatori su verificirani na realnim mjerenjima DP Log arhive dizaličara i cjevopolagača Saipem 7000 tijekom postupka polaganja cijevi na Projektu Ormen Lange (Norveška, 2006.).Dynamic positioning (DP) systems are used for maintaining position, heading and speed of the vessels, but also a predefined motion path, position mooring, etc. To ensure performing of these operations, it is necessary, among other things, to determine an accurate estimation of low-frequency position, heading and speed of the vessel. Additionally, it is necessary to ensure the estimation of wind and slowly-varying loads caused by other environmental disturbances. In actual DP systems, the vessel observer is usually an extended Kalman filter (EKF) which is traditionally used in marine control systems. In this doctoral thesis the classical base concepts of the existing commercial DP systems are analysed. Furthermore, the characteristics of DP systems are analysed both from the theoretical and practical point of view, especially in the part which is closely related to filtering, identification, estimation and prediction. Numerous advantages of existing solutions are identified, but also the several disadvantages which can be eliminated by using modern computational technologies such as machine learning and computational intelligence algorithms are pointed out. For these reasons, structures based on static, dynamic and hybrid intelligent identifiers and estimators have been proposed for the purpose of intelligent identification and estimation in DP systems. Proposed hybrid system of intelligent identifiers and estimators combined with EKF, as well as the intelligent identifiers for the sensor fusion and reconstruction of lost signals, are of particular interest. Intelligent identifiers and estimators are further adjusted, tested, and verified with real measurements from the DP Log archive of the heavy-lift and J-lay pipe vessel Saipem 7000

INTELLIGENT ESTIMATION IN DYNAMIC POSITIONING SYSTEMS OF MARINE VESSELS

Sustavi za dinamičko pozicioniranje plovnih objekata koriste se za održavanje njihove pozicije, smjera napredovanja i brzine, održavanje unaprijed definirane putanje gibanja, potpomognuto sidrenje i sl. Da bi se ove operacije uopće mogle provoditi, nužno je, između ostalog, omogućiti i određivanje precizne estimacije niskofrekventne pozicije, smjera napredovanja i brzine plovnog objekta, te estimaciju vjetrovnog i sporopromjenjivog opterećenja koje uzrokuju ostali vanjski poremećaji. U realnim sustavima za pozicioniranje plovnih objekata funkciju observera, tj. estimatora, ima neka od inačica Kalmanovog filtra koji ima već dugu tradiciju u brodskim sustavima upravljanja. U radu su analizirani klasični koncepti na kojima su temeljeni postojeći sustavi za dinamičko pozicioniranje te su istražene značajke dinamičkog pozicioniranja plovnih objekata s teoretske i praktične strane, posebno u dijelu koji se odnosi na problematiku filtriranja, identifikacije, estimacije i predikcije. Uočene su brojne prednosti, ali i nedostaci postojećih rješenja koji se mogu otkloniti primjenom novijih računalnih tehnologija kao što su algoritmi strojnog učenja i računalne inteligencije. Iz navedenih razloga, predložene su i konstruirane strukture statičkih, dinamičkih i hibridnih inteligentnih identifikatora i estimatora za potrebe identifikacije i estimacije u sustavima za dinamičko pozicioniranje. Od posebnog značaja su predloženi hibridni sustavi inteligentnih identifikatora i estimatora s proširenim Kalmanovim filtrom te inteligentni identifikatori za fuziju senzorskih informacija i rekonstrukciju signala u prekidu. Predloženi inteligentni identifikatori i estimatori su verificirani na realnim mjerenjima DP Log arhive dizaličara i cjevopolagača Saipem 7000 tijekom postupka polaganja cijevi na Projektu Ormen Lange (Norveška, 2006.).Dynamic positioning (DP) systems are used for maintaining position, heading and speed of the vessels, but also a predefined motion path, position mooring, etc. To ensure performing of these operations, it is necessary, among other things, to determine an accurate estimation of low-frequency position, heading and speed of the vessel. Additionally, it is necessary to ensure the estimation of wind and slowly-varying loads caused by other environmental disturbances. In actual DP systems, the vessel observer is usually an extended Kalman filter (EKF) which is traditionally used in marine control systems. In this doctoral thesis the classical base concepts of the existing commercial DP systems are analysed. Furthermore, the characteristics of DP systems are analysed both from the theoretical and practical point of view, especially in the part which is closely related to filtering, identification, estimation and prediction. Numerous advantages of existing solutions are identified, but also the several disadvantages which can be eliminated by using modern computational technologies such as machine learning and computational intelligence algorithms are pointed out. For these reasons, structures based on static, dynamic and hybrid intelligent identifiers and estimators have been proposed for the purpose of intelligent identification and estimation in DP systems. Proposed hybrid system of intelligent identifiers and estimators combined with EKF, as well as the intelligent identifiers for the sensor fusion and reconstruction of lost signals, are of particular interest. Intelligent identifiers and estimators are further adjusted, tested, and verified with real measurements from the DP Log archive of the heavy-lift and J-lay pipe vessel Saipem 7000

INTELLIGENT ESTIMATION IN DYNAMIC POSITIONING SYSTEMS OF MARINE VESSELS

Sustavi za dinamičko pozicioniranje plovnih objekata koriste se za održavanje njihove pozicije, smjera napredovanja i brzine, održavanje unaprijed definirane putanje gibanja, potpomognuto sidrenje i sl. Da bi se ove operacije uopće mogle provoditi, nužno je, između ostalog, omogućiti i određivanje precizne estimacije niskofrekventne pozicije, smjera napredovanja i brzine plovnog objekta, te estimaciju vjetrovnog i sporopromjenjivog opterećenja koje uzrokuju ostali vanjski poremećaji. U realnim sustavima za pozicioniranje plovnih objekata funkciju observera, tj. estimatora, ima neka od inačica Kalmanovog filtra koji ima već dugu tradiciju u brodskim sustavima upravljanja. U radu su analizirani klasični koncepti na kojima su temeljeni postojeći sustavi za dinamičko pozicioniranje te su istražene značajke dinamičkog pozicioniranja plovnih objekata s teoretske i praktične strane, posebno u dijelu koji se odnosi na problematiku filtriranja, identifikacije, estimacije i predikcije. Uočene su brojne prednosti, ali i nedostaci postojećih rješenja koji se mogu otkloniti primjenom novijih računalnih tehnologija kao što su algoritmi strojnog učenja i računalne inteligencije. Iz navedenih razloga, predložene su i konstruirane strukture statičkih, dinamičkih i hibridnih inteligentnih identifikatora i estimatora za potrebe identifikacije i estimacije u sustavima za dinamičko pozicioniranje. Od posebnog značaja su predloženi hibridni sustavi inteligentnih identifikatora i estimatora s proširenim Kalmanovim filtrom te inteligentni identifikatori za fuziju senzorskih informacija i rekonstrukciju signala u prekidu. Predloženi inteligentni identifikatori i estimatori su verificirani na realnim mjerenjima DP Log arhive dizaličara i cjevopolagača Saipem 7000 tijekom postupka polaganja cijevi na Projektu Ormen Lange (Norveška, 2006.).Dynamic positioning (DP) systems are used for maintaining position, heading and speed of the vessels, but also a predefined motion path, position mooring, etc. To ensure performing of these operations, it is necessary, among other things, to determine an accurate estimation of low-frequency position, heading and speed of the vessel. Additionally, it is necessary to ensure the estimation of wind and slowly-varying loads caused by other environmental disturbances. In actual DP systems, the vessel observer is usually an extended Kalman filter (EKF) which is traditionally used in marine control systems. In this doctoral thesis the classical base concepts of the existing commercial DP systems are analysed. Furthermore, the characteristics of DP systems are analysed both from the theoretical and practical point of view, especially in the part which is closely related to filtering, identification, estimation and prediction. Numerous advantages of existing solutions are identified, but also the several disadvantages which can be eliminated by using modern computational technologies such as machine learning and computational intelligence algorithms are pointed out. For these reasons, structures based on static, dynamic and hybrid intelligent identifiers and estimators have been proposed for the purpose of intelligent identification and estimation in DP systems. Proposed hybrid system of intelligent identifiers and estimators combined with EKF, as well as the intelligent identifiers for the sensor fusion and reconstruction of lost signals, are of particular interest. Intelligent identifiers and estimators are further adjusted, tested, and verified with real measurements from the DP Log archive of the heavy-lift and J-lay pipe vessel Saipem 7000

Nature Inspired Metaheuristics for Optimizing Problems at a Container Terminal

Nowadays, maritime transport is the backbone of the international trade of goods. Therefore, seaports play a very important role in global transport. The use of containers is significantly represented in the maritime transport. Considering the increased number of container shipments in the global transport, seaport container terminals have to be adapted to a new situation and provide the best possible service of container transfer by reducing the transfer cost and the container transit time. Therefore, there is a need for optimization of the whole container transport process within the terminal. The logistic problems of the container terminals have become very complex and logistics experts cannot manually adjust the operations of terminal processes that will optimize the usage of resources. Hence, to achieve further improvements of terminal logistics, there is a need to introduce scientific methods such as metaheuristics that will enable better and optimized use of the terminal resources in an automated way. There is a large number of research papers that have successfully proposed the solutions of optimizing the container logistic problems with well-known metaheuristics inspired by the nature. However, there is a continuous emergence of new nature inspired metaheuristics today, like artificial bee colony algorithm, firefly algorithm and bat algorithm, that outperform the well-known metaheuristics considering the most popular optimization problems like travel salesman problem. Considering these results of comparing algorithms, we assume that better results of optimization of container terminal logistic problems can be achieved by introducing these new nature inspired metaheuristics. In this paper we have described and classified the main subsystems of the container terminal and its logistic problems that need to be optimized. We have also presented a review of new nature inspired metaheuristics (bee, firefly and bat algorithm) that could be used in the optimization of these problems within the terminal

Neural Network Prediction of Open Water Characteristics of Ducted Propeller

Dynamic positioning system is a computer-controlled system which enables maintaining the marine vessel’s position and heading by means of propellers and active thrusters. In order to improve the efficiency of the heavily loaded propeller at low advance velocity, the azimuth thrusters with a nozzle around the propeller are mostly used. The paper deals with the development of the neural network prediction model for the Wageningen ducted propeller series. The aim is to create a prediction tool that provides the estimation of the open water characteristics of the four blade Ka series in combination with the 19A accelerating nozzle. For this purpose, two layered feedforward neural network is used. The proposed neural network model provides the possibility for a more general modelling of azimuth thruster in usually very complex hydrodynamic propulsion model of the dynamically positioned marine vessel

IDENTIFICATION AND SIMULATION MODELS OF OPERATING SYSTEMS BASED ON ARTIFICIAL NEURAL NETWORKS

Rezultati identifikacije i simulacije dinamičkog ili statičkog radnog sustava značajno ovise o kvaliteti i odabiru ulaznih parametara. U radu je dat uopćen model identifikacije i simulacije radnog sustava u ovisnosti o različitim klasama parametara, temeljen na generaliziranoj regresijskoj neuronskoj (GRNN) mreži. Predložen je i model iteracijskog postupka kojim se pomoću vjerojatnosne neuronske (PNN) mreže vrši ocjena uspješnosti dobivenih simulacijskih rezultata nastalih kao odzivi GRNN mreža. Oba modela su testirana na parametrima sustava upravljanja i regulacije parnoturbinskog postrojenja, a u tu svrhu je korišten programski paket MATLAB 7.0.1.The identification and simulation results of dynamic and static operating systems significantly depend upon the quality and choice of input parameters. The paper presents a generalised identification and simulation model of an operating system dependant on different classes of parameters based on a generalised regressive neural network (GRNN). In addition the iterative procedure model is proposed here which, in virtue of the probability neuron network (PNN), makes it possible to effect efficiency assessment of the results developed as GRNN network responses. Both models have been tested on system parameters for the control and regulation of steam turbine installations utilising for the purpose the software package MATLAB 7.0.1

On Global Ionospheric Maps based winter-time GPS ionospheric delay with reference to the Klobuchar model: Case study of the Northern Adriatic

Modelling of the ionospheric Total Electron Content (TEC) represents a challenging and demanding task in Global Navigation Satellite Systems (GNSS) positioning performance. In terms of satellite Positioning, Navigation and Timing (PNT), TEC represents a significant cause of the satellite signal ionospheric delay. There are several approaches to TEC estimation. The Standard (Klobuchar) ionospheric delay correction model is the most common model for Global Positioning System (GPS) single-frequency (L1) receivers. The development of International GNSS Service (IGS) Global Ionospheric Maps (GIM) has enabled the insight into global TEC dynamics. GIM analyses in the Northern Adriatic area have shown that, under specific conditions, local ionospheric delay patterns differ from the one defined in the Klobuchar model. This has been the motivation for the presented research, with the aim to develop a rudimentary model of the TEC estimation, with emphasis on areas where ground truth data are not available. The local pattern of the ionospheric delay has been modelled with wave functions based on the similarity of waveforms, considering diurnal differences in TEC behavior from defined TEC patterns. The model represents a spatiotemporal winter-time ionospheric delay correction with the Klobuchar model as a basis. The evaluation results have shown accurate approximation of the local pattern of the ionospheric delay. The model was verified in the same seasonal period in 2007, revealing it successfulness under pre-defined conditions. The presented approach represents a basis for the further work on the local ionospheric delay modelling, considering local ionospheric and space weather conditions, thus improving the satellite positioning performance for single-frequency GNSS receivers