Classificaton of the objects detected in front of the vehicle using front view camera

U današnje doba pokušava se postići što veća autonomija vozila, a za to su zaduženi napredni sustavi za pomaganje vozaču (ADAS, engl. Advanced driver-assistance systems). Krajnji cilj razvoja jednog takvog sustava jest omogućiti potpunu autonomiju vozila. Kako bi jedan takav sustav mogao donositi odluke, treba raspolagati informacijama koje pružaju uvid u okolinu vozila, tj. objekte koji okružuju automobil. Prilikom normalne vožnje (unaprijed), sustavu je najbitnije raspolagati informacijama o objektima koji se nalaze ispred vozila. Ovaj rad se bavi klasifikacijom objekata detektiranih s prednje strane vozila gdje ulaz u algoritam klasifikacije predstavljaju slike objekata. Diplomski rad opisuje klasifikaciju objekata koristeći duboke konvolucijske mreže te je kao dio rada opisan postupak izrade jedne takve mreže, od prikupljanja slika koje predstavljaju podatke pa do samog učenja mreže. Također, opisani su i pojedini slojevi od kojih se takva duboka neuronska mreža sastoji. U radu je izvršena evaluacija izrađenih mreža i razmatrani su problemi koji se mogu stvoriti kod krive klasifikacije i zašto do njih dolazi.Nowadays, we are trying to achieve as much vehicle autonomy as possible by developing Advanced driver-assistance systems (ADAS). The ultimate goal of developing such a system is allowing complete autonomy of the vehicle. In order for such a system to make decisions, it should have information that provides insight into the environment of the vehicle, i.e. the objects surrounding the car. During forward driving, it is most important for the system to have information about the objects in front of the vehicle. This paper describes the classification method of using deep convolutional neural networks of the objects detected in the front of the vehicle. The input into the classification algorithm is represented by the image of the object. This paper describes the process of making such a network, from collecting and grouping the images that represent the data up to the learning of the network itself. This paper also describes individual layers which comprise such a deep neural network. In this paper, an evaluation of created networks has been carried out and the paper also discusses why and what problems could occur when using such a method for classification

A novel fuzzy logic scheme for PID controller auto-tuning

This paper presents a novel method for PID (proportional–integral–derivative) controller auto-tuning based on expert knowledge incorporated into a fuzzy logic inference system. The proposed scheme iteratively tries to improve the performance of the closed-loop system. As performance measures, the proposed scheme uses the characteristics of the step response (rise time, overshoot, and settling time). PID parameters in the first iteration can be calculated based on the basic open-loop step response experiment or it is possible to use current parameters. In each successive iteration, step response characteristics are measured and the relative changes expressed in the percentage of value in the first iteration are calculated and converted into linguistic values. The fuzzy expert system computes fuzzy values that are used after defuzzification as multiplying factors for current PID parameters. To achieve a balance between the aggressive and robust closed-loop response, as well as between the slower and the faster one, the fuzzy expert system works in three operating modes: the one for speeding up the system, the one for reducing the overshoot, and the one for a balanced reduction of rise time and overshoot. The performance and robustness are verified by computer simulation using an extensive range of different processes

Veb bazirana aplikacija za izbor optimalnog sistema gajenja biljaka u kontrolisanim uslovima

In this paper is shown a description of the application based on the latest web technologies for optimal greenhouse production system selection. Web application is based on pre-formed expert model implemented in the web application with parameters stored in a database for easy maintenance. Using latest web technologies is achieved massive scale and availability of such expert knowledge to anyone with the Internet access and with standard web browser. The application in a very intuitive way to an agricultural producer, based on his initial idea and implemented expert model, guides throughout the process of selecting the optimal greenhouse production system and system parameters.U ovom radu je dat opis aplikacije bazirane na najsavremenijim veb tehnologijama za izbor optimalnog sistema biljne proizvodnje u kontrolisanim uslovima. Veb aplikacija se oslanja na prethodno formiran ekspertski model izbora tehnološko- tehničkog sistema gajenja u kontrolisanim uslovima. Model je implementiran u okviru veb aplikacije dok su parametri modela smešteni u bazi podataka radi lakšeg održavanja. Razvijena veb aplikacija je bazirana na najnovijim veb i internet tehnologijama koje omogućavaju masovnost i dostupnost takvog ekspertskog znanja do svakog ko ima pristup internetu i standardní veb pregledač. Aplikacija na veoma intuitivan način krajnjeg poljoprivrednog proizvođača, na osnovu njegovih početnih ideja, vodi kroz postupak izbora optimalnog sistema. Sam postupak je organizovan u koracima radi lakše interakcije sa korisnikom. Na osnovu željene biljne kulture i raspoložive površine, oslanjajući se na ekspertsko znanje implementirano u modelu kao i dodatnih zahteva korisnika, aplikacija daje predlog u obliku izveštaja. Izveštaj sadrži predlog tipa konstrukcije i orijentacije objekta, tehnološkog sistema gajenja, tehničko-tehnološkog sistema ventilacije, grejanja i navodnjavanja kao i njihove kapacitete, površine neophodnih pratećih objekata i površine predviđene za buduće proširenje. Svaki generisani izveštaj korisnik može sačuvati za kasniju upotrebu. Na taj način korisnik može upoređivati dva ili više predloga aplikacije i doneti konačnu odluku o podizanju sistema biljne proizvodnje u kontrolisanim uslovima po predloženoj specifikaciji

Mitochondrial unfolded protein response, mitophagy and other mitochondrial quality control mechanisms in heart disease and aged heart

Mitochondria are involved in crucial homeostatic processes in the cell: the production of adenosine triphosphate and reactive oxygen species, and the release of pro-apoptotic molecules. Thus, cell survival depends on the maintenance of proper mitochondrial function by mitochondrial quality control. The most important mitochondrial quality control mechanisms are mitochondrial unfolded protein response, mitophagy, biogenesis, and fusion-fission dynamics. This review deals with mitochondrial quality control in heart diseases, especially myocardial infarction and heart failure. Some previous studies have demonstrated that the activation of mitochondrial quality control mechanisms may be beneficial for the heart, while others have shown that it may lead to heart damage. Our aim was to describe the mechanisms by which mitochondrial quality control contributes to heart protection or damage and to provide evidence that may resolve the seemingly contradictory results from the previous studies

Classificaton of the objects detected in front of the vehicle using front view camera

U današnje doba pokušava se postići što veća autonomija vozila, a za to su zaduženi napredni sustavi za pomaganje vozaču (ADAS, engl. Advanced driver-assistance systems). Krajnji cilj razvoja jednog takvog sustava jest omogućiti potpunu autonomiju vozila. Kako bi jedan takav sustav mogao donositi odluke, treba raspolagati informacijama koje pružaju uvid u okolinu vozila, tj. objekte koji okružuju automobil. Prilikom normalne vožnje (unaprijed), sustavu je najbitnije raspolagati informacijama o objektima koji se nalaze ispred vozila. Ovaj rad se bavi klasifikacijom objekata detektiranih s prednje strane vozila gdje ulaz u algoritam klasifikacije predstavljaju slike objekata. Diplomski rad opisuje klasifikaciju objekata koristeći duboke konvolucijske mreže te je kao dio rada opisan postupak izrade jedne takve mreže, od prikupljanja slika koje predstavljaju podatke pa do samog učenja mreže. Također, opisani su i pojedini slojevi od kojih se takva duboka neuronska mreža sastoji. U radu je izvršena evaluacija izrađenih mreža i razmatrani su problemi koji se mogu stvoriti kod krive klasifikacije i zašto do njih dolazi.Nowadays, we are trying to achieve as much vehicle autonomy as possible by developing Advanced driver-assistance systems (ADAS). The ultimate goal of developing such a system is allowing complete autonomy of the vehicle. In order for such a system to make decisions, it should have information that provides insight into the environment of the vehicle, i.e. the objects surrounding the car. During forward driving, it is most important for the system to have information about the objects in front of the vehicle. This paper describes the classification method of using deep convolutional neural networks of the objects detected in the front of the vehicle. The input into the classification algorithm is represented by the image of the object. This paper describes the process of making such a network, from collecting and grouping the images that represent the data up to the learning of the network itself. This paper also describes individual layers which comprise such a deep neural network. In this paper, an evaluation of created networks has been carried out and the paper also discusses why and what problems could occur when using such a method for classification

Website and ordering service for beauty salon

U ovom završnom radu cilj je bio izrada funkcionirajuće web stranice koja se prilagođava zaslonu te web aplikacije koja olakšava rezervaciju termina za neku uslugu. Pri odabiru tehnologije za izradu responzivne web stranice, uz HTML, CSS i JavaScript koji čine osnovu izrade web stranica, bilo je ključno odabrati framework koji ima ugrađen sustav mreže (engl. grid system) te koji ima dobru dokumentaciju. Odabran je Bootstrap koji ima daleko najveću podršku te najviše dokumentacije kao najpopularniji projekt na GitHubu. Uz pomoć bootstrap, ne izostavljajući HTML, CSS, i JavaScript, izrađena je funkcionalna web stranica koja se prilagođava zaslonu. Odabiru tehnologije za izradu web aplikacije pao je na Angular, najpopularniji JavaScript framework. Uz pomoć Angulara izrađena je aplikacija koja omogućuje odabir usluge ovisno o izabranoj kategoriji te odabir dva termina.The main task of this Bachelor thesis was developing a functional web site which adapts its structure depending on the size of the screen and to develop a web application which allows the user to arrange an appointment for a service. When choosing the technology for development of a responsive web site, not counting HTML, CSS or JavaScript, it was crucial to choose a framework which had grid system embedded into it and also a framework which had lots of documentation, hence Bootstrap was chosen. Bootstrap was a logical choice since it’s the most popular project on GitHub. A functional page was created using Bootstrap and also elementary technologies of front-end; HTML, CSS and JavaScript. Choosing a technology for web application hasn’t been that hard since Angular is the most popular JavaScript framework. So, the web application was made using Angular, which allows the user to arrange two appointments and a service according to the category

Classificaton of the objects detected in front of the vehicle using front view camera

U današnje doba pokušava se postići što veća autonomija vozila, a za to su zaduženi napredni sustavi za pomaganje vozaču (ADAS, engl. Advanced driver-assistance systems). Krajnji cilj razvoja jednog takvog sustava jest omogućiti potpunu autonomiju vozila. Kako bi jedan takav sustav mogao donositi odluke, treba raspolagati informacijama koje pružaju uvid u okolinu vozila, tj. objekte koji okružuju automobil. Prilikom normalne vožnje (unaprijed), sustavu je najbitnije raspolagati informacijama o objektima koji se nalaze ispred vozila. Ovaj rad se bavi klasifikacijom objekata detektiranih s prednje strane vozila gdje ulaz u algoritam klasifikacije predstavljaju slike objekata. Diplomski rad opisuje klasifikaciju objekata koristeći duboke konvolucijske mreže te je kao dio rada opisan postupak izrade jedne takve mreže, od prikupljanja slika koje predstavljaju podatke pa do samog učenja mreže. Također, opisani su i pojedini slojevi od kojih se takva duboka neuronska mreža sastoji. U radu je izvršena evaluacija izrađenih mreža i razmatrani su problemi koji se mogu stvoriti kod krive klasifikacije i zašto do njih dolazi.Nowadays, we are trying to achieve as much vehicle autonomy as possible by developing Advanced driver-assistance systems (ADAS). The ultimate goal of developing such a system is allowing complete autonomy of the vehicle. In order for such a system to make decisions, it should have information that provides insight into the environment of the vehicle, i.e. the objects surrounding the car. During forward driving, it is most important for the system to have information about the objects in front of the vehicle. This paper describes the classification method of using deep convolutional neural networks of the objects detected in the front of the vehicle. The input into the classification algorithm is represented by the image of the object. This paper describes the process of making such a network, from collecting and grouping the images that represent the data up to the learning of the network itself. This paper also describes individual layers which comprise such a deep neural network. In this paper, an evaluation of created networks has been carried out and the paper also discusses why and what problems could occur when using such a method for classification

Short-term load forecasting in large scale electrical utility using artificial neural network

739-745This paper presents a novel method for short-term load forecasting (STLF), based on artificial neural network (ANN), targeted for use in large-scale systems such as distribution management system (DMS). The system comprises of a preprocessing unit (PPU) and a feed forward ANN ordered in a sequence. PPU prepares the data and feeds them as input to the ANN, which calculates the hourly load forecasts. Preprocessing of the entering data reduces the size of the input space to the ANN, which improves the generalization capability and shortens the training time of the network. Reduced dimension of the input space also diminishes the number of parameters to be set in a training procedure, allowing smaller training set, and thus online usage and adaptation. This is important for a real-world power system where a sufficient set of historical data (training points) may not always be available, for different reasons. Ease of use and fast adaptation are necessary when predictions need to carry out in a large number of nodes in the power grid. Functionality of the proposed method has been tested on recorded data from Serbian electrical utility. Results demonstrate that even with a simple configuration such as this one, fair accuracy can be achieved in forecasting the hourly load. The simplicity and reusability are very important factors for installation of the proposed system in a large-scale DMS, considering the technical requirements (e.g. training data availability, processing power and memory capacity)

Fuzzy expert system for adaptive vessel traffic control on one-way section on navigable canal

This paper analyses the management process of the vessel traffic control on one-way section on navigable canal with the adaptive time-sequential filter (traffic lights). One-way section on canal significantly decreases waterway capacity and requests special attention in control and regulation of the vessel traffic. The vessel traffic is a stochastic variable, and the vessel traffic control needs to be flexible and adaptive in order to achieve the required traffic flow with minimal delays. On the one-way section, two independent variable vessel flows from opposite directions are encountered, and fixed (predefined) signal plans lead to an increase in vessel delays. An appropriate solution is development of a Fuzzy Control System (FCS) for the vessel traffic control. A control algorithm is designed according to a set of linguistic rules that describes input parameters for the control strategy. The estimated and approximate input parameters are implemented in the algorithm as fuzzy sets. The final result of the developed algorithm is the traffic light scheme (duration of green light for certain direction). The presented control system can be used as an adaptive automatic control system for the vessel traffic control processes on navigable canals or on critical sections of other waterways