JGrassTools è una libreria di moduli di analisi territoriale basata sull'utilizzo di dati raster e vettoriali scritta in Java esviluppata da HydroloGIS, Dipartimento di Ingegneria Civile e Ambientale ...dell'Università di Trento e CUDAM. JGrassTools is a library of terrain analysis tools for raster and vector data The library is written in Java and devel-oped by HydroloGIS, the Department of Civil and Environmental Engineering of the University of Trento and the CUDAM. The objective of the project is on one hand to make it easier to a wide group of people to access existing data and models and on the other hand to make the implementa-tion of new models by scientists of differ-ent research centers easier. The modules currently integrated in JGrassTools are mainly geomorphologic and hydrologic algorithms for the evalu-ation of hydro-geologic risk and tools for the analysis of field data.
JGrassTools è una libreria di moduli di analisi territoriale basata sull'utilizzo di dati raster e vettoriali scritta in Java esviluppata da HydroloGIS, Dipartimento di Ingegneria Civile e Ambientale ...dell'Università di Trento e CUDAM. JGrassTools is a library of terrain analysis tools for raster and vector data The library is written in Java and devel-oped by HydroloGIS, the Department of Civil and Environmental Engineering of the University of Trento and the CUDAM. The objective of the project is on one hand to make it easier to a wide group of people to access existing data and models and on the other hand to make the implementa-tion of new models by scientists of differ-ent research centers easier. The modules currently integrated in JGrassTools are mainly geomorphologic and hydrologic algorithms for the evalu-ation of hydro-geologic risk and tools for the analysis of field data.
Learning and teaching of sorting algorithms are very challenging for students and teachers as well. The issues are more highlighted in the online form of teaching which was the only form of schooling ...from March to June of the school year 2019/2020 in Croatia due to the COVID-19 pandemic. The use of visualization tools could be used for lowering the abstraction of complex programming concepts such as sorting algorithms. We conducted research among two high-school classes (n=52) in one science and mathematics high school while teaching sorting algorithms during online schooling. In the experimental group, we used visualization tools VisuAlgo and Python Tutor for teaching sorting algorithms and their implementation in Python as well. We examined students’ attitudes towards programming and online teaching as well. We present the results of the research in this paper.
Clustering of uncertain objects in large uncertain databases and problem of mining uncertain data has been well studied. In this paper, clustering of uncertain objects with location uncertainty is ...studied. Moving objects, like mobile devices, report their locations periodically, thus their locations are uncertain and best described by a probability density function. The number of objects in a database can be large which makes the process of mining accurate data, a challenging and time consuming task. Authors will give an overview of existing clustering methods and present a new approach for data mining and parallel computing of clustering problems. All existing methods use pruning to avoid expected distance calculations. It is required to calculate the expected distance numerical integration, which is time-consuming. Therefore, a new method, called Segmentation of Data Set Area-Parallel, is proposed. In this method, a data set area is divided into many small segments. Only clusters and objects in that segment are observed. The number of segments is calculated using the number and location of clusters. The use of segments gives the possibility of parallel computing, because segments are mutually independent. Thus, each segment can be computed on multiple cores.
Paralelno klasteriranje nesigurnih podatka koristeći se segmentacijom područja podataka i Voronojevim dijagramima. Klasteriranje podataka s nesigurnošću je vrlo proučavano područje u velikim bazama nesigurnih podataka. U takvim bazama podataka teško je pronaći korisne podatke u mnoštvu podataka s nesigurnošću. U ovom radu proučavano je klasteriranje objekata koji imaju nesigurnost položaja. Većina pokretnih objekata, kao što su mobilni uređd
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aji, periodički izvještava svoj položaj, stoga je njihov položaj neprecizan te se mora opisati funkcijom gustoće vjerojatnosti. Broj objekata u bazi podataka može biti jako velik i doći do točnih podataka je izazovan zadatak i zahtijeva puno vremena. Sve metode za klasteriranje nesigurnih podataka koriste slične principe. Ovim radom predložen je nov pristup. Prvo je dan pregled postojećih metoda, a nakon toga predložena je nova metoda za paralelno klasteriranje nesigurnih podataka. Sve postojeće metode koriste se različitim postupcima pročišćavanja kako bi se izbjeglo računanje očekivane udaljenosti jer ono uključuje numeričke integracije i zahtijeva puno vremena. Predložili smo metodu nazvanu paralelna segmentacija područja podataka. U toj metodi, klastersko područje podijeljeno je u mnogo malih segmenata te se promatraju samo klasteri i objekti u tim malim segmentima. Broj segmenata izračunava se pomoću broja i položaja klastera u prostoru. To nam daje mogućnost za paralelno računanje jer segmenti su međd
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usobno neovisni te se tako svaki segment može računati na više procesorskih jezgri.
Neizrazita logika (eng. fuzzy logic) je matematičko-logički aparat razvijen za prikazivanje neizvjesnosti u prirodnim, društvenim i tehničkim disciplinama. Pod pojmom neizvjesnosti podrazumijeva se ...statistička neizvjesnost, kao nepreciznost mjerenja i opažanja, ali i neizvjesnost kod definiranja samih svojstava pojava. Neizrazita logika opisuje neizvjesnost na način koji se može mjeriti sa statističkim (vjerojatnosnim) opisom, a pokazuje se i prikladnijima od njega za sve veći broj slučajeva. Primjena neizrazite logike nameće se stoga kao logičan korak u normizaciji pokazatelja onečišćenja u situacijama praćenja emisija u okoliš, kao i stanja okoliša uzrokovanih onečišćenjem, posebno za vode, gdje je već razvijena metodologija praćenja koja se temelji na statističkim metodama. Ovaj rad stoga daje ocjenu mogućnosti primjene neizrazite logike u praćenju i kontroli pokazatelja onečišćenja okoliša, koja bi se trebala temeljiti na razvoju i primjeni neizrazitih normi, razvijajući pri tome osnovne algoritme. U brojnoj literaturi detaljno se obrazlaže primjena metoda neizrazite logike, što je poslužilo i u ovom radu za razradu potrebnog metodološkog aparata. Nisu zanemareni ni drugi aspekti ovog procesa, kao problem razumijevanja takvih normi od širokog kruga sudionika u postupcima zaštite okoliša, kao što je javnost.
This paper proposes a novel hybrid control of induction motor, based on the combination of the direct torque control DTC and the backstepping one, optimized by Genetic Algorithm (GA). First the basic ...evolution of DTC is explained, where the torque and stator flux are controlled by non linear hysteresis controllers which cause large ripple in motor torque at steady state operation. A Backstepping control is applied to overcome these problems, however the used parameters are often chosen arbitrarily, which may affect the controller quality. To find the best parameters, an optimization technique based on genetic algorithm is used. Also, in order to obtain accurate information about stator flux, torque and load torque, open loops estimators are used for this Backstepping control. At last, experimental results are presented in order to prove the efficiency of the above mentioned control technique.
Choosing and implementing a Maximum Power Point Tracking (MPPT) algorithm in a photovoltaic (PV) system, along with choosing the power converter, constitutes the fundamental basic capabilities of a ...photovoltaic system. MPPT techniques are implemented in photovoltaic systems to achieve full utilization of PV array output power. Today there is a wide variety of MPPT algorithms, each one has its advantages and disadvantages. This paper presents theoretical, as well as experimental comparison results, in several aspects regarding four MPPT methods based on the basic two MPPT algorithms (Perturb and Observe, Incremental Conductance), implemented on a single DC/DC converter, under the same experimental conditions. The theoretical and experimental comparison is performed for characteristics of ripple around the MPP and the convergence time. The experimental results are provided and supported by theoretical analysis and show that gradient based methods have better convergence time as well as ripple values in comparison to fixed step methods.
Evolutionary computation has been widely used in computer science for decades. Even though it started as far back as the 1960s with simulated evolution, the subject is still evolving. During this ...time, new metaheuristic optimization approaches, like evolutionary algorithms, genetic algorithms, swarm intelligence, etc., were being developed and new fields of usage in artificial intelligence, machine learning, combinatorial and numerical optimization, etc., were being explored. However, even with so much work done, novel research into new techniques and new areas of usage is far from over. This book presents some new theoretical as well as practical aspects of evolutionary computation. This book will be of great value to undergraduates, graduate students, researchers in computer science, and anyone else with an interest in learning about the latest developments in evolutionary computation.
Problemi s upravljanjem mnogih procesa u industriji vezani su s nemogućnošću on-line mjerenja nekih važnih procesnih veličina. Ovaj se problem može u značajnoj mjeri riješiti estimacijom ovih ...teško-mjerljivih procesnih veličina. Estimator je pri tome odgovarajući matematički model procesa koji na temelju informacije o ostalim (lako-mjerljivim) procesnim veličinama procjenjuje trenutni iznos teško-mjerljive veličine. Budući da su procesi po prirodi promjenjivi, točnost estimacije zasnovane na modelu procesa izgra.enog na starim podacima u pravilu opada s vremenom. Kako bi se ovo izbjeglo, parametre modela procesa je potrebno kontinuirano prepodešavati kako bi model što bolje opisivao (trenutno) vladanje procesa. Ovisno o tipu matematičkog modela, za prepodešavanje njegovih parametara na raspolaganju je više metoda. Kao osnova estimatora teško-mjerljive veličine u radu se koristi PLSR model procesa, dok se njegovi parametri prepodešavaju na više načina – metodom pomičnog prozora, rekurzivnim NIPALS algoritmom, rekurzivnim kernel algoritmom te Just-in-Time Learning metodom. Svojstva navedenih metoda adaptacije PLSR modela procesa ispitana su na odabranom primjeru. Nadalje, metode adaptacije su analizirane i s obzirom na računalnu i memorijsku zahtjevnost.
I progetti di costruzione devono soddisfare requisiti in termini di tempi, costi e qualità e il Project Management è fondamentalmente concentrato su questo compromesso. In ogni progetto questi tre ...parametri sono interdipendenti: non si può modificare un elemento senza influenzare almeno uno degli altri due. Questo lavoro presenta l’uso di una tecnica di intelligenza artificiale per la pianificazione delle attività di un progetto infrastrutturale, in vista di una ottimizzazione multi obbiettivo in termini di tempi-costi-qualità. L’approccio si basa sull’utilizzo di algoritmi genetici, implementati in Matlab, per valutare set di combinazioni di opzioni per le attività di costruzione. Lo scopo è quello di fornire una tecnica efficace per esplorare adeguatamente tutte le possibili configurazioni progettuali.
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