Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned

Optimization of energy storage usage

This paper suggests the use of dynamic population size throughout the optimization process which is applied on the numerical model of a medium voltage post insulator. The main objective of the dynamic population is reducing population size, to achieve faster convergence. Change of population size can be done in any iteration by proposed method. The multiobjective optimization process is based on the PSO algorithm, which is suitably modifiedin order to operate with the principle of the optimal Pareto front

Optimization of electrical power network elements by using evolutionary algorithms

V doktorski disertaciji je predstavljen pristop k reševanju različnih optimizacijskih problemov v domeni elektroenergetskega omrežja. Pri tem so predstavljeni rezultati in podrobnosti aplikacij optimizacijskih algoritmov pri reševanju optimizacijskih problemov elementov kot so transformator, podporni kompozitni izolator, plinski odvodnik ter skoznjik, kot tudi primer določanja optimalne konfiguracije srednjenapetostnega omrežja. Kot primarna optimizacijska tehnika je bil uporabljen evolucijski algoritem imenovan diferenčna evolucija. Njegova slabost, ki je sorodna vsem evolucijskim algoritmom, je veliko število cenitev kriterijske funkcije, potrebnih za doseganje konvergence optimizacijskega postopka. Izboljšanje omenjene slabosti je bilo v okviru disertacije realizirano z razširitvijo izvirnega algoritma oz. z njegovo dopolnitvijo s Kriging interpolacijsko metodo. Preskusi na različnih standardnih testnih funkcijah kot tudi na realnem inženirskem problemu so pokazali, da potrebuje novi algoritem v primerjavi z izvirnim povprečno pol manj cenitev dejanske kriterijske funkcije.In this thesis an approach for solving various optimization problems in area of electrical power network elements is presented. Results and details of optimization algorithm applications for solving various elements, such as transformer, post composite insulator, gas discharge arrester, bushing and network configuration problem are also given. Well known evolutionary algorithm, Differential evolution, has been selected as a fundamental optimization technique. Its downside, which is common to all Evolutionary algorithms, is the required large number of iterations, respectively the number of function evaluations in order to achieve the convergence. To improve this property of Differential evolution algorithm, one has been extended to use Kriging interpolation method. Newly developed Kriging-assisted differential evolution algorithm enables the reduction of required function calls. Experiments, which had been conducted on several standard test functions, and also on a real engineering problem, have shown that new algorithm, compared to original Differential evolution algorithm, generally reduces a number of actual function calls by half

MANAGING OF HYDROELECTRIC POWER PLANTS IN THE HOLDING SLOVENSKE ELEKTRARNE GROUP

V diplomskem delu je izdelana analiza različnih stanj obratovanja hidroenergetskih objektov, ki spadajo v skupino Holding Slovenske elektrarne. Precejšnji del teh hidroenergetskih objektov je popolnoma avtomatiziran. Zaradi tega je težnja k obratovanju le-teh brez posadke, saj se na ta način znižujejo stroški obratovanja. Kljub temu, da avtomatizirani objekti obratujejo brez posadke, so še vedno potrebni dežurni elektrarn ter vzdrževalci, ki so lahko zaposleni na enem ali istočasno več hidroenergetskih objektih. Njihova skrb je pravilno delovanje objektov in zagotavljanje oz. povečevanje njihove zanesljivosti in razpoložljivosti. Pri tem so natančno definirane naloge in pristojnosti, s katerimi lahko proces vzdrževanja optimiramo glede na vrsto vzdrževanja. V okviru diplomskega dela je izvedena optimizacija procesa vzdrževanja s pomočjo algoritma diferenčne evolucije, ki ga uvrščamo med evolucijske algoritme, saj pri svojem delovanju posnema naravno evolucijo. Rezultat optimizacije je prikaz porazdelitve posameznih vzdrževalnih del znotraj enomesečnega intervala. Pri tem so kot referenca uporabljeni podatki iz leta 2010, ki smo jih za izbrane objekte pridobili iz sistema Maximo.Different ways of managing hydroelectric power plants in the Holding Slovenske elektrarne Group have been analyzed. A considerable part of hydroelectric power plants have been completely automated and they operate without staff. However, in order to increase the availability and reliability of devices, automated plants still require maintenance, which is carried out by the employees. In this case, the roles and responsibilities for maintenance are precisely determined, therefore the maintenance process can be optimized in accordance to the maintenance type. As part of the thesis, the maintenance process is optimized by using the differential evolution algorithm, which mimics the natural evolution. The optimization results show the distribution of individual maintenance type labour during one month long intervals. The reference data obtained from the information system Maximo has been used for the optimization process. This data is valid for the selected hydroelectric power plant and for year 2010

Optimization of Electrical Energy Production by using Modified Differential Evolution Algorithm

Doktorska disertacija obravnava področje optimizacije proizvodnje električne energije iz hidroelektrarn in termoelektrarn. Nanaša se na kratkoročno obdobje in predstavlja kompleksen optimizacijski problem. Kompleksnost problema izhaja iz velikega števila odvisnih spremenljivk in številnih omejitev elektrarn. Glede na kompleksnost problema je za optimizacijo uporabljen algoritem diferenčne evolucije, ki je sicer znan kot uspešen in robusten optimizacijski algoritem. Uspešnost delovanja algoritma diferenčne evolucije je tesno povezana z izbiro krmilnih parametrov, zmogljivost pa je mogoče izboljšati med drugim tudi s paralelizacijo algoritma. V doktorski disertaciji je predstavljen modificiran algoritem diferenčne evolucije, ki z novim načinom paralelizacije izboljša zmogljivost doseganja globalno optimalnih rešitev pri optimizaciji proizvodnje električne energije. Uspešnost delovanja algoritma pa je izboljšana tudi z novim načinom dinamičnega spreminjanja velikosti populacije. S tem se poleg doseganja kvalitetnejših rešitev v primerjavi s klasičnim algoritmom diferenčne evolucije doseže hitrejša konvergenca postopka oz. se skrajša čas optimizacijskega procesa. Rešitve, ki so predstavljene v disertaciji, so poleg preverjanja na testnih modelih elektrarn, uporabljenih v številnih strokovnih in znanstvenih publikacijah, preverjene tudi na modelih realnih elektrarn. Pri optimizaciji proizvodnje električne energije iz hidroelektrarn in termoelektrarn je zasledovanih več kriterijev: zadovoljitev dane sistemske zahteve oz. voznega reda, minimiziranje porabe vode na enoto proizvedene električne energije, znižanje oz. eliminiranje prelivanja vode, zadovoljitev končnih stanj rezervoarjev hidroelektrarn ter minimiziranje stroškov energenta in emisij pri termoelektrarnah.The dissertation addressed the optimization of electrical energy production from hydro power plants and thermal power plants. It refers to short-term optimization and presents a complex optimization problem. The complexity of the problem arises from an extensive number of co-dependent variables and power plant constraints. According to the complexity of the problem, the differential evolution algorithm known as the successful and robust optimization algorithm was selected as an appropriate algorithm for optimization. The performance of this differential evolution algorithm is closely connected with a control parameters’ set and its capabilities being inter alia improved by the algorithm’s parallelization. The capabilities of achieving a global optimal solution within the optimization of electrical energy production are improved by the proposed modified differential evolution algorithm with new parallelization mode. This algorithm’s performance is also improved by its proposed dynamic population size throughout the optimization process. In addition to achieving better optimization results in comparison with the classic differential evolution algorithm, the proposed dynamic population size reduces convergence time. The improvements of this algorithm presented in the dissertation, besides power plant models mostly used in scientific publications, were also tested on the power plant models represented by real parameters’. The optimization of electrical energy from hydro and thermal power plants is followed by certain criteriasatisfying system demand, minimizing usage of water quantity per produced electrical energy unit, minimizing or eliminating water spillage, satisfying the final reservoir states of hydro power plants and minimizing fuel costs and emissions of thermal power plants

SOCIAL NETWORKS AND THEIR INFLUENCE ON INTERPERSONAL RELATIONS

Priljubljenost spletnih socialnih omrežij zadnje čase presega vsa pričakovanja. Portali na katerih si uporabniki delijo informacije, se povezujejo in izmenjujejo stike, mnenja in izkušnje so nov komunikacijski fenomen in so del vsakdana mnogih ljudi. »Če nisi na Facebooku, ne obstajaš«, je rek, ki postaja resničnost. Diplomsko delo je razdeljeno na dve zaokroženi celoti. V teoretičnem delu diplomskega dela so predstavljene značilnosti, pomen in uporaba socialnih omrežij in podobnosti ter razlike med komunikacijo in medosebnimi interakcijami uporabnikov spletnega socialnega omrežja v realnem in virtualnem okolju. Drugi del diplomskega dela zajema empirična raziskava. V empiričnem delu diplomskega dela sta predstavljena analiza profilov dvajsetih uporabnikov spletnega socialnega omrežja Facebook, opazovanih po določenih kriterijih opazovanja ter anketni vprašalniki, katere so izpolnili omenjeni analizirani.The popularity of social network websites is increasing above all expectations. Those sites where users share information, create links between each other and exchange contacts, opinions and experiences are the whole new communication phenomena and a part of everyday occurrence among many people, respectively. Thus the sentence »If you are not on Facebook, than you don\u27t exist« is becoming a reality. This diploma work is divided on two major parts. Theoretical part of the diploma presents characteristics, importance and the use of social network websites. Similarities and differences between communication and interpersonal interactions of social network website users have been presented in both real and virtual environment. The second part of the diploma work includes the empirical research, where the profile analysis results for the twenty users of the social network website Facebook have been presented. Also the criteria for the profile observation have been presented together with the survey that was submitted to the previously mentioned users

Post insulator optimization based on dynamic population size

This paper suggests the use of dynamic population size throughout the optimization process which is applied on the numerical model of a medium voltage post insulator. The main objective of the dynamic population is reducing population size, to achieve faster convergence. Change of population size can be done in any iteration by proposed method. The multiobjective optimization process is based on the PSO algorithm, which is suitably modifiedin order to operate with the principle of the optimal Pareto front