Modeling preference time in middle distance triathlons

Modeling preference time in triathlons means predicting the intermediate times of particular sports disciplines by a given overall finish time in a specific triathlon course for the athlete with the known personal best result. This is a hard task for athletes and sport trainers due to a lot of different factors that need to be taken into account, e.g., athlete's abilities, health, mental preparations and even their current sports form. So far, this process was calculated manually without any specific software tools or using the artificial intelligence. This paper presents the new solution for modeling preference time in middle distance triathlons based on particle swarm optimization algorithm and archive of existing sports results. Initial results are presented, which suggest the usefulness of proposed approach, while remarks for future improvements and use are also emphasized.Comment: ISCBI 201

Making up for the deficit in a marathon run

To predict the final result of an athlete in a marathon run thoroughly is the eternal desire of each trainer. Usually, the achieved result is weaker than the predicted one due to the objective (e.g., environmental conditions) as well as subjective factors (e.g., athlete's malaise). Therefore, making up for the deficit between predicted and achieved results is the main ingredient of the analysis performed by trainers after the competition. In the analysis, they search for parts of a marathon course where the athlete lost time. This paper proposes an automatic making up for the deficit by using a Differential Evolution algorithm. In this case study, the results that were obtained by a wearable sports-watch by an athlete in a real marathon are analyzed. The first experiments with Differential Evolution show the possibility of using this method in the future.Comment: ISMSI 201

New Perspectives in the Development of the Artificial Sport Trainer

ABSTRACT: The rapid development of computer science and telecommunications has brought new ways and practices to sport training. The artificial sport trainer, founded on computational intelligence algorithms, has gained momentum in the last years. However, artificial sport trainer usually suffers from a lack of automatisation in realization and control phases of the training. In this study, the Digital Twin is proposed as a framework for helping athletes, during realization of training sessions, to make the proper decisions in situations they encounter. The digital twin for artificial sport trainer is based on the cognitive model of humans. This concept has been applied to cycling, where a version of the system on a Raspberry Pi already exists. The results of porting the digital twin on the mentioned platform shows promising potential for its extension to other sport disciplines.Akemi Galvez and Andres Iglesias have received funding from the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme under the Marie SklodowskaCurie grant agreement no. 778035, and from the project TIN2017-89275-R funded by MCIN/AEI/10.13039/501100011033/FEDER “Una manera de hacer Europa”

Accurate Long-term Air Temperature Prediction with a Fusion of Artificial Intelligence and Data Reduction Techniques

In this paper three customised Artificial Intelligence (AI) frameworks, considering Deep Learning (convolutional neural networks), Machine Learning algorithms and data reduction techniques are proposed, for a problem of long-term summer air temperature prediction. Specifically, the prediction of average air temperature in the first and second August fortnights, using input data from previous months, at two different locations, Paris (France) and C\'ordoba (Spain), is considered. The target variable, mainly in the first August fortnight, can contain signals of extreme events such as heatwaves, like the mega-heatwave of 2003, which affected France and the Iberian Peninsula. Thus, an accurate prediction of long-term air temperature may be valuable also for different problems related to climate change, such as attribution of extreme events, and in other problems related to renewable energy. The analysis carried out this work is based on Reanalysis data, which are first processed by a correlation analysis among different prediction variables and the target (average air temperature in August first and second fortnights). An area with the largest correlation is located, and the variables within, after a feature selection process, are the input of different deep learning and ML algorithms. The experiments carried out show a very good prediction skill in the three proposed AI frameworks, both in Paris and C\'ordoba regions.Comment: 33 pages, 14 figures, 7 tables, under revie

Artificial intelligence for managing the portfolio of stocks

Izziv dela predstavlja snovanje, načrtovanje in praktična izvedba avtomatiziranega trgovalnega sistema, ki neodvisno in brez posredovanja uporabnikov sprejema in izvaja trgovalne odločitve. Jedro trgovalnega sistema predstavlja trgovalna strategija, ki spremlja pretekle ter aktualne podatke borznih kotacij, izvaja tehnično analizo in, če je tega sposobna, se prilagaja sprotnim razmeram na finančnih trgih. Obravnavamo dve skupini trgovalnih strategij, klasične, ki niso sposobne sprotnega prilagajanja niti učenja, in dve trgovalni strategiji na osnovi naprednih algoritmov umetne inteligence, eno izmed njih predstavnico umetnih nevronskih mrež najnovejše tretje generacije. Izvedemo obširna simulacijska eksperimentiranja na osnovi nemškega delniškega trga v zadnjih desetih letih, zasnujemo in izvedemo pa tudi eksperimentiranja na namenski strojni opremi, ki močno pohitri kompleksnost časovnega izvajanja, ter eksperimentiranja na analognem elektronskem vezju, s pomočjo katerega se podrobno seznanimo z načinom propagiranja informacij umetnih nevronskih mrež tretje generacije. Rezultati eksperimentov prinašajo tako vsebinske kot tehnične ugotovitve, najpomembnejšo med njimi, da se enoten model ki hkrati trguje z večjim številom finančnih instrumentov obnaša podobno kot kopica posamično prilagojenih modelov na točno določen finančni instrument, kakor tudi novo ugotovljene izkušnje vezane na propagiranje in izrabo najnovejše generacije umetnih nevronskih mrež.Challenge of this work is about the design, planning and practical implementation of the automated trading system that independently and user-interference free generates the trading decisions and realizes them on the open market. The core of the trading system is a trading strategy that monitors past and current financial data, performs the technical analyses and, if capable adapts to the latest conditions on financial markets. Two groups of the trading strategies, the classics, traditionally not capable of adapting nor learning, and two adapting and learning capable trading strategies, based on the latest artificial intelligence methods, one of them a representative of the third-generation neural networks, are implemented. Comprehensive simulation experiments and tests are concluded using the data on German stock market in past ten years, with additional digital and purely analogue hardware experiments on the dedicated equipment, that demand significantly lower time complexities on one hand, and offer an outstanding chance to get familiarized with the concept of information propagation in the third-generation artificial neural networks. Results of experiments communicate both substantive and technical findings, more important among them, that the universal model that manages several financial instruments concurrently behaves similar as a bunch of specific models that are specialized for only a single financial instrument at a time, as well as newly discovered experiences on propagation and exploitation of the latest third-generation neural networks

Development of advanced adaptive controller for mechatronic systems

V magistrski nalogi predstavljamo, opisujemo ter razlagamo princip delovanja nelinearnega naprednega adaptivnega hitrostnega regulatorja, ki smo ga izdelali za potrebe napredne regulacije na enoosnem robotu. Tega krmilimo z algoritmom evolucijskih strategij, ki je sposoben dinamičnega iskanja rešitev, kjer se vrednost funkcije uspešnosti spreminja s časom. Pri tem smo vrednost funkcije uspešnosti napovedovali s pomočjo nevronske mreže (angl. Artificial Neural Network, krajše ANN). Predlagano metodo smo testirali na realnem laboratorijskem robotskem sistemu z eno stopnjo prostosti (angl. one degree of freedom, krajše 1 D.O.F.) in ugotovili, da je primeren za regulacijo v realnem času (odzivni čas 1-5 ms). Izvedena je bila primerjava z linearnim PI-hitrostnim regulatorjem, rezultati pa so pokazali uspešnejše delovanje nelinearnega hitrostnega regulatorja.In this M.Sc. Thesis, a non-linear advanced adaptive controller is proposed and described, which was used for the purpose of velocity control on a single degree of freedom robotic mechanism. The controller is based on the algorithm of dynamic evolution strategy, which is capable of searching the global optimum dynamically, i.e. when the fitness function is changing through time. The fitness function calculation has been done by implementing an artificial neural network, which was simulating the behavior of the real system. We tested the proposed algorithm on the robotic mechanism and compared the results to the PI-velocity controller. We concluded, that the proposed approach of identification and optimization is appropriate for online real-time control (response time 1-5 ms). Furthermore, non-linear controller outperformed the linear controller, according to conducted tests

Self-tuning controller design of 2 DOF robot using BA algorithm

Algoritmi po vzoru iz narave dandanes pokrivajo več raziskovalnih področij. Aplikacije, s katerimi rešujemo različne optimizacijske probleme v industriji in drugih področjih človekove dejavnosti, povečujejo kakovost proizvoda, zmanjšujejo časovni okvir načrtovanja ali kako drugače lajšajo reševanja problema. Naš problem predstavlja načrtovanje parametrov položajnega regulatorja na dvoosnem robotskem mehanizmu s pomočjo algoritmov po vzoru iz narave. Pri tem med seboj primerjamo genetski algoritem ter algoritem po vzoru obnašanja netopirjev. Spoznamo in potrdimo osnovne značilnosti obeh algoritmov, ju preizkusimo na realni laboratorijski aplikaciji, ter ugotovimo, da algoritem po vzoru obnašanja netopirjev izboljšuje rezultate genetskega algoritma na našem problemu. S tem smo prišli do sklepa, da lahko algoritme po vzoru iz narave uspešno uporabimo za reševanje realnih problemov iz prakse.Nature-inspired algorithms present a bright star of researching. Implemented on different optimizational, industrial or researchable applications they offer an excellent opportunity to increase quality of product, reduce time needed for design or ease the procedure of solving modern problems. Our main topic presents the 2 dof robotic mechanism, equipped with nature-inspired algorithms in order to design self-tuning controller. We tested and compared two algorithms - Genetic algorithm and Bat algorithm, met their basics of working and according to real laboratory tests confirmed the execution of optimizational process. Bat algorithm did prove better working in the laboratory experiments. We conclude, that it was worth to use the nature-inspired algorithms for our problem