16 research outputs found
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