Empirical Analysis of the Necessary and Sufficient Conditions of the Echo State Property

The Echo State Network (ESN) is a specific recurrent network, which has gained popularity during the last years. The model has a recurrent network named reservoir, that is fixed during the learning process. The reservoir is used for transforming the input space in a larger space. A fundamental property that provokes an impact on the model accuracy is the Echo State Property (ESP). There are two main theoretical results related to the ESP. First, a sufficient condition for the ESP existence that involves the singular values of the reservoir matrix. Second, a necessary condition for the ESP. The ESP can be violated according to the spectral radius value of the reservoir matrix. There is a theoretical gap between these necessary and sufficient conditions. This article presents an empirical analysis of the accuracy and the projections of reservoirs that satisfy this theoretical gap. It gives some insights about the generation of the reservoir matrix. From previous works, it is already known that the optimal accuracy is obtained near to the border of stability control of the dynamics. Then, according to our empirical results, we can see that this border seems to be closer to the sufficient conditions than to the necessary conditions of the ESP.Comment: 23 pages, 14 figures, accepted paper for the IEEE IJCNN, 201

ANOVA Method Applied to PEMFC Ageing Forecasting Using an Echo State Network

International audienceAccording to the International Energy Agency, an increase of the requests of energy of 40% could arise in the next decades, mainly due to the emergence of developing countries. The problem with the nowaday energy system is the use of fossil energy, which is limited and attempt to disappear in the near future. Thus an energy transition has to begin in order to replace the fossil fuels and anticipate their disappearance. Consequently, in recent years, the promotion and development of renewable energy have been realized. One of this renewable energy, the energy vector hydrogen, appears to be a promising solution, mainly due to interesting performance of Fuel Cells (FC) systems and hydrogen abundance on Earth (it is still important to underline that the hydrogen does not exist in natural form). However, this research area is still subject to scientific and technological bottlenecks. One of these major bottlenecks preventing the industrialization of FC systems is it limited useful lifetime. It is therefore important to develop reliable tools for the diagnosis and prognosis of FC system in order to optimize its efficiency. The aim of this article is to present the results of a sensibility analysis applied to a prognosis tools called Echo State Network

Odhad emocí a duševní koncentrace pomocí technik Deep Learningu

The purpose of this work is to evaluate the brain waves of humans with deep learn- ing methods and evolutionary computation techniques, and to verify the performance of applied techniques. In this thesis, we apply well–known metaheuristics and Artificial Neural Networks for classifying human mental activities using electroencephalographic signals. We developed a Brain–Computer Interface system that is able to process elec- troencephalographic signals and classify mental concentration versus relaxation. The system is able to automatically extract and learn representation of the given data. Based on scientific protocols we designed the Brain–Computer Interface experiments and we created an original and relevant data for the industrial and academic community. Our experimental data is available to the scientific community. In the experiments we used an electroencephalographic based device for collecting brain information form the subjects during specific activities. The collected data represents brain waves of subjects who was stimulated by writing tasks. Furthermore, we selected the best combination of the input features (brain waves information) using the following two metaheuristic techniques: Simulated Annealing and Geometric Particle Swarm Optimization. We applied a specific type of Artificial Neural Network, named Echo State Network, for solving the mapping between brain information and subject activities. The results indicate that it is possible to estimate the human con- centration using few electroencephalographic signals. In addition, the proposed system is developed with a fast and robust learning technique that can be easily adapted accord- ing to each subject. Moreover, this approach does not require powerful computational resources. As a consequence, the proposed system can be used in environments which are computationally limited and/or where the computational time is an important issue.Cílem práce je ohodnocení lidských mozkových vln s využitím metod hlubokého učení (deep learning) a evolučních výpočetních technik a pro ověření výkonu aplikovaných technik. V diplomové práci jsou využity dobře známé metaheuristiky a umělé neuronové sítě pro klasifikaci lidských mentálních aktivit za použití elektroencefalografických signálů. Bylo vyvinuto rozhraní mozek-počítač, které je schopno zpracovat elektroencefalografické signály a klasifikovat mentální soustředění v porovnání s relaxací. Systém je schopen automaticky extrahovat a naučit se reprezentaci daných dat. Na základě vědeckých protokolů byl navržen experiment pro rozhraní mozek-počítač a byla vytvořena původní a relevantní data pro průmyslovou a akademickou komunitu. Vygenerovaná pokusná data jsou přístupné pro vědeckou komunitu. V rámci experimentů bylo využito zařízení založené na encefalografii pro sběr mozkových signálů subjektu během specifických aktivit. Nasbíraná data reprezentují mozkové vlny subjektu, který byl stimulován psaním úloh. Dále byla vybrána nejlepší kombinace vstupních vlastností (informace o mozkové vlně) s využitím následujících dvou metaheuristických metod: simulovaného žíhání a geometrické optimalizace hejnem částic. Umělá neuronová síť, která se nazývá Echo State síť, byla aplikována pro řešení mapování mezi informacemi z mozku a aktivitami subjektu. Výsledky ukazují, že je možné odhadnout lidskou aktivitu pomocí několika encefalografických signálů. Kromě toho, navrhovaný systém je vyvinut s využitím rychlých a robustních učících technik, které mohou být jednoduše přizpůsobeny podle jednotlivých subjektů. Tento přístup navíc nevyžaduje výkonné výpočetní prostředky. V důsledku toho může být systém využit v prostředí, které jsou výpočetně omezeny a/nebo v případech, kdy výpočetní čas je důležitým hlediskem.460 - Katedra informatikyvýborn

Computation Of Microbial Ecosystems in Time and Space (COMETS): An open source collaborative platform for modeling ecosystems metabolism

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are also emerging as a valuable avenue for predicting, understanding and designing microbial communities. COMETS (Computation Of Microbial Ecosystems in Time and Space) was initially developed as an extension of dynamic flux balance analysis, which incorporates cellular and molecular diffusion, enabling simulations of multiple microbial species in spatially structured environments. Here we describe how to best use and apply the most recent version of this platform, COMETS 2, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, as well as several new biological simulation modules, including evolutionary dynamics and extracellular enzyme activity. COMETS 2 provides user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, and comprehensive documentation and tutorials, facilitating the use of COMETS for researchers at all levels of expertise with metabolic simulations. This protocol provides a detailed guideline for installing, testing and applying COMETS 2 to different scenarios, with broad applicability to microbial communities across biomes and scales.Comment: 146 pages, 12 figures, 2 supplementary figures, 3 supplementary video

Traffic Characteristics and Queueing Theory: Implications and Applications to Web Server Systems

Businesses rely increasingly on Internet services as the basis of their income. Downtime and poor performance of such services can therefore be directly translated into loss of revenue. In order to plan and design services sufficiently capable of meeting minimumQuality of Service (QoS) requirements and Service Level Agreements(SLA), an understanding of how network traffic and job service demand affect the system is necessary. Traditionally, arrival and service processes have been modelled as Poisson processes. However, research done over the years suggests that the assumption of Poisson traffic is fallible in many cases. This work considers performance of a web server under different traffic and service demand conditions. Moreover, we consider theoretical models of queues, response time formulas derived from this models and their validity for a web server system. We try to make a simple approach to a complex problem by modelling a web server as one simple queueing system. In addition, we investigate the phenomenon known as self-similarity which has been observed in web traffic inter-arrival processes. We have found indications that traffic with identical expectation values for inter-arrival and service time differing in distribution type affects the response time differently. Moreover, classical queueingmodels are found unsuited for doing capacity planning. Instead we suggest ”a worst case scenario” approach in order for service providers to meet service level targets. Much of the previous work within these areas is of a highly mathematical and theoretical nature. We investigate from a more pragmatic viewpoint

The Growth and Morphology of Small Ice Crystals in a Diffusion Chamber

Small water ice crystals are the main component of cold tropospheric clouds such as cirrus. Because these clouds cover large areas of our planet, their role in the radiation budget of incoming and outgoing radiation to the planet’s surface is important. At present, the representation of these clouds in climate and weather models is subject to improvements: a large part of the uncertainty error stems from the lack of precise micro-physical and radiation model schemes for ice crystal clouds. To improve the cloud representations, a better understanding of the life time dynamics of the clouds and their composition is necessary, comprising a detailed understanding of the ice particle genesis, and development over their lifetime. It is especially important to understand how the development of ice crystals over time is linked to the changes in observable variables such as water vapour content and temperature and how they change the light scattering properties of the crystals. Recent remote and aircraft based in-situ measurements have shown that many ice particles show a light scattering behaviour typical for crystals having rough surfaces or being of complex geometrical shapes. The aim of this thesis was to develop the experimental setup and experiments to investigate this further by studying the surface morphology of small water ice crystals using scanning electron microscopy (SEM). The experiments I developed study the growth of water ice crystals inside an SEM chamber under controlled environmental conditions. The influence of water vapour supersaturation, pressure and temperature is investigated. I demonstrate how to retrieve the surface topology from observed crystals for use as input to computational light scattering codes to derive light scattering phase functions and asymmetry parameters, which can be used as input into atmospheric models. Difficulties with the method for studying the growth of water ice crystals, such as the effect of the electron beam-gas ionization and charging effects, the problem of facilitating repeated and localized ice growth, and the effect of radiative influences on the crystal growth are discussed. A broad set of nucleation target materials is studied. In a conclusion, I demonstrate that the method is suitable to study the surface morphologies, but is experimentally very challenging and many precautions must be taken, such as imaging only once and preventing radiative heat exchange between the chamber walls and the crystals to avoid unwanted effects on the crystal morphology. It is also left as a question if a laboratory experiment, where crystals will need to be grown in connection to a substrate, can represent the real world well enough. Deriving the required light scattering data in-situ might be an alternative, easier way to collect data for modelling use