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

Evolutionary computation based on nanocomposite training: application to data classification

Research into novel materials and computation frameworks by-passing the limitations of the current paradigm, has been identified as crucial for the development of the next generation of computing technology. Within this context, evolution in materio (EiM) proposes an approach where evolutionary algorithms (EAs) are used to explore and exploit the properties of un-configured materials until they reach a state where they can perform a computational task. Following an EiM approach, this thesis demonstrates the ability of EAs to evolve dynamic nanocomposites into data classifiers. Material-based computation is treated as an optimisation problem with a hybrid search space consisting of configuration voltages creating an electric field applied to the material, and the infinite space of possible states the material can reach in response to this field. In a first set of investigations, two different algorithms, differential evolution (DE) and particle swarm optimisation (PSO), are used to evolve single-walled carbon nanotube (SWCNT) / liquid crystal (LC) composites capable of classifying artificial, two-dimensional, binary linear and non-linear separable and merged datasets at low SWCNT concentrations. The difference in search behaviour between the two algorithms is found to affect differently the composite’ state during training, which in turn affects the accuracy, consistency and generalisation of evolved solutions. SWCNT/LC processors are also able to scale to complex, real-life classification problems. Crucially, results suggest that problem complexity influences the properties of the processors. For more complex problems, networks of SWCNT structures tend to form within the composite, creating stable devices requiring no configuration voltages to classify data, and with computational capabilities that can be recovered more than several hours after training. A method of programming the dynamic composites is demonstrated, based on the reapplication of sequences of configuration voltages which have produced good quality SWCNT/LC classifiers. A second set of investigations aims at exploiting the properties presented by the dynamic nanocomposites, whilst also providing a means for evolved device encapsulation, making their use easier in out-of-the lab applications. Novel composites based on SWCNTs dispersed in one-part UV-cure epoxies are introduced. Results obtained with these composites support their choice for use in subsequent EiM research. A final discussion is concerned with evolving an electro-biological processor and a memristive processor. Overall, the work reported in the thesis suggests that dynamic nanocomposites present a number of unexpected, potentially attractive properties not found in other materials investigated in the context of EiM

Deep neural network generation for image classification within resource-constrained environments using evolutionary and hand-crafted processes

Constructing Convolutional Neural Networks (CNN) models is a manual process requiringexpert knowledge and trial and error. Background research highlights the following knowledge gaps. 1) existing efficiency-focused CNN models make design choices that impact model performance. Better ways are needed to construct accurate models for resourceconstrained environments that lack graphics processing units (GPU’s) to speed up model inference time such as CCTV cameras and IoT devices. 2) Existing methods for automatically designing CNN architectures do not explore the search space effectively for the best solution and 3) existing methods for automatically designing CNN architectures do not exploit modern model architecture design patterns such as residual connections. The lack of residual connections means the model depth is limited owing to the vanishing gradient problem. Furthermore, existing methods for automatically designing CNN architectures adopt search strategies that make them vulnerable to local minima traps. Better techniques to construct efficient CNN models, and automated approaches that can produce accurate deep model constructions advance many areas such as hazard detection, medical diagnosis and robotics in both academia and industry. The work undertaken during this research are 1) the proposal of an efficient and accurate CNN architecture for resource-constrained environments owing to a novel block structure containing 1x3 and 3x1 convolutions to save computational cost, 2) proposed a particle swarm optimization (PSO) method of automatically constructing efficient deep CNN architectures with greater accuracy by proposing a novel encoding and search strategy, 3) proposed a PSO based method of automatically constructing deeper CNN models with improved accuracy by proposing a novel encoding scheme that employs residual connections, in novel search mechanism that follows the global and neighbouring best leaders. The main findings of this research are 1) the proposed efficiency-focused CNN model outperformed MobileNetV2 by 13.43% in respect to accuracy, and 39.63% in respect to efficiency, measured in floating-point operations. A reduction in floating-point operations means the model has the potential for faster inference times which is beneficial to applications within resource-constrained environments without GPU’s such as CCTV cameras. 2) the proposed automatic CNN generation technique outperformed existing methods by 7.58% in respect to accuracy and a 63% improvement in search time efficiency owing to the proposal of more efficient architectures speeding up the search process and 3) the proposed automatic deep residual CNN generation method improved model accuracy by 4.43% when compared against related studies owing to deeper model construction and improvements in the search process. The proposed search process embeds human knowledge of constructing deep residual networks and provides constraint settings which can be used to limit the proposed models depth and width. The ability to constrain a models depth and width is important as it ensures the upper bounds of a proposed model will fit within the constraints of resource-constrained environments

XXV Congreso Argentino de Ciencias de la Computación - CACIC 2019: libro de actas

Trabajos presentados en el XXV Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de Río Cuarto los días 14 al 18 de octubre de 2019 organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y Facultad de Ciencias Exactas, Físico-Químicas y Naturales - Universidad Nacional de Río CuartoRed de Universidades con Carreras en Informátic

XXV Congreso Argentino de Ciencias de la Computación - CACIC 2019: libro de actas

Trabajos presentados en el XXV Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de Río Cuarto los días 14 al 18 de octubre de 2019 organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y Facultad de Ciencias Exactas, Físico-Químicas y Naturales - Universidad Nacional de Río CuartoRed de Universidades con Carreras en Informátic