Data modelling and data processing generated by human eye movements

Data modeling and data processing are important activities in any scientific research. This research focuses on the modeling of data and processing of data generated by a saccadometer. The approach used is based on the relational data model, but the processing and storage of the data is done with client datasets. The experiments were performed with 26 randomly selected files from a total of 264 experimental sessions. The data from each experimental session was stored in three different formats, respectively text, binary and extensible markup language (XML) based. The results showed that the text format and the binary format were the most compact. Several actions related to data processing were analyzed. Based on the results obtained, it was found that the two fastest actions are respectively loading data from a binary file and storing data into a binary file. In contrast, the two slowest actions were storing the data in XML format and loading the data from a text file, respectively. Also, one of the time-consuming operations turned out to be the conversion of data from text format to binary format. Moreover, the time required to perform this action does not depend in proportion on the number of records processed

A Design of New Brands of Martenzite Steels by Artificial Neural Networks

The paper proposes a model-based approach for the design of martenzite structure steels with improved mechanical and plastic characteristics using proper composition and thermal treatment. For that purpose, artificial neural models approximating the dependence of steels strength characteristics on the percentage content of alloying components were trained. These non-linear models are further used within an optimization gradient procedure based on backpropagation of utility function through neural network structure. In order to optimizing the steel characteristics via its chemical composition, several steel brands with high values of tensile strenght, yield strenght and relative elongation were designed. A steel composition having economical alloying and proper for practical application was determined comparing several obtained decisions. The usage of that steel will lead to lightening of the hardware for automobile industry

Efficient Adaptation of Structure Metrics in Prototype-Based Classification

Mokbel B, Paaßen B, Hammer B. Efficient Adaptation of Structure Metrics in Prototype-Based Classification. In: Wermter S, Weber C, Duch W, et al., eds. Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Lecture Notes in Computer Science. Vol 8681. Springer; 2014: 571-578.More complex data formats and dedicated structure metrics have spurred the development of intuitive machine learning techniques which directly deal with dissimilarity data, such as relational learning vector quantization (RLVQ). The adjustment of metric parameters like relevance weights for basic structural elements constitutes a crucial issue therein, and first methods to automatically learn metric parameters from given data were proposed recently. In this contribution, we investigate a robust learning scheme to adapt metric parameters such as the scoring matrix in sequence alignment in conjunction with prototype learning, and we investigate the suitability of efficient approximations thereof

Data Analysis from Two-choice Decision Tasks in Visual Information Processing

Data analysis are important tasks in research. The present study focuses on the analysis of data sets from human eye movement experiments. The results of the experiments were analyzed according to two criteria – gender and age of the participants. The participants were divided into 3 groups, respectively group 1: between 20 and 35 years, group 2: between 36 and 55 years and group 3: between 56 and 85 years. The results showed that 75% of the two-choice decision tasks were solved correctly. This trend was maintained among the participants from group 1 – respectively 75.4%. The participants from group 2 gave more correct answers – respectively 82.2%, but the participants from group 3 gave fewer correct answers – respectively 70.2%. The average value of the response time indicator (of all participants) was 1455 ms. The response time of the participants from groups 1 and 2 was shorter than the average (respectively with 483 ms and 235 ms). The response time of the participants from group 3 was longer than the average (respectively with 626 ms)

Intelligent Optimization of a Mixed Culture Cultivation Process

In the present paper a neural network approach called "Adaptive Critic Design" (ACD) was applied to optimal tuning of set point controllers of the three main substrates (sugar, nitrogen source and dissolved oxygen) for PHB production process. For approximation of the critic and the controllers a special kind of recurrent neural networks called Echo state networks (ESN) were used. Their structure allows fast training that will be of crucial importance in on-line applications. The critic network is trained to minimize the temporal difference error using Recursive Least Squares method. Two approaches - gradient and heuristic - were exploited for training of the controllers. The comparison is made with respect to achieved improvement of the utility function subject of optimization as well as with known expert strategy for control the PHB production process

Neural Networks Approach to Optimization of Steel Alloys Composition

Part 16: Multi Layer ANNInternational audienceThe paper presents modeling of steels strength characteristics in dependence from their alloying components quantities using neural networks as nonlinear approximation functions. Further, for optimization purpose the neural network models are used. The gradient descent algorithm based on utility function backpropagation through the models is applied. The approach is aimed at synthesis of steel alloys compositions with improved strength characteristics by solving multi-criteria optimization task. The obtained optimal alloying compositions fall into martenzite region of steels. They will be subject of further experimental testing in order to synthesize new steels with desired characteristics

International Conference on Artificial Neural Networks (ICANN)

The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new algorithms for prototype selection, and group structure discovering. Moreover, the book discusses one-class support vector machines for pattern recognition, handwritten digit recognition, time series forecasting and classification, and anomaly identification in data analytics and automated data analysis. By presenting the state-of-the-art and discussing the current challenges in the fields of artificial neural networks, bioinformatics and neuroinformatics, the book is intended to promote the implementation of new methods and improvement of existing ones, and to support advanced students, researchers and professionals in their daily efforts to identify, understand and solve a number of open questions in these fields.