Catálogo de publicaciones - libros

The analysis of electrocardiogram (ECG) is used in the diagnosis of the heart disease. Various algorithms have been proposed and used to detect ECG characteristics while it is difficult to detect P wave in real-time accurately. In the present study, we developed the real-time exercise ECG analyzer and the performance of this system was discussed. The method of defining P and R wave is based on a template-matching algorithm. In particular, the computerized algorithm analyzes a single channel ECG signal. The system extracts P-P, R-R and P-R interval from an incoming ECG signal sampled at 1KHz, and also performs spectral analysis of P-P and P-R intervals. The system was evaluated on recordings from 5 healthy subjects. The proposed algorithm had high correlation between P-P and R-R intervals (more than 0.97), for this reason we considered that algorithm was able to detect P wave accurately. Results were displayed on the screen immediately, and we obtained that a correlation diagram of P-P and P-R intervals. The developed system provides the good quality in real-time and the additional information on the fluctuation of P-R intervals.

Cultured cardiac myocytes from neonatal rats show spontaneous and rhythmic contractions. The intracellular concentration of free Ca also changes rhythmically associated with the rhythmic contraction of myocytes (Ca oscillation). This study aims to elucidate whether spontaneous rhythmic contraction affects the intercellular synchronization of intracellular Ca oscillation in cultured cardiac myocytes. In cultures at 4 days in vitro (4 DIV), spontaneous Ca oscillation was synchronized among myocytes. Treatment of cultures with an uncoupler of E-C coupling resulted in a cessation of the spontaneous contraction of cardiac myocytes, but did not affect the intercellular synchronization of intracellular Ca oscillation among myocytes. In addition, the treatment significantly increased the intervals of intracellular Ca oscillation when the spontaneous rhythmic contraction was terminated. We also performed computer simulations using mutually interacting Bonhoeffervan der Pol (BVP) oscillators to understand the relationship between the cyclic movements of cardiac myocytes and the intercellular synchronization of intracellular Ca oscillation. The simulation results supported the experimental findings obtained here. The present study demonstrated that mechanical factors associated with rhythmic contraction did not affect the intercellular synchronization of intracellular Ca oscillation in cultured cardiac myocytes.

The increasing use of auctions has led to a growing interest in the subject. The objective of this paper is to develop a genetic algorithm to find optimum bidding strategies for Ausubel auctions. The algorithm provides the strategy (defined as the action to be taken under different market conditions) that maximizes pay off to the bidder. The algorithm is tested under a series of scenarios that differ in their composition of utility functions, number of bidders and quantity of lots auctioned. The results suggest that the approach leads to strategies that outperform sincere bidding systems.

Human fatigue is one of the most important problems of Interactive Evolutionary Algorithms (IEAs) that requires addressing. The problem of fatigue usually arises out of intensive interaction between the IEA system and the respondent. Consequently, due to lack of interest or attention, the respondent will provide biased answers either intentionally or unintentionally. To reduce the times of interaction in IEAs effectively, we adopt a learning approach to learn responedent’s preference model and then use this model to predict the fitness values of any given individuals. Unlike other research, we propose a novel system called ALP-IGA that integrates the theorem of incremental machine learning, the Algorithmic Probability (ALP), with Interactive Genetic Algorithm (IGA). Since the ALP model will utilize each interaction effectively to improve the accuracy of prediction, it is very likely our ALP-IGA system can predict respondent’s preferences precisely just after a few interactions. We have investigated the performance of our ALP-IGA via a Monte Carlo simulation. Experimental results indicated that the number of interactions needed by ALP-IGA is very small for some cases. In addition, we have also compared the prediction correctness of ALP-IGA with a contrast IEA system whose learning scheme is implemented by a neural network algorithm. The results showed that ALP-IGA is superior to IEA with neural network for all cases.

Inspired by the efficient method of locomotion of the rattlesnake Crotalus cerastes, the objective of this work is to investigate the emergent properties of the automatically designed through genetic programming (GP) fastest possible (sidewinding), robust and adaptive locomotion gaits of simulated snake-like robot (Snakebot). Considering the notion of “emergent intelligence” as the ability of Snakebot to achieve its goals (of moving fast) without the need to be explicitly taught about how to do so, we present the empirical results on the emergence of sidewinding locomotion as fastest locomotion of the Snakebot. The emergent properties of evolved robust sidewinding gaits featuring desired velocity characteristics of Snakebot in challenging environment are discussed. The ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics, and the emergent properties of obtained adaptive gaits are elaborated. Concluding on the practical implications of the analogy between the emergent properties of the robust and the adaptive locomotion gaits, this work could be viewed as a step towards building real Snakebots, which are able to perform robustly in difficult environment.

Traditional evolutionary computations use random sampling strategy to gene rate their first generation resulting in very few (if any) good solutions found in the first generation. Over-sampling is a strategy of the deliberate selection of individuals of a rare type in order to obtain reasonably precise estimates of the properties of this type. We believe that the use of over-sampling in generating the first generation of IEC would result in better performance. We proposed two types of over-sampling process in IEC (OIEC_1 and OIEC_2), and used mineral water bottle design as a research case to verify the proposed models’ performance. The initial results shown that both proposed models performed better than traditional IEC.

We evaluate the convergence speed of an Interactive Evolutionary Computation (IEC) using a rating-scale mapping for user fatigue reduction. First, we introduce the concept of mapping users’ relative ratings to an “absolute scale”; this allows us to improve the performance of the IEC subjective evaluation characteristic predictor, which can in turn accelerate EC convergence and reduce user fatigue. Second, we experimentally evaluate the effectiveness of the proposed method using seven benchmark functions instead of a hunman user. The experimental results show that the convergence speed of an IEC using the proposed absolute rating data-trained predictor is much faster than an IEC using a conventional predictor trained using relative rating data.

this paper presents a new approach to interactive evolutionary computation that helps the user in the difficult task of finding an optimal solution between multiple possibilities. There are several ways of applying algorithms in interactive evolutionary computation; in this paper we explain three of them in order to make an experimental comparative study. Proceeding with a main goal of solving complex problems as fast as possible, we take the Rastrigin test function as a benchmark and it is executed with the three algorithms described. The aim is to show clearly the results of the algorithms in terms of solution quality and number of iterations. The results clearly show that the use of the proposed method based on chromosome learning heuristics works well even for non Interactive Evolutionary Computation frameworks.

This paper presents how a Multi-Agent system can support the design of concrete bridges in the 3D CAD environment where 3D product model enables the interoperability among various application systems. We developed multi-agent programs that can check interference, lack of cover and space of members such as reinforcing bars, prestressing strands, etc., and integrated them with the previously developed 3D CAD environment consisting of 3D CAD system, PC bridge structural design system, and the product model. Application test of this integrated system showed efficiency improvement.

In the CAD drawing standard of the Ministry of Land, Infrastructure and Transport, it is clearly written that the CAD drawing’s exchange format will conform to the international standard ISO10303-202 (STEP/AP202). On the other hand, at the Japan Construction Information Center’s (JACIC) CAD Data Exchange Standard Development Consortium (SCADEC: Standard CAD data exchange format in japanese Construction field), exchange format (SXF:SCADEC eXchange Format) based on STEP/AP202 was developed. In the future, for the electronic delivery of CAD data related to construction, SXF will be adopted. In addition, in Osaka Prefecture, securing CAD data related to construction is raised as an issue for electronic delivery system/information sharing system. However, in the current situation, system that can distinguish identity of 2 distinct CAD data does not exist. For this reason, in this research, a system, specifically called Logical Smart, that determines the identity of CAD data by using SXF is developed.