The method developed for blind separation of highly synchronised pulse sources means a breakthrough novelty in biomedical research. It is for the first time that the activity of motor units in neurodegenerative diseases is made observable, such as in essential and Parkinsonian tremor.
COBISS.SI-ID: 16676630
Teaching-Learning-Based Optimization (TLBO) seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances. It is reported that it outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. Such a breakthrough hassteered us towards investigating the secrets of TLBOćs dominance. This paper reports our findings on TLBO qualitatively and quantitatively through code-reviews and experiments, respectively. Our findings have revealed three important mistakes regarding TLBO: (1) at least one unreported but important step; (2) incorrect formulae on a number of fitness function evaluations; and (3) misconceptions about parameter-less control. Additionally, unfair experimental settings/conditions were used to conduct experimental comparisons(e.g., different stopping criteria). The experimental results for constrained and unconstrained benchmark functions under fairly equal conditions failed to validate its performance supremacy. The ultimate goal of this paper is to provide reminders for metaheuristics researchers and practitioners in order to avoid similar mistakes regarding both the qualitative and quantitative aspects, and to allow fair comparisons of the TLBO algorithm to be made with other metaheuristic algorithms.
COBISS.SI-ID: 16111638
An unsupervised incremental algorithm for grammar inference and its application to domain-specific language development are described. Grammatical inference is the process of learning a grammar from the set of positive and optionally negative sentences. Learning general context-free grammars is still considered a hard problem in machine learning and is not completely solved yet. The main contribution of the paper is a newly developed memetic algorithm, which is a population-based evolutionary algorithm enhanced with local search and a generalization process. The learning process is incremental since a new grammar is obtained from the current grammar and false negative samples, which are not parsed by the current grammar. Despite being incremental, the learning process is not sensitive to the order of samples. All important parts of this algorithm are explained and discussed. Finally, a case study of a domain specific language for rendering graphical objects is used to show the applicability of this approach.
COBISS.SI-ID: 15603734
Textile simulation models are notorious for being difficult to tune. The physically-based derivations of energy functions, as mostly used for mapping the characteristics of real-world textiles on to simulation models, are labour-intensive and not guarantee satisfactory results. The extremely complex behaviour of textiles requires additional adjustment over a wide-range of parameters in order to achieve realistic real-life behaviour of the model. Furthermore, such derivations might not even be possible when dealing with mass-spring particle system-based models. Since there is no explicit correlation between the physical characteristics of textiles and the stiffnesses of springs that control a model's behaviour, this remains an unresolved issue. This paper proposes a hybrid Evolutionary Algorithm (EA), in order to solve this problem. The initial parameters of the model are written in individual's genes, where the number of genes is predefined for different textile types in order to limit the search-space. By mimicking the evolution processes, the EA is used to search the stability domain of the model to find a set of parameters that persuasively imitate the behaviour of a given real-world textile (e.g. silk, cotton or wool). This evaluation is based on the drape measurement, a characteristic often used when evaluating fabrics within the textile industry. The proposed EA is multi-objective, as textile drape is analysed using different quantifications. Local search is used to heuristically improve convergence towards a solution, while the efficiency of the method is demonstrated in comparison to a simple EA. To the best of our knowledge, this problem is being solved using an EA for the first time.
COBISS.SI-ID: 15310102
In this paper, a multimethod approach for heartbeat and respiration detection from an optical interferometric signal is proposed. Optical interferometer is a sensitive device that detects physical changes of optical-fiber length due to external perturbations. When in direct or indirect contact with human body (e.g., hidden in a bed mattress), mechanical and acoustic activity of cardiac muscle and respiration reflect in the interferometric signal, enabling entirely unobtrusive monitoring of heartbeat and respiration. A novel, two-phased multimethod approach was developed for this purpose. The first phase selects best performing combinations of detection methods on a training set of signals. The second phase applies the selected methods to test set of signals and fuses all the detections of vital signs. The test set consisted of14 subjects cycling an ergometer until reaching their submaximal heart rate.The following resting periods were analyzed showing high efficiency (98.18 1.40% sensitivity and 97.04 4.95% precision) and accuracy (mean absolute error of beat-to-beat intervals 22 9 ms) for heartbeat detection, andacceptable efficiency (90.06 7.49% sensitivity and 94.21 3.70% precision) and accuracy (mean absolute error of intervals between respiration events 0.33 0.14 s) for respiration detection.
COBISS.SI-ID: 16264470