Farey Trees Explain Sequential Effects in Choice Response Time

The latencies of successive two-alternative, forced-choice response times display intricately patterned sequential effects, or dependencies. They vary as a function of particular trial-histories, and in terms of the order and identity of previously presented stimuli and registered responses. This article tests a novel hypothesis that sequential effects are governed by dynamic principles, such as those entailed by a discrete sine-circle map adaptation of the Haken Kelso Bunz (HKB) bimanual coordination model. The model explained the sequential effects expressed in two classic sequential dependency data sets. It explained the rise of a repetition advantage, the acceleration of repeated affirmative responses, in tasks with faster paces. Likewise, the model successfully predicted an alternation advantage, the acceleration of interleaved affirmative and negative responses, when a task’s pace slows and becomes more variable. Detailed analyses of five studies established oscillatory influences on sequential effects in the context of balanced and biased trial presentation rates, variable pacing, progressive and differential cognitive loads, and dyadic performance. Overall, the empirical patterns revealed lawful oscillatory constraints governing sequential effects in the time-course and accuracy of performance across a broad continuum of recognition and decision activities.

Adaptive training of vibration-based anomaly detector for wind turbine condition monitoring

Adaptive training of a vibration-based anomaly detector for wind turbine condition monitoring system (CMS) is carried out to achieve high-performance detection from the early stages of monitoring. Machine learning-based wind turbine CMSs are required to collect large-scale data to yield reli-able predictions. Existing studies in this area have postulated that both data for training a monitoring system and those during the operation of the system are obtained from identical devices. In addition, constant monitoring of data is desirable, but in practice, the data can be observed periodically (e.g., several tens of seconds of data are observed every two hours). In this case, collecting sufficient data is time consuming, making it difficult to conduct accurate predictions at the early stage of the CMS operation. To address this problem, a small amount of vibration data observed at a target wind turbine is utilized to adapt the anomaly detector that is trained on relatively large-scale vibration signals obtained from other wind turbines. In the present study, maximum a posteriori (MAP) adaptation is applied to a Gaussian mixture model (GMM)-based anomaly detector. Experimental comparisons using vibration data from the gearbox in the ex- perimental environment and those used in the wind turbine demonstrated that MAP-based GMM adaptation yielded an improvement in anomaly detection accuracy even when only a small amount of data is observed at the target gearbox.

Language Identification based on Support Vector Machine using GMM Super vectors

This paper proposes a novel approach that combines the power of generative Gaussian mixture models (GMM) and discriminative support vector machines (SVM). The main objective this paper is to incorporating the GMM super vectors based on SVM classifier for language identification (LID) task. The GMM based LID system to capture all the variations present in phonotactic constraints imposed by the language requires large amount of training data. The Gaussian mixture model (GMM)- universal background model (UBM) modeling require less amount of training data. In GMM-UBM LID system, a language model is created by maximum a posterior (MAP) adaptation of the means of the universal background model (UBM). Here the GMM super vectors are created by concatenating the means of the adapted mixture components from UBM. Then these super vectors are applied to a SVM for classification purpose. In this paper, the performance of GMM-UBM LID system based on SVM is compared with the conventional GMM LID system. Form the performance analysis it is found that GMM-UBM LID system based on SVM is performed well when compared to GMM based LID system.