Abstract

Available control methods for underactuated Euler–Lagrange (EL) systems rely on structure-specific constraints that may be appropriate for some systems, but restrictive for others. A generalized (structure-independent) control framework is to a large extent missing, especially in the presence of uncertainty. This paper introduces an adaptive-robust control framework for a quite general class of uncertain underactuated EL systems. Compared to existing literature, the important attributes of the proposed solution are: (i) avoiding structure-specific restrictions, namely, symmetry condition property of the mass matrix, and a priori bounds on non-actuated states or state derivatives; (ii) considering Coriolis, centripetal, friction and gravity terms to be unknown, while only requiring the knowledge of maximum perturbation around a nominal value of the mass matrix; (iii) handling state-dependent uncertainties irrespective of their linear or nonlinear in parameters structure. These features significantly widen the range of underactuated EL systems the proposed solution can handle in comparison to the available methods. Stability is studied analytically and the performance is verified in simulation using offshore boom crane dynamics.

Abstract

The literature has proven that attaining good transient behavior in leakage-based robust adaptive control of uncertain switched systems is intrinsically challenging. In fact, because the gains of the inactive subsystems must exponentially vanish during inactive times as an effect of leakage action, new learning transients will repeatedly arise at each switching instant. In this paper, a new leakage-based mechanism is designed for robust adaptive control of uncertain switched systems: in contrast to the available designs, the key innovation of the proposed one is that the adaptive gains of the inactive subsystems can be kept constant to their switched-off values, thus preventing vanishing gains. Bounded stability of the closed-loop switched system is guaranteed thanks to the introduction of an auxiliary gain playing the role of leakage. A benchmark example commonly adopted in adaptive switched literature shows that the proposed strategy can consistently improve the transient behavior under various families of switching signals.

Abstract

Due to advances in technology for better output/production, there has been an increased demand for technology with optimized resource utilization in order to conserve resources. Advancements in sensors have improved resource optimization in many fields such as electric power consumption, water management and quality management. In this regard, air conditioner (AC) management is an important aspect of electric power consumption. In this paper, we address the issue of reducing power consumption for air conditioning of a given room. In previous works, the whole room is maintained at a fixed required temperature by inherently assuming that all of the users prefer the same temperature; this is generally not true in practice. In this paper, we propose a sensor-based framework to place and manage ACs for maintaining diverse temperature zones in a given layout to reduce power consumption. Through a simulation study, we demonstrate that the proposed framework indeed has the potential to reduce power consumption significantly as compared to the naive approach by achieving user satisfaction.

Abstract

Pattern mining is an important task of data mining and involves the extraction of interesting associations from large databases. However, developing fast and efficient parallel algorithms for handling large volumes of data is a challenging task. The MapReduce framework enables the distributed processing of huge amounts of data in large-scale distributed environment with robust fault-tolerance. In this paper, we propose a parallel algorithm for extracting coverage patterns. The results of our performance evaluation with real-world and synthetic datasets demonstrate that it is indeed feasible to extract coverage patterns effectively under the MapReduce framework.

Abstract

Weighted Frequent Itemset (WFI) mining is an important model in data mining. It aims to discover all itemsets whose weighted sum in a transactional database is no less than the user-specified threshold value. Most previous works focused on finding WFIs in a transactional database and did not recognize the spatiotemporal characteristics of an item within the data. This paper proposes a more flexible model of Weighted Frequent Neighborhood Itemsets (WFNI) that may exist in a spatiotemporal database. The recommended patterns may be found very useful in many real-world applications. For instance, an WFNI generated from an air pollution database indicates a geographical region where people have been exposed to high levels of an air pollutant, say PM2.5. The generated WFNIs do not satisfy the anti-monotonic property. Two new measures have been presented to effectively reduce the search space and the computational cost of finding the desired patterns. A pattern-growth algorithm, called Spatial Weighted Frequent Pattern-growth, has also been presented to find all WFNIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also describe a case study in which our model has been used to find useful information in air pollution database.

Abstract

Music is appreciated for emotional reasons across cultures, but knowledge on the cross-cultural similarities and differences of music-evoked emotions is still sparse. The current study compared music-evoked emotions in Finland and in India, contextualizing them within the perceived psychological functionality of music in an individualistic versus collectivistic culture. Participants ( N = 230) answered an online survey on music-evoked emotions and related personal meanings. A mixed-method approach using factor analysis and qualitative content analysis was used to identify the concepts for cross-cultural comparison. Results show that both cultures value music for positive emotional experiences, but the prevalence of more detailed emotional nuances and underlying meanings is distinctively different. The highest-scoring emotion factor for Finns was Power-Empowerment while for Indians it was Peaceful-Transcendence. For Finns, the personal relevance of music was distinctively related to self-enhancement, self-reflective insights, and self-expression, while for Indians the relevance was particularly related to using music as a mood management tool for reaching positive, relaxed, and motivated affective states. Both cultures found music important for experiencing social connection. The results partly reflect the individualistic-collectivistic dimensionality of cultures and emphasize the relevance of contextualizing music psychological knowledge of music-evoked emotions in individuals’ culturally bound meaning-making processes.

Abstract

In this paper, we present a multispeaker localization method using the time delay estimates obtained from the spectral features derived from the single frequency filter (SFF) representation. The mixture signals are transformed into SFF domain from which the temporal envelopes are extracted at each frequency. Subsequently, the spectral features such as mean and variance of temporal envelopes across frequencies are correlated for extracting the time delay estimates. Since these features emphasize the high SNR regions of the mixtures, correlation of the corresponding features across the channels leads to robust delay estimates in real acoustic environments. We study the efficacy of the developed approach by comparing its performance with the existing correlation based time delay estimation techniques. Both, a standard data set recorded in real-room acoustic environments and simulated data set are used for …

Abstract

n generation of emotional speech, there are deviations in the speech production features when compared to neutral (non-emotional) speech. The objective of this study is to capture the deviations in features related to the excitation component of speech and to develop a system for automatic recognition of emotions based on these deviations. The emotions considered in this study are anger, happiness, sadness and neutral state. The study shows that there are useful features in the deviations of the excitation features, which can be exploited to develop an emotion recognition system. The excitation features used in this study are the instantaneous fundamental frequency (), the strength of excitation, the energy of excitation and the ratio of the high-frequency to low-frequency band energy (). A hierarchical binary decision tree approach is used to develop an emotion recognition system with neutral speech …

Abstract

This paper presents a Bayesian multilingual topic model for learning language-independent document embeddings. Our model learns to represent the documents in the form of Gaussian distributions, thereby encoding the uncertainty in its covariance. We propagate the learned uncertainties through linear classifiers for zero-shot cross-lingual topic identification. Our experiments on 5 language Europarl and Reuters (MLDoc) corpora show that the proposed model outperforms multi-lingual word embedding and BiLSTM sentence encoder based systems with significant margins in the majority of the transfer directions. Moreover, our system trained under a single day on a single GPU with much lower amounts of data performs competitively as compared to the state-of-the-art universal BiLSTM sentence encoder trained on 93 languages. Our experimental analysis shows that the amount of parallel data improves the overall performance of embeddings. Nonetheless, exploiting the uncertainties is always beneficial.

Abstract

This paper presents the Question Answering (QA) system for a low resource language like ‘Telugu’ named ‘AVADHAN’. This work started with preparing a pre-tagged data set for Telugu Question Classification (QC). We also explained the ambiguities and complexities involved in the data set. AVADHAN exhibits the comparisons between Support Vector Machine (SVM), Logistic Regression (LR) and Multi-Layer Perceptron (MLP) classifiers for achieving the plausible answers. After performing various experiments the overall accuracies obtained, for both ‘exact match’ and ‘partial match’ based approaches, were for SVM (31.6%, 68.5%), LR (31%, 66.6%) and for MLP (30%, 67%) respectively