We present a multimodal data set for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance, and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool. An extensive analysis of the participants' ratings during the experiment is presented. Correlates between the EEG signal frequencies and the participants' ratings are investigated. Methods and results are presented for single-trial classification of arousal, valence, and like/dislike ratings using the modalities of EEG, peripheral physiological signals, and multimedia content analysis. Finally, decision fusion of the classification results from different modalities is performed. The data set is made publicly available and we encourage other researchers to use it for testing their own affective state estimation methods.

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DEAP: A Database for Emotion Analysis ;Using Physiological Signals

Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.

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Evolving artificial neural networks

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Art of electronics

Hybrid metaheuristics have received considerable interest these recent years in the field of combinatorial optimization. A wide variety of hybrid approaches have been proposed in the literature. In this paper, a taxonomy of hybrid metaheuristics is presented in an attempt to provide a common terminology and classification mechanisms. The taxonomy, while presented in terms of metaheuristics, is also applicable to most types of heuristics and exact optimization algorithms.

As an illustration of the usefulness of the taxonomy an annoted bibliography is given which classifies a large number of hybrid approaches according to the taxonomy.

A Taxonomy of Hybrid Metaheuristics

This chapter presents an introduction to recurrent neural networks for readers familiar with artificial neural networks in general, and multi-layer perceptrons trained with gradient descent algorithms (back-propagation) in particular. A recurrent neural network (RNN) is an artificial neural network with internal loops. These internal loops induce recursive dynamics in the networks and thus introduce delayed activation dependencies across the processing elements (PEs) in the network.

Recurrent Neural Networks

Two-stage oil-free centrifugal air compressors can bring significant advantages and open new market opportunities for compressor manufacturers. One of the core technologies behind this compressor type is the high-speed electrical machine supported by active magnetic bearings. In this paper, the requirements set by the compressor on the electrical machine design are presented. The design solutions aimed to satisfy these requirements are discussed. Two case studies illustrate possible design approaches for the target application with examples of a 120-kW, 60 000-r/min induction machine with a solid rotor and a 225-kW, 50 000-r/min permanent-magnet synchronous machine (PMSM) with a full cylindrical magnet. The system design and simulation results are confirmed by measurements of a PMSM prototype.

Design Aspects of High-Speed Electrical Machines With Active Magnetic Bearings for Compressor Applications

Audio-visual speech recognition (AVSR) using acoustic and visual signals of speech has received attention because of its robustness in noisy environments. In this paper, we present a late integration scheme-based AVSR system whose robustness under various noise conditions is improved by enhancing the performance of the three parts composing the system. First, we improve the performance of the visual subsystem by using the stochastic optimization method for the hidden Markov models as the speech recognizer. Second, we propose a new method of considering dynamic characteristics of speech for improved robustness of the acoustic subsystem. Third, the acoustic and the visual subsystems are effectively integrated to produce final robust recognition results by using neural networks. We demonstrate the performance of the proposed methods via speaker-independent isolated word recognition experiments. The results show that the proposed system improves robustness over the conventional system under various noise conditions without a priori knowledge about the noise contained in the speech.

Robust Audio-Visual Speech Recognition Based on Late Integration

This paper presents an application of evolutionary programming to parameter optimization in the design of a voltage reference circuit. Designing circuits consists of two steps: topological design and parameter determination. After designing a topology suitable for the circuit, the designer selects an appropriate value for each circuit element from a circuit analysis and his experience. This step is difficult and time consuming because the designer must consider many factors simultaneously. As more precise circuits are required, parameter optimization becomes more complex. The voltage reference circuit, which requires a precise reference voltage independent of power fluctuation and temperature change, is such an example. In this paper, evolutionary programming is used as an effective method of finding good parameter values for the elements of the voltage reference circuit. Simulation results show that this method provides good performance and can be used as an effective method for circuit design.

Parameter optimization of an on-chip voltage reference circuit using evolutionary programming

A new neuro-fuzzy identification model of nonlinear dynamic-systems

While automatic speech recognition technologies have been successfully applied to realworld applications, there still exist several problems which need to be solved for wider application of the technologies. One of such problems is noise-robustness of recognition performance; although a speech recognition system can produce high accuracy in quiet conditions, its performance tends to be significantly degraded under presence of background noise which is usually inevitable in most of the real-world applications. Recently, audio-visual speech recognition (AVSR), in which visual speech information (i.e., lip movements) is used together with acoustic one for recognition, has received attention as a solution of this problem. Since the visual signal is not influenced by acoustic noise, it can be used as a powerful source for compensating for performance degradation of acousticonly speech recognition in noisy conditions. Figure 1 shows the general procedure of AVSR: First, the acoustic and the visual signals are recorded by a microphone and a camera, respectively. Then, salient and compact features are extracted from each signal. Finally, the two modalities are integrated for recognition of the given speech.

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Cheol Hoon Park

Papers

Design Aspects of High-Speed Electrical Machines With Active Magnetic Bearings for Compressor Applications

Robust Audio-Visual Speech Recognition Based on Late Integration

Parameter optimization of an on-chip voltage reference circuit using evolutionary programming

A new neuro-fuzzy identification model of nonlinear dynamic-systems

Adaptive Decision Fusion for Audio-Visual Speech Recognition