Study on Optimized Elman Neural Network Classification Algorithm Based on PLS and CA

High-dimensional large sample data sets, between feature variables and between samples, may cause some correlative or repetitive factors, occupy lots of storage space, and consume much computing time. Using the Elman neural network to deal with them, too many inputs will influence the operating efficiency and recognition accuracy; too many simultaneous training samples, as well as being not able to get precise neural network model, also restrict the recognition accuracy. Aiming at these series of problems, we introduce the partial least squares (PLS) and cluster analysis (CA) into Elman neural network algorithm, by the PLS for dimension reduction which can eliminate the correlative and repetitive factors of the features. Using CA eliminates the correlative and repetitive factors of the sample. If some subclass becomes small sample, with high-dimensional feature and fewer numbers, PLS shows a unique advantage. Each subclass is regarded as one training sample to train the different precise neural network models. Then simulation samples are discriminated and classified into different subclasses, using the corresponding neural network to recognize it. An optimized Elman neural network classification algorithm based on PLS and CA (PLS-CA-Elman algorithm) is established. The new algorithm aims at improving the operating efficiency and recognition accuracy. By the case analysis, the new algorithm has unique superiority, worthy of further promotion

Computational and Psycho-Physiological Investigations of Musical Emotions

The ability of music to stir human emotions is a well known fact (Gabrielsson & Lindstrom. 2001). However, the manner in which music contributes to those experiences remains obscured. One of the main reasons is the large number of syndromes that characterise emotional experiences. Another is their subjective nature: musical emotions can be affected by memories, individual preferences and attitudes, among other factors (Scherer & Zentner, 2001). But can the same music induce similar affective experiences in all listeners, somehow independently of acculturation or personal bias? A considerable corpus of literature has consistently reported that listeners agree rather strongly about what type of emotion is expressed in a particular piece or even in particular moments or sections (Juslin & Sloboda, 2001). Those studies suggest that music features encode important characteristics of affective experiences, by suggesting the influence of various structural factors of music on emotional expression. Unfortunately, the nature of these relationships is complex, and it is common to find rather vague and contradictory descriptions. This thesis presents a novel methodology to analyse the dynamics of emotional responses to music. It consists of a computational investigation, based on spatiotemporal neural networks sensitive to structural aspects of music, which "mimic" human affective responses to music and permit to predict new ones. The dynamics of emotional responses to music are investigated as computational representations of perceptual processes (psychoacoustic features) and self-perception of physiological activation (peripheral feedback). Modelling and experimental results provide evidence suggesting that spatiotemporal patterns of sound resonate with affective features underlying judgements of subjective feelings. A significant part of the listener's affective response is predicted from the a set of six psychoacoustic features of sound - tempo, loudness, multiplicity (texture), power spectrum centroid (mean pitch), sharpness (timbre) and mean STFT flux (pitch variation) - and one physiological variable - heart rate. This work contributes to new evidence and insights to the study of musical emotions, with particular relevance to the music perception and emotion research communities

Atypical eye contact in autism: Models, mechanisms and development

An atypical pattern of eye contact behaviour is one of the most significant symptoms of Autism Spectrum Disorder (ASD). Recent empirical advances have revealed the developmental, cognitive and neural basis of atypical eye contact behaviour in ASD. We review different models and advance a new ‘fast-track modulator model’. Specifically, we propose that atypical eye contact processing in ASD originates in the lack of influence from a subcortical face and eye contact detection route, which is hypothesized to modulate eye contact processing and guide its emergent specialization during development

Breaking Down the Barriers To Operator Workload Estimation: Advancing Algorithmic Handling of Temporal Non-Stationarity and Cross-Participant Differences for EEG Analysis Using Deep Learning

This research focuses on two barriers to using EEG data for workload assessment: day-to-day variability, and cross- participant applicability. Several signal processing techniques and deep learning approaches are evaluated in multi-task environments. These methods account for temporal, spatial, and frequential data dependencies. Variance of frequency- domain power distributions for cross-day workload classification is statistically significant. Skewness and kurtosis are not significant in an environment absent workload transitions, but are salient with transitions present. LSTMs improve day- to-day feature stationarity, decreasing error by 59% compared to previous best results. A multi-path convolutional recurrent model using bi-directional, residual recurrent layers significantly increases predictive accuracy and decreases cross-participant variance. Deep learning regression approaches are applied to a multi-task environment with workload transitions. Accounting for temporal dependence significantly reduces error and increases correlation compared to baselines. Visualization techniques for LSTM feature saliency are developed to understand EEG analysis model biases

Individual differences in navigating and experiencing presence in virtual environments

The effort of making Virtual Environments (VEs) more useful and satisfactory to use lie at the core of usability research. Because of their development and widespread accessibility, VEs are being used by an ever-increasing diversity of users, whose individual differences impact on both task performance and level of satisfaction. This aspect raises a major challenge in terms of designing adaptive VEs, suitable not for the average user but for each individual user. One way to address this challenge is through the study of individual differences and their implications, which should lead to new effective ways to accommodate them. Adaptivity reflects the system’s capability to automatically tailor itself to dynamically changing user behaviour. This capability is enabled by a user model, acquired on the basis of identifying the user’s patterns of behaviour. This thesis addresses the issue of studying and accommodating individual differences with the purpose of designing adaptive VEs. The individual differences chosen to be investigated are those that impact particularly on two fundamental aspects underlying each interaction with a VE, namely navigation and sense of presence. Both these aspects are related to the perceived usability of VEs. The impact that a set of factors like empathy, absorption, creative imagination and willingness to be transported within the virtual world has on presence has been investigated and described through a prediction equation. Based on these findings, a set of guidelines has been developed for designing VEs able to accommodate these individual differences in order to support users to experience a higher level of presence. The individual differences related to navigation within VE have been investigated in the light of discriminating between efficient versus inefficient search strategies. Building a user model of navigation affords not only a better understanding of user spatial behaviour, but also supports the development of an adaptive VE which could help low spatial users to improve their navigational skills by teaching them the efficient navigational rules and strategies

OBSERVER-BASED-CONTROLLER FOR INVERTED PENDULUM MODEL

This paper presents a state space control technique for inverted pendulum system. The system is a common classical control problem that has been widely used to test multiple control algorithms because of its nonlinear and unstable behavior. Full state feedback based on pole placement and optimal control is applied to the inverted pendulum system to achieve desired design specification which are 4 seconds settling time and 5% overshoot. The simulation and optimization of the full state feedback controller based on pole placement and optimal control techniques as well as the performance comparison between these techniques is described comprehensively. The comparison is made to choose the most suitable technique for the system that have the best trade-off between settling time and overshoot. Besides that, the observer design is analyzed to see the effect of pole location and noise present in the system

A Review of Resonant Converter Control Techniques and The Performances

paper first discusses each control technique and then gives experimental results and/or performance to highlights their merits. The resonant converter used as a case study is not specified to just single topology instead it used few topologies such as series-parallel resonant converter (SPRC), LCC resonant converter and parallel resonant converter (PRC). On the other hand, the control techniques presented in this paper are self-sustained phase shift modulation (SSPSM) control, self-oscillating power factor control, magnetic control and the H-∞ robust control technique

A Review of Resonant Converter Control Techniques and The Performances

paper first discusses each control technique and then gives experimental results and/or performance to highlights their merits. The resonant converter used as a case study is not specified to just single topology instead it used few topologies such as series-parallel resonant converter (SPRC), LCC resonant converter and parallel resonant converter (PRC). On the other hand, the control techniques presented in this paper are self-sustained phase shift modulation (SSPSM) control, self-oscillating power factor control, magnetic control and the H-∞ robust control technique

State-Feedback Controller Based on Pole Placement Technique for Inverted Pendulum System

This paper presents a state space control technique for inverted pendulum system using simulation and real experiment via MATLAB/SIMULINK software. The inverted pendulum is difficult system to control in the field of control engineering. It is also one of the most important classical control system problems because of its nonlinear characteristics and unstable system. It has three main problems that always appear in control application which are nonlinear system, unstable and non-minimumbehavior phase system. This project will apply state feedback controller based on pole placement technique which is capable in stabilizing the practical based inverted pendulum at vertical position. Desired design specifications which are 4 seconds settling time and 5 % overshoot is needed to apply in full state feedback controller based on pole placement technique. First of all, the mathematical model of an inverted pendulum system is derived to obtain the state space representation of the system. Then, the design phase of the State-Feedback Controller can be conducted after linearization technique is performed to the nonlinear equation with the aid of mathematical aided software such as Mathcad. After that, the design is simulated using MATLAB/Simulink software. The controller design of the inverted pendulum system is verified using simulation and experiment test. Finally the controller design is compared with PID controller for benchmarking purpose

Embodied language learning and cognitive bootstrapping: methods and design principles

Co-development of action, conceptualization and social interaction mutually scaffold and support each other within a virtuous feedback cycle in the development of human language in children. Within this framework, the purpose of this article is to bring together diverse but complementary accounts of research methods that jointly contribute to our understanding of cognitive development and in particular, language acquisition in robots. Thus, we include research pertaining to developmental robotics, cognitive science, psychology, linguistics and neuroscience, as well as practical computer science and engineering. The different studies are not at this stage all connected into a cohesive whole; rather, they are presented to illuminate the need for multiple different approaches that complement each other in the pursuit of understanding cognitive development in robots. Extensive experiments involving the humanoid robot iCub are reported, while human learning relevant to developmental robotics has also contributed useful results. Disparate approaches are brought together via common underlying design principles. Without claiming to model human language acquisition directly, we are nonetheless inspired by analogous development in humans and consequently, our investigations include the parallel co-development of action, conceptualization and social interaction. Though these different approaches need to ultimately be integrated into a coherent, unified body of knowledge, progress is currently also being made by pursuing individual methods