Adaptive Integrated Control for Omnidirectional Mobile Manipulators Based on Neural-Network

An omnidirectional mobile manipulator, due to its large-scale mobility and dexterous manipulability, has attracted lots of attention in the last decades. However, modeling and control of such systems are very challenging because of their complicated mechanism. In this paper, an unified dynamic model is developed by Lagrange Formalism. In terms of the proposed model, an adaptive integrated tracking controller, based on the computed torque control (CTC) method and the radial basis function neural-network (RBFNN), is presented subsequently. Although CTC is an effective motion control strategy for mobile manipulators, it requires precise models. To handle the unmodeled dynamics and the external disturbance, a RBFNN, serving as a compensator, is adopted. This proposed controller combines the advantages of CTC and RBFNN. Simulation results show the correctness of the proposed model and the effectiveness of the control approach.

Modified Gabor Wavelets for Image Decomposition and Perfect Reconstruction

This article presents a scheme for image decomposition and perfect reconstruction based on Gabor wavelets. Gabor functions have been used extensively in areas related to the human visual system due to their localization in space and bandlimited properties. However, since the standard two-sided Gabor functions are not orthogonal and lead to nearly singular Gabor matrices, they have been used in the decomposition, feature extraction, and tracking of images rather than in image reconstruction. In an attempt to reduce the singularity of the Gabor matrix and produce reliable image reconstruction, in this article, the authors used single-sided Gabor functions. Their experiments revealed that the modified Gabor functions can accomplish perfect reconstruction.

Important Attributes Selection Based on Rough Set for Speech Emotion Recognition

Speech emotion recognition is becoming more and more important in such computer application fields as health care, children education, etc. In order to improve the prediction performance or providing faster and more cost-effective recognition system, an attribute selection is often carried out beforehand to select the important attributes from the input attribute sets. However, it is time-consuming for traditional feature selection method used in speech emotion recognition to determine an optimum or suboptimum feature subset. Rough set theory offers an alternative, formal and methodology that can be employed to reduce the dimensionality of data. The purpose of this study is to investigate the effectiveness of Rough Set Theory in identifying important features in speech emotion recognition system. The experiments on CLDC emotion speech database clearly show this approach can reduce the calculation cost while retaining a suitable high recognition rate.

Kansei Experience

We propose that Information and Communication Technology should deliver a new experience to the user. We call this experience Kansei Experience. To deliver it we advocate the reliance on a new computing paradigm called cultural computing. Within this paradigm, we develop the concept of Kansei Media and how it could be implemented via Kansei Mediated Interaction. Kansei Media is about sharing implicit knowledge such as feelings, emotions and moods. We aim for a Kansei Experience, rendered by Kansei Media, which relates to reality, and enhances it to enlighten the user. To do so we investigate the aesthetics of the experience we wish to produce. Finally, to develop the concept of Kansei Mediated Experience we refer two cultures (Western and Eastern) and use famous stories from both as means of delivering guidelines for the implementation of Kansei Experience.

Knowledge Adquisition in a Cooperative and Competitive Framework

In this chapter, we modelize an interchange commerce system basen on the economic concept of utility function. A cognitive agent controls the interchanges of the clients in her market. When interchanges are not possible any more, the agent becomes a client of a higher market, giving place to a hierarchical market system. Now, she behaves according to what she has learned from her clients. Apart from physical resources, intangible goods such as knowledge are also interchanged. This cooperative and competitive structure is formalized via process algebra.

On Cognitive Foundations of Creativity and the Cognitive Process of Creation

Creativity is a gifted ability of human beings in thinking, inference, problem solving, and product development. A creation is a new and unusual relation between two or more objects that generates a novel and meaningful concept, solution, method, explanation, or product. This article formally investigates into the cognitive process of creation and creativity as one of the most fantastic life functions. The cognitive foundations of creativity are explored in order to explain the space of creativity, the approaches to creativity, the relationship between creation and problem solving, and the common attributes of inventors. A set of mathematical models of creation and creativity is established on the basis of the tree structures and properties of human knowledge known as concept trees. The measurement of creativity is quantitatively analyzed, followed by the formal elaboration of the cognitive process of creation as a part of the Layered Reference Model of the Brain (LRMB).

Dynamic Negotiation Mechanism for Improving Service Quality for Replicas in Data Grids

In order not to be limited in term of calculation, storage and communication, the concept of grid, which does not cease evolving, makes it possible to offer a practical operation of work unified as well as a great storage and computing power. To manage the division in the data grid, technical replication is used, but in spite of their advantages, the competitor access to the data could involve inconsistencies, from where the great challenge to ensure the consistency management between replicas of object. In this chapter, we describe model double-layered adapted to the applications on a large scale and which represents the support of the hybrid approach of consistency management of replicas based on pessimistic and optimistic approaches. This hybrid approach present an adapted mechanism based on the various negotiation forms between virtual consistency agents to be able to reduce the number of conflicts between replicas in data grids.

Autonomic Agent Systems

A new computing paradigm is currently on the spot: interaction based on series of actions. Most of autonomic agent systems (AASs) exploit this type of interaction to self-adjust their autonomous behaviors as a fundamental operational paradigm. At an interaction interface, actions evolve over time, hence series of actions occurs as a royal candidate for modeling, specifying, programming, and verifying AASs. For considering AASs, series of actions and adaptation relations; our formal approach consists, in particular, of categorical models and behaviors such that, firstly , AASs, series of actions and adaptation relations will categorically be modeled to provide algebraic frameworks for development of reasoning on their behaviors and, secondly, categorical behaviors of AASs, series of action and adaptation relations will be investigated and developed taking advantage of their categorical models.

A Cognitive Informatics Reference Model of Autonomous Agent Systems (AAS)

Despite the fact that the origin of software agent systems has been rooted in autonomous artificial intelligence and cognitive psychology, their implementations are still based on conventional imperative computing techniques rather than autonomous computational intelligence. This paper presents a cognitive informatics perspective on autonomous agent systems (AAS’s). A hierarchical reference model of AAS’s is developed, which reveals that an autonomous agent possesses intelligent behaviors at three layers known as those of imperative, autonomic, and autonomous from the bottom up. The theoretical framework of AAS’s is described from the facets of cognitive informatics, computational intelligence, and denotational mathematics. According to Wang’s abstract intelligence theory, an autonomous software agent is supposed to be called as an intelligent-ware, shortly, an intelware, parallel to hardware and software in computing, information science, and artificial intelligence.

Noise Cancellation in ECG Signals with an Unbiased Adaptive Filter

The electrocardiographic (ECG) signal is a transthoracic manifestation of the electrical activity of the heart and is widely used in clinical applications. This chapter describes an unbiased linear adaptive filter (ULAF) to attenuate high-frequency random noise present in ECG signals. The ULAF does not contain a bias in its summation unit and the filter coefficients are normalized. During the adaptation process, the normalized coefficients are updated with the steepest-descent algorithm to achieve efficient filtering of noisy ECG signals. A total of 16 ECG signals were tested in the adaptive filtering experiments with the ULAF, the least-mean-square (LMS), and the recursive-least-squares (RLS) adaptive filters. The filtering performance was quantified in terms of the root-mean-squared error (RMSE), normalized correlation coefficient (NCC), and filtered noise entropy (FNE). A template derived from each ECG signal was used as the reference to compute the measures of filtering performance. The results indicated that the ULAF was able to provide noise-free ECG signals with an average RMSE of 0.0287, which was lower than the second-best RMSE obtained with the LMS filter. With respect to waveform fidelity, the ULAF provided the highest average NCC (0.9964) among the three filters studied. In addition, the ULAF effectively removed more noise, measured by FNE, in comparison with the LMS and RLS filters in most of the ECG signals tested. The issues of adaptive filter setting for noise reduction in ECG signals are discussed at the end of this chapter.