Kuk-hyun Han

The huggable: a therapeutic robotic companion for relational, affective touch

Numerous studies have shown the positive benefits of companion animal therapy. Unfortunately, companion animals are not always available. The Huggable is a new type of robotic companion being designed specifically for such cases. It features a full body “sensitive skin” for relational affective touch, silent, muscle-like, voice coil actuators, an embedded PC with data collection and networking capabilities. In this paper we briefly describe the Huggable and propose a live demonstration of the robot.

Quantum-inspired Multiobjective Evolutionary Algorithm for Multiobjective 0/1 Knapsack Problems

This paper proposes a multiobjective evolutionary algorithm (MOEA) inspired by quantum computing, which is named quantum-inspired multiobjective evolutionary algorithm (QMEA). In the previous papers, quantum-inspired evolutionary algorithm (QEA) was proved to be better than conventional genetic algorithms for single-objective optimization problems. To improve the quality of the nondominated set as well as the diversity of population in multiobjective problems, QMEA is proposed by employing the concept and principles of quantum computing such as uncertainty, superposition, and interference. Experimental results pertaining to the multiobjective 0/1 knapsack problem show that QMEA finds solutions close to the Pareto-optimal front while maintaining a better spread of nondominated set.

Face detection using quantum-inspired evolutionary algorithm

This work proposes a new face detection system using quantum-inspired evolutionary algorithm (QEA). The proposed detection system is based on elliptical blobs and principal component analysis (PCA). The elliptical blobs in the directional image are used to find the face candidate regions, and then PCA and QEA are employed to verify faces. Although PCA related algorithms have shown outstanding performance, there still exist some problems such as optimal decision boundary or learning capabilities. By PCA, we can obtain the optimal basis but they may not be the optimal ones for discriminating faces from non-faces. Moreover, a threshold value should be selected properly considering the success rate and false alarm rate. To solve these problems, QEA is employed to find out the optimal decision boundary under the predetermined threshold value which distinguishes between face images and non-face images. The proposed system provides learning capability by reconstructing the training database, which means that system performance can be improved as failure trials occur.

On the Analysis of the Quantum-inspired Evolutionary Algorithm with a Single Individual

This paper discusses the reason why QEA works and verifies how QEA works. The theoretical analysis of the simplified model of the segment process of QEA shows that QEA with a single individual for OneMax problem guarantees the global solution in terms of expected running number of generations. The analysis for exploration shows clearly that QEA starts with a global search scheme and changes automatically into a local search scheme as generation advances because of its inherent probabilistic mechanism, which leads to a good balance between exploration and exploitation. For comparison purpose, simulated annealing is considered with three test functions. The results support the conclusions derived from the theoretical analysis of QEA with a single individual.

Evolutionary algorithm-based face verification

This paper proposes a novel face verification method using principal components analysis (PCA) and evolutionary algorithm (EA). Although PCA related algorithms have shown outstanding performance, the problem lies in making decision rules or distance measures. To solve this problem, quantum-inspired evolutionary algorithm (QEA) is employed to find out the optimal weight factors in the distance measure for a predetermined threshold value which distinguishes between face images and non-face images. Experimental results show the effectiveness of the proposed method through the improved verification rate and false alarm rate.

The huggable: a new type of therapeutic robotic companion

Much research has shown the many positive benefits of companion animal therapy in improving the lives of people in hospitals and nursing home facilities (Allen, Blascovich et al. 1991). Unfortunately, in many facilities companion animal therapy is not offered due to fears of allergies, bites, or disease. Even in facilities that do offer this form of therapy, it is only offered for a few hours each day once or twice a week with a trained professional present at all times. As a response to these restrictions robot assisted therapy, using robots such as Sony’s AIBO and the Paro (Wada, Shibata et al. 2002) has emerged for cases in which companion animals are not available. These current robotic companions lack a full body sense of touch capable of understanding the relational and affective content provided to the robot, such as if the robot is held in someone’s arms, tickled, or petted. These aspects of touch are one of the ways in which companion animals provide comfort.