Kwee-Bo Sim

Parameter-setting-free harmony search algorithm

Abstract Various phenomenon-mimicking algorithms, such as genetic algorithm, simulated annealing, tabu search, ant colony optimization, and particle swarm optimization, have their own algorithm parameters. These parameters need to be skillfully assigned in order to obtain good results. It is burdensome, especially to novice users, to assign these parameters. The same is true for the harmony search algorithm which was inspired by music performance. Thus, this study proposes a novel technique to eliminate tedious and experience-requiring parameter assigning efforts. The new parameter-setting-free (PSF) technique which this study suggests contains one additional matrix which contains an operation type (random selection, memory consideration, or pitch adjustment) for every variable in harmony memory. Three examples illustrate that the PSF technique can find good solutions robustly.

Internet-Based Teleoperation of an Intelligent Robot With Optimal Two-Layer Fuzzy Controller

Research on Internet-based teleoperation has received increased attention in the past few years. In this paper, an Internet-based teleoperation system was implemented. In order to robustly transmit the surroundings and control information of the robot, packet-type data were used. The central problem in Internet-based teleoperation is data transmission latency or data loss. For this specific problem, an autonomous mobile robot with optimal two-layer fuzzy controller (2LFC) was introduced. When data transmission is failed, the robot automatically moves and protects itself. In addition, a color detection system was implemented so that the robot can perceive an object and move to another object. The fuzzy controller was optimized by using the schema coevolutionary algorithm (SCEA), which finds an optimal solution. Using these technologies, the efficacies of the 2LFC, the SCEA, and the teleoperation system were verified

Emotion recognition and acoustic analysis from speech signal

Recently, robot technique has been developed remarkably. But, they cannot do emotional tasks and the work they can is limited. In this view, it is important for machine to understand humans emotion. Also, emotion recognition is necessary to make an intimate robot. This paper shows simulation results which classify emotions by learning 4 pitch patterns and results from some analyses. The pitch contour includes emotion information. This is why the pitch has been widely used for emotion recognition. However, because the pitch contour is not sufficient for recognizing emotion, we should add other elements. Thus, several analyses are done and the analyzed elements are called acoustic elements for convenience. These elements are the fundamental for more accurate recognition. In addition to this, we analyze the relation between emotion and acoustic elements. The brain is high-dimensional nonlinear dynamical system. So, it is essential to utilize a system that is capable of storing internal states and utilize a system that is capable of storing internal states and implementing complex dynamics. DRNN fits such a system. The simulator is composed of the DRNN (dynamic recurrent neural network), feature extraction.

Artificial immune network-based cooperative control in collective autonomous mobile robots

In this paper, we propose a method of cooperative control based on immune system in distributed autonomous robotic system (DARS). Immune system is living bodys self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying the immune system to DARS, a robot is regarded as a B lymphocyte (B cell), each environmental condition as an antigen and a behavior strategy as an antibody respectively. The executing process of proposed method is as follows: when the environmental condition changes, a robot selects an appropriate behavior strategy, and its behavior strategy is stimulated and suppressed by other robot using communication. Finally, such stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis, and it is used for decision making of an optimal swarm strategy.

Development of context aware system based on Bayesian network driven context reasoning method and ontology context modeling

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts: context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Network for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

Realization of cooperative strategies and swarm behavior in distributed autonomous robotic systems using artificial immune system

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on the immune system in a distributed autonomous robotic system (DARS). The immune system is a living bodys self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in a dynamically changing environment. To apply the immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. When the environmental condition changes, a robot selects an appropriate behavior strategy, and its behavior strategy is stimulated and suppressed by other robots using communication. Finally, the most stimulated strategy is adopted as the swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis. It is used for decision making of the optimal swarm strategy. By T-cell modeling, the adaptation ability of the robot is enhanced in dynamic environments.

Genetic Algorithm Based Feature Selection Method Development for Pattern Recognition

An important problem of pattern recognition is to extract or select feature set, which is included in the pre-processing stage. In order to extract feature set, principal component analysis has been usually used and SFS (sequential forward selection) and SBS (sequential backward selection) have been used as a feature selection method. This paper applies genetic algorithm which is a popular method for nonlinear optimization problem to the feature selection problem. So, we call it genetic algorithm feature selection (GAFS) and this algorithm is compared to other methods in the performance aspect

On developing an adaptive neural-fuzzy control system

An adaptive neural-fuzzy control scheme for intelligent control is proposed. The control system consists of a fuzzy-neural controller (FNC) and model neural network (MNN). In the FNC, the antecedence and consequence of the fuzzy rule are constructed by a clustering method and a multilayer neural network. In the MNN, a multilayer neural network is utilized to identify an unknown controlled plant. The error backpropagation algorithm has been adopted as a learning technique. The effectiveness of the scheme is demonstrated by computer simulations of a cart-pole and a two-d.o.f. robot manipulator.

Development of a Facial Emotion Recognition Method Based on Combining AAM with DBN

In this paper, novel methods for facial emotion recognition in facial image sequences are presented. Our facial emotional feature detection and extracting based on Active Appearance Models (AAM) with Ekman’s Facial Action Coding System (FACS). Our approach to facial emotion recognition lies in the dynamic and probabilistic framework based on Dynamic Bayesian Network (DBN) with Kalman Filter for modeling and understanding the temporal phases of facial expressions in image sequences. By combining AAM and DBN, the proposed method can achieve a higher recognition performance level compare with other facial expression recognition methods. The result on the BioID dataset show a recognition accuracy of more than 90% for facial emotion reasoning using the proposed method.

SLAM of mobile robot in the indoor environment with Digital Magnetic Compass and Ultrasonic Sensors

In the moving of the mobile robot, the mobile robot acquires a map of its environment while simultaneously localizing itself relative to the map. Simultaneous localization ad mapping (SLAM) problems arise when the robot does not have access to a map of the environment, nor does it know its own pose. In this paper, we study the SLAM of mobile robot in the indoor environment with Digital Magnetic Compass and Ultrasonic Sensors. Digital Magnetic Compass has a strong feature against interference in the indoor environment better than compass which is can easily be disturbed by electromagnetic sources or large ferromagnetic structures. Ultrasonic Sensors are cheap and can give relatively accurate range readings. Autonomous mobile robot is aware of robots moving direction and position by the restricted data. Also robot must localize as quickly as possible. As application for the SLAM on the autonomous mobile robot system, robot can find the localization and the mapping and can solve the Kid Napping situation for itself. Especially, in the Kid Napping situation, autonomous mobile robot use Ultrasonic sensors and Digital Magnetic Compass(DMC)s data for moving. When robot receives the similar data by sensors, robot uses Computation Intelligence(CI) for perceiving in the robots position.