Jinung An

Modeling of a magnetorheological actuator including magnetic hysteresis

Magnetorheological (MR) actuators provide controlled torque through control of an applied magnetic field. Therefore knowledge of the relationship between the applied current and output torque is required. This paper presents a new nonlinear modeling of MR actuators considering magnetic hysteresis to determine the torque-current nonlinear relationship. Equations for transmitted torque are derived according to mechanical shear configurations of the MR actuator. Hodgdons hysteresis model is used to capture the characteristics of hysteresis nonlinearity in the MR actuators. An MR actuator test setup has been constructed using a commercial MR brake to evaluate the proposed model. The measured torque shows hysteresis effects as the current increases and decreases. Using Hodgdons hysteresis model of the magnetic circuit and Bingham model of the MR fluid, a novel nonlinear model of the MR actuator is obtained as a torque estimator for practical torque control purpose. The validity of the theoretical results is verified by comparison between experiments and simulations. Furthermore, the current versus torque frequency response of the MR actuator is examined to evaluate its applicability to torque control. The bandwidth of the MR actuator is high enough for especially haptic applications.

Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs

This paper presents a novel framework of the estimation of particle swarm distribution algorithms (EPSDAs). The aim is to effectively combine particle swarm optimization (PSO) with the estimation of distribution algorithms (EDAs) without losing their unique features. This aim is achieved by incorporating the following mechanisms: (1) selection is applied to the local best solutions in order to obtain more promising individuals for model building, (2) a probabilistic model of the problem is built from the selected solutions, and (3) new individuals are generated by a stochastic combination of the EDAs model sampling method and the PSOs particle moving mechanism. To exhibit the utility of the EPSDA framework, an extended compact particle swarm optimization (EcPSO) is developed by combining the strengths of the extended compact genetic algorithm (EcGA) with binary PSO (BPSO), along the lines of the suggested framework. Due to its effective nature of harmonizing the global search of EcGA with the local search of BPSO, EcPSO is able to discover the optimal solution in a fast and reliable manner. Experimental results on artificial to real-world problems have adduced grounds for the effectiveness of the proposed approach.

Haptic experimentation on a hybrid active/passive force feedback device

This paper describes the design and implementation of a hybrid active/passive force feedback device. It has a single degree of freedom that is actuated by a motor and a brake pair. The use of a motor and a brake allows various objects to be simulated without a stability problem and related safety issues involved with using high torque motors only. The device performance is measured by its ability to simulate various test objects. Simple test objects are modeled as a benchmark test of the systems performance. The force feedback device is capable of simulating forces in a variety of virtual environments. This device demonstrates the effectiveness of a hybrid active/passive haptic actuator. An analysis of the use of hybrid active/passive actuators in the 2 degrees of freedom case finds that they are capable of simulating a rigid body in a broad range.

Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms

Recent advances in neuroimaging demonstrate the potential of functional near-infrared spectroscopy (fNIRS) for use in brain-computer interfaces (BCIs). fNIRS uses light in the near-infrared range to measure brain surface haemoglobin concentrations and thus determine human neural activity. Our primary goal in this study is to analyse brain haemodynamic responses for application in a BCI. Specifically, we develop an efficient signal processing algorithm to extract important mental-task-relevant neural features and obtain the best possible classification performance. We recorded brain haemodynamic responses due to frontal cortex brain activity from nine subjects using a 19-channel fNIRS system. Our algorithm is based on continuous wavelet transforms (CWTs) for multi-scale decomposition and a soft thresholding algorithm for de-noising. We adopted three machine learning algorithms and compared their performance. Good performance can be achieved by using the de-noised wavelet coefficients as input features for the classifier. Moreover, the classifier performance varied depending on the type of mother wavelet used for wavelet decomposition. Our quantitative results showed that CWTs can be used efficiently to extract important brain haemodynamic features at multiple frequencies if an appropriate mother wavelet function is chosen. The best classification results were obtained by a specific combination of input feature type and classifier.

Stability and Performance of Haptic Interfaces with Active/Passive Actuators--Theory and Experiments

This paper addresses both theoretical and experimental studies of the stability and performance of haptic interfaces containing active/passive actuators. Three different realizations of haptic interfaces are introduced to investigate their stability and performance: an active system equipped with a motor; a passive system equipped with a brake; and a hybrid system equipped with both brake and motor. The first objective is to demonstrate that a hybrid system is superior in its stability and performance to an active system via passivity theorem and Z-width. The second objective of this paper is to show that the conditions for the asymptotic stability of haptic interfaces during the static friction display are investigated via the absolute stability theory. Theoretical and experimental results are compared. An alternative haptic interface is proposed that provides its highly stable haptic interaction with high performance.

Decision Support Algorithm for Diagnosis of ADHD Using Electroencephalograms

Attention deficit hyperactivity disorder is a complex brain disorder which is usually difficult to diagnose. As a result many literature reports about the increasing rate of misdiagnosis of ADHD disorder with other types of brain disorder. There is also a risk of normal children to be associated with ADHD if practical diagnostic criteria are not supported. To this end we propose a decision support system in diagnosing of ADHD disorder through brain electroencephalographic signals. Subjects of 10 children participated in this study, 7 of them were diagnosed with ADHD disorder and remaining 3 children are normal group. Our main goal of this sthudy is to present a supporting diagnostic tool that uses signal processing for feature selection and machine learning algorithms for diagnosis.Particularly, for a feature selection we propose information theoretic which is based on entropy and mutual information measure. We propose a maximal discrepancy criterion for selecting distinct (most distinguishing) features of two groups as well as a semi-supervised formulation for efficiently updating the training set. Further, support vector machine classifier trained and tested for identification of robust marker of EEG patterns for accurate diagnosis of ADHD group. We demonstrate that the applicability of the proposed approach provides higher accuracy in diagnostic process of ADHD disorder than the few currently available methods.

Portable fire evacuation guide robot system

Robot technology is emerging for applications in disaster prevention with devices such as fire-fighting robots, rescue robots, and surveillance robots. In this paper, we suggest an portable fire evacuation guide robot system that can be thrown into a fire site to gather environmental information, search displaced people, and evacuate them from the fire site. This spool-like small and light mobile robot can be easily carried and remotely controlled by means of a laptop-sized tele-operator. It contains the following functional units: a camera to capture the fire site; sensors to gather temperature data, CO gas, and O2 concentrations; and a microphone with speaker for emergency voice communications between firefighter and victims. The robots design gives its high-temperature protection, excellent waterproofing, and high impact resistance. Laboratory tests were performed for evaluating the performance of the proposed evacuation guide robot system.

In haptics, the influence of the controllable physical damping on stability and performance

Physical damping is essential to achieve passivity in haptic display. To improve stability, an additional physical damper was used with motor. This fixed damping approach, however, has limitation to display free motion, so that the motor actually has to work for damping cancellation. This paper suggests that the controllable damping approach without necessity for damping cancellation to improve both stability and performance. Firstly, we analyze the effect of the controllable physical damping on stability and also propose a controllable physical damping approach applying controllable magnetorheological brake to haptic display. Finally, we experimentally show the contribution of the proposed controllable physical damping approach to the performance in terms of Z-width.

Neural Network Classification of Brain Hemodynamic Responses from Four Mental Tasks

We investigate subjects’ brain hemodynamic activities during mental tasks using a nearinfrared spectroscopy. A wavelet and neural network-based methodology is presented for recognition of brain hemodynamic responses. The recognition is performed by a single layer neural network classifier according to a backpropagation algorithm with two error minimizing techniques. The performance of the classifier varied depending on the neural network model, but the performance was usually at least 90%. The classifier usually converged faster and attained a somewhat greater level of performance when an input was presented with only relevant features. The overall classification rate was higher than 94%. The study demonstrates the accurate classifiablity of human brain hemodynamic useful in various brain studies.

Effects of torsional stiffness, knee angle, and link ratio on the design of a biologically inspired mobile robot with two-segment legs

In this study we discuss two-segment legs parameters, torsional stiffness, knee angle, and link ratio, for a biologically inspired terrestrial robot with four legs. Each leg has two-segment links and a passive joint. Simulation results then verify soft legs effectiveness compared to a robot with stiff legs.