Byungchan Kim

Highly sensitive localized surface plasmon resonance immunosensor for label-free detection of HIV-1

UNLABELLED A highly sensitive label-free immunosensor for the detection of HIV-1 is newly developed based on localized surface plasmon resonance (LSPR) method. Uniform nanopattern of circular Au-dots (10-20nm) was fabricated on indium tin oxide (ITO) coated glass substrate by simple electrochemical deposition method. The surface of Au nanopattern was modified with HIV-1 neutralizing gp120 monoclonal antibody fragments. The modified substrate was employed to measure various concentrations of HIV-1 particles quantitatively based on the shift of longitudinal wavelength in the UV-Vis spectrum which results from the changes of local refractive index induced by specific antigen-antibody recognition events. The detection limit of the HIV-1 particles was estimated to be 200fg/mL, which is 10 fold higher than that of previously reported virus detection method based on LSPR. Since fabricated LSPR immunosensor has high sensitivity and selectivity, it is a promising approach for biological/medical sample analysis and various kinds of virus detection. FROM THE CLINICAL EDITOR A localized surface plasmon resonance-based virus detection method is shown to have an order of magnitude improvement in detectable concentration of HIV-1 particles. Similar approaches may be used for screening other viral particles as well.

Impedance Learning for Robotic Contact Tasks Using Natural Actor-Critic Algorithm

Compared with their robotic counterparts, humans excel at various tasks by using their ability to adaptively modulate arm impedance parameters. This ability allows us to successfully perform contact tasks even in uncertain environments. This paper considers a learning strategy of motor skill for robotic contact tasks based on a human motor control theory and machine learning schemes. Our robot learning method employs impedance control based on the equilibrium point control theory and reinforcement learning to determine the impedance parameters for contact tasks. A recursive least-square filter-based episodic natural actor-critic algorithm is used to find the optimal impedance parameters. The effectiveness of the proposed method was tested through dynamic simulations of various contact tasks. The simulation results demonstrated that the proposed method optimizes the performance of the contact tasks in uncertain conditions of the environment.

Estimation of Multijoint Stiffness Using Electromyogram and Artificial Neural Network

The human arm exhibits outstanding manipulability in executing various tasks by taking advantage of its intrinsic compliance, force sensation, and tactile contact clues. By examining human strategy in controlling arm impedance, we may be able to understand underlying human motor control and develop control methods for dexterous robotic manipulation. This paper presents a novel method for estimating multijoint stiffness by using electromyogram (EMG) and an artificial neural network model. The artificial network model developed in this paper relates EMG data and joint motion data to joint stiffness. With the proposed method, the multijoint stiffness of the arm was estimated without complex calculation or specialized apparatus. The feasibility of the proposed method was confirmed through experimental and simulation results.

Novel Framework of Robot Force Control Using Reinforcement Learning

Over the past decades, robotic technologies have advanced remarkably and have been proven to be successful, especially in the field of manufacturing. In manufacturing, conventional position-controlled robots perform simple repeated tasks in static environments. In recent years, there are increasing needs for robot systems in many areas that involve physical contacts with human-populated environments. Conventional robotic systems, however, have been ineffective in contact tasks. Contrary to robots, humans cope with the problems with dynamic environments by the aid of excellent adaptation and learning ability. In this sense, robot force control strategy inspired by human motor control would be a promising approach. There have been several studies on biologically-inspired motor learning. Cohen et al. suggested impedance learning strategy in a contact task by using associative search network (Cohen et al., 1991). They applied this approach to wall-following task. Another study on motor learning investigated a motor learning method for a musculoskeletal arm model in free space motion using reinforcement learning (Izawa et al., 2002). These studies, however, are limited to rather simple problems. In other studies, artificial neural network models were used for impedance learning in contact tasks (Jung et al., 2001; Tsuji et al., 2004). One of the noticeable works by Tsuji et al. suggested on-line virtual impedance learning method by exploiting visual information. Despite of its usefulness, however, neural network-based learning involves heavy computational load and may lead to local optimum solutions easily. The purpose of this study is to present a novel framework of force control for robotic contact tasks. To develop appropriate motor skills for various contact tasks, this study employs the following methodologies. First, our robot control strategy employs impedance control based on a human motor control theory the equilibrium point control model. The equilibrium point control model suggests that the central nervous system utilizes the springlike property of the neuromuscular system in coordinating multi-DOF human limb movements (Flash, 1987). Under the equilibrium point control scheme, force can be controlled separately by a series of equilibrium points and modulated stiffness (or more generally impedance) at the joints, so the control scheme can become simplified considerably. Second, as the learning framework, reinforcement learning (RL) is employed to optimize the performance of contact task. RL can handle an optimization problem in an