Papers by Author: Sung Ho Hwang

Abstract: This study introduces a nano smart material to develop a novel sensor for Structural Health Monitoring (SHM) of mechanical and civil systems. Mechanical, civil, and environmental systems need to become self-sensing and intelligent to preserve their integrity, optimize their performance, and provide continuous safety for the users and operators. Present smart materials and structures have fundamental limitations in their sensitivity, size, cost, ruggedness, and weight. Smart materials developed using nanotechnology have the potential to improve the way we generate and measure motion in devices from the nano to the macro scale in size. Among several possible smart nanoscale materials, Carbon Nanotubes (CNT) have aroused great interest in the research community because of their remarkable mechanical, electrochemical, piezoresistive, and other physical properties. To address the need for new intelligent sensing based on CNT, this study presents piezoresistivity and electrochemical properties and preliminary experiments that can be applied for SHM. This study is anticipated to develop a new multifunctional sensor which can simultaneously monitor strain, stress and corrosion on a structure with a simple electric circuit.

Abstract: Vehicle stability in 4 wheel drive(4WD) vehicles has been pursued by torque split based technology and brake based technology. The brake based methods are essentially brake maneuver strategies using the active control of the individual wheel brake. By comparison, the torque split based technologies realize stability by varying the traction torque split through powertrain to create an offset yaw moment. In the 4WD hybrid electric vehicle adopting separate front and rear motor, the vehicle stability enhancement algorithm using the rear motor control has some advantages such as faster response, braking energy recuperation, etc. However, since the left and right wheels are controlled by the same driving and regenerative torque from one motor, stability enhancement only by the front and rear motor control has a limitation in satisfying the required offset yaw moment. Therefore, to obtain the demanded offset yaw moment, a brake force distribution at each wheel is required. In this paper, a vehicle stability control logic using the front and rear motor and electrohydraulic brake(EHB) is proposed for a 4WD hybrid electric vehicle. A fuzzy control algorithm is suggested to compensate the error of the sideslip angle and the yaw rate by generating the direct yaw moment. Performance of the vehicle stability control algorithm is evaluated using ADAMS and MATLAB Simulink co-simulation.

Abstract: Drivers are becoming more fatigued and uncomfortable as traffic densities increase, and so, can show slower reaction time. They then face the danger of traffic accidents due to their inability to cope with frequent shifting. To reduce this risk, some drivers prefer automatic transmissions to manual transmissions. However, automatic transmission requires both higher fuel consumptions and costs. For this reason, attention to automated manual transmission that can provide high efficiency, low cost and easy manufacturability has been increasing. In addition, the function and performance of the electronic control unit of automobiles has improved continually and rapidly with the growing electronics technology. The ECU is a representative embedded system in automobiles, which has to satisfy high performance and reliability under the constraints of size and cost. In this paper, the embedded system platform for automobiles is developed on the basis of MPC565, and a test rig is developed to perform the basic function test for automatic clutch actuation. The developed embedded system and clutch control algorithm are validated by the experimental results performed on the test rig.

Abstract: We recognized EMG signal patterns of lower limb muscles by using neural networks and performed feature evaluation during the recovery of postural balance of human body. Surface electrodes were attached to lower limb and EMG signals were collected during the balance recovery process from a perturbation without permitting compensatory stepping. A waist pulling system was used to apply transient perturbations in five horizontal directions. The EMG signals of fifty repetitions of five motions were analyzed for ten subjects. Twenty features were extracted from EMG signals of one event. Feature evaluation was also performed by using DB (Davies-Bouldin) index. By using neural networks, EMG signals were classified into five categories, such as forward perturbation, backward perturbation, lateral perturbation and two oblique perturbations. As results, motions were recognized with mean success rates of 75 percent. With the neural networks classifier of this study, the EMG patterns of lower limb muscles during the recovery of postural balance can be classified with high accuracy of recognition.

Abstract: This paper presents a discrete analysis approach to investigate performance of the DMF. An arcspring installed between the flywheels is modeled as n - discrete elements. Each element consists of mass, spring and nonlinear friction element. The nonlinear friction model is proposed to describe Stribeck effect and viscous friction depending on the relative sliding velocity. The DMF performance such as hysterisis characteristics are investigated by comparing the experimental result. In addition, the torque characteristics transmitted to the driveshaft are evaluated by comparing the test result from manual transmission bench tester. It is found that discrete DMF model described the automotive driveline behavior closely. It is also found that the friction characteristics of the arcspring depends on the relative sliding velocity between the friction surfaces, which varies depending on the relative position of the DMF arcspring.

Abstract: This paper introduces a new sensor design based on a carbon nanotube structural neuron for structural health monitoring applications. The carbon nanotube neuron is a thin and narrow polymer film sensor that is bonded or deposited onto a structure. The electrochemical impedance (resistance and capacitance) of the neuron changes due to deterioration of the structure where the neuron is located. A network of the long carbon nanotube neurons can form a structural neural system to provide large area coverage and an assurance of the operational health of a structure without the need for actuators and complex wave propagation analyses that are used with other SHM methods. The neural system can also reduce the cost of health monitoring by using biomimetic signal processing to minimize the number of channels of data acquisition needed to detect damage. The carbon nanotube neuron is lightweight and easily applied to the structural surface, and there is no stress concentration, no piezoelectrics, no amplifier, and no storage of high frequency waveforms. The carbon nanotube neuron is expected to find applications in detecting damage and corrosion in large complex structures including composite and metallic aircraft and rotorcraft, bridges, and almost any type of structure with almost no penalty to the structure.