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Development of Particle Flow-Based Inflatable Robot Body for Shape Rigidity Modulation
Hyunho Kim,Sangjoon Jonathan Kim,Junghoon Park,Handdeut Chang,Namkeun Kim,Yeongjin Kim 한국정밀공학회 2020 International Journal of Precision Engineering and Vol.21 No.10
Disaster robots are needed to perform various tasks through narrow gaps between building debris to be used for rescue. A soft material-based disaster robot can have easy access to the rescue site through the narrow gaps. To ensure the robust control and better performance of the soft robot operation, a joint stiff ness modulation mechanism is required. In this paper, we have proposed a noble stiff ness modulation mechanism that includes shape change and self-assembly by using a particle flow-based inflatable robot body. We analyzed the particle filling completion time by injecting air and particles at a constant pressure into the soft chamber depending on several parameters (the size of the particle, the size of the reservoir, the volume ratio between the chamber volume and the total volume of the particle, and the injected air pressure). Of these, the most dominant factors influencing the completion time were particle size and pressure. It was observed that the smaller the size of the particle, the shorter time. The completion time tended to decrease as the air pressure increased.
Kim, Jun Woo,Bae, Kiho,Kim, Hyun Joong,Son, Ji-won,Kim, Namkeun,Stenfelt, Stefan,Prinz, Fritz B.,Shim, Joon Hyung Elsevier 2018 JOURNAL OF ALLOYS AND COMPOUNDS Vol.752 No.-
<P><B>Abstract</B></P> <P>Nickel-yttria-stabilized zirconia (Ni-YSZ) cermet is widely used as an anode material in solid oxide fuel cells (SOFCs); however, Ni re-oxidation causes critical problems due to volume expansion, which causes high thermal stress. We fabricated a Ni-YSZ anode functional layer (AFL), which is an essential component in high-performance SOFCs, and re-oxidized it to investigate the related three-dimensional (3D) microstructural and thermo-mechanical effects. A 3D model of the re-oxidized AFL was generated using focused ion beam-scanning electron microscope (FIB-SEM) tomography. Re-oxidation of the Ni phase caused significant volumetric expansion, which was confirmed via image analysis and calculation of the volume fraction, connectivity, and two-phase boundary density. Finite element analysis (FEA) with simulated heating to 500–900 °C confirmed that the thermal stress in re-oxidized Ni-YSZ is concentrated at the boundaries between YSZ and re-oxidized NiO (nickel oxide). NiO is subjected to more stress than YSZ. Stress exceeding the fracture stress of 8 mol% YSZ appears primarily at 800 °C or higher. The stress is also more severe near the electrolyte-anode boundary than in the Ni-YSZ cermet and the YSZ regions. This may be responsible for the electrolyte membrane delamination and fracture that are observed during high-temperature operation.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Re-oxidized NiO-YSZ was 3D-reconstructed via FIB-SEM tomography. </LI> <LI> Re-oxidation of Ni-YSZ removes pores via Ni volume expansion. </LI> <LI> The NiO-YSZ interface and the YSZ electrolyte exhibit high thermal stress. </LI> <LI> Thermal stresses greater than the fracture stress of YSZ appear at some interfaces. </LI> </UL> </P>
Walking pattern recognition with smartphone accelerometer and gyroscope
Jaehyun Park,Namkeun Kim 대한인간공학회 2015 대한인간공학회 학술대회논문집 Vol.2015 No.10
Objective: This study aims to investigate the human walking patterns using smartphone sensors. Accelerometer and gyroscope were used in the study. Background: With technological advancements, inertial measurement unit (IMU) sensors including accelerometer, gyroscope and magnetometer are widely used in smartphones. Method: We gathered the human walking data from accelerometer and gyroscope of smartphones in participants’ pockets. Participants were required to walk on treadmills for three minutes. Result: The threshold-based step counter algorithm and machine learning algorithm were applied for analyzing the human walking patterns. As a result, using the support vector machine (SVM) algorithm, the average accuracy of pattern recognitions was more than 90%. Conclusion: When the sampling rate of sensor data was more than 20 Hz, the result turned out to be reliable. Application: The results of this study can be extended for human behavior recognition research.