Kinematic Analysis and Experimental Verification on the Locomotion of Gecko

This paper presents a kinematic analysis of the locomotion of a gecko,and experimental verification of the kinematicmodel.Kinematic analysis is important for parameter design,dynamic analysis,and optimization in biomimetic robot research.The proposed kinematic analysis can simulate,without iteration,the locomotion of gecko satisfying the constraint conditionsthat maintain the position of the contacted feet on the surface.So the method has an advantage for analyzing the climbing motionof the quadruped mechanism in a real time application.The kinematic model of a gecko consists of four legs based on 7-degreesof freedom spherical-revolute-spherical joints and two revolute joints in the waist.The motion of the kinematic model issimulated based on measurement data of each joint.The motion of the kinematic model simulates the investigated real gecko’smotion by using the experimental results.The analysis solves the forward kinematics by considering the model as a combinationof closed and open serial mechanisms under the condition that maintains the contact positions of the attached feet on the ground.The motions of each joint are validated by comparing with the experimental results.In addition to the measured gait,three othergaits are simulated based on the kinematic model.The maximum strides of each gait are calculated by workspace analysis.Theresult can be used in biomimetic robot design and motion planning.

Design of 3D Space Following Positioning System Based on ZigBee

The research on positioning system and spatial alignment is a big topic. In this paper, we proposed a design (that) studies two issues. One is the study of range positioning algorithm based on ZigBee communication system. The other one is spatial alignment platform which is controlled with two servos. Hardware and software control system was realized, which also consists of two parts, ZigBee network positioning system and automatic orientation platform.

Multi-Agent Deep Reinforcement Learning-Based Resource Allocation in HPC/AI Converged Cluster

As the complexity of deep learning(DL)networks and training data grows enormously,methods that scale with computation are becoming the future of artificial intelligence(AI)development.In this regard,the interplay between machine learning(ML)and high-performance computing(HPC)is an innovative paradigm to speed up the efficiency of AI research and development.However,building and operating an HPC/AI converged system require broad knowledge to leverage the latest computing,networking,and storage technologies.Moreover,an HPC-based AI computing environment needs an appropriate resource allocation and monitoring strategy to efficiently utilize the system resources.In this regard,we introduce a technique for building and operating a high-performance AI-computing environment with the latest technologies.Specifically,an HPC/AI converged system is configured inside Gwangju Institute of Science and Technology(GIST),called GIST AI-X computing cluster,which is built by leveraging the latest Nvidia DGX servers,high-performance storage and networking devices,and various open source tools.Therefore,it can be a good reference for building a small or middlesized HPC/AI converged system for research and educational institutes.In addition,we propose a resource allocation method for DL jobs to efficiently utilize the computing resources with multi-agent deep reinforcement learning(mDRL).Through extensive simulations and experiments,we validate that the proposed mDRL algorithm can help the HPC/AI converged cluster to achieve both system utilization and power consumption improvement.By deploying the proposed resource allocation method to the system,total job completion time is reduced by around 20%and inefficient power consumption is reduced by around 40%.

Effect of Laryngopharyngeal Anatomy on Expiratory Airflow and Submicrometer Particle Deposition in Human Extrathoracic Airways

The objective of this study is to systematically assess the influences of the larynopharyneal anatomical details on airflow and particle behaviors during exhalation by means of image-based modeling. A physiologically realistic nose-throat airway was developed with medical images. Individual airway anatomy such as uvula, pharynx, and larynx were then isolated for examination by progressively simplifying this image-based model geometry. Low Reynolds number (LRN) k-w model and Langrangian tracking model were used to simulate the dynamics of airflow and particle transport for a wide range of exhalation conditions (4-45 L/min) and particle sizes (1 nm-1 μm). Results showed that pharyngeal anatomical details exerted a significant impact on breathing resistance and particle profiles. Abrupt pressure drop resulting from the uvula-related airway obstruction was observed. Even though the total deposition rate in the nasal airway is largely unaffected by the upstream effect, the local deposition patterns vary notably. Results of this study also indicate that the pressure drop appears to be an appropriate parameter to characterize the geometric variations for diffusive depositions. Inclusion of pressure drop (D0.5Q-0.62dp0.07) gives an improved correlation than using the conventional diffusion factor (D0.5Q﹣0.28).

Microtubule Biomechanical Properties under Deformation and Vibration

Microtubules (MT) are of great engineering importance due to their potential applications as sensors, actuators, drug delivery, and others. The MT properties/mechanics are greatly affected by their biomechanical environment and it is important to understand their biological function. Although microtubule mechanics has been extensively studied statically, very limited studies are devoted to the biomechanical properties of microtubule undergoing deformation and vibration. In this study, we investigate the biomechanical properties of the microtubule under bending deformation and free vibration using 3D finite element analysis. Results of force-deformation and vibration frequencies and mode shapes obtained from the finite element analysis are presented. The results indicate that the force-deformation characteristics vary with time/phases and become non-linear at higher time intervals. The modes of MT vibration and frequencies are in the GHz range and higher modes will involve combined bending, torsion and axial deformations. These higher modes and shapes change their deformation which might have implications for physiological and biological behavior, especially for sensing and actuation and communication to cells. The bending force-deformation characteristics and vibration modes and frequencies should help further understand the biomechanical properties of self-assembled microtubules.