http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
An Adaptive Goal-Based Model for Autonomous Multi-Robot Using HARMS and NuSMV
Yongho Kim,Jin-Woo Jung,John C. Gallagher,Eric T. Matson 한국지능시스템학회 2016 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.16 No.2
In a dynamic environment autonomous robots often encounter unexpected situations that the robots have to deal with in order to continue proceeding their mission. We propose an adaptive goal-based model that allows cyber-physical systems (CPS) to update their environmental model and helps them analyze for attainment of their goals from current state using the updated environmental model and its capabilities. Information exchange approach utilizes Human-Agent-Robot-Machine-Sensor (HARMS) model to exchange messages between CPS. Model validation method uses NuSMV, which is one of Model Checking tools, to check whether the system can continue its mission toward the goal in the given environment. We explain a practical set up of the model in a situation in which homogeneous robots that has the same capability work in the same environment.
Convolutional Neural Networks for Analyzing Unmanned Aerial Vehicles Sound
Shulin Li,HyunJong Kim,Sukhoon Lee,John C. Gallagher,Daeun Kim,SungWook Park,Eric T. Matson 제어로봇시스템학회 2018 제어로봇시스템학회 국제학술대회 논문집 Vol.2018 No.10
The emergence of Unmanned Aerial Vehicles (UAV) is pervasive throughout society. A growing segment of usage is of a dubious nature for harassment, illegal activity and terrorism. Detection of unknown UAV’s has become a requirement for many organizations and agencies to thwart the emergence of UAV’s that are in some way threatening. To detect UAV, the use of acoustic signals has become an useful area of research. Convolutional Neural Networks (CNNs) are one of several models of deep learning, applied in various fields such as image recognition and natural language processing. In this project, we design a system to detect the presence of possible detection and payload detection using CNNs on the basis of sound data generated from UAV flights. The sound of recorded drones is pre-processed into spectral data by Fast Fourier Transform (FFT) and Mel-Frequency Cepstrum (MFCC) and given as the input value to the CNN model. The results show that it is possible to detect and differentiate UAVs which have standard weight and also with additional payload. In short, the project has two detection goals. One is the acoustic detection of a UAV, and the second is the determination if that UAV has a payload.
An Adaptive Goal-Based Model for Autonomous Multi-Robot Using HARMS and NuSMV
Kim, Yongho,Jung, Jin-Woo,Gallagher, John C.,Matson, Eric T. Korean Institute of Intelligent Systems 2016 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.16 No.2
In a dynamic environment autonomous robots often encounter unexpected situations that the robots have to deal with in order to continue proceeding their mission. We propose an adaptive goal-based model that allows cyber-physical systems (CPS) to update their environmental model and helps them analyze for attainment of their goals from current state using the updated environmental model and its capabilities. Information exchange approach utilizes Human-Agent-Robot-Machine-Sensor (HARMS) model to exchange messages between CPS. Model validation method uses NuSMV, which is one of Model Checking tools, to check whether the system can continue its mission toward the goal in the given environment. We explain a practical set up of the model in a situation in which homogeneous robots that has the same capability work in the same environment.