Search

Your search keyword '"Park, Minkyu"' showing total 776 results
Start Over Author "Park, Minkyu"
776 results on '"Park, Minkyu"'

137. Receding Horizon-Based Infotaxis With Random Sampling for Source Search and Estimation in Complex Environments

Author

Park, Minkyu, Ladosz, Pawel, Kim, Jongyun, and Oh, Hyondong

Abstract

This article proposes a receding horizon-based information-theoretic source search and estimation strategy for a mobile sensor in an urban environment in which an invisible harmful substance is released into the atmosphere. The mobile sensor estimates the source term including its location and release rate by using sensor observations based on Bayesian inference. The sampling-based sequential Monte Carlo method, particle filter, is employed to estimate the source term state in a highly nonlinear and stochastic system. Infotaxis, the information-theoretic gradient-free search strategy is modified to find the optimal search path that maximizes the reduction of the entropy of the source term distribution. In particular, receding horizon Infotaxis (RHI) is introduced to avoid falling into the local optima and to find more successful information gathering paths in obstacle-rich urban environments. Besides, a random sampling method is introduced to reduce the computational load of the RHI for real-time computation. The random sampling method samples the predicted future measurements based on current estimation of the source term and computes the optimal search path using sampled measurements rather than considering all possible future measurements. To demonstrate the benefit of the proposed approach, comprehensive numerical simulations are performed for various conditions. The proposed algorithm increases the success rate by about 30% and reduces the mean search time by about 40% compared with the existing information-theoretic search strategy.

Published
2023
Full Text
View/download PDF

138. Deep Learning-Based Human Detection Using RGB and IR Images from Drones

Author

Bae, Seonguk, Shin, Heejung, Kim, Hyeongseop, Park, Minkyu, Choi, Myong-Yol, and Oh, Hyondong

Abstract

This paper proposes an object detection algorithm based on a deep neural network which utilizes RGB and infrared (IR) images for human detection with drones. Although there are some public RGB image datasets in aerial view, there is no publicly available dataset in which both RGB and IR images are taken simultaneously with drones. Thus, we collect RGB and IR images at various altitudes on our own. However, the detection performance of the RGB and IR-based algorithm is limited, because there are more available RGB images for training than IR images or RGB and IR images taken simultaneously. To address this data imbalance, we use a generative adversarial neural network model called CycleGAN to generate IR images from RGB images in the public and self-collected dataset. Furthermore, the neural network for object detection is accelerated by neural network quantization and optimization to execute the algorithm faster on an embedded computing board. To shorten the search time and improve the robustness of detection, we employ a formation flight of multiple drones for human detection. The effectiveness of the integrated system of formation flight and onboard human detection algorithm is validated by the real flight experiment to find humans who are in a wide and wild area.

Published
2023
Full Text
View/download PDF

147. Improving responsiveness of soft aperiodic tasks using proportional slack time

Author

Kim, Heeheon, Piao, Xuefeng, Han, Sangchul, Park, Moonju, Park, Minkyu, Cho, Seongje, and Cho, Yookun

Subjects
Algorithms -- Usage, Computer science -- Usage, Algorithm, Computers, Electronics, Engineering and manufacturing industries
Abstract

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.compeleceng.2009.12.012 Byline: Heeheon Kim (a), Xuefeng Piao (a), Sangchul Han (d), Moonju Park (b), Minkyu Park (d), Seongje Cho (c), Yookun Cho (a) Keywords: Real-time system; Aperiodic job; Response time; Earliest deadline first Abstract: In a real-time system with both hard real-time periodic jobs and soft real-time aperiodic jobs, it is important to guarantee that the deadline of each periodic job is met, as well as to provide a fast response time for each aperiodic job. We propose an algorithm, called Proportional Slack Reserve (PSR), that produces an efficient schedule for such an environment. For every execution unit of a periodic job, the PSR algorithm reserves time which can be used for execution of aperiodic jobs. If reserved time is not available, the algorithm assigns a deadline to an aperiodic job for achieving better responsiveness of aperiodic jobs. The proposed algorithm can fully utilize processing power while meeting all deadlines of periodic jobs. It can also easily reclaim the time unused by the periodic job. We analytically show that for each aperiodic job, the response time in a PSR schedule is no longer than that in a TBS schedule, which is known to be efficient for servicing aperiodic jobs. We also present simulation results in which the response time of PSR is significantly improved over that of TBS, and moreover the performance of PSR compares favorably with TB(N) considering scheduling overhead. Author Affiliation: (a) Seoul National University, School of Computer Science and Engineering, 599 Gwanak-ro, Gwanak-gu, Seoul 151-742, South Korea (b) University of Incheon, Dept. of Computer Science and Engineering, 319 Incheondaegil, Nam-gu, Incheon 402-749, South Korea (c) Dankook University, Dept. of Computer Science, 126 Jukjeon-dong, Suji-gu, Yongin-si, Gyeonggi-do 448-701, South Korea (d) Konkuk University, Dept. of Computer Science, 322 Danwol-dong, Chungju-si, Chungcheongbuk-do 380-701, South Korea Article History: Received 25 February 2009; Revised 24 November 2009; Accepted 22 December 2009

Published
2010

Catalog

Books, media, physical & digital resources
See catalog results