• Geophysics and Geophysical Exploration
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• v.5 no.1
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• pp.64-67
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• 2002

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.487-495
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• 2023

This paper presents a method for autonomous exploration of indoor environments using a 2-dimensional Light Detection And Ranging (LiDAR) scanner. The proposed frontier-based exploration method considers navigability from the current robot position to extracted frontier targets. An approach to constructing the point cloud grid map that accurately reflects the occupancy probability of glass obstacles is proposed, enabling identification of safe frontier grids on the safety grid map calculated from the point cloud grid map. Navigability, indicating whether the robot can successfully navigate to each frontier target, is calculated by applying the skeletonization-informed rapidly exploring random tree algorithm to the safety grid map. While conventional exploration approaches have focused on frontier detection and target position/direction decision, the proposed method discusses a safe navigation approach for the overall exploration process until the completion of mapping. Real-world experiments have been conducted to verify that the proposed method leads the robot to avoid glass obstacles and safely navigate the entire environment, constructing the point cloud map and calculating the navigability with low computing time deviation.

• ETRI Journal
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• v.38 no.6
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• pp.1240-1249
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• 2016

With the number of IP cores in a multicore system-on-chip increasing to up to tens or hundreds, the role of on-chip interconnection networks is vital. We propose a networks-on-chip-style bus network as a compromise and redefine the exploration problem to find the best IP tiling patterns and communication path combinations. Before solving the problem, we estimate the time complexity and validate the infeasibility of the solution. To reduce the time complexity, we propose two fast exploration algorithms and develop a program to implement these algorithms. The program is executed for several experiments, and the exploration time is reduced to approximately 1/22 and 7/1,200 at the first and second steps of the exploration process, respectively. However, as a trade-off for the time saving, the time cost (TC) of the searched architecture is increased to up to 4.7% and 11.2%, respectively, at each step compared with that of the architecture obtained through full-case exploration. The reduction ratio can be decreased to 1/4,000 by simultaneously applying both the algorithms even though the resulting TC is increased to up to 13.1% when compared with that obtained through full-case exploration.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.401-408
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• 2023

In this study, a career exploration program was developed for athletic students. Therefore, existing research on career exploration for athletics was analyzed, requirements were identified, and a learning plan was designed. Based on this, a step-by-step educational program was developed. In addition, since research on career exploration for athletic students was not active in previous studies, 'problem definition' - 'data collection' - 'data preprocessing' - 'data analysis' by referring to existing career exploration studies that were studied in the school field. - 'Data visualization' - 'Simulation analysis' were divided into stages to conduct the study. Through this study, it is expected that research on vocational education for athletic students will be more active.

• Journal of Engineering Education Research
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• v.27 no.1
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• pp.13-31
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• 2024

In recent times, STEM graduates are confronting a decline in employment rates influenced by economic, social, cultural, and policy-related factors. Career decisions are closely linked to education, college experiences, and university settings. To comprehend the reasons behind the decline in STEM employment, it is essential to explore the relationships among these factors. This study aims to comprehensively examine differences in career motivation and career exploration behavior among 2,393 STEM undergraduates in Korea. Additionally, factors affecting career motivation and career exploration behavior were investigated. The findings indicate significant differences in perceived career motivation and career exploration behavior based on individual backgrounds and university characteristics. And analyzing the data, 37.8% of career motivation is explained by contextual supports, career barriers, individual backgrounds (grade, GPA), university characteristics (major fields, location), field to enter after graduation, and timing of job preparation. For career exploration behavior, 30.1% is explained by contextual supports, career barriers, individual backgrounds (gender, grade, GPA), university characteristics (major field, location), field to enter after graduation, and timing of job preparation. Practical implications underscore the need for tailored educational and policy support, considering individual backgrounds and university characteristics, to effectively address challenges faced by STEM graduates in the evolving employment landscape.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.313-316
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• 2007

Ground-penetrating radar (GPR) is an effectiveness tool for imaging spatial distribution of hydrogeologic parameters. An artificial groundwater recharge test has been conducted in Nagaoka City in Japan, and time-lapse crosshole GPR data were collected to monitor infiltration processes in a vadose zone. Since radiowave velocities in a vadose zone are largely controlled by variations in water content, the increase in traveltimes is interpreted as an increase in saturation in the test zone. We use a finite-difference time-domain method in two-dimensional cylindrical coordinates to simulate field results. Numerical modeling successfully reproduces the major feature of velocity changes in the filtration process.

• The Journal of Korea Robotics Society
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• v.8 no.4
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• pp.256-265
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• 2013

A local map-based exploration algorithm for mobile robots is presented. Segmented frontiers and their relative transformations constitute a tree structure. By the proposed efficient frontier segmentation and a local map management method, a robot can reduce the unknown area and update the local grid map which is assigned to each frontier node. Although this local map-based exploration method uses only local maps and their adjacent node information, mapping completion and efficiency can be greatly improved by merging and updating the frontier nodes. Also, we suggest appropriate graph search exploration methods for corridor and hall environments. The simulation demonstrates that the entire environment can be represented by well-distributed frontier nodes.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.559-570
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• 2008

This paper proposes an improved particle swarm optimization named PSO with Controllable Random Exploration Velocity (PSO-CREV) behaving an additional exploration behavior. Different from other improvements on PSO, the updating principle of PSO-CREV is constructed in terms of stochastic approximation diagram. Hence a stochastic velocity independent on cognitive and social components of PSO can be added to the updating principle, so that particles have strong exploration ability than those of conventional PSO. The conditions and main behaviors of PSO-CREV are described. Two properties in terms of "divergence before convergence" and "controllable exploration behavior" are presented, which promote the performance of PSO-CREV. An experimental method based on a complex test function is proposed by which the proper parameters of PSO-CREV used in practice are figured out, which guarantees the high exploration ability, as well as the convergence rate is concerned. The benchmarks and applications on FCRNN training verify the improvements brought by PSO-CREV.

• Proceedings of the KSEEG Conference
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• 2000.04b
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• pp.153-154
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• 2000

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.343-344
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• 2011