High-performance computing (HPC) is an essential element that supports innovation across science, technology, economy, and society. Recently, as rapidly spreading intelligent information such as big data and artificial intelligence has become larger and more sophisticated, HPC is actively working as a core infrastructure that supports this smoothly. However, there is a lack of training systems to develop, operate, and utilize HPC, making it difficult the professional HPC manpower training. Accordingly, we analyzed the results of HPC training conducted over the past year by KISTI, Korea's only HPC public service institution, and we would like to suggest a strategy to overcome these.
There has been increasing interest in artificial intelligence (AI) in various fields. This phenomenon calls for human resources to be equipped with the knowledge and skills of AI and data. The Korean Ministry of Education has opened up introductory courses in AI to high school students since the second half of 2021. It will also include AI education in the 2022 revised curriculum for elementary, middle, and high school students. Despite these efforts to enhance students’ digital literacy through the innovation of the national curriculum, opportunities for taking advantage of AI and data education should be reached for more diverse learners. At the same time, the courses need to be designed with not only theoretical but practical contents and activities based on learner needs. Under these circumstances, the Science Data Education Center at the Korean Institute of Science and Technology Information (KISTI) has been providing AI and data education programs either online or face-to-face for university members, such as undergraduates, graduates, researchers, and professors. In this study, we aim to present cases of educational programs on AI and data operated by the Science Data Education Center, especially regarding those for the university components. Pertinent implications derived from the results of operating the programs will be discussed.
In recent years, the research of 3D mapping technique in urban environments obtained by mobile robots equipped with multiple sensors for recognizing the robot’s surroundings is being studied actively. However, the map generated by simple integration of multiple sensors data only gives spatial information to robots. To get a semantic knowledge to help an autonomous mobile robot from the map, the robot has to convert low-level map representations to higher-level ones containing semantic knowledge of a scene. Given a 3D point cloud of an urban scene, this research proposes a method to recognize the objects effectively using 3D graph model for autonomous mobile robots. The proposed method is decomposed into three steps: sequential range data acquisition, normal vector estimation and incremental graph-based segmentation. This method guarantees the both real-time performance and accuracy of recognizing the objects in real urban environments. Also, it can provide plentiful data for classifying the objects. To evaluate a performance of proposed method, computation time and recognition rate of objects are analyzed. Experimental results show that the proposed method has efficiently in understanding the semantic knowledge of an urban environment.
A map of complex environment can be generated using a robot carrying sensors. However, representation of environments directly using the integration of sensor data tells only spatial existence. In order to execute high-level applications, robots need semantic knowledge of the environments. This research investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The proposed system is decomposed into five steps: sequential LIDAR scan, point classification, ground detection and elimination, segmentation, and object classification. This method could classify the various objects in urban environment, such as cars, trees, buildings, posts, etc. The simple methods minimizing time-consuming process are developed to guarantee real-time performance and to perform data classification on-the-fly as data is being acquired. To evaluate performance of the proposed methods, computation time and recognition rate are analyzed. Experimental results demonstrate that the proposed algorithm has efficiency in fast understanding the semantic knowledge of a dynamic urban environment.
In this study, Life Cycle Assessment(LCA) has been carried out to evaluate the environmental impacts of a metallic can. A 360 mL volume of an aluminum can bottle was used as the functional unit. The results of Life Cycle Inventory(LCI) showed that iron ore and coal were the major parts of the input materials, whereas aluminum can products, carbon dioxide, wastewater, and hazardous wastes were those of the output ones. According to LCA weighting, it was observed that the most significant impact potential was found to be global warming(49.11%) followed by abiotic resource depletion(47.72%). In the whole system, cold rolled steel coil showed the largest environmental impact potential(86%), followed by electricity(14%). Meanwhile, lubricating oil and industrial water had the minor portion of the total environmental impact potentials. It was suggested that the use of cold rolled steel and electricity should be the main source for CO2, resulting in the big impact on global warming.