• New Physics: Sae Mulli
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• v.68 no.12
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• pp.1338-1346
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• 2018

The purpose of this study is to analyze the curriculum of a university-affiliated science gifted education center based on the core competencies and to suggest a direction for improving the education at the gifted education center. For this purpose, we set the 12 core competencies as follows: 6 cognitive competencies such as knowledge, creativity, scientific thinking ability, inquiry ability, problem solving ability and fusion ability, and 6 non-cognitive competencies such as task commitment, self-directed learning ability, motivation reinforcement and challenge, communication skills, collaboration ability and leadership. The curricula of the science gifted education centers reflect all the competencies, but some competencies are only potentially included in the contents of the programs. In this study, we present examples of education programs by each competences and suggest additional descriptions for the development of gifted education centers.

• Korean Journal of Construction Engineering and Management
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• v.22 no.4
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• pp.11-19
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• 2021

Despite of the various advantages of modular construction method and the continued growth of related markets, it is difficult to activate them because no specific system has been established in Korea. Accordingly, this study derived improvement areas and tasks for activating modular construction methods through existing literature reviews and preliminary surveys. Then, AHP analysis and expected utility evaluation were conducted for expert groups to derive priority for improvement areas and tasks. In addition, opinions of enterprise and architectural research were compared and analyzed, and the analysis results suggested the direction of policy establishment and system improvement. This study is expected to be used as a basic study for policy decision-making to activate modular construction method.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.1-8
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• 2022

In this paper, we design and implement a GPU-based parallel algorithm to effectively solve the traveling salesman problem through an ant color system. The repetition process of generating hundreds or thousands of tours simultaneously in TSP utilizes GPU's task-level parallelism, and the update process of pheromone trails data actively exploits data parallelism by 32x32 thread blocks. In particular, through simultaneous memory access of multiple threads, the coalesced accesses on continuous memory addresses and concurrent accesses on shared memory are supported. This experiment used 127 to 1002 city data provided by TSPLIB, and compared the performance of sequential and parallel algorithms by using Intel Core i9-9900K CPU and Nvidia Titan RTX system. Performance improvement by GPU parallelization shows speedup of about 10.13 to 11.37 times.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.2
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• pp.183-188
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• 2022

It is a difficult task to estimate parameters in even a simple stock assessment model such as a surplus production model, using only data about temporal catch-per-unit-effort (CPUE) (or survey index) and fishery yields. Such difficulty is exacerbated when time-varying parameters are treated as random effects (aka state variables). To overcome the difficulty, previous studies incorporated somewhat subjective assumptions (e.g., B₁=K) or informative priors of parameters. A key is how to build an objective joint prior of parameters, reducing subjectivity. Given the limited data on temporal CPUEs and fishery yields from 1999-2020 for common squid Todarodes pacificus, we built a joint prior of only two parameters, intrinsic growth rate (r) and carrying capacity (K), based on the resilience level of the population (Froese et al., 2017), and used a Bayesian state-space production assessment model. We used template model builder (TMB), a R package for implementing the assessment model, and estimating all parameters in the model. The predicted annual biomass was in the range of 0.76×10⁶ to 4.06×10⁶ MT, the estimated MSY was 0.13×10⁶ MT, the estimated r was 0.24, and the estimated K was 2.10×10⁶ MT.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.5
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• pp.811-816
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• 2022

Even though continuous management and supervision of reinforcement of policies to safeguard accidents at workplace and work sites were implemented. Accident prevention activities such as inspection and diagnosis are urgently required to induce a preliminary investigation to identify the risk factors for each type of work, before the work task to eliminate risks at the worksites. Since safety inspections at work sites were generally conducted through visual inspections, the results of safety inspections may vary depending on the findings and proficiency of the safety officers. The results of those inspections may have loopholes to prevent potential accidents at work. Therefore, the purpose of this study was to develop a risk identification checklist that can effectively perform safety inspections to prevent accidents at work sites. This study initially analyzed the previously developed accident checklist to identify current complications and issues in safety checklists. Based on the findings of major industrial accidents over the past three years, the relationship between accident, workplace, and work type were analyzed refereeing the safety inspection standards. A risk recognition-checklist was developed to provide basic data on identifying risk factors, and inspection guidance at work sites. To prepare for potential accidents by identifying and taking countermeasures to mitigate the high risk and serious accidents at sites by the guidelines of the checklist. The developed inspection checklist has been practically used by experts at work sites to perform safety inspections, and it has been verified its suitability, and feasibility, to prevent or mitigate workplace accidents, including securing the safety and health of field workers. The role of the developed safety checklist has been considered effective at worksites.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.249-255
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• 2022

Potholes are an important clue to the structural defects of asphalt pavement and cause many casualties and property damage. Therefore, accurate pothole detection is an important task in road surface maintenance. Many machine learning technologies are being introduced for pothole detection, and data preprocessing is required to increase the efficiency of deep learning models. In this paper, we propose a preprocessing method that emphasizes important textures and shapes in pothole datasets. The proposed preprocessing method uses intensity transformation to reduce unnecessary elements of the road and emphasize the texture and shape of the pothole. In addition, the feature of the porthole is detected using Superpixel and Sobel edge detection. Through performance comparison between the proposed preprocessing method and the existing preprocessing method, it is shown that the proposed preprocessing method is a more effective method than the existing method in detecting potholes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1769-1776
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• 2022

Automatically summarizing long sentences is an important technique. The BART model is one of the widely used models in the summarization task. In general, in order to generate a summarization model of a specific domain, fine-tuning is performed by re-training a language model trained on a large dataset to fit the domain. The fine-tuning is usually done by changing the number of nodes in the last fully connected layer. However, in this paper, we propose a fine-tuning method by adding an attention layer, which has been recently applied to various models and shows good performance. In order to evaluate the performance of the proposed method, various experiments were conducted, such as accumulating layers deeper, fine-tuning without skip connections during the fine tuning process, and so on. As a result, the BART model using two attention layers with skip connection shows the best score.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2D
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• pp.255-265
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• 2009

Because the current disaster management task is being processed by using a separated operation system and an insufficient information system by each division, construction facilities suffer great damage by disaster. This research has classified the disaster management business phase and information by each phase through analyzing the existing disaster management information and business process. Then, it has built a disaster management information breakdown structure for integrating of individual disaster information, and also established a standard XML (eXtensible Markup Language) schema of disaster management electronic documents for a real-time utilization. The suggested methods in this research are verified by developing a manual system of electronic type. The disaster management IETM developed in this study provides the consistency for processing the disaster management tasks and the prevention of information omission. And it can be used as a electronic decision making tool for providing of integrated disaster management information.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.289-296
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• 2023

The purpose of this study aims to identify the key factors that contribute to successful online learning experiences for college students in the pre-course, during course, and post-course phases. A survey was conducted college students, and a total of 95 questionnaires were used for statistical analysis. The main findings revealed that in the pre-course phase, task value, academic self-efficacy, and control beliefs were significant factors. During course, interaction emerged as a crucial factor. Notably, students' satisfaction in the post-course phase is significantly influenced by academic self-efficacy and interaction. Understanding these factors will help inform the design and operation of effective college-level online courses to improve student experience and satisfaction.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.17-22
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• 2024

History, which began with the emergence of mankind, has a means of recording. Today, we can check the past through data. Generated data may only be generated and stored at a certain moment, but it is not only continuously generated over a certain time interval from the past to the present, but also occurs in the future, so making predictions using it is an important task. In order to find out trends in the use of time series data among numerous data, this paper analyzes the concept of time series data, analyzes Recurrent Neural Network and Long-Short Term Memory, which are mainly used for time series data analysis in the machine learning field, and analyzes the use of these models. Through case studies, it was confirmed that it is being used in various fields such as medical diagnosis, stock price analysis, and climate prediction, and is showing high predictive results. Based on this, we will explore ways to utilize it in the future.