• Korean Medical Education Review
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• v.15 no.3
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• pp.120-124
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• 2013

The clinical clerkship focuses students on developing their ability to perform comprehensive diagnosis and management of patients with common undifferentiated problems by the integration of knowledge and clinical reasoning. Therefore, the clerkship evaluation system should assess their actual problem solving and professional behavior. However, concern remains that clerkship evaluations are imprecise and highly variable. This review is designed to provide faculty members with concepts, options, and a methodology to actively teach and evaluate the clinical clerkship, as well as offer encouragement and inspiration to medical students. We reviewed past and current clinical clerkship evaluations and discuss several tips to improve clinical excellence such as continuity, transparency of the evaluation process, a faculty development program, practical examination of clinical skills, implementation of a checklist for recording exposure and skills, providing prompt and constructive feedback to students, self-evaluation of professional performance, varying multi-faceted assessment combinations, being outpatient clinic-centered, and having dedicated faculty members who give students one-on-one contact with a preceptor.

• Annual Conference of KIPS
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• 2019.05a
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• pp.458-460
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• 2019

최근 클라우드 시스템 환경이 점차 늘어남에 따라 데이터 센터(IDC) 구축이 점차 늘어나가고 있다. 데이터 센터는 최근 부각하고 있는 4 차 산업 영역에서 사물 인터넷(IoT), 자율주행차 등 에서 처리될 대용량 데이터로 인한 이를 처리하는 중요한 역할을 담당하고 있다. 데이터센터 운영에는 대량의 에너지가 필요하다. 수 많은 컴퓨터에서 발생하는 열에너지를 처리하기 위하여 대량의 전력 냉방 에너지를 소비하고 있다. 냉방 공조 운영은 데이터 센터 운영에 중요한 역할을 한다. 이유는 많은 컴퓨터를 가동하는 비용보다 부대 시설로 운영되는 냉방 에너지를 보다 많이 소비하는 현상까지 발생하고 있다. 이에 최근 데이터 센터 냉방 공조 운영을 효율화하는 것에 연구를 맞추고 있다. 본 논문에서는 냉방 공조 운영 효율화 하도록 하기 위해서 다중 기계 학습을 활용한 데이터 센터의 냉방 에너지 절감 시스템을 제안하고자 한다. 기존의 단수 알고리즘을 활용하여 머신 러닝의 모델구현 방식이 아닌 다중의 기계 학습을 통하여 최적화된 모델을 일일 배치로 생성하여 예측을 하는 시스템이다. 본 시스템을 통하여 사전에 최적화된 냉방 운영을 하여 기존 데이터 센터의 운영되는 과다 냉방을 감축 시켜 에너지를 절감해주는 기능을 제공한다. 본 논문 시스템 연구 결과는 폭발적으로 늘어가고 있는 데이터 센터의 에너지 효율화에 기여할 수 있고, 클라우드 사업에서 경쟁력을 줄 수 있는 운영 시스템 방안을 제시한다.

• Journal of the Korea Society of Computer and Information
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• v.7 no.1
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• pp.174-183
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• 2002

In this paper a control chart pattern recognition methodology based on the back propagation algorithm and Multi layer perceptron, a neural computing theory, is presented. This pattern recognition algorithm, suitable for real time statistical process control. evaluates observations routinely collected for control charting to determine whether a Pattern, such as a cycle. trend or shift, which is exists in the data. This approach is promising because of its flexible training and high speed computation with low-end workstation. The artificial neural network methodology is developed utilizing the delta learning rule, sigmoid activation function with two hidden layers. In a computer integrated manufacturing environment, the operator need not routinely monitor the control chart but, rather, can be alerted to patterns by a computer signal generated by the proposed system.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.213-221
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• 2021

In this study, an simulation app was developed as one of the methods to help learners better understand the eight process scheduling policies of multi-programming. In learning, an app in the form of a simulation should provide a realistic environment and allow learners to practice. To do this, the needs of the learners were investigated and analyzed, and the purpose was set, designed, and programmed based on the learners' understanding. And it was shown that the apps as a tool to simulate the created eight scheduling policies are performing well. In particular, it was shown that the problem of not having a step-by-step various diagram and explanation for step-by-step various inputs, which is a limitation of paper textbooks, can be solved using these tools.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.1-9
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• 2023

The problem of classifying a non-line-of-sight (NLOS) signal in a multipath channel is important to improve global navigation satellite system (GNSS) positioning accuracy in urban areas. Conventional deep learning-based NLOS signal classifiers use GNSS satellite measurements such as the carrier-to-noise-density ratio (CN_0), pseudorange, and elevation angle as inputs. However, there is a computational inefficiency with use of these measurements and the NLOS signal features expressed by the measurements are limited. In this paper, we propose a Convolutional Neural Network (CNN)-based NLOS signal classifier that receives successive Auto-correlation function (ACF) outputs according to a time-series, which is the most primitive output of GNSS signal processing. We compared the proposed classifier to other DL-based NLOS signal classifiers such as a multi-layer perceptron (MLP) and Gated Recurrent Unit (GRU) to show the superiority of the proposed classifier. The results show the proposed classifier does not require the navigation data extraction stage to classify the NLOS signals, and it has been verified that it has the best detection performance among all compared classifiers, with an accuracy of up to 97%.

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1141-1155
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• 2022

Multicultural education is not about learning about a specific ethnic group, but rather developing the ability to cross the border of one's own culture and have conversations with people of other cultures. I think the purpose is to promote empathy and consideration. This study emphasizes the importance of developing multi-dimensional educational programs for all members of society for multicultural social integration, and it is necessary to lead personal, social and civic action movements to create a fair society through media-based multicultural literacy education. said that In order to achieve harmony and integration in a multicultural society, it is the most important to acknowledge cultural diversity and to discard cultural prejudices and inequalities for symbiosis between the mainstream culture and the minority culture. In particular, the United States and Germany, which have successfully led multicultural social integration, are comprehensive in all areas, including interculturalism based on peaceful coexistence and respect, labor market issues, vocational education issues, housing and health issues, and communication issues through media literacy. He led a multicultural national integration system with approaches and methods. Therefore, our multicultural education policy should also pursue a new paradigm that presupposes a change in the public's awareness of a multicultural society.

• Korean Journal of Construction Engineering and Management
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• v.24 no.4
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• pp.60-73
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• 2023

The need for adopting automation technologies to improve inefficiencies in interior finishing modeling work is increasing during the Building Information Modeling (BIM) design stage. As a result, the use of visual programming languages (VPL) for practical applications is growing. However, undefined or incorrect space designations in BIM models can hinder the development of automated finishing modeling processes, resulting in erroneous corrections and rework. To address this challenge, this study first developed a rule-based automated interior finishing detailing module for floors, walls, and ceilings. In addition, an automated space integrity checking module with 86.69% ACC using the Multi-Layer Perceptron (MLP) model was developed. These modules were integrated into a design automation module for interior finishing, which was then verified for practical utility. The results showed that the automation module reduced the time required for modeling and integrity checking by 97.6% compared to manual work, confirming its utility in assisting BIM model development for interior finishing works.

• Steel and Composite Structures
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• v.51 no.5
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• pp.473-485
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• 2024

This paper proposes a composite form of fuzzy adaptive control plan based on a robust observer. The fuzzy 2D control gains are regulated by the parameters in the LMIs. Then, control and learning performance indices with weight matrices are constructed as the cost functions, which allows the regulation of the trade-off between the two performance by setting appropriate weight matrices. The design of 2D control gains is equivalent to the LMIs-constrained multi-objective optimization problem under dual performance indices. By using this proposed smart tracking design via fuzzy nonlinear criterion, the data link can be further extended. To evaluate the performance of the controller, the proposed controller was compared with other control technologies. This ensures the execution of the control program used to track position and trajectory in the presence of great model uncertainty and external disturbances. The performance of monitoring and control is verified by quantitative analysis. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.

• Safety and Health at Work
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• v.15 no.3
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• pp.373-378
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• 2024

Introduction: Following the Republic of Korea electric power industry site-specific safety management system, this paper proposes a novel safety autonomous platform (SAP) architecture that can automatically and precisely manage on-site safety through ensemble artificial intelligence (AI) models. The ensemble AI model was generated from video information and worker's biometric information as learning data and the estimation results of this model are based on standard operating procedures of the workplace and safety rules. Methods: The ensemble AI model is designed and implemented by the Hadoop ecosystem with Kafka/NiFi, Spark/Hive, HUE, and ELK (Elasticsearch, Logstash, Kibana). Results: The functional evaluation shows that the main function of this SAP architecture was operated successfully. Discussion: The proposed model is confirmed to work well with safety mobility gateways to provide some safety applications.