• 대한조선학회논문집
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• 제56권2호
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• pp.135-142
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• 2019

Ice accretions on the ship equipment and areas are the most common issues for vessels operating in cold climate and ice-covered waters and it has effect on the vessel safety and operability of equipment and systems, thus ship machineries and structures exposed to low temperature environments should satisfy the winterization requirements specified in ice class rules. The main objective of this study is to review the state-of-the-art of winterization trend for vessels navigating in ice-covered waters. The hazard of icing and how ice accretions affect operations and safety are investigated firstly, and then winterized notations for each classification are summarized. In addition, winterization methods currently used in vessels operating in ice-covered waters are investigated for a better understanding of effective approach and its application. This information will provide a framework for future winterization issues to mitigate the ice accretion phenomena.

• 스마트미디어저널
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• 제10권2호
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• pp.22-29
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• 2021

In the past decade, deep learning has been applied to various medical image analysis tasks. Skeletal bone age estimation is clinically important as it can help prevent age-related illness and pave the way for new anti-aging therapies. Recent research has applied deep learning techniques to the task of bone age assessment and achieved positive results. In this paper, we propose a bone age prediction method using a deep convolutional neural network. Specifically, we first train a classification model that automatically localizes the most discriminative region of an image and crops it from the original image. The regions of interest are then used as input for a regression model to estimate the age of the patient. The experiments are conducted on a whole-body scintigraphy dataset that was collected by Chonnam National University Hwasun Hospital. The experimental results illustrate the potential of our proposed method, which has a mean absolute error of 3.35 years. Our proposed framework can be used as a robust supporting tool for clinicians to prevent age-related diseases.

• 한국멀티미디어학회논문지
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• 제23권12호
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• pp.1540-1551
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• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.

• 대한의용생체공학회:의공학회지
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• 제42권5호
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• pp.232-240
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• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.

• 대한한의학원전학회지
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• 제36권1호
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• pp.79-107
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• 2023

Objectives : This study aims to improve the diagnosis and treatment of contemporary insomnia by examining Zhang Jiebin's discussion on treating insomnia. Methods : The classical texts from the 'Insomnia' chapter of the Jingyue Quanshu were examined threefold in terms of symptom, treatment, and prescription analysis, after which the treatment discussion part was examined within the historical context of discussions on insomnia in major medical texts starting from the Huangdineijing. Results : According to Zhang, the cause of insomnia could be divided into two, after which criteria for diagnosis and treatment were set as excess pathogen and vital qi deficiency. He argued that insomnia could be naturally resolved through improvement of various pathogenic situations. Discussions on insomnia from various medical texts since the Huangdineijing suggest that pathology related to psychological function and emotions gradually increased and expanded over time. Conclusions : Zhang's discussion on symptom, treatment and prescriptions of insomnia suggests a new framework that could improve treatment effects through a Korean Medical Mind-Body approach, rather than the contemporary classification of organic insomnia and non-organic insomnia.

• 동아시아경상학회지
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• 제10권1호
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• pp.71-81
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• 2022

Purpose - Foodservice production is predominantly susceptible to rampant calamities since it trusts on social gatherings and interactions. This research aims to elaborate a brief framework of the literature review on the research conducted for the Coronavirus outburst regarding the food service sector. Research design, Data, and methodology - The method used in research involving interpretation of the subject content in a text data through a systematic process of classification to identify the themes and coding is referred to as the qualitative content analysis. It can also be defined as a useful research approach method of analysis instead of an empirical analysis. Result - Based on ultimate systematic literature analysis, the author figured out that the vendors should be given importance to the digital traveling interventions as the shortest factor in foodservice processing firms. Designing new sources of revenue and implementing numerous canceled regulations are other resolution that helps challenges in food service industries Conclusion - The Coronavirus pandemic has affected the foodservice business leading to the permanent closure of some businesses. There is a need for a stimulus package from the state to revive these businesses since they play a great role in the economy's growth and are regarded as part of the economy, and most of their activity is undocumented.

• 한국소프트웨어감정평가학회 논문지
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• 제15권1호
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• pp.63-70
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• 2019

최근 딥 러닝(Deep leaning)을 이용한 이미지 인식 분야의 연구가 활발히 진행되고 있다. 본 연구에서는 육안으로 관찰하여 분류하기 어려운 암석을 이미지만으로 분류하기 위해 딥 러닝 오픈 소스 프레임워크인 Tensorflow 기반의 CNN모델을 사용하여 고등학교 교육과정에서 다루는 암석 18종(화성암 6종, 변성암 6종, 퇴적암 6종)의 이미지를 통해 암석을 분류하는 시스템을 제안한다. 암석의 이미지를 학습시켜 암석을 구별하는 분류기를 개발하여 분류 성능을 확인하였으며 최종적으로 구현한 모바일 어플리케이션을 통해 교실 내 학습 또는 현장체험학습 등에서 학생들의 학습 보조도구로서 사용할 수 있다.

• 로봇학회논문지
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• 제18권4호
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• pp.436-443
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• 2023

With the advancement of robot intelligence, the concept of man and unmanned teaming (MUM-T) has garnered considerable attention in military research. In this paper, we present a transformer-based architecture for predicting the health status of agents, with the help of multi-head attention mechanism to effectively capture the dynamic interaction between friendly and enemy forces. To this end, we first introduce a framework for generating a dataset of battlefield situations. These situations are simulated on a virtual simulator, allowing for a wide range of scenarios without any restrictions on the number of agents, their missions, or their actions. Then, we define the crucial elements for identifying the battlefield, with a specific emphasis on agents' status. The battlefield data is fed into the transformer architecture, with classification headers on top of the transformer encoding layers to categorize health status of agent. We conduct ablation tests to assess the significance of various factors in determining agents' health status in battlefield scenarios. We conduct 3-Fold corss validation and the experimental results demonstrate that our model achieves a prediction accuracy of over 98%. In addition, the performance of our model are compared with that of other models such as convolutional neural network (CNN) and multi layer perceptron (MLP), and the results establish the superiority of our model.

• 항공우주시스템공학회지
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• 제18권2호
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• pp.95-103
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• 2024

무인기에 적용되는 다양한 추진기관 기술은 항공의 중요한 개발 방향 중의 하나인 무인기의 비행 성능에 크게 관련이 있다. 본 논문에서는 무인기의 내연기관(왕복엔진, 로타리엔진, 가스터빈엔진), 하이브리드 및 순수한 전기 추진시스템에 대하여 조사를 수행하였다. 특히 이러한 추진기관들의 분류, 작동사이클, 특성 및 주요 기술들에 대하여 제시하였다. 그러므로 미래의 무인기 추진시스템의 개발 틀, 종합적인 예측 및 다양한 비교를 정립하는데 도움을 줄 것으로 판단된다.

• Advances in nano research
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• 제16권6호
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• pp.531-548
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• 2024

The study focuses on using remote sensing to gather data about the Earth's surface, particularly in urban environments, using satellites and aircraft-mounted sensors. It aims to develop a classification framework for road targets using multi-spectral imagery. By integrating Convolutional Neural Networks (CNNs) with XGBoost, the study seeks to enhance the accuracy and efficiency of road target identification, aiding urban infrastructure management and transportation planning. A novel aspect of the research is the incorporation of quantum sensors, which improve the resolution and sensitivity of the data. The model achieved high predictive accuracy with an MSE of 0.025, R-squared of 0.85, RMSE of 0.158, and MAE of 0.12. The CNN model showed excellent performance in road detection with 92% accuracy, 88% precision, 90% recall, and an f1-score of 89%. These results demonstrate the model's robustness and applicability in real-world urban planning scenarios, further enhanced by data augmentation and early stopping techniques.