• 한국환경교육학회지:환경교육
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• 제11권2호
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• pp.14-25
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• 1998

The Quality of human living depends on the environmental quality of the region sustaining the life. The environmental deterioration of the modern society is due to mechanical environmentalism. For the better quality of the life, The changes of recognition and attitude on the environments are required. These changes of mind are also important in environmental education for teenagers. The 7th national curriculum, officially anounced December 1998, focuses on the change of attitude to environments and practical behavior in real life for “Environments”, the environmental education curriculum in middle school. Basic elements of the curriculum are cultivation of the pro-environmental thinking, multi-levelling of teaching materials and methods, and encouraging of student participating activity. Actually, the curriculum construction is composed of stepped-levelling of teaching and learning, reasonable contents volume, encouraging of student practice, and suggesting of evaluation standards of textbook writing. Three main subjects of environmental education for middle school consist of (1) man and environment, (2) recognition of environmental problem, and (3) protection activity for environment. Methodology of environmental education can include multi-disciplinary approaches, variable teaching methods, and continuing evaluation of student practice and participation attitude. Environmental education for teenagers relating to the 7th national curriculum focuses on recognition of the environmental problems and practice activity in daily life. The recognition includes considering relationship of human life to environment, solving environmental problems in regional context, and development of comprehensive understanding concept of the environments. For the practice education, variable teaching methods, such as field survey and application of multi-media, are needed.

• 전자통신동향분석
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• 제36권3호
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• pp.106-118
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• 2021

The field of brain science (or neuroscience in a broader sense) has inspired researchers in artificial intelligence (AI) for a long time. The outcomes of neuroscience such as Hebb's rule had profound effects on the early AI models, and the models have developed to become the current state-of-the-art artificial neural networks. However, the recent progress in AI led by deep learning architectures is mainly due to elaborate mathematical methods and the rapid growth of computing power rather than neuroscientific inspiration. Meanwhile, major limitations such as opacity, lack of common sense, narrowness, and brittleness have not been thoroughly resolved. To address those problems, many AI researchers turn their attention to neuroscience to get insights and inspirations again. Biologically plausible neural networks, spiking neural networks, and connectome-based networks exemplify such neuroscience-inspired approaches. In addition, the more recent field of brain network analysis is unveiling complex brain mechanisms by handling the brain as dynamic graph models. We argue that the progress toward the human-level AI, which is the goal of AI, can be accelerated by leveraging the novel findings of the human brain network.

• 한국간호교육학회지
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• 제28권1호
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• pp.27-36
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• 2022

Purpose: This study aimed to understand the current status of global health curricula and characteristics in nursing schools, focusing on the changes since 2015. Methods: Data were collected from the websites of 202 nursing schools nationwide in Korea. Global health curricula were analyzed using a structured framework developed by the authors. Results: Among 202 nursing schools, 173 (85.6%) schools offer global health-related courses. Of these, 72 (35.6%) schools offer a 'Multiculturalism' course, and 42 (20.8%) schools offer a 'Global Nursing' course. Fifty-nine schools (29.2%) offer both courses. Compared to the study findings in 2015, the number of global health-related courses and the percentage of global health-related courses designated as a requirement dramatically increased. An additional analysis of five syllabi of global-health related courses found several differences in the courses' aims, contents and evaluation methods. Conclusions: Due to social and political changes, nursing schools are more likely to offer global health curricula. However, there is still a lack of consensus on the core contents and approaches of such curricula, necessitating systematic discussions about the core contents and effective learning methods to increase nursing student competency in global health nursing.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.53-63
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• 2023

Many researchers are trying hard to minimize the incidence of cancers, mainly Gastric Cancer (GC). For GC, the five-year survival rate is generally 5-25%, but for Early Gastric Cancer (EGC), it is almost 90%. Predicting the onset of stomach cancer based on risk factors will allow for an early diagnosis and more effective treatment. Although there are several models for predicting stomach cancer, most of these models are based on unbalanced datasets, which favours the majority class. However, it is imperative to correctly identify cancer patients who are in the minority class. This research aims to apply three class-balancing approaches to the NHS dataset before developing supervised learning strategies: Oversampling (Synthetic Minority Oversampling Technique or SMOTE), Undersampling (SpreadSubsample), and Hybrid System (SMOTE + SpreadSubsample). This study uses Naive Bayes, Bayesian Network, Random Forest, and Decision Tree (C4.5) methods. We measured these classifiers' efficacy using their Receiver Operating Characteristics (ROC) curves, sensitivity, and specificity. The validation data was used to test several ways of balancing the classifiers. The final prediction model was built on the one that did the best overall.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.541-543
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• 2022

In recent years, retinal disorders have become a serious health concern. Retinal disorders develop slowly and without obvious signs. To avoid vision deterioration, early detection and treatment are critical. Optical coherence tomography (OCT) is a non-invasive and non-contact medical imaging technique used to acquire informative and high-resolution image of retinal area and underlying layers. Disease signs are difficult to detect because OCT images have many areas which are not related to any disease. In this paper, we present a deep learning-based method to perform multi-label classification on a long-tailed OCT dataset. Our method first extracts the region of interest and then performs the classification task. We achieve 98% accuracy, 92% sensitivity, and 99% specificity on our private OCT dataset. Using the heatmap generated from trained convolutional neural network, our method is more robust and explainable than previous approaches because it focuses on areas that contain disease signs.

• 전기전자학회논문지
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• 제27권4호
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• pp.430-438
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• 2023

The Modular Multilevel Converter (MMC) has emerged as a key component in HVDC systems due to its ability to efficiently transmit large amounts of power over long distances. In such systems, accurate estimation of the MMC capacitor voltage is of utmost importance for ensuring optimal system performance, stability, and reliability. Traditional methods for voltage estimation may face limitations in accuracy and robustness, prompting the need for innovative approaches. In this paper, we propose a novel distributed neural network observer specifically designed for MMC capacitor voltage estimation. Our observer harnesses the power of a multi-layer neural network architecture, which enables the observer to learn and adapt to the complex dynamics of the MMC system. By utilizing a distributed approach, we deploy multiple observers, each with its own set of neural network layers, to collectively estimate the capacitor voltage. This distributed configuration enhances the accuracy and robustness of the voltage estimation process. A crucial aspect of our observer's performance lies in the meticulous initialization of random weights within the neural network. This initialization process ensures that the observer starts with a solid foundation for efficient learning and accurate voltage estimation. The observer iteratively updates its weights based on the observed voltage and current values, continuously improving its estimation accuracy over time. The validity of proposed algorithm is verified by the result of estimated voltage at each observer in capacitor of MMC.

• 디지털콘텐츠학회 논문지
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• 제17권6호
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• pp.509-521
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• 2016

본 논문에서는 파킨슨 병 진단 및 바이오 표지자 검출을 위한 극한 기계학습을 결합하는 새로운 균형 표본 유전 알고리즘(SBGA-ELM)을 제안하였다. 접근법은 정확한 파킨슨 병 진단 및 바이오 표지자 검출을 위해 공개 파킨슨 병 데이터베이스로부터 22,283개의 유전자의 발현 데이터를 사용하며 다음의 두 가지 주요 단계를 포함하였다 : 1. 특징(유전자) 선택과 2. 분류단계이다. 특징 선택 단계에서는 제안된 균형 표본 유전 알고리즘에 기반하고 파킨스병 데이터베이스(ParkDB)의 유전자 발현 데이터를 위해 고안되었다. 제안된 제안 된 SBGA는 추가적 분석을 위해 ParkDB에서 활용 가능한 22,283개의 유전자 중에서 강인한 서브셋을 찾는다. 특징분류 단계에서는 정확한 파킨슨 병 진단을 위해 선택된 유전자 세트가 극한 기계학습의 훈련에 사용된다. 발견 된 강인한 유전자 서브세트는 안정된 일반화 성능으로 파킨슨 병 진단을 할 수 있는 ELM 분류기를 생성하게 된다. 제안된 연구에서 강인한 유전자 서브셋은 파킨슨병을 관장할 것으로 예측되는 24개의 바이오 표지자를 발견하는 데도 사용된다. 논문을 통해 발견된 강인 유전자 하위 집합은 SVM이나 PBL-McRBFN과 같은 기존의 파킨슨 병 진단 방법들을 통해 검증되었다. 실시된 두 가지 방법(SVM과 PBL-McRBFN)에 대해 모두 최대 일반화 성능을 나타내었다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 추계학술대회-지능형 정보기술과 미래조직 Information Technology and Future Organization
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• pp.365-373
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• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• International Journal of Contents
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• 제10권4호
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• pp.11-17
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• 2014

The influence of acquisition environment on music score images captured by a camera has not yet been seriously examined. All existing Optical Music Recognition (OMR) systems attempt to recognize music score images captured by a scanner under ideal conditions. Therefore, when such systems process images under the influence of distortion, different viewpoints or suboptimal illumination effects, the performance, in terms of recognition accuracy and processing time, is unacceptable for deployment in practice. In this paper, a novel, lightweight but effective approach for dealing with the issues caused by camera based music scores is proposed. Based on the staff line information, musical rules, run length code, and projection, all regions of interest are determined. Templates created from inverse filter are then used to recognize the music symbols. Therefore, all fragmentation and deformation problems, as well as missed recognition, can be overcome using the developed method. The system was evaluated on a dataset consisting of real images captured by a smartphone. The achieved recognition rate and processing time were relatively competitive with state of the art works. In addition, the system was designed to be lightweight compared with the other approaches, which mostly adopted machine learning algorithms, to allow further deployment on portable devices with limited computing resources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권1호
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• pp.414-430
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• 2016

Verifier-local revocation (VLR) seems to be the most flexible revocation approaches for any group signature scheme, because it just only requires the verifiers to possess some up-to-date revocation information, but not the signers. Langlois et al. (PKC 2014) proposed the first VLR group signature based on lattice assumptions in the random oracle model. Their scheme has at least Õ(n²) ⋅ log N bit group public key and Õ(n) ⋅ log N bit signature, respectively. Here, n is the security parameter and N is the maximum number of group members. In this paper, we present a simpler lattice-based VLR group signature, which is more efficient by a O(log N) factor in both the group public key and the signature size. The security of our VLR group signature can be reduced to the hardness of learning with errors (LWE) and small integer solution (SIS) in the random oracle model.