• Journal of The Korean Association For Science Education
• /
• v.37 no.5
• /
• pp.847-857
• /
• 2017

This study examined the effect of the Collaborative Problem-Solving for Character Competence (CoProC) instruction model within the context of secondary science education. The participants of this study were comprised of 143 Korean students, each of whom was in the 10th grade spread across four class cohorts. These cohorts were further divided into an experimental group (comprised of 73 students from two different classes), which received the CoProC program; and a control group (70 students from two other classes), which did not. In order to assess the effect of CoProC instruction model upon participants' character competence, we designed and administered a Character Competence Test for participants. The CoProC instruction model consists of 3 fundamental steps: Preparation, Problem-solving, and Evaluation. Key character competence targeted in the CoProC program include caring, collaboration, communication, responsibility, respect, honesty, self-regulation, and the development of positive self-image. Thus, these same qualities were targeted and analyzed in the Character Competence Test, which was administered before and after the CoProC activities. The results show a significant increase in the experimental group's competency for caring, collaboration, responsibility, respect, and self-regulation when compared to the control group. Based on these results, we have found that CoProC instruction model to be an effective teaching intervention toward cultivating character competence in a secondary science education setting.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.101-125
• /
• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.

• Journal of Korea Entertainment Industry Association
• /
• v.15 no.8
• /
• pp.255-267
• /
• 2021

The purpose of this study is to present basic data for systematic and effective basic medical education by developing a flipped learning class design model using smart tools and verifying its validity. To this end, in this study, a model proposal was developed based on literature review, and its validity was verified through expert review and field application. In this study, as a flipped learning class design model using smart tools, RECIPE(R: Ready, E: Establish a Plan, C: Create and Connect Media, I: Into the Classroom, P: Process-focused Assessment, E: Evaluation) model was developed. This model is a model that enhances the learning effect by applying an appropriate smart tool at each stage of designing flipped learning. As a result of applying this model to the development of'Anatomy'and'Neuroscience'lectures in the first semester of 2019, students' interest and satisfaction are high, and it is proposed as a specialized model in the field of basic medicine. Therefore, the RECIPE model developed in this study can be applied to various basic medicine-related classes, and it is expected that students will be able to understand basic medicine through the design of the flipped learning class based on this.

• KIPS Transactions on Software and Data Engineering
• /
• v.13 no.1
• /
• pp.35-49
• /
• 2024

Video captioning technology, as a significant outcome of the integration between computer vision and natural language processing, has emerged as a key research direction in the field of artificial intelligence. This technology aims to achieve automatic understanding and language expression of video content, enabling computers to transform visual information in videos into textual form. This paper provides an initial analysis of the research trends in deep learning-based video captioning and categorizes them into four main groups: CNN-RNN-based Model, RNN-RNN-based Model, Multimodal-based Model, and Transformer-based Model, and explain the concept of each video captioning model. The features, pros and cons were discussed. This paper lists commonly used datasets and performance evaluation methods in the video captioning field. The dataset encompasses diverse domains and scenarios, offering extensive resources for the training and validation of video captioning models. The model performance evaluation method mentions major evaluation indicators and provides practical references for researchers to evaluate model performance from various angles. Finally, as future research tasks for video captioning, there are major challenges that need to be continuously improved, such as maintaining temporal consistency and accurate description of dynamic scenes, which increase the complexity in real-world applications, and new tasks that need to be studied are presented such as temporal relationship modeling and multimodal data integration.

• Journal of the Korean GEO-environmental Society
• /
• v.19 no.10
• /
• pp.21-26
• /
• 2018

Soil-water characteristic curve, which is called soil retention curve, is required to explore water flows in unsaturated soils, relative permeability of water in multi-phase fluids flow, and change to stiffness and volume of soils. Thus, the understanding of soil-water characteristic curves of soils help us explore the behavior of soils inclduing fluids. Biopolymers are environmental-friendly materials, which can be completely degraded by microbes and have been believed not to affect the nature. Thus, various biopolymers such as deacetylated power, polyethylene oxide, xanthan gum, alginic acid sodium salt, and polyacrylic acid have been studies for the application to soil remediation, soil improvement, and enhanced oil recovery. PAA (polyacrylic acid) is one of biopolymers, which have shown a great effect in enhanced oil recovery as well as soil remediation because of the improvement of water-flood performance by mobility control. The study on soil-water characteristic curves of sandy soils containing PAA (polyacrylic acid) has been conducted through experimentations and theoretical models. The results show that both capillary entry pressure and residual water saturation dramatically increase according to the increased concentration of PAA (polyacrylic acid). Also, soil-water characteristic curves by theoretical models are quite well consistent with the results by experimental studies. Thus, soil-water characteristic curves of sandy soils containing biopolymers such as PAA (polyacrylic acid) can be estimated using fitting parameters for the theoretical model.

• Journal of Korea Multimedia Society
• /
• v.13 no.5
• /
• pp.722-729
• /
• 2010

Prediction problems are widely used in medical domains. For example, computer aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs with nonlinear kernels like RBF kernels, have shown superior accuracy in prediction problems. However, they are not preferred by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized Radial Basis Function (LRBF) was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models, VRIFA visualizes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The area under the ROC curve (AUC) can be used to evaluate the prediction result when the data set is highly imbalanced. The tool can be used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.43-45
• /
• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• Journal of Venture Innovation
• /
• v.7 no.1
• /
• pp.77-90
• /
• 2024

While discussions on ESG are actively taking place in Europe and other countries, the number of countries pushing for mandatory ESG information disclosure related to non-financial information of listed companies is rapidly increasing. However, as companies respond to mandatory global ESG information disclosure, problems are emerging such as the stringent requirements of global ESG disclosure standards, the complexity of data management, and a lack of understanding and preparation of the ESG system itself. In addition, it requires a reasonable analysis of how business management opportunities and risk factors due to climate change affect the company's financial impact, so it is expected to be quite difficult to analyze the results that meet the disclosure standards. In order to perform tasks such as ESG management activities and information disclosure, data of various types and sources is required and management through an information system is necessary to measure this transparently, collect it without error, and manage it without omission. Therefore, in this study, we designed an ESG data integrated management model to integrate and manage various related indicators and data in order to transparently and efficiently convey the company's ESG activities to various stakeholders through ESG information disclosure. A framework for implementing an information system to handle management was developed. These research results can help companies facing difficulties in ESG disclosure at a practical level to efficiently manage ESG information disclosure. In addition, the presentation of an integrated data management model through analysis of the ESG disclosure work process and the development of an information system to support ESG information disclosure were significant in the academic aspects needed to study ESG in the future.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10b
• /
• pp.43-45
• /
• 2004

웹 서비스의 등록, 검색, 조합, 실행을 자동적으로 수행할 수 있도록 웹 서비스에 시맨틱 웹 기술을 적용시킨 것이 시맨틱 웹 서비스(Semantic Web Services)이다. 시맨틱 웹 서비스는 원하는 서비스를 찾으려는 사용자의 노력을 줄이기 위해 기계가 이해할 수 있는 정보를 서비스에 추가시킴으로써 정확하게 원하는 서비스를 찾을 수 있게 해준다. 수많은 서비스들 중 같은 역할을 하는 서비스들이 있을 수가 있다. 그 중 하나의 서비스만을 선택해야 할 때 서비스의 기능이 아닌 질적인 측면에서 QoS(Quality of Service)를 고려하면 최선의 서비스를 선택을 하는데 도움을 줄 수 있다. 따라서 시맨틱 웹 서비스의 검색과 조합의 측면에서 매치메이킹(matchmaking)에 대한 연구의 하나로써 QoS의 매칭에 대한 연구를 수행하게 되었다. 기존의 QoS 매칭과 관련된 연구에서 QoS의 유사도를 계산하는 방법은 QoS 요소의 값의 특성을 반영하지 않는다는 것과 QoS 유사도의 순위가 놓더라도 좋은 서비스라는 것을 의미하지 않는다는 두 가지 문제점을 발견하였다. 따라서 본 논문에서는 기존 연구의 문제점을 해결할 수 있는 방법을 제시하고자 한다. 먼저 기존의 웹 서비스 관련 연구에서 논의된 여러 QoS 모델을 수렴하여 QoS 모델의 요소들을 결정하였다. 그리고 기존 연구의 두 가지 문제점을 해결하기 위해 각 QoS 요소의 표준편차를 이용한 표준 값을 구하여 QoS 요소의 값의 특성을 반영하였다. 또한 매칭 결과 순위가 높은 것이 사용자에게 더 선호되는 좋은 서비스라는 것을 보장하는 메커니즘을 제안하였다.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.15 no.5
• /
• pp.82-88
• /
• 2011

In the present work, FEM analyses are carried out to investigate the fractures occurred within the structural part in the course of combustion experiment. The loss of structural integrity stems from the localized deformation and the damage induced due to a severe change in the thermal load. Moreover, the two-back stress evolution model is proposed using the Armstrong-Frederick and the Phillips' rules to depict the plastic deformation, and the continuum damage mechanics is also incorporated into the present model. It is noted that the present model is able to formulate a wide range of constitutive description with ease. The numerical results depicts that a severe strain localization and damage evolution can be obtained depending on the dominant back stress.