• Journal of Educational Research in Mathematics
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• v.15 no.4
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• pp.419-444
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• 2005

As a theoretical background for this research, the literatures which focus on teaching and loaming of connecting with mathematics and science were investigated. And the rationale of integrated curriculum on the basis of the 7th mathematics curriculum and the goal of mathematics education and the forms of integrated curriculum and the integrated curriculum in foreign school were investigated. Depending on this review, the implement method of the integrated curriculum of mathematics and science in Korea school is suggested as the following: It requires designing inter-disciplinary into-grated problem or various teaching and learning materials which are based upon concept, skill, and principle by commonality found across the subject matter. Based on the analyses upon described above, three inter-disciplinary integrated teaching and learning materials were developed. And then, based on the case stud)', the research questions were analyzed in depth. Students could understand the developing process of linear function, develop the formula and grape representing the relationship between time and velocity, time and distance, and interpret realistic meaning of the slope.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• Journal of Web Engineering
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• v.21 no.1
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• pp.27-52
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• 2021

With recent increases in the number of network-connected devices, the number of edge computing services that provide similar functions has increased. Therefore, it is important to recommend an optimal edge computing service, based on quality-of-service (QoS). However, in the real world, there is a cold-start problem in QoS data: highly sparse invocation. Therefore, it is difficult to recommend a suitable service to the user. Deep learning techniques were applied to address this problem, or context information was used to extract deep features between users and services. However, edge computing environment has not been considered in previous studies. Our goal is to predict the QoS values in real edge computing environments with improved accuracy. To this end, we propose a GAIN-QoS technique. It clusters services based on their location information, calculates the distance between services and users in each cluster, and brings the QoS values of users within a certain distance. We apply a Generative Adversarial Imputation Nets (GAIN) model and perform QoS prediction based on this reconstructed user service invocation matrix. When the density is low, GAIN-QoS shows superior performance to other techniques. In addition, the distance between the service and user slightly affects performance. Thus, compared to other methods, the proposed method can significantly improve the accuracy of QoS prediction for edge computing, which suffers from cold-start problem.

• Archives of design research
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• v.16 no.3
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• pp.5-14
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• 2003

This study attempted to explore the guidelines for designing the contents of on-line learning in design field. The studies on the characteristics of design education and design major students as well as the guidelines for on-line learning were also reviewed. The guidelines for designing the contents of distance learning in design field were suggested through the synthesis and analysis of the former studies and the characteristics of design education and the learning style of Korean students. It could be concluded that the considerations were categorized into the following factors; quality of instruction, quality of presentation technique, quality of interaction, quality of evaluation and other surrounding factors. The finding can be used as a helpful guideline for the instructors of the design field who start on-line learning.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.1
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• pp.32-42
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• 2000

This paper presents an on-line learning algorithm for sequential construction of generalized radial basis function networks (GRBFNs) to model nonlinear systems from empirical data. The GRBFN, an extended from of standard radial basis function (RBF) networks with constant weights, is an architecture capable of representing nonlinear systems by smoothly integrating local linear models. The proposed learning algorithm has a two-stage learning scheme that performs both structure learning and parameter learning. The structure learning stage constructs the GRBFN model using two construction criteria, based on both training error criterion and Mahalanobis distance criterion, to assign new hidden units and the linear local models for given empirical training data. In the parameter learning stage the network parameters are updated using the gradient descent rule. To evaluate the modeling performance of the proposed algorithm, simulations and their results applied to two well-known benchmarks are discussed.

• Journal of the Korean earth science society
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• v.41 no.3
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• pp.291-306
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• 2020

In this study, we developed a learning progression for the structure of the solar system using multi-tier supply form items and validated its appropriateness. To this end, by applying Wilson's (2005) construct modeling approach, we set up 'solar system components,' 'size and distance pattern of solar system planets,' and 'solar system modeling' as the progress variables of the learning progression and constructed multi-tier supply form items for each of these variables. The items were applied to 150 fifth graders before and after the classes that dealt with the 'solar system and star' unit. To describe the results of the assessment, the students' responses to each item were categorized into five levels. By analyzing the Wright map that was created by applying the partial credit Rasch model, we validated the appropriateness of the learning progression based on the students' responses. In addition, the validity of the hypothetical pathway that was established in the learning progression was verified by tracking changes in the developmental level of students before and after the classes. The results of the research are as follows. The bottom-up research method that used multi-tier supply form items was able to elaborately set the empirical learning progression for the conceptualization of the structure of the solar system that is taught in elementary school. In addition, the validity of the learning progression was high, and the development of students was found to change with the learning progression.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.115-122
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• 2024

Multi-object tracking (MOT) is a vital component in understanding the surrounding environments. Previous research has demonstrated that MOT can successfully detect and track surrounding objects. Nonetheless, inaccurate classification of the tracking objects remains a challenge that needs to be solved. When an object approaching from a distance is recognized, not only detection and tracking but also classification to determine the level of risk must be performed. However, considering the erroneous classification results obtained from the detection as the track class can lead to performance degradation problems. In this paper, we discuss the limitations of classification in tracking under the classification uncertainty of the detector. To address this problem, a class update module is proposed, which leverages the class uncertainty estimation of the detector to mitigate the classification error of the tracker. We evaluated our approach on the VisDrone-MOT2021 dataset,which includes multi-class and uncertain far-distance object tracking. We show that our method has low certainty at a distant object, and quickly classifies the class as the object approaches and the level of certainty increases.In this manner, our method outperforms previous approaches across different detectors. In particular, the You Only Look Once (YOLO)v8 detector shows a notable enhancement of 4.33 multi-object tracking accuracy (MOTA) in comparison to the previous state-of-the-art method. This intuitive insight improves MOT to track approaching objects from a distance and quickly classify them.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.232-234
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• 2006

본 논문에서는 마르코프 결정 문제 (Markov decision problem)의 풀이 효율을 잴 수 있는 척도를 알아보기 위해 복잡계 네트워크 (complex network) 의 관점에서 MDP를 하나의 그래프로 나타내고, 그 그래프의 위상학적 성질들을 여러 네트워크 척도 (network measurements)들을 이용하여 측정하고 그 MDP의 풀이 효율과의 관계를 분석하였다. 실세계의 여러 문제들이 MDP로 표현될 수 있고, 모델이 알려진 경우에는 평가치 반복(value iteration)이나 모델이 알려지지 않은 경우에도 강화 학습(reinforcement learning) 알고리즘등을 사용하여 풀 수 있으나, 이들 알고리즘들은 시간 복잡도가 높아 크기가 큰 실세계 문제에 적용하기 쉽지 않다. 이 문제를 해결하기 위해 제안된 것이 MDP를 계층적으로 분할하거나, 여러 단계를 묶어서 수행하는 등의 시간적 추상화(temporal abstraction) 방법들이다. 시간적 추상화를 도입할 경우 MDP가 보다 효율적으로 풀리는 꼴로 바뀐다는 사실에 착안하여, MDP의 풀이 효율을 네트워크 척도를 이용하여 측정할 수 있는 여러 위상학적 성질들을 기반으로 분석하였다. 다양한 구조와 파라미터를 가진 MDP들을 사용해 네트워크 척도들과 MDP의 풀이 효율간의 관계를 분석해 본 결과, 네트워크 척도들 중 평균 측지 거리 (mean geodesic distance) 가 그 MDP의 풀이 효율을 결정하는 가장 중요한 기준이라는 사실을 알 수 있었다.

• Journal of the Korean Society for Library and Information Science
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• v.34 no.3
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• pp.61-76
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• 2000

A study on the remote library users showed that remote users preferred online databases and internet as information resources because of easy access and easy of use. However, they have used books and journals, which are printed resources, as main information resources because online databases and internet cannot satisfy their information needs. Also, 86.7 percent of remote users consider library services as an important issue to choose a distance learning program, and remote users extremely motivated by their professors to use library so that there are needs to have partnership between distance learning programs and library services. Finally, remote users consider authority and accuracy and contents of obtained information as important issues for their information rather than design of the databases, so that library should secure more digitalized information for remote users needs. This study is to contribute to improve quality of information services for remote users, however, it is not thoroughgoing enough, and there should be successive researches in the future.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.209-216
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• 2022

The modern development of higher education in Ukraine is the result of two main factors. One of them - the factor of social progress - reflects the transformations inherent in modern Ukrainian society. These include, first of all, the processes of democratization and the development of civic responsibility. European the choice of Ukraine, the integration of our state into the European space determine accordingly, the second factor influencing the development of domestic higher education - the trends that guide the progress of the European Higher Education Area (European Higher Education Area (hereinafter - EHEA) and the European Research Area Research Area, hereinafter - ERA). The strategy of information support of the educational process (approved by the European Commission in 2010) recognizes the leading role of higher education as a driver of social progress, accordingly states the priority - the development of free economic education and identifies indicators of such progress - the achievement and international attractiveness of European free economic education. The information support of modernization challenges in higher education are aimed at the educational process, the leadership position of students, in particular through promotion and implementation of leading achievements and best practices in the context of globalization.