• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3984-4005
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• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

• Industry Promotion Research
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• v.1 no.2
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• pp.63-69
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• 2016

This study analyzed the effect of Chung-nam province small manufaturing firm male workers' participation in training on perceptions of effectiveness. The study results showed that Off-the-job training satisfaction significantly affected satisfaction of training system and helpfulness of self-development. It means that workers considered lecture or e-learning method is better than on-the-job training, so it is suggested that small manufacturing businesses should establish more organized training system for on-the-job training because workers' perception of satisfaction and effectiveness of OJT was lower than Off-JT. This study provided implications for verifying the effectiveness depend on the type of training and presenting important points to enhance workers' satisfaction of education and training.

• Structural Engineering and Mechanics
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• v.82 no.3
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• pp.271-282
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• 2022

The active learning surrogate model based on adaptive sampling strategy is increasingly popular in reliability analysis. However, most of the existing sampling strategies adopt the trial and error method to determine the size of the Monte Carlo (MC) candidate sample pool which satisfies the requirement of variation coefficient of failure probability. It will lead to a reduction in the calculation efficiency of reliability analysis. To avoid this defect, a new method for determining the optimal size of the MC candidate sample pool is proposed, and a new structural reliability analysis method combining polynomial chaos-based Kriging model (PC-Kriging) with adaptive sampling region is also proposed (PCK-ASR). Firstly, based on the lower limit of the confidence interval, a new method for estimating the optimal size of the MC candidate sample pool is proposed. Secondly, based on the upper limit of the confidence interval, an adaptive sampling region strategy similar to the radial centralized sampling method is developed. Then, the k-means++ clustering technique and the learning function LIF are used to complete the adaptive design of experiments (DoE). Finally, the effectiveness and accuracy of the PCK-ASR method are verified by three numerical examples and one practical engineering example.

• Research in Mathematical Education
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• v.18 no.3
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• pp.171-185
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• 2014

Mathematically deductive reasoning skill is one of the major learning objectives stated in senior secondary curriculum (CDC & HKEAA, 2007, page 15). Ironically, student performance during routine assessments on geometric reasoning, such as proving geometric propositions and justifying geometric properties, is far below teacher expectations. One might argue that this is caused by teachers' lack of relevant subject content knowledge. However, recent research findings have revealed that teachers' knowledge of teaching (e.g., Ball et al., 2009) and their deductive reasoning skills also play a crucial role in student learning. Prior to a comprehensive investigation on teacher competency, we use a case study to investigate teachers' knowledge competency on how to teach their students to mathematically argue that, for example, two triangles are congruent. Deductive reasoning skill is essential to geometry. The initial findings indicate that both subject and pedagogical content knowledge are essential for effectively teaching this challenging topic. We conclude our study by suggesting a method that teachers can use to further improve their teaching effectiveness.

• Journal of the Korea Safety Management & Science
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• v.22 no.1
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• pp.9-14
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• 2020

Artificial intelligence (AI) has been applied to most industries by enhancing automation and contributing greatly to efficient processes and high-quality production. This research analyzes the applications of AI-based automobile accident prevention systems. It deals with AI-based collision prevention systems that learn information from various sensors attached to cars and AI-based accident detection systems that automatically report accidents to the control center in the event of a collision. Based on the literature review, technological and institutional changes are taking place at the national levels, which recognize the effectiveness of the systems. In addition, start-ups at home and abroad as well as major car manufacturers are in the process of commercializing auto parts equipped with AI-based collision prevention technology.

• The Journal of Korean Association of Computer Education
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• v.5 no.1
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• pp.109-115
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• 2002

Nowadays, Web-based bulletin board system is widely used for e-learning because of its effectiveness in communicating and information sharing. But harmful or irrelevant contents are frequently posted and these affect students in learning community based on Web-boards and the ill effect of Web-board is revealed. Some Web-boards provide features that allow users can filter specific words to resolve this problem but those features have shortcomings. Using these features the content that contains harmful words is always removed even if the whole meaning of it isn't harm and user who uses harmful words habitually cannot have an opportunity of having his/her bad habit be corrected. In this paper, we proposed new way of dealing with harmful words that overcomes those shortcomings.

• 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 Korean Academy of Nursing
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• v.40 no.2
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• pp.151-160
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• 2010

Purpose: The purpose of this study was to evaluate the effects of teaching method using Standardized Patients (SPs) on nursing competence, self-directed learning readiness, and problem solving ability-focusing on subcutaneous insulin injection. Methods: This research was a nonequivalent control group non-synchronized post-test design. The subjects consisted of 62 junior nursing students at E University. Scenarios to train SPs and checklists to evaluate the students' competence were developed by our research team. The experimental group (n=31) participated in the teaching class using SPs. The control group (n=31) received traditional practice education. The collected data were analyzed with descriptive analysis, $\chi^2$/Fisher's exact test, t-test, Pearson's correlation coefficient, and Cronbach's $\alpha$ using SPSS WIN 14.0 Program. Results: The mean scores of competence, self-directed learning readiness, and problem solving were significantly higher in the experimental group than the control group. Conclusion: As confirmed by this research findings, the teaching method using SPs was more effective than the traditional method to improve junior nursing students' competence, self-directed learning readiness, and problem solving. Therefore, It is necessary to develop a various of scenarios and to testify their effectiveness.

• Journal of The Korean Association For Science Education
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• v.39 no.1
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• pp.89-99
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• 2019

The purpose of this study is to investigate the effect of students' formation of evolutionary concept and learning on the development of digital teaching materials. The explanation of biological evolution, which explains the changes that living organisms undergo over a long period of time, can provide various contents for use in a book. The production and editing of images in digital textbooks would provide explanation of difficult concepts in a fun way. For this study, we designed instructional materials consisting of four class hours using iBooks Author, an electronic book authoring tool based on the 5E learning cycle model. In order to verify the effectiveness of the developed digital textbooks, we compared instructions by the general textbooks to those using digital textbooks. Both teaching through general textbook form and teaching using digital textbook materials had a significant effect on the formation of the concept of evolution, but interest in biological science and evolution increased significantly only in the group taught using digital textbooks. As a result of testing the instruction effect by the digital textbooks by classifying the students by type, the group that is familiar with smart devices was more active and interesting in class depending on digital literacy. The satisfaction of the developed digital textbooks also showed a positive score in the group with high digital literacy. The results of this study suggest that the development of digital textbooks in the unit of evolution can be an instructional material for easy and interesting approach to difficult concepts in the teaching of evolution.

• Journal of The Korean Association of Information Education
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• v.18 no.2
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• pp.357-370
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• 2014

The purpose of this research was to develop the tools needed to evaluate the effectiveness of using digital textbooks and smart education. We then developed the tools to evaluate the effectiveness of smart education and digital textbook utilization, which were an identification of 1) seven essential 21st century skills, definitions of each, and prerequisite abilities; 2) five 21st century teacher competencies, definitions of each, and prerequisite abilities; To develop the questionnaire, we conducted a literature review in this area, consulted experts, observed classes, interviewed members of focus groups, and met with policy makers from the Ministry of Education and KERIS. The student questionnaire(26 Questions developed) included; creativity and innovation, critical thinking and problem solving, communication, collaboration, ICT literacy, self-directed learning, and adaptability. The teacher questionnaire(24 questions developed) included; 21st Century Skills, ICT Literacy, Rapport building with learners, Instructional design, Evaluation and reflection. The tools we developed will be able to use for evaluating the effectiveness of smart education and digital textbooks.