• KIEAE Journal
• /
• v.17 no.5
• /
• pp.95-100
• /
• 2017

Purpose: The purpose of this study is to investigate the prior art based on deep learning to objectively calculate the metabolic rate which is the subjective factor for the PMV optimum control and to make a plan for future research based on this study. Methods: For this purpose, the theoretical and technical review and applicability analysis were conducted through various documents and data both in domestic and foreign. Results: As a result of the prior art research, the machine learning model of artificial neural network and deep learning has been used in various fields such as speech recognition, scene recognition, and image restoration. As a representative case, OpenCV Background Subtraction is a technique to separate backgrounds from objects or people. PASCAL VOC and ILSVRC are surveyed as representative technologies that can recognize people, objects, and backgrounds. Based on the results of previous researches on deep learning based on metabolic rate for occupational metabolic rate, it was found out that basic technology applicable to occupational metabolic rate calculation technology to be developed in future researches. It is considered that the study on the development of the activity quantity calculation model with high accuracy will be done.

• Educational Technology International
• /
• v.11 no.1
• /
• pp.27-46
• /
• 2010

The purpose of this paper is to apply the newly developed SICAT teaching and learning model to the actual scene of teaching and learning and draw a point of discussion for utilizing teaching and learning model, by uncovering the satisfaction of students and the inhibiting/facilitating elements when using the model. SICAT(Scientific Inquiry and Creative Activity with Technology; from here on SICAT), a teaching and learning model custom-built for engineering education, was developed, as more and more people paid attention to the demand for creative engineers. It was developed from the basis of PBL(Problem Based Learning), includes three sub-types which can be applied to the actual theory, design, and experimentation fields within engineering education. The three sub-types, which are ARDA(Analysis-Reasoning Activity & Discussion-Argumentation Activity), CoCD (Collaboration Activity & Capstone Design Activity), and ReSh(Reflection Activity & Sharing Activity), respectively support deductive and argumentation activities, creative design and collaboration activities, and retrospection and sharing activities. However, no research has been conducted to investigate whether or not there are inhibiting or facilitating elements in the application procedure, or what the rate of satisfaction for students is, when applying the SICAT model, which was newly developed to innovate existing engineering education, to the actual site of teaching and learning. Therefore, this research applied three types of SICAT teaching and learning models to the theory, design, and experimentation classes at the department of materials science and engineering at Hanyang University for eight weeks. After application, the students, teachers and tutors were surveyed and interviewed, and then the results analyzed in order to uncover inhibiting/facilitating elements and the rate of satisfaction. The satisfaction rate of students from the SICAT teaching and learning model was 3.78(in a perfect score of 5: The A type-3.65, The C type-3.80, The R type-3.90), and inhibiting/facilitating elements were drawn from the aspects of learning activities, support system. In conclusion, they can be contributed for implications of SICAT teaching and learning model universal use at engineering education in University.

• Journal of Korean language education
• /
• v.29 no.4
• /
• pp.93-112
• /
• 2018

The purpose of this study is to deal with vocabulary in KFL. As a result of this study, learners learned vocabulary on average 43 points through contextual inference and introduction of the class to activate background knowledge. In particular, the implicit method showed the highest learning rate of 52 points, and the thematic method had a 41 point-learning rate. In contrast, the semantic method was the lowest with a 25 point-learning rate. There was no significant difference in the improvement rate of upper vocabulary learners, but in the case of the lower learner, there was significant difference in the improvement rate. The difference was not significant in the post-test relative gain rate of upper learners, but there was significant in lower learners. In the delayed test relative gain rate, the difference was significant in all groups. There was correlation between vocabulary difficulty and score, but there was no correlation with the thematic method. And there was no correlation between vocabulary difficulty, improvement rate and relative gain rate in all three classes. However, content understanding, lexical grade, improvement rate, and relative gain rate showed a significant correlation.

• Journal of Engineering Education Research
• /
• v.21 no.1
• /
• pp.37-43
• /
• 2018

The purpose of this study is to investigate the effects of peer tutoring and feedback on academic learning in university mathematics. We compared subject satisfaction and academic achievement between the test group and the control group. We classified the test group(82 participants) and the control group(134 non-participants) and then applied peer tutoring and feedback to the test group. The rest of the environment was the same except for participation in the program. According to results, it was confirmed that the subject satisfaction were significantly higher(significance level .05) in the test group, where the subject satisfaction were learning objectives and expectation, learning satisfaction, and learning effect. Furthermore, in the change of academic achievement, the rate of decrease was lower and the rate of increase was higher in the test group than the control group. The satisfaction of participants was 4.33(Likert scale 5), and this trend tended to be same regardless of gender, high school course, or admission process.

• Journal of Information Processing Systems
• /
• v.16 no.5
• /
• pp.1048-1063
• /
• 2020

In this study, we propose a reinforcement learning agent to control the data transmission rates of nodes in carrier sensing multiple access with collision avoidance (CSMA/CA)-based wireless networks. We design a reinforcement learning (RL) agent, based on Q-learning. The agent learns the environment using the timeout events of packets, which are locally available in data sending nodes. The agent selects actions to control the data transmission rates of nodes that adjust the modulation and coding scheme (MCS) levels of the data packets to utilize the available bandwidth in dynamically changing channel conditions effectively. We use the ns3-gym framework to simulate RL and investigate the effects of the parameters of Q-learning on the performance of the RL agent. The simulation results indicate that the proposed RL agent adequately adjusts the MCS levels according to the changes in the network, and achieves a high throughput comparable to those of the existing data transmission rate adaptation schemes such as Minstrel.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.7 no.5
• /
• pp.853-862
• /
• 2003

With the data base subject of first grade paper test of information handling technician, We proposed special method of evaluating learning ability directivity to judge that student can understand the contents of each chapter exactly or not, using assigned function and fuzzy deduction in this thesis. Using fuzzy logic, the proposed method of evaluating learning ability is dividing the presenting frequency of setting questions for examination about the subject of database into three rank and we can define this as the important. We applied the fuzzy assigned rate about the number of times of studying through the important of studying and the fuzzy assigned rate about formative evaluation to each of nine fuzzy deduction theories and than evaluated comprehension rate of learning. With the fuzzy grade about learning comprehension of each chapter and assigned rate about the score of generalized evaluation; We applied these two thing to the deduction rule of fuzzy and made it as defuzzifier and finally evaluated learning. We made that the result of eventual evaluating learning is very useful for learners to diagnosis learned contents by themselves and also it can be great material to judge that learners can get the goal of learning or not synthetically.

• The Journal of Information Technology
• /
• v.8 no.1
• /
• pp.119-124
• /
• 2005

Learning procedure in the neural network is updating of weights between neurons. Unadequate initial learning coefficient causes excessive iterations of learning process or incorrect learning results and degrades learning efficiency. In this paper, adaptive learning algorithm is proposed to increase the efficient in the learning algorithms of Kohonens Self-Organization Neural networks. The algorithm updates the weights adaptively when learning procedure runs. To prove the efficiency the algorithm is experimented to clustering of the random weight. The result shows improved learning rate about 42~55% ; less iteration counts with correct answer.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.8 no.2
• /
• pp.464-470
• /
• 2004

We propose an enhanced backpropagation algorithm by auto-tuning of learning rate using fuzzy control system for performance improvement of backpropagation algorithm. We propose two methods, which improve local minima and loaming times problem. First, if absolute value of difference between target and actual output value is smaller than $\varepsilon$ or the same, we define it as correctness. And if bigger than $\varepsilon$, we define it as incorrectness. Second, instead of choosing a fixed learning rate, the proposed method is used to dynamically adjust learning rate using fuzzy control system. The inputs of fuzzy control system are number of correctness and incorrectness, and the output is the Loaming rate. For the evaluation of performance of the proposed method, we applied the XOR problem and numeral patterns classification The experimentation results showed that the proposed method has improved the performance compared to the conventional backpropagatiot the backpropagation with momentum, and the Jacob's delta-bar-delta method.

• Journal of Korea Multimedia Society
• /
• v.25 no.6
• /
• pp.878-885
• /
• 2022

Research in the field of computer vision based on deep learning is being actively conducted. However, deep learning-based models have vulnerabilities in adversarial attacks that increase the model's misclassification rate by applying adversarial perturbation. In particular, in the case of FGSM, it is recognized as one of the effective attack methods because it is simple, fast and has a considerable attack success rate. Meanwhile, as one of the efforts to visualize deep learning models, Grad-CAM enables visual explanation of convolutional neural networks. In this paper, I propose a method to generate adversarial examples with high attack success rate by applying Grad-CAM to FGSM. The method chooses fixels, which are closely related to labels, by using Grad-CAM and add perturbations to the fixels intensively. The proposed method has a higher success rate than the FGSM model in the same perturbation for both targeted and untargeted examples. In addition, unlike FGSM, it has the advantage that the distribution of noise is not uniform, and when the success rate is increased by repeatedly applying noise, the attack is successful with fewer iterations.

• Journal of KIISE:Computing Practices and Letters
• /
• v.8 no.3
• /
• pp.344-355
• /
• 2002

Having a variety of good characteristics against other pattern recognition techniques, Multilayer Perceptron (MLP) has been used in wide applications. But, it is known that Error Backpropagation (EBP) algorithm which MLP uses in learning has a defect that requires relatively long leaning time. Because learning data in pattern recognition contain abundant redundancies, in order to increase learning speed it is very effective to use online-based teaming methods, which update parameters of MLP pattern by pattern. Typical online EBP algorithm applies fixed learning rate for each update of parameters. Though a large amount of speedup with online EBP can be obtained by choosing an appropriate fixed rate, fixing the rate leads to the problem that the algorithm cannot respond effectively to different leaning phases as the phases change and the learning pattern areas vary. To solve this problem, this paper defines learning as three phases and proposes a Instant Learning by Varying Rate and Skipping (ILVRS) method to reflect only necessary patterns when learning phases change. The basic concept of ILVRS is as follows. To discriminate and use necessary patterns which change as learning proceeds, (1) ILVRS uses a variable learning rate which is an error calculated from each pattern and is suppressed within a proper range, and (2) ILVRS bypasses unnecessary patterns in loaming phases. In this paper, an experimentation is conducted for speaker verification as an application of pattern recognition, and the results are presented to verify the performance of ILVRS.