• Journal of Korean Elementary Science Education
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• v.37 no.3
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• pp.254-266
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• 2018

The aim of this study is to analyze STEAM lessons focused on the learner's learning. This study was conducted on 4th-graders in Y city, Kyung-gi province. The lessons were based on a joint teaching plan for students through the teacher learning community (TLC) with three teachers from the same school. Each of the three classes that conducted the class was selected and analyzed as the main center of observation by three students. The conclusions from this study are as follows: First, we identified that different levels of learners are learning in STEAM lessons through a learner-centered class analysis. Some students arrived on their own by taking the initiative in class, others by consulting with a group of friends, and others needed active teacher guidance to learn. Second, Depending on the level and characteristics of the students, some learning criteria were not reached. Some students need guidance at a glance level, and others need individually instructed or guided activities. Teachers need to keep an eye out for students and give them an appropriate level of guidance during class. In STEAM lessons, it appears that students of different levels and characteristics can immerse themselves in their own way, as well as the clear guidance of activity for their students.

• Journal of The Korean Association of Information Education
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• v.2 no.2
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• pp.209-214
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• 1998

The advent of powerful hardware and advances in high speed networks enabled synchronous learning, with teachers and students being geographically distributed but connected via computer networks. Today's synchronous learning systems are mainly based on video conferencing technology which provides audio, video, and joint editing of documents only, but does not take into account the specific requirements of teaching, for instance, controlling the course of instruction, raising hands, or reference pointing. A shared whiteboard is often the core part of these systems. Therefore, in this paper, we look into implementation issues in whiteboards of distance learning systems. Particularly, system architectures and concurrency control mechanisms are considered.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.5
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• pp.438-444
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• 2013

This paper presents the replication of a desired vibration response by iterative learning control (ILC) system for a vibration motion replication actuator. The vibration motion replication actuator has parameter uncertainties including system nonlinearity and joint nonlinearity. Vehicle manufacturers worldwide are increasingly relying on road simulation facilities that put simulated loads and stresses on vehicles and subassemblies in order to reduce development time. Road simulation algorithm is the key point of developing road simulation system. With the rapid progress of digital signal processing technology, more complex control algorithms including iterative learning control can be utilized. In this paper, ILC algorithm was utilized to produce simultaneously the six channels of desired responses using the Stewart platform composed of six linear electro-magnetic actuators with Halbach magnet array. The convergence rate and accuracy showed reasonable results to meet the requirement. It shows that the algorithm is acceptable to replicate multi-channel vibration responses.

• The Mathematical Education
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• v.53 no.2
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• pp.201-217
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• 2014

This research has an intention of developing a professional development program. We proposed an alternative mathematics teacher training model based on the situated learning theory, community of practice, and reflective practitioner. The developed mathematics teacher professional development program recruited participation unit that was consisted of 3 or 4 teachers in same school and total of 28 teachers from 9 schools. Also, there were 18 mentors to support each school. In this sense, it can be called multi-tired teacher community professional development program. Through the program, the teachers improve their teaching competency. Also, the operation ability of teacher learning community was improved. Learning community culture has been formed in each school. It shows ability that the explorative learning community can be operated voluntarily although the program finished. Furthermore, community shared corporate responsibility about open class. They recognize open class as a new method to improve community teaching ability than a tool to evaluate individual teaching ability.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.137-146
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• 2021

When controlling manipulator, degree of freedom is lost in singularity so specific joint velocity does not propagate to the end effector. In addition, control problem occurs because jacobian inverse matrix can not be calculated. To avoid singularity, we apply Deep Deterministic Policy Gradient(DDPG), algorithm of reinforcement learning that rewards behavior according to actions then determines high-reward actions in simulation. DDPG uses off-policy that uses 𝝐-greedy policy for selecting action of current time step and greed policy for the next step. In the simulation, learning is given by negative reward when moving near singulairty, and positive reward when moving away from the singularity and moving to target point. The reward equation consists of distance to target point and singularity, manipulability, and arrival flag. Dual arm manipulators hold long rod at the same time and conduct experiments to avoid singularity by simulated path. In the learning process, if object to be avoided is set as a space rather than point, it is expected that avoidance of obstacles will be possible in future research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3780-3797
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• 2022

The estimation of radio map (RM) is a fundamental and critical task for the network planning and optimization performance of mobile communication. In this paper, a RM estimation method is proposed based on a deep dual learning structure. This method can simultaneously and accurately reconstruct the urban building map (UBM) and estimate the RM of the whole cell by only part of the measured reference signal receiving power (RSRP). Our proposed method implements UBM reconstruction task and RM estimation task by constructing a dual U-Net-based structure, which is named RadioCycle. RadioCycle jointly trains two symmetric generators of the dual structure. Further, to solve the problem of interference negative transfer in generators trained jointly for two different tasks, RadioCycle introduces a dynamic weighted averaging method to dynamically balance the learning rate of these two generators in the joint training. Eventually, the experiments demonstrate that on the UBM reconstruction task, RadioCycle achieves an F1 score of 0.950, and on the RM estimation task, RadioCycle achieves a root mean square error of 0.069. Therefore, RadioCycle can estimate both the RM and the UBM in a cell with measured RSRP for only 20% of the whole cell.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2764-2781
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• 2024

Federated learning (FL) has been proposed as an emerging distributed machine learning framework, which lowers the risk of privacy leakage by training models without uploading original data. Therefore, it has been widely utilized in the Industrial Internet of Things (IIoT). Despite this, FL still faces challenges including the non-independent identically distributed (Non-IID) data and heterogeneity of devices, which may cause difficulties in model convergence. To address these issues, a local surrogate function is initially constructed for each device to ensure a smooth decline in global loss. Subsequently, aiming to minimize the system energy consumption, an FL approach for joint CPU frequency control and bandwidth allocation, called FCBAFL is proposed. Specifically, the maximum delay of a single round is first treated as a uniform delay constraint, and a limited-memory Broyden-Fletcher-Goldfarb-Shanno bounded (L-BFGS-B) algorithm is employed to find the optimal bandwidth allocation with a fixed CPU frequency. Following that, the result is utilized to derive the optimal CPU frequency. Numerical simulation results show that the proposed FCBAFL algorithm exhibits more excellent convergence compared with baseline algorithm, and outperforms other schemes in declining the energy consumption.

• Journal of the Korean Society of Physical Medicine
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• v.5 no.3
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• pp.405-412
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• 2010

Purpose : The purpose of this study was to assess the effects of the trunk muscle activity on bridging exercise according to the knee joint angle. Methods : Twenty-five healthy adults volunteered to participate in this study. Subjects were required to complete following four bridging exercises; knee joint flexion $120^{\circ}$, $90^{\circ}$, $60^{\circ}$, $45^{\circ}$. Surface electromyography from selected trunk muscles was normalized to maximum voluntary isometric contraction. Muscle activity was measured by QEMG-4 system(LXM 3204, Laxtha Korea). A repeated measures of one-way ANOVA with post-hoc Bonferroni's correction was used to determine the influence of bridging exercise on muscle activity for each muscle and descriptive statistics was used to determine local/global muscle ratio. Results : The internal oblique of bridging exercises $120^{\circ}$, $90^{\circ}$ showed significantly(p<.05). The erctor spinae of all bridging exercises showed significant excepted between $60^{\circ}$ and $45^{\circ}$(p<.05). Median of internal oblique/rectus abdominis ratio of $120^{\circ}$ was 4.41, $90^{\circ}$ was 3.94, $60^{\circ}$ was 3.58, $45^{\circ}$ was 3.39. Median of internal oblique/external oblique ratio of $120^{\circ}$ was 2.66, $90^{\circ}$ was 2.43, $60^{\circ}$ was 2.87, $45^{\circ}$ was 2.64. Conclusion : Angular motion decreasing with knee joint flexion made erector spinae activation increase. on the other hand, as decreasing abdomen muscle activation, the more performing motor learning is required for abdomen muscle strength and co-contraction for the trunk stabilization.

• Journal of Ocean Engineering and Technology
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• v.18 no.6 s.61
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• pp.79-83
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• 2004

The curved blocks which compose the bow and stem of a ship contain many skewed joints that are inclined horizontally and vertically. Most of these joints have a large fitness error and are continuously changing their form and are not easily accessible. The welding position and parameter values should be appropriately set in correspondence to the shape and the inclination of the joints. The welding parameters such as current, voltage, travel speed, and melting rate, are related to each other and their values must be in a specific limited range for the sound welding. These correlations and the ranges are dependent up on the kind and size of wire, shielding gas, joint shape and fitness. To determine these relationships, extensive welding experiments were performed. The experimental data were processed using several information processing technologies. The regression method was used to determine the relationship between current voltage, and deposition rate. When a joint is inclined, the weld bead should be confined to a the limited size, inorder to avoid undercut as well as overlap due to flowing down of molten metal by gravity. The dependency of the limited weld size which is defined as the critical deposited area on various factors such as the horizontally and vertically inclined angle of the joint, skewed angle of the joint, up or down welding direction and weaving was investigated through a number of welding experiments. On the basis of this result, an ANN system was developed to estimate the critical deposited area. The ANN system consists of a 4 layer structure and uses an error back propagation learning algorithm. The estimated values of the ANN were validated using experimental values.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.4
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• pp.259-265
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• 2016

In this paper, new method was proposed to classify lung tissues such as Broncho vascular, Emphysema, Ground Glass Reticular, Ground Glass, Honeycomb, Normal for early lung disease detection. 459 Statistical features was extraced from joint histogram matrix based on multi resolution analysis, volumetric LBP, and CT intensity, then dominant features was selected by using adaboost learning. Accuracy of proposed features and 3D AMFM was 90.1% and 85.3%, respectively. Proposed joint histogram based features shows better classification result than 3D AMFM in terms of accuracy, sensitivity, and specificity.