• Korean Institute of Interior Design Journal
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• v.17 no.1
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• pp.138-145
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• 2008

This study is conducted to formulate various spatial compositions for mixed-use elementary schools in order to use them as community centers. Accordingly, this study is conducted to analyze currently operating community-use elementary schools to find out about the current condition of mixed-use facilities, and the types of mixed-use facilities will be derived from the analysis. In addition, a more effective method of planning mixed-use elementary schools is investigated. By incorporating various educational curricula and teaching/learning activities, major points, which should be considered when planning elementary schools, will be suggested to effectively respond to the changes of educational environment in the future. The results show that in terms of inner traffic flow type, 9 schools out of the schools under study showed a unificated traffic flow type or a cross traffic flow type. When traffic overlaps, problems of noise, visual distraction, and decreased learning performance can appear. To prevent the conflict between areas and to manage areas effectively, each area should be differentiated by installing doors and shutters and by marking guides, and the facility used for community residents and students should also be scheduled separately to avoid time conflict. In addition, to accommodate the changes of users, flexible space planning options should be considered.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.10a
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• pp.231-234
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• 2005

The high temperature deformation behavior of AZ 31 Mg alloy was investigated by designing a back propagation neural network that uses a gradient descent-learning algorithm. A neural network modeling is an intelligent technique that can solve non-linear and complex problems by learning from the samples. Therefore, some experimental data have been firstly obtained from continuous compression tests performed on a thermo-mechanical simulator over a range of temperatures $(250-500^{\circ}C)$ with strain rates of $0.0001-100s^{-1}$ and true strains of 0.1 to 0.6. The inputs for neural network model are strain, strain rate, and temperature and the output is flow stress. It was found that the trained model could well predict the flow stress for some experimental data that have not been used in the training. Workability of a material can be evaluated by means of power dissipation map with respect to strain, strain rate and temperature. Power dissipation map was constructed using the flow stress predicted from the neural network model at finer Intervals of strain, strain rates and subsequently processing maps were developed for hot working processes for AZ 31 Mg alloy. The safe domains of hot working of AZ 31 Mg alloy were identified and validated through microstructural investigations.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.45-50
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• 2022

The vehicle motion in urban environment is determined by surrounding traffic flow, which cause understanding the flow to be a factor that dominantly affects the motion planning of the vehicle. The traffic flow in this urban environment is accessed using various urban infrastructure information. This paper represents a color recognition algorithm for traffic lights to perceive traffic condition which is a main information among various urban infrastructure information. Deep learning based vision open source realizes positions of traffic lights around the host vehicle. The data are processed to input data based on whether it exists on the route of ego vehicle. The colors of traffic lights are estimated through pixel values from the camera image. The proposed algorithm is validated in intersection situations with traffic lights on the test track. The results show that the proposed algorithm guarantees precise recognition on traffic lights associated with the ego vehicle path in urban intersection scenarios.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.289-294
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• 2015

This paper proposes an autonomous machine learning method applicable to the BCI(Brain-Computer Interface) is based on the self-organizing Kohonen method, one of the exemplary method of unsupervised learning. In addition we propose control method of learning region and self machine learning rule using an interactive function. The learning region control and machine learning was used to control the side effects caused by interaction function that is based on the self-organizing Kohonen method. After determining the winner neuron, we decided to adjust the connection weights based on the learning rules, and learning region is gradually decreased as the number of learning is increased by the learning. So we proposed the autonomous machine learning to reach to the network equilibrium state by reducing the flow toward the input to weights of output layer neurons.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.4
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• pp.283-292
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• 2020

Artificial intelligence, which is one of the representative images of the 4th industrial revolution, has been highly recognized since 2016. This paper analyzed domestic paper trends for 'Artificial Intelligence', 'Machine Learning', and 'Deep Learning' among the domestic papers provided by the Korea Academic Education and Information Service. There are approximately 10,000 searched papers, and word count analysis, topic modeling and semantic network is used to analyze paper's trends. As a result of analyzing the extracted papers, compared to 2015, in 2016, it increased 600% in the field of artificial intelligence, 176% in machine learning, and 316% in the field of deep learning. In machine learning, a support vector machine model has been studied, and in deep learning, convolutional neural networks using TensorFlow are widely used in deep learning. This paper can provide help in setting future research directions in the fields of 'artificial intelligence', 'machine learning', and 'deep learning'.

• English Language & Literature Teaching
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• v.7 no.2
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• pp.55-75
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• 2002

The present study examined whether Kang's (1999) model of the relationships among language learning strategy use and language proficiency for the Asian students could be applied to a more heterogeneous group. In Kang's study, he collected information of language learning strategies of 957 foreign students learning English as a second language in American colleges through a questionnaire. He also measured the subjects' language proficiency with the Institutional Testing Program TOEFL (Test of English as a Foreign Language). This study analyzed the same data without the limitation of cultural identity. Structural equation modeling was used to model the relationships among strategy use and language proficiency. Then, the model of the present study was descriptively compared with Kang's (1999) model for the Asian students. The overall flow of the relationship paths appeared to vary very little across the two models, which would have indicated that the generalizability of Kang's (1999) model could be extended more than originally examined. (156)

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.9-15
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• 2021

In this paper, the Embedded MEMS system to the power apparatus used Edge Impulse machine learning tools and therefore an improved predictive system design is implemented. The proposed MEMS embedded system is developed based on nRF52840 system and the sensor with 3-Axis Digital Magnetometer, I2C interface and magnetic measurable range ±120 uT, BM1422AGMV which incorporates magneto impedance elements to detect magnetic field and the ARM M4 32-bit processor controller circuit in a small package. The MEMS embedded platform is consisted with Edge Impulse Machine Learning and system driver implementation between hardware and software drivers using SensorQ which is special queue including user application temporary sensor data. In this paper by experimenting, TensorFlow machine learning training output is applied to the power apparatus for analyzing the status such as "Normal, Warning, Hazard" and predicting the performance at level of 99.6% accuracy and 0.01 loss.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.383-391
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• 2005

The paper is devoted to some flow shop scheduling problems, where job processing times are defined by functions dependent on their positions in the schedule. An example is constructed to show that the classical Johnson's rule is not the optimal solution for two different models of the two-machine flow shop scheduling to minimize makespan. In order to solve the makespan minimization problem in the two-machine flow shop scheduling, we suggest Johnson's rule as a heuristic algorithm, for which the worst-case bound is calculated. We find polynomial time solutions to some special cases of the considered problems for the following optimization criteria: the weighted sum of completion times and maximum lateness. Some furthermore extensions of the problems are also shown.

• Journal of the Korean Society of Visualization
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• v.19 no.2
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• pp.32-40
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• 2021

Vapor-driven solutal Marangoni flow is governed by the concentration distribution of solutes on a liquid-gas interface. Typically, the flow structure is investigated by particle image velocimetry (PIV). However, to develop a theoretical model or to explain the working mechanism, the concentration distribution of solutes at the interface should be known. However, it is difficult to achieve the concentration profile theoretically and experimentally. In this paper, to find the concentration distribution of solutes around 2D droplet, the reverse tracking method with an artificial neural network based on PIV data was performed. Using the method, the concentration distribution of solutes around a 2D droplet was estimated for actual flow data from PIV experiment.