• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.270-278
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• 2023

We propose a container orchestration system for process workloads that combines the potential of big data and machine learning technologies to integrate enterprise process-centric workloads. This proposed system analyzes big data generated from industrial automation to identify hidden patterns and build a machine learning prediction model. For each machine learning case, training data is loaded into a data store and preprocessed for model training. In the next step, you can use the training data to select and apply an appropriate model. Then evaluate the model using the following test data: This step is called model construction and can be performed in a deployment framework. Additionally, a visual hierarchy is constructed to display prediction results and facilitate big data analysis. In order to implement parallel computing of PCA in the proposed system, several virtual systems were implemented to build the cluster required for the big data cluster. The implementation for evaluation and analysis built the necessary clusters by creating multiple virtual machines in a big data cluster to implement parallel computation of PCA. The proposed system is modeled as layers of individual components that can be connected together. The advantage of a system is that components can be added, replaced, or reused without affecting the rest of the system.

• Journal of The Korean Association For Science Education
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• v.25 no.3
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• pp.400-410
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• 2005

According to the cognitive conflict process model, student anxiety factor is known to have both positive and negative effects on student response behaviors in a conflict situation for conceptual change learning. However, there is little research that reveals what type of anxiety, either constructive or destructive, is related when conducting step-by-step experiments to resolve cognitive conflicts. This study attempted to learn the characteristic of explanatory hypothesis according to anxiety type after conducting five step-by-step experiments related to action and reaction concept. Results found that students who belonged to the types of 'conviction in logical misconception', 'insisting on additional variables', and 'reasonable modification' suggested explanatory hypothesis close to physical nature. On the other hand, those who showed the other five types of anxiety ('compatible predictions', 'dependence on others', 'fusion of past experience', 'lack of confidence', and 'conflict with past experience') suggested temporary supported hypothesis or simple explanatory hypothesis according to student intuition and simple observation. These results indicate that students in the above-mentioned five categories need more external interactions with instructors based on the type of anxiety related to student behavior. In addition, the results present student characteristics which instructors should be more attentive to when using step-by-step experiments to resolve cognitive conflicts.

• Communications of Mathematical Education
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• v.28 no.3
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• pp.395-422
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• 2014

In this paper, we obtain the step inteaching high school leve-based class utilizing cooperative learning lessons using level-type tutoring to improve academic achievement and mathematics attitudes. The details are as follows. First, we develop the teaching and learning model for the level-type instructional development and for the application to project work. Second, we seek to height academic achievement by applying the level-type work sheets in conjunction with cooperative learning. For this problem, we will focus on the following issues. First, how will you using level-type tutoring level TAI cooperative learning in order to improve academic achievement and develop the learning ability in mathematics? Second, how can you step utilizing TAI instructional level of cooperative learning in mathematics classes to improve mathematics learning attitudes? Third, how will you some reaction step work sheets utilizing level TAI cooperative learning of students for mathematics. Results of this study are as follows. First, in the experimental group compared to the comparison group was improved academic achievement. Second, in the experimental group compared to the comparison group learning attitudes could help. Third, the level of cooperative learning instructional model utilizing the TAI in the experimental group compared to the comparison group represents a significant response was seen.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.77-88
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• 2011

In distributed cognitive radio networks, cognitive radio devices which perform the channel sensing individually, are seriously affected by radio channel environments such as noise, shadowing and fading such that they can not property satisfy the maximum allowable interference level to the primary user. In the paper, we propose a Q-learning based channel access scheme for cognitive radios so as to satisfy the maximum allowable interference level to the primary user as well as to improve the throughput of cognitive radio by opportunistically accessing on the idle channels. In the proposed scheme, the pattern of channel usage of the primary user will be learned through Q-learning during the pre-play learning step, and then the learned channel usage pattern will be utilized for improving the sensing performance during the Q-learning normal operation step. Through the simulation, it is shown that the proposed scheme can provide bettor performance than the conventional energy detector in terms of the interference level to primary user and the throughput of cognitive radio under both AWGN and Rayleigh fading channels.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.149-156
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• 2019

The existing subtract image based intrusion detection system for CCTV digital images has a problem that it can not distinguish intruders from moving backgrounds that exist in the natural environment. In this paper, we tried to solve the problems of existing system by designing real - time intrusion detection system for CCTV digital image by combining subtract image based intrusion detection method and background learning artificial neural network technology. Our proposed system consists of three steps: subtract image based intrusion detection, background artificial neural network learning stage, and background artificial neural network evaluation stage. The final intrusion detection result is a combination of result of the subtract image based intrusion detection and the final intrusion detection result of the background artificial neural network. The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.

• Journal of The Korean Association For Science Education
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• v.24 no.1
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• pp.171-182
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• 2004

The purpose of this study is to analyze lesson plans from third to sixth grades of science and to find out teaching strategies in respects of learning functions provided by preservice elementary teachers in education university. On the whole, to control students' learning process preservice teachers used more shared-regulation strategy than strong teacher-regulation one. Teaching activities for regulative learning function were most used in strategy of strong teacher-regulation, and in strategy of shared-regulation those for cognitive learning functions were most used. But teaching activities for affective learning functions were used a little considered in both teaching strategies. In introduction step of instruction, affective and regulative learning functions were more instructed by strong teacher-regulation strategy and cognitive learning functions were more instructed by shared-regulation strategy. The affective, cognitive, and regulative learning functions were largely planned by shared-regulation teaching strategy in development. The regulative learning functions were planned by strong teacher-regulation strategy than by shared-regulation strategy and affective learning functions were considered a little bit in consolidation. There was a tendency that strong teacherregulation strategy was increased in lessons for fifth and sixth grade.

• Design Convergence Study
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• v.14 no.1
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• pp.1-13
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• 2015

The purpose of this paper is to find out what values users pursue from a smart device based education service. For this purpose, a survey was conducted using a questionnaire that was developed based on the results of literature review and user interviews. Thirteen user expectation values were developed from the results of the survey: individually customized learning, anytime anywhere learning, learning for career, learning through interaction, learning from diverse resources, learning through cooperation, learning through interchange, self-directed learning, learning within actual context, learning with feedback, learning in spare time, learning with motivation & compensation, and step-by-step learning. In addition, the results of the survey also showed that the user expectation values of women, high school students and people who responded that they knew the smart device based education service very well were higher than those of other users.

• Journal of Korean Elementary Science Education
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• v.28 no.2
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• pp.178-186
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• 2009

The science writing is recognized for useful learning method to boost up scientific thinking for all class course as well as traditional lecture and experiment. Many researches say that science writing is helpful to extend students' science knowledge and scientific attitude. By the way, the researchers thought that science writing can also improve the science process skill if students participate in delicately organized learning program. In this study, we had contrived the teaching & learning strategy of science writing to improve science process skills. The learning program covers all field of Klopfer's process skills with various forms of writing; explaining writing, logical writing, critical writing, and creative writing. The learning program has been developed for 5th grade students in the regular classes in order to enhance science process skills as well as knowledge and scientific attitude. Not to miss any process skill or various kinds of writing, we used 3 dimensional frame. The axes of the frames are science process skills, forms of writing, and science curriculum contents. The students are given the final writing theme at the beginning of each chapter. They drill science process skills step by step during the classes, and have a chance to talk each other before the final writing. They practice writing skills from one sentence to full article by degrees. The effect of the program was examined by students' work and TSPS (Test of Science Process Skill). The result showed that 5th grade students had a meaningful progress in science process skills as well as knowledge and scientific attitude. we could confirm it with examining students' work in the class.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.76-85
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• 2022

One of the most fundamental challenges when designing controllers for dynamic systems is the adjustment of controller parameters. Usually the system model is used to get the initial controller, but eventually the controller parameters must be manually adjusted in the real system to achieve the best performance. To avoid this manual tuning step, data-driven methods such as machine learning were used. Recently, reinforcement learning became one alternative of this problem to be considered as an agent learns policies in large state space with trial-and-error Markov Decision Process (MDP) which is widely used in the field of robotics. However, on initial training step, as an agent tries to explore to the new state space with random action and acts directly on the controller parameters in real systems, MDP can lead the system safety-critical system failures. Therefore, the issue of 'safe exploration' became important. In this paper we meet 'safe exploration' condition with Control Barrier Function (CBF) which converts direct constraints on the state space to the implicit constraint of the control inputs. Given an initial low-performance controller, it automatically optimizes the parameters of the control law while ensuring safety by the CBF so that the agent can learn how to predict and control unknown and often stochastic environments. Simulation results on a quadrotor UAV indicate that the proposed method can safely optimize controller parameters quickly and automatically.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.45-55
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• 2021

This paper has proposed an algorithm that detecting for dense small vehicle in large image efficiently. It is consisted of two Ensemble Deep-Learning Network algorithms based on Coarse to Fine method. The system can detect vehicle exactly on selected sub image. In the Coarse step, it can make Voting Space using the result of various Deep-Learning Network individually. To select sub-region, it makes Voting Map by to combine each Voting Space. In the Fine step, the sub-region selected in the Coarse step is transferred to final Deep-Learning Network. The sub-region can be defined by using dynamic windows. In this paper, pre-defined mapping table has used to define dynamic windows for perspective road image. Identity judgment of vehicle moving on each sub-region is determined by closest center point of bottom of the detected vehicle's box information. And it is tracked by vehicle's box information on the continuous images. The proposed algorithm has evaluated for performance of detection and cost in real time using day and night images captured by CCTV on the road.