• ETRI Journal
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• v.41 no.3
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• pp.298-307
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• 2019

This paper investigates the use of the inverse-free sparse Bayesian learning (SBL) approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM)-based multiuser massive multiple-input multiple-output (MIMO) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought-after low-PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation-maximization (EM) iterative algorithm. The matrix inversion involved in the expectation step (E-step) is averted by invoking a relaxed evidence lower bound (relaxed-ELBO). The resulting inverse-free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations.

• Journal of Elementary Mathematics Education in Korea
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• v.13 no.1
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• pp.51-74
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• 2009

The purpose of this study is to search for various strategies that could self-regulated learning within cyber home study efficiently, to operate the cyber home study based on such strategies, to manage and support students' learning and to investigate what effects it would have on the ability of self-regulated learning and attitude. In this study, an operational strategy for cyber home study according to the compositional elements of self-regulated learning based on prior studies. Then, the study developed the learning contents of cyber home study and operated cyber home study according to the operational strategy. From the results of the analysis obtained in this study, the following conclusions can be drawn as follows. First, A learner's self-regulated learning capability is able to be improved by self-regulated leaning strategies. Cyber home study that would enable students to implement the leaning on their own through learning contents and operating strategies corresponding to them was the environment that could help their self-regulated learning. Second, in order to find out students' satisfaction for the application of cyber home study, the study compared the survey of cyber home study with the frequency and percentage by each question and the mean value of technical statistics. Cyber home study let students have positive recognition on mathematical learning, and especially as shown in the results of the interview, it was helpful to improve students' interest and confidence as well as their mathematical learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.5
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• pp.1042-1048
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• 2017

Recently rapid changes in the industrial environment require new talents in companies. Flipped learning is drawing attention as an effective teaching-learning method. The existing traditional lecture teaching-learning method have various problems that the dropout rates of the student is high and the creative problem solving ability is hindered. In the case of the IT engineering college, most of the major theoretical courses require prior learning of the prerequisite coursework subjects. Therefore, effective teaching-learning methods must be developed to improve student participation and academic achievement. This paper proposes the flipped learning model consisting of five sets that combine the flipped learning and practice to improve student motivation and self - directed learning. Also, this paper analyzes the learning effect by applying it to the algorithm lecture of computer engineering and presents problem and utilization plan according to the result.

• International Journal of Advanced Culture Technology
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• v.3 no.1
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• pp.13-20
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• 2015

The self-cleaning properties of nano-zinc oxide on cotton fabrics have been investigated. The cotton fabric has been prepared by pad-dry method. The nano-zinc oxide was encapsulated in the polystyrene particle by mini-emulsion process prior used. The loading amount of zinc oxide particles into the mini-emulsion were various from 1% wt to 40%wt. The particles sizes of ZnO-encapsulated polystyrene mini-emulsion were determined using dynamic light scattering. It was showed that the particle size of the mini-emulsion was in the range of 124-205 nm. The topography and morphology of ZnO-encapsulated polystyrene which coated on cotton fabrics was observed using scanning electron microscopy. The crystal structure of ZnO-coated on cotton fabrics was explored by X-ray diffraction spectroscopy. The photocatalytic activities of zinc oxide were present through the self-cleaning properties. The presents of the zinc oxide on cotton fabrics significantly showed the improving of the self-cleaning properties under UV radiation.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.203-210
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• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.495-503
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• 2014

In recent days, a study of the intrusion detection system collecting and analyzing network data, packet or logs, has been actively performed to response the network threats in computer security fields. In particular, Bayesian network has advantage of the inference functionality which can infer with only some of provided data, so studies of the intrusion system based on Bayesian network have been conducted in the prior. However, there were some limitations to calculate high detection performance because it didn't consider the problems as like complexity of the relation among network packets or continuos input data processing. Therefore, in this paper we proposed two methodologies based on K-menas clustering to improve detection rate by reforming the problems of prior models. At first, it can be improved by sophisticatedly setting interval range of nodes based on K-means clustering. And for the second, it can be improved by calculating robust CPT through applying weighted-leaning based on K-means clustering, too. We conducted the experiments to prove performance of our proposed methodologies by comparing K_WTAN_EM applied to proposed two methodologies with prior models. As the results of experiment, the detection rate of proposed model is higher about 7.78% than existing NBN(Naive Bayesian Network) IDS model, and is higher about 5.24% than TAN(Tree Augmented Bayesian Network) IDS mode and then we could prove excellence our proposing ideas.

• The Journal of Korean Academic Society of Nursing Education
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• v.20 no.2
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• pp.341-349
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• 2014

Purpose: This study was conducted to compare blended practicum with clinical and lab combined e-learning between cooperative and individual group on learning outcomes. Method: A total of 63 junior Nursing students were recruited from C University in G city from May, 2012 to June, 2012. Ten hours lab practicum for two weeks was provided for both two groups during the period of adult nursing practicum. Prior to blended practicum, e-learning was conducted. For cooperative group, two hours off line team learning with a tutor for eight weeks was provided, in other hands, for individual group, any off line team learning was not provided and self study on line was not evaluated by the tutor. Results: The result of ANCOVA showed that critical thinking and self directed learning were significantly improved in the individual group compared to the cooperative group (F=-18.15, p<.001; F=28.12, p<.001). In other hands, clinical competence was significantly higher in the cooperative group than in the individual group (F=16.61, p<.001). Conclusion: Through development of self-leaning facilitating online contents, the blended practicum combined e-learning could be effective in critical thinking, self-directed learning and clinical competence. Further studies about e-learning strategies of off-line learning are still needed.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.224-229
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• 2020

We propose a machine learning-based method for proactively detecting faults in LDPE processes and predicting equipment lifespan. It is important to detect and prevent unexpected faults in chemical processes in order to maximize safety and productivity. Since LDPE process is a high-pressure process up to 3,000 kg/㎠g or more, once ESD occurs, it can result in productivity loss due to increased maintenance periods. By collecting key variables operation data of the process and using unsupervised machine leaning methods, we developed a fault detection model which detected 4 ESDs 2.4 days prior to the occurrence. In addition, it was confirmed that the life expectancy of a hyper compressor can be predicted by using the physically significant key variables.

• Korean Educational Research Journal
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• v.43 no.1
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• pp.183-213
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• 2022

The purpose of this study is to examine the implications of Deleuze and Guattari's Machinism and Pedagogy of Assemblages. A slow, empirical process offered by Deleuze and Guattari is possible only if they experience a repetition of the duration in time. The identity of this world, a combination of potential and reality, is expressed as a machine. The identity of the 'machine' is the generation. The identity of the information society that exists everywhere in the cloud and unconsciously collects big data is also the information society. The information society is at risk of leaning toward a society in which individual desires are managed prior to the manifestation of a self-reliance a machine consisting of unmarked and mechanical arrangements. Social science based on the theory of layout shares the characteristics of repetition patterns, coexistence of linguistic and materiality, attention to boundary and negation to total whole. The pedagogy of layout, in which the collective pattern is structurally deformed in time, conforms to the original problem consciousness of Deleuze and Guattari, slow and empirical education. In addition, the work of examining the materiality and expression of the education-machine will contribute to the establishment of a new learning theory, an educational theory in the era of trans-human.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.31-46
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• 2023

This study classified and confirmed the learning modes about start-ups that are based on the accelerator's program which was focusing on the Korean accelerators in the ICT field targeting global market. Eight accelerator practitioners were interviewed who were in charge of operating programs for accelerators, qualitatively analyzing method of the interview was conducted. The interview results to identify various learning modes that accelerators provide to startups through programs. In order to identify and classify learning modes, the researcher reviewed various prior documents and using categories of experience accumulation, observation, experimentation, trial and error, and improvisation as a priori code for the qualitative analysis. The interview results were analyzed through a subject analysis. As the result of the study, the learning modes offered by the accelerator's programs to startups were confirmed, with two subcategories identified for each of the five categories: experiential, learning from others, experimental, trial and error, and improvisation. Given the limited research on accelerator programs and their main function, the main function of accelerators, this study identified the types of learning modes that offered by the accelerator's programs to startups from the perspective of learning. This study provides important insights into the types of learning modes that offered by the accelerator programs, which can help to improve our understanding of how accelerators support organizational learning for startups. Additionally, this information can be useful for startups considering in participating in the accelerator programs, as it can help them making informed decisions about their involvement.