• International Commerce and Information Review
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• v.16 no.3
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• pp.431-463
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• 2014

This paper analyzed the effect of the alliance capabilities needed for each stage of alliance lifecycle(search & negociation, contract, operation, evaluation/termination) according to the alliance life cycle of SMEs consulting firms on the performance, and the moderating effect of the partnership & entrepreneurship between the process capabilities and performance using the multi-group analysis The result shows that searching & operational capabilities have a positive impact on the customer & learning performance, not contracting and termination capabilities, and the partnership & entrepreneurship moderated between the process capabilities and alliance performance. This study shows that the operation stage in the alliance life cycle is the most important, in this process alliance partners show the higher partnership & entrepreneurship than any other stages.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.8
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• pp.801-806
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• 2015

Journal bearings support rotors using fluid film between the rotor and the stator. Generally, journal bearings are used in large rotor systems such as turbines in a power plant, because even in high-speed and load conditions, journal bearing systems run in a stable condition. To enhance the reliability of journal-bearing systems, in this paper, we study health-diagnosis algorithms that are based on the supervised learning method. Specifically, this paper focused on defining the unit of features, while other previous papers have focused on defining various features of vibration signals. We evaluate the features of various lengths or units on the separable ability basis. From our results, we find that one cycle datum in the time-domain and 60 cycle datum in the frequency domain are the optimal datum units for real-time journal-bearing diagnosis systems.

• Communications of Mathematical Education
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• v.34 no.2
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• pp.135-160
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• 2020

The purpose of this study is to draw implications for improving the method of reflecting the competencies in Korea mathematics curriculum, by analyzing what competencies are reflected in foreign mathematics and curriculum. As a result of the study, foreign countries were reflecting their competencies in mathematics curriculum in various ways. In France mathematics curriculum, the achievement standards of learning competencies(compétences travaillées) that students should reach by cycle were presented, and the related common competencies(socle commun) were indicated. In Australia's mathematics curriculum, the general capabilities for achievement standards were identified, and the achievement criteria for proficiency strands to be reached by grade level were presented. British Columbia's mathematics curriculum actively reflected its competencies. In the mathematics curriculum, domains were reorganized based on the competencies, and achievement standards of the competencies were proposed. The results of this study will help in improving the ways in which were reflected competencies in mathematics curriculum.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.1-9
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• 2020

In this paper, we propose a model that performs more natural facial attribute editing by utilizing mask information in the hair and hat region. STGAN, one of state-of-the-art research of facial attribute editing, has shown results of naturally editing multiple facial attributes. However, editing hair-related attributes can produce unnatural results. The key idea of the proposed method is to additionally utilize information on the face regions that was lacking in the existing model. To do this, we apply three ideas. First, hair information is supplemented by adding hair ratio attributes through masks. Second, unnecessary changes in the image are suppressed by adding cycle consistency loss. Third, a hat segmentation network is added to prevent hat region distortion. Through qualitative evaluation, the effectiveness of the proposed method is evaluated and analyzed. The method proposed in the experimental results generated hair and face regions more naturally and successfully prevented the distortion of the hat region.

• Journal of Platform Technology
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• v.8 no.4
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• pp.20-28
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• 2020

In this paper, a voice, object, and optical character recognition platform including voice recognition-based smart wearable devices, smart devices, and web AI servers was proposed as an appropriate technology to help the visually impaired to live independently by learning the life cycle of the visually impaired in advance. The wearable device for the visually impaired was designed and manufactured with a reverse neckband structure to increase the convenience of wearing and the efficiency of object recognition. And the high-sensitivity small microphone and speaker attached to the wearable device was configured to support the voice recognition interface function consisting of the app of the smart device linked to the wearable device. From experimental results, the voice, object, and optical character recognition service used open source and Google APIs in the web AI server, and it was confirmed that the accuracy of voice, object and optical character recognition of the service platform achieved an average of 90% or more.

• Journal of the Korean Child Neurology Society
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• v.25 no.3
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• pp.204-207
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• 2017

Hyperornithinemia-hyperammonemia-homocitrullinuria syndrome (HHH syndrome) is a neurometabolic disorder with highly variable clinical severity ranging from mild learning disability to severe encephalopathy. Diagnosis of HHH syndrome can easily be delayed or misdiagnosed due to insidious symptoms and incomplete biochemical findings, in that case, genetic testing should be considered to confirm the diagnosis. HHH syndrome is caused by biallelic mutations of SLC25A15, which is involved in the urea cycle and the ornithine transport into mitochondria. Here we report a boy with spastic paraplegia and asymptomatic younger sister who have compound heterozygous mutations of c.535C>T (p.R179^*) and c.116C>A (p.T39K) in the SLC25A15 gene. We identified that p.T39K mutation is a novel pathogenic mutation causing HHH syndrome and that p.R179^*, which is prevalent in Japanese and Middle Eastern heritage, is also found in the Korean population.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.299-306
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• 2022

In recent, sensors embedded in robots, equipment, and circuits have become common, and research for diagnosing device failures by learning measured sensor data is being actively conducted. This failure diagnosis study is divided into a classification model for predicting failure situations or types and a regression model for numerically predicting failure conditions. In the case of a classification model, it simply checks the presence or absence of a failure or defect (Class), whereas a regression model has a higher learning difficulty because it has to predict one value among countless numbers. So, the reason that regression modeling is more difficult is that there are many irregular situations in which it is difficult to determine one output from a similar input when predicting by matching input and output. Therefore, in this paper, we focus on input and output data with periodicity, analyze the input/output relationship, and secure regularity between input and output data by performing sliding window-based input data patterning. In order to apply the proposed method, in this study, current and temperature data with periodicity were collected from MMC(Modular Multilevel Converter) circuit system and learning was carried out using ANN. As a result of the experiment, it was confirmed that when a window of 2% or more of one cycle was applied, performance of 97% or more of fit could be secured.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.133-140
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• 2023

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.227-241
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• 2022

Full waveform inversion (FWI) in the field of seismic data processing is an inversion technique that is used to estimate the velocity model of the subsurface for oil and gas exploration. Recently, deep learning (DL) technology has been increasingly used for seismic data processing, and its combination with FWI has attracted remarkable research efforts. For example, DL-based data processing techniques have been utilized for preprocessing input data for FWI, enabling the direct implementation of FWI through DL technology. DL-based FWI can be divided into the following methods: pure data-based, physics-based neural network, encoder-decoder, reparameterized FWI, and physics-informed neural network. In this review, we describe the theory and characteristics of the methods by systematizing them in the order of advancements. In the early days of DL-based FWI, the DL model predicted the velocity model by preparing a large training data set to adopt faithfully the basic principles of data science and apply a pure data-based prediction model. The current research trend is to supplement the shortcomings of the pure data-based approach using the loss function consisting of seismic data or physical information from the wave equation itself in deep neural networks. Based on these developments, DL-based FWI has evolved to not require a large amount of learning data, alleviating the cycle-skipping problem, which is an intrinsic limitation of FWI, and reducing computation times dramatically. The value of DL-based FWI is expected to increase continually in the processing of seismic data.