• Journal of Agricultural Extension & Community Development
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• v.4 no.1
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• pp.305-315
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• 1997

The propose of this study was to analyze the relationships among the levels of training & development evaluation (reaction, learning, transfer). The study has been conducted on 730 trainees who attended in the basic accounting program in L training and development institution through three incidents of tracked research such as reaction survey right after the conclusion of training, learning evaluation through test, and an evaluation of the transferability after 3 months of training. Questionnaires and test papers for analyses were used after their reliability, validity, difficulty, and discrimination have been verified on a pre-test. The research has been conducted for six months from 4 March 1996 to the end of August 1996, and data have been collected through direct research and survey through mail. The collected data have been worked on at SAS program for Windows with a statistical significance level of 5%. Statistical method that had been used was Pearson's correlation coefficient. The result and conclusion acquired from this study were as follows: Between reaction and learning, learning and transfer of training, only a weak positive correlation exists and explanation or prediction variance showing hierarchical relationship was quite weak with 1%. Thus, this research not only does not strongly support Kirkpatrick(1976)'s hierarchical model of $reaction{\rightarrow}learning{\rightarrow}transfer$, but also indicates that the separate measurement on each levels of training evaluation needs to be done. On the other hand, there was a relatively strong positive correlation between reaction and transfer of training. Based on the result, the conclusion, and the restriction perceived through this study, the following suggestions were made. 1. There is a need to empirically analyze and verify the hierarchy of all levels of training evaluation including the evaluation of the fourth level (result) such as organizational productivity, organizational satisfaction, and separation rate. 2. A great deal of efforts will be needed to systematically analyze what the relationships are among the methods measuring the level of evaluation of the training and development, and to apply this result to the training field.

• Convergence Security Journal
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• v.21 no.3
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• pp.57-66
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• 2021

As the detection performance using deep learning and machine learning of the intrusion detection field has been verified, the cases of using it are increasing day by day. However, it is difficult to collect the data required for learning, and it is difficult to apply the machine learning performance to reality due to the imbalance of the collected data. Therefore, in this paper, A mixed sampling technique using t-SNE visualization for imbalanced data processing is proposed as a solution to this problem. To do this, separate fields according to characteristics for intrusion detection events, including payload. Extracts TF-IDF-based features for separated fields. After applying the mixed sampling technique based on the extracted features, a data set optimized for intrusion detection with imbalanced data is obtained through data visualization using t-SNE. Nine sampling techniques were applied through the open intrusion detection dataset CSIC2012, and it was verified that the proposed sampling technique improves detection performance through F-score and G-mean evaluation indicators.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.47-55
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• 2024

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.

• Journal of Korean Home Economics Education Association
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• v.9 no.1
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• pp.19-37
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• 1997

The aim of this study is to research and analyse how junior-high students, both male and female in Inchon area, are recognizing the contents of the curriculum in the subject of home economics and how effectively they are learning and applying it in their actual life. 772 students, both male and female, who started to learn the subject of home economics from the 7th grade as compulsory are the respondents, and the survey is done by using questionnaire. The result shows that after taking the course of home economics, both male and female students have got more positive view on the necessity of learning the subject. But still, on the whole, female students are more intersted and more active than males the subject in learning. As for food and nutrition part, large percentage of the respondents, both male and female, answer that it is very helpful. They tend to be on more balanced diet and when they purchase food or when they eat at restaurant they refer what they learn about nutrition at school more often than not. A number of the students are re-practicing cooking at home after they learn it at school. Also the fact in the survey shows that more and more mothers are getting active in asking their children to re-practice cooking. One of the difficulties for male students to take the course is stereo-typed thinking on the separate role of man and woman in the family. But many of them started cooking some food, even though it is very simple, and the survey shows that their interest in nutrition and health increased after they were initiated into this course.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.33-43
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• 2020

Retrieving a 3D model from a 3D database and augmenting the retrieved model in the Augmented Reality system simultaneously became an issue in developing the plausible AR environments in a convenient fashion. It is considered that the sketch-based 3D object retrieval is an intuitive way for searching 3D objects based on human-drawn sketches as query. In this paper, we propose a novel deep learning based approach of retrieving a sketch-based 3D object as for an Augmented Reality Model. For this work, we introduce a new method which uses Sketch CNN, Wasserstein CNN and Wasserstein center loss for retrieving a sketch-based 3D object. Especially, Wasserstein center loss is used for learning the center of each object category and reducing the Wasserstein distance between center and features of the same category. The proposed 3D object retrieval and augmentation consist of three major steps as follows. Firstly, Wasserstein CNN extracts 2D images taken from various directions of 3D object using CNN, and extracts features of 3D data by computing the Wasserstein barycenters of features of each image. Secondly, the features of the sketch are extracted using a separate Sketch CNN. Finally, we adopt sketch-based object matching method to localize the natural marker of the images to register a 3D virtual object in AR system. Using the detected marker, the retrieved 3D virtual object is augmented in AR system automatically. By the experiments, we prove that the proposed method is efficiency for retrieving and augmenting objects.

• Proceedings of the KSPS conference
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• 1996.10a
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• pp.274-274
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• 1996

This presents the preliminary results from work in progress of a paired study of the acquisition of voiceless stops by Spanish speakers learning English, and American English speakers learning Spanish. For this study the hypothesis was that the American speakers would have no difficulty suppressing the aspiration in Spanish unaspirated stops; the Spanish speakers would have difficulty acquiring the aspiration necessary for English voiceless stops, according to Eckman's Markedness Differential Hypothesis. The null hypothesis was proved. All subjects were given the same set of disyllabic real words of English and Spanish in carrier phrases. The tokens analyzed in this report are limited to word-initial voiceless stops, followed by a low back vowel in stressed syllables. Tokens were randomized and then arranged in a list with the words appearing three separate times. Aspiration was measured from the burst to the onset of voicing(VOT). Both the first language (Ll) tokens and second language (L2) tokens were compared for each speaker and between the two groups of language speakers. Results indicate that the Spanish speakers, as a group, were able to reach the accepted target language VOT of English, but English speakers were not able to reach the accepted range for Spanish, in spite of statistically significant changes of p<.OOl by speakers in both groups of learners. A closer analysis of the speech samples revealed wide variability within the speech of native speakers of English. Not only is variability in English due to the wide range of VOT (120 msecs. for English labials, for example) but individual speakers showed different patterns. These results are revealing for the demands requied in experimental designs and the number of speakers and tokens requied for an adequate description of different languages. In addition, a simple report of means will not distinguish the speakers and the respective language learning situation; measurements must also include the RANGE of acceptability of VOT for phonetic segments. This has immediate consequences for the learning and teaching of foreign languages involving aspirated stops. In addition, the labelling of spoken language in speech technology is shown to be inadequate without a fuller mathematical description.

• Journal of The Korean Astronomical Society
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• v.52 no.6
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• pp.217-225
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• 2019

We apply a modified Convolutional Neural Network (CNN) model in conjunction with transfer learning to predict whether an active region (AR) would produce a ≥C-class or ≥M-class flare within the next 24 hours. We collect line-of-sight magnetogram samples of ARs provided by the SHARP from May 2010 to September 2018, which is a new data product from the HMI onboard the SDO. Based on these AR samples, we adopt the approach of shuffle-and-split cross-validation (CV) to build a database that includes 10 separate data sets. Each of the 10 data sets is segregated by NOAA AR number into a training and a testing data set. After training, validating, and testing our model, we compare the results with previous studies using predictive performance metrics, with a focus on the true skill statistic (TSS). The main results from this study are summarized as follows. First, to the best of our knowledge, this is the first time that the CNN model with transfer learning is used in solar physics to make binary class predictions for both ≥C-class and ≥M-class flares, without manually engineered features extracted from the observational data. Second, our model achieves relatively high scores of TSS = 0.640±0.075 and TSS = 0.526±0.052 for ≥M-class prediction and ≥C-class prediction, respectively, which is comparable to that of previous models. Third, our model also obtains quite good scores in five other metrics for both ≥C-class and ≥M-class flare prediction. Our results demonstrate that our modified CNN model with transfer learning is an effective method for flare forecasting with reasonable prediction performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.403-413
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• 2009

We compare three predistortion methods to prevent signal distortion and spectral re-growth due to the high PAPR (peak-to-average ratio) of OFDM signal and the non-linearity of high-power amplifiers. The three predistortion methods are pth order inverse, indirect learning architecture and look up table. The pth order inverse and indirect learning architecture methods requires less memory and has a fast convergence because these methods use a polynomial model that has a small number of coefficients. Nevertheless the convergence is fast due to the small number of coefficients and the simple computation that excludes manipulation of complex numbers by separate compensation for the magnitude and phase. The look up table method is easy to implement due to simple computation but has the disadvantage that large memory is required. Computer simulation result reveals that indirect learning architecture shows the best performance though the gain is less than 1 dB at $BER\;=\;10^{-4}$ for 64-QAM. The three predistorters are adaptive to the amplifier aging and environmental changes, and can be selected to the requirements for implementation.

• Journal of Industrial Convergence
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• v.17 no.2
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• pp.15-20
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• 2019

The purpose of this study is to provide customized experience learning service platform that enables consumers to easily search for various content information about on - site experiential learning, exhibitions, events, and culture, and to provide services. To provide customized experiential learning information that meets the requirements of the consumer. Beacon technology implemented through this study is a BLE technology that broadcasts a URL in Eddystone format developed by Google. This means that even if a user does not install a separate application, Making it easier and faster to access. Based on this, when the database of local cultural contents is completed, it will be expanded to the whole country, and it is expected that more diverse and high quality self - directed cultural contents experiential learning activity education programs will be provided to consumers by diversifying contents and expanding the market.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.1-6
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• 2021

In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.