• Journal of Venture Innovation
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• v.6 no.1
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• pp.161-179
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• 2023

More than 2 million foreigners were staying in Korea. In this study, the effect of individual and environmental characteristics of foreign workers on organizational commitment was empirically analyzed. A research model for empirical analysis was established. A research model was established independent variables by dividing them into four parts: personal desire, adaptability, organizational life, and social support and setting organizational commitment as dependent variables. Based on this research model, data collection for analysis was conducted in a survey method for foreign workers in Korea. An empirical analysis was conducted using SPSS 24 based on 200 valid sample of the respondents. The results of the empirical analysis were as follows. First, it was found that foreign workers' desire for achievement, language adaptability, relationships with supervisors and colleagues, future vision of jobs, and social support had a significant positive (+) effect on organizational commitment. On the other hand, the significant influence relationship of cultural adaptability was not tested. Second, factors that significantly affect organizational commitment was in the order of social support, relationships with supervisors and colleagues, future vision of jobs, language adaptability, and desire for achievement. Based on these research results, academic implications were presented, and practical implications were presented to increase the organizational commitment of foreign workers.

• International Journal of Contents
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• v.14 no.1
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• pp.1-6
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• 2018

Text, as one of the most influential inventions of humanity, has played an important role in human life since ancient times. The rich and precise information embodied in text is very useful in a wide range of vision-based applications such as the text data extracted from images that can provide information for automatic annotation, indexing, language translation, and the assistance systems for impaired persons. Therefore, natural-scene text detection with active research topics regarding computer vision and document analysis is very important. Previous methods have poor performances due to numerous false-positive and true-negative regions. In this paper, a fully-convolutional-network (FCN)-based method that uses supervised architecture is used to localize textual regions. The model was trained directly using images wherein pixel values were used as inputs and binary ground truth was used as label. The method was evaluated using ICDAR-2013 dataset and proved to be comparable to other feature-based methods. It could expedite research on text detection using deep-learning based approach in the future.

• Journal of English Language & Literature
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• v.64 no.1
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• pp.25-37
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• 2018

The turning point is one of the more evocative concepts in the critic's arsenal, as it is equally suited to the evaluation and analysis of a given moment in one's day as to those of a historical event. But how does one recognize a turning point? As we find ourselves always "in the middest," both spatially and temporally, we inhabit sites that may be points at which many things may be seen to turn. Indeed, it is usually only possible to identify a turning point, as it were, from a distance, from the remove of space and time which allows for a sense of recognition, based in part on original context and in part of perceived effects. In this article, Robert T. Tally Jr. argues that the apprehension and interpretation of a turning point involves a fundamentally critical activity. Examining three models by which to understand the concept of the turning point-the swerve, the trope, and peripety (or the dialectical reversal)-Tally demonstrates how each represents a different way of seeing the turning point and its effects. Thus, the swerve is associated with a point of departure for a critical project; the trope is connected to continuous and sustained critical activity in the moment, and peripety enables a retrospective vision that, in turn, inform future research. Tally argues for the significance of the turning point in literary and cultural theory, and concludes that the identification, analysis, and interpretation of turning points is crucial to the project of criticism today.

• Journal of agricultural medicine and community health
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• v.42 no.1
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• pp.1-12
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• 2017

Objectives: In this study, we analyzed vision statements of the 6th community health plan of local government in Korea. Methods: We examined vision statements letters, missions and strategy plans, and long-term missions of 6th community health plans of 229 local government in Korea. We also analyzed the numbers of vision letters, sentence examination, word frequency, each vision statement with frequency analysis, chi-square test, and one-way ANOVA. Results: Among 229 local government, 172(75.1%) of local government had the number of letters (Korean) less than 17 of vision statements, and there were a significant differences according to type of community health centers (p<0.05). Figuration (37.1%) were the most used in an expression of vision statement sentence, and special characters (43.2%) were the most used language except Korean. The most commonly used words of vision statement in order of frequency were 'health', 'happiness', 'with', 'citizen', 'city', '100 years old' etc. Chungcheong provinces and Daejeon metropolitan city had a highest score in directionality on phrase evaluation, and there were a significant differences according to regional classes of local government (p<0.01). Gyeongsang provinces, Ulsan, Daegu, and Busan metropolitan cities had a highest score in future orientation and sharing possibilities on phrase evaluation, and there were a significant differences according to regional classes of local government (p<0.01). Conclusions: Vision is one of the most important component of community health plan. We need more detailed 'vision statement guideline' and the community health care centers of local government should effort to make more clear and complete their vision.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.35-49
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• 2024

Video captioning technology, as a significant outcome of the integration between computer vision and natural language processing, has emerged as a key research direction in the field of artificial intelligence. This technology aims to achieve automatic understanding and language expression of video content, enabling computers to transform visual information in videos into textual form. This paper provides an initial analysis of the research trends in deep learning-based video captioning and categorizes them into four main groups: CNN-RNN-based Model, RNN-RNN-based Model, Multimodal-based Model, and Transformer-based Model, and explain the concept of each video captioning model. The features, pros and cons were discussed. This paper lists commonly used datasets and performance evaluation methods in the video captioning field. The dataset encompasses diverse domains and scenarios, offering extensive resources for the training and validation of video captioning models. The model performance evaluation method mentions major evaluation indicators and provides practical references for researchers to evaluate model performance from various angles. Finally, as future research tasks for video captioning, there are major challenges that need to be continuously improved, such as maintaining temporal consistency and accurate description of dynamic scenes, which increase the complexity in real-world applications, and new tasks that need to be studied are presented such as temporal relationship modeling and multimodal data integration.

• Journal of Families and Better Life
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• v.30 no.3
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• pp.83-103
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• 2012

This thesis is based on an in-depth interview on participation experience of ten immigrants who reside in S Multi-cultural Family Support Center that is located in Gyeonggi-do in Korea Immigration & Integration Program(KIIP). The purpose of this research is to present through it's basic data for improvement in the operation of KIIP in the future by grasping participation process in KIIP that the immigrants have experienced, problems involved in their operation, and related requirements. Major results of the research are as follows: First, the motive for the immigrants' participation in KIIP was to acquire Korean nationality, learn Korean, and prepare for their future in Korea. Second, as a difficulty in participation in KIIP, access to educational institutions loomed large. Third, regarding improvements in the operation of KIIP, marriage immigrants needed to continue Korean language education, whereas other immigrants revealed a demand for opening evening classes or weekend classes. In the final analysis, it seems that for KIIP to provide an opportunity for the immigrants to have a vision for their life in the future, as well as for its realization in Korean society, policy-oriented institutional support that pays attention to their life situation and demands is badly needed.

• Journal of Biosystems Engineering
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• v.17 no.1
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• pp.65-78
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• 1992

When using a computer vision system for a measurement, the geometrically distorted input image usually restricts the site and size of the measuring window. A geometrically distorted image caused by the image sensing and processing hardware degrades the accuracy of the visual measurement and prohibits the arbitrary selection of the measuring scope. Therefore, an image calibration is inevitable to improve the measuring accuracy. A calibration process is usually done via four steps such as measurement, modeling, parameter estimation, and compensation. In this paper, the efficient error calibration technique of a geometrically distorted input image was developed using a neural network. After calibrating a unit pixel, the distorted image was compensated by training CMLAN(Cerebellar Model Linear Associator Network) without modeling the behavior of any system element. The input/output training pairs for the network was obtained by processing the image of the devised sampled pattern. The generalization property of the network successfully compensates the distortion errors of the untrained arbitrary pixel points on the image space. The error convergence of the trained network with respect to the network control parameters were also presented. The compensated image through the network was then post processed using a simple DDA(Digital Differential Analyzer) to avoid the pixel disconnectivity. The compensation effect was verified using known sized geometric primitives. A way to extract directly a real scaled geometric quantity of the object from the 8-directional chain coding was also devised and coded. Since the developed calibration algorithm does not require any knowledge of modeling system elements and estimating parameters, it can be applied simply to any image processing system. Furthermore, it efficiently enhances the measurement accuracy and allows the arbitrary sizing and locating of the measuring window. The applied and developed algorithms were coded as a menu driven way using MS-C language Ver. 6.0, PC VISION PLUS library functions, and VGA graphic functions.

• Journal of KIISE
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• v.43 no.7
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• pp.795-800
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• 2016

In image classification, recent CNNs compete with human performance. However, there are limitations in more general recognition. Herein we deal with indoor images that contain too much information to be directly processed and require information reduction before recognition. To reduce the amount of data processing, typically variational inference or variational Bayesian methods are suggested for object detection. However, these methods suffer from the difficulty of marginalizing over the given space. In this study, we propose an image-text integrated recognition system using active vision based on Spatial Transformer Networks. The system attempts to efficiently sample a partial region of a given image for a given language information. Our experimental results demonstrate a significant improvement over traditional approaches. We also discuss the results of qualitative analysis of sampled images, model characteristics, and its limitations.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.628-631
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• 2023

We present a new approach to evaluate the generated texts by Large Language Models (LLMs) for meme classification. Analyzing an image with embedded texts, i.e. meme, is challenging, even for existing state-of-the-art computer vision models. By leveraging large image-to-text models, we can extract image descriptions that can be used in other tasks, such as classification. In our methodology, we first generate image captions using BLIP-2 models. Using these captions, we use GPT-4 to evaluate the relationship between the caption and the meme text. The results show that OPT_6.7B provides a better rating than other LLMs, suggesting that the proposed method has a potential for meme classification.

• The Journal of the Convergence on Culture Technology
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• v.5 no.1
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• pp.371-376
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• 2019

Creative thinking appears even before it is expressed in language, and its existence is revealed through emotion, intuition, image and body feeling before logic or linguistics rules work. In this study, Lego is intended to present experimental child interactive content that is applied with a computer vision based on image processing techniques. In the case of infants, the main purpose of this content is the development of hand muscles and the ability to implement imagination. The purpose of the analysis algorithm of the OpenCV library and the image processing using the 'VVVV' that is implemented as a 'Node' in the midst of perceptual changes in image processing technology that are representative of object recognition, and the objective is to use a webcam to film, recognize, derive results that match the analysis and produce interactive content that is completed by the user participating. Research shows what Lego children have made, and children can create things themselves and develop creativity. Furthermore, we expect to be able to infer a diverse and individualistic person's thinking based on more data.