• International journal of advanced smart convergence
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• 제11권3호
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• pp.36-41
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• 2022

In the 21st century, information collection and information provision based on digital informatization and intelligent automation are emerging as one of the social problems in the society for the elderly and the vulnerable groups in the welfare society including the disabled, and various methods are being studied to find realistic alternatives. Among these factors, the problem of the elderly living alone is emerging as the most serious, and as a realistic approach to solve some problems by applying information devices, it is a monitoring system using the Internet of Things(IoT). The need for an optimized system is emerging. In this study, the state of the elderly and the elderly living alone can be measured remotely by applying IoT technology. We present the research cases of a Breathing Sensor-based Smart Monitoring System that is used as a smart information system and used as a monitoring system for the elderly and infirm when it is identified as deceased through state detection

• 한국멀티미디어학회논문지
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• 제24권9호
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• pp.1224-1241
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• 2021

Recent development of deep learning techniques for image generation has led to straightforward generation of sophisticated deepfakes. However, as a result, privacy violations through deepfakes has also became increased. To solve this issue, a number of techniques for deepfake detection have been proposed, which are mainly focused on RGB channel-based analysis. Although existing studies have suggested the effectiveness of other color model-based analysis (i.e., Grayscale), their effectiveness has not been quantitatively validated yet. Thus, in this paper, we compare the effectiveness of Grayscale channel-based analysis with RGB channel-based analysis in deepfake detection. Based on the selected CNN-based models and deepfake datasets, we measured the performance of each color model-based analysis in terms of accuracy and time. The evaluation results confirmed that Grayscale channel-based analysis performs better than RGB-channel analysis in several cases.

• 동아시아경상학회지
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• 제8권1호
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• pp.71-80
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• 2020

Purpose - The purpose of this paper is to advance the understanding of the relationship between host country image and cultural intelligence (CQ) on the attractiveness of expatriate destinations. Specifically, this study compares the different impacts of CQ on the relationship between host country image and subsidiary attractiveness by comparing a US-based subsidiary and a Vietnam-based one. Research design and methodology - A total of 445 Korean full-time workers responded through an online survey. The survey randomly showed the participants one of two conditions for a potential expatriate location. The participants were asked to answer a series of questions on the organizational attractiveness of the subsidiaries from the perspective of an expatriate candidate and respond to a series of questions on individual CQ. Results - Through a two-way ANOVA test, the results reveal that Korean expatriate candidates perceive that a Vietnam-based subsidiary is a less attractive destination for international assignment when compared to a US-based subsidiary. In addition, the positive moderating effect of cultural intelligence on the relationship between the host location and the subsidiary's attractiveness is stronger when Vietnam, rather than the US, is the assignment location. Conclusions - Drawing upon AUM theory, this study confirmed that unfavorable country image affects subsidiaries' attractiveness for expatriate candidates, due to anxiety. However, this study showed the role of employees' CQ to mitigate these challenges. This study suggests providing information on positive conditions of expatriate locations and building systematic process for enhancing individual CQ for organizations.

• 대한의용생체공학회:의공학회지
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• 제9권1호
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• pp.61-66
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• 1988

Because the amount of radiation emerging from the thorax behind the lungs is often literally thousands of times that exiting behind the mediastinum, the dynamic range of X-ray chest image is very large. In order to solve the dynamic range problem, we propose a signal adaptive algorithm which enhances the local contrast and contracts the enhancement of quantum noise by local mean/valiance estimator.

• 한국실내디자인학회논문집
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• 제16호
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• pp.182-189
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• 1998

The existential space concept which introduced the sense of place that began emerging recently together with the change of space concept results from people's fundamental existential humane aspect as the subject of perceptive experience. The tendencies of enlargement layering and diversification which we can see in contemporary architecture imply the possibility that elements of urban image can be incorporated into interior design. This study utilizes Kevin Lynch's theory which tries to relate environment to existential space. In this study we re-analyze and apply Lynch's $\ulcorner$the Theory of Urban Image$\lrcorner$to interior space and using the conclusion from a case study examine the applicability of the theory about visual perception to interior design. This study considers a new method of analysis to perceive and grasp interior space through the understanding of the cognitive map and image concept.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1205-1223
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• 2018

Image enhancement is an emerging method for analyzing the images clearer for interpretation and analysis in the spatial domain. The goal of image enhancement is to serve an input image so that the resultant image is more suited to the particular application. In this paper, a novel method is proposed based on Mamdani fuzzy inference system (FIS) using multiple fuzzy membership functions. It is observed that the shape of membership function while converting the input image into the fuzzy domain is the essential important selection. Then, a set of fuzzy If-Then rule base in fuzzy domain gives the best result in image contrast enhancement. Based on a different combination of membership function shapes, a best predictive solution can be determined which can be suitable for different types of the input image as per application requirements. Our result analysis shows that the quality attributes such as PSNR, Index of Fuzziness (IOF) parameters give different performances with a selection of numbers and different sized membership function in the fuzzy domain. To get more insight, an optimization algorithm is proposed to identify the best combination of the fuzzy membership function for best image contrast enhancement.

• Current Optics and Photonics
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• 제6권3호
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• pp.260-269
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• 2022

Segmented planar imaging detector for electro-optical reconnaissance (SPIDER) is an emerging technology for optical imaging. However, this novel detection approach is faced with degraded imaging quality. In this study, a 6 × 6 planar waveguide is used after each lenslet to expand the field of view. The imaging principles of field-plane waveguide structures are described in detail. The local multiple-sampling simulation mode is adopted to process the simulation of the improved imaging system. A novel image-reconstruction algorithm based on deep learning is proposed, which can effectively address the defects in imaging quality that arise during image reconstruction. The proposed algorithm is compared to a conventional algorithm to verify its better reconstruction results. The comparison of different scenarios confirms the suitability of the algorithm to the system in this paper.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• 유통과학연구
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• 제19권2호
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• pp.15-24
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• 2021

Purpose: The research is to find out the perception of consumers for the convenience store (c - store) image in an emerging market, with a sample from Ho Chi Minh City. Research design, data, and methodology: Data were collected using a 24 - item structured questionnaire into six factors, namely: store atmospheric, merchandise, supplementary service, customer convenience, sales personnel, promotion. Five hundred consumers shopped at the different c - stores were collected for a multidimensional scaling technique that creates a perceptual map illustrating of c - store image. Results: The results point out that consumers' perception of a different c - store is different. The trend of c- stores are focusing on the dimensions of the function aspect. The customers also put their attention on the psychological dimension, which, in this case, is customer convenience with a sharp point. Almost all stores are bad on store atmospheric in customer- based. Conclusions: The modern retail store chains need to focus on elements to create a store image positioning and improve the perceptions of the consumers towards the store. Besides, customers not only visit the stores, not due to its convenient location, mass media or shopping experience, but also a strong identity for the store's brand image.

• 한국멀티미디어학회논문지
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• 제24권12호
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• pp.1624-1631
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• 2021

With the continued development of image processing technology, we live in a time when it is difficult to visually discriminate processed (or tampered) images from real images. However, as the risk of fake images being misused for crime increases, the importance of image forensic science for identifying fake images is emerging. Currently, various deep learning-based identifiers have been studied, but there are still many problems to be used in real situations. Due to the inherent characteristics of deep learning that strongly relies on given training data, it is very vulnerable to evaluating data that has never been viewed. Therefore, we try to find a way to improve generalization ability of deep learning-based fake image identifiers. First, images with various contents were added to the training dataset to resolve the over-fitting problem that the identifier can only classify real and fake images with specific contents but fails for those with other contents. Next, color spaces other than RGB were exploited. That is, fake image identification was attempted on color spaces not considered when creating fake images, such as HSV and YCbCr. Finally, dropout, which is commonly used for generalization of neural networks, was used. Through experimental results, it has been confirmed that the color space conversion to HSV is the best solution and its combination with the approach of increasing the training dataset significantly can greatly improve the accuracy and generalization ability of deep learning-based identifiers in identifying fake images that have never been seen before.