• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.69-77
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• 2018

The malignant melanoma accounts for about 1 to 3% of the total malignant tumor in the West, especially in the US, it is a disease that causes more than 9,000 deaths each year. Generally, skin lesions are difficult to detect the features through photography. In this paper, we propose a computer-aided diagnosis algorithm based on deep learning for classification of malignant melanoma and benign skin tumor in RGB channel skin images. The proposed deep learning model configures the tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to segment a skin lesion area in the dermoscopic image. We could implement algorithms to classify malignant melanoma and benign tumor using skin lesion image and results of expert's labeling in ResNet. The U-Net model obtained a dice similarity coefficient of 83.45% compared with results of expert's labeling. The classification accuracy of malignant melanoma obtained the 83.06%. As the result, it is expected that the proposed artificial intelligence algorithm will utilize as a computer-aided diagnosis algorithm and help to detect malignant melanoma at an early stage.

• Journal of Distribution Science
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• v.20 no.4
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• pp.111-119
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• 2022

Purpose: This study seeks to address the omission through examining the manner in which demographic similarity affects the responses of employees in the retail sector towards horizontal pay dispersion. Through doing so the study will be effective in bolstering the recent efforts of more careful exploration of conditions. Research design, data, and methodology: Scant past studies are available to guide for practitioners in retail sector which compensation strategy might lead adequate job performance for retail sales workers. To suggest possible solution, the present authors used variables of pay dispersion and obtained 317 US retail sale workers in distribution channels to measure the association between pay dispersion and employee job performance. Results: The statistical findings indicated both first and second hypothesis could be acceptable with favorable Beta and T values, resulting high degree of pay dispersion leads a low level of job performance, while a low degree of pay dispersion can motivate retail sales workers to improve their performance. Conclusions: The findings of this study raises an argument that processes of social comparison work in a more vigorous manner. This is thus a representation of the propensity of a retail sales worker to voluntarily resign from an organization when dispersion rates are higher.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.417-426
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• 2023

To improve the effect of image restoration and solve the image detail loss, an image dehazing enhancement algorithm based on mean guided filtering is proposed. The superpixel calculation method is used to pre-segment the original foggy image to obtain different sub-regions. The Ncut algorithm is used to segment the original image, and it outputs the segmented image until there is no more region merging in the image. By means of the mean-guided filtering method, the minimum value is selected as the value of the current pixel point in the local small block of the dark image, and the dark primary color image is obtained, and its transmittance is calculated to obtain the image edge detection result. According to the prior law of dark channel, a classic image dehazing enhancement model is established, and the model is combined with a median filter with low computational complexity to denoise the image in real time and maintain the jump of the mutation area to achieve image dehazing enhancement. The experimental results show that the image dehazing and enhancement effect of the proposed algorithm has obvious advantages, can retain a large amount of image detail information, and the values of information entropy, peak signal-to-noise ratio, and structural similarity are high. The research innovatively combines a variety of methods to achieve image dehazing and improve the quality effect. Through segmentation, filtering, denoising and other operations, the image quality is effectively improved, which provides an important reference for the improvement of image processing technology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.15-29
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• 2024

A fetoscope is an optical endoscope, which is often applied in fetoscopic laser photocoagulation to treat twin-to-twin transfusion syndrome. In an operation, the clinician needs to observe the abnormal placental vessels through the endoscope, so as to guide the operation. However, low-quality imaging and narrow field of view of the fetoscope increase the difficulty of the operation. Introducing an accurate placental vessel segmentation of fetoscopic images can assist the fetoscopic laser photocoagulation and help identify the abnormal vessels. This study proposes a method to solve the above problems. A novel encoder-decoder network with a dual-path structure is proposed to segment the placental vessels in fetoscopic images. In particular, we introduce a channel attention mechanism and a continuous convolution structure to obtain multi-scale features with their weights. Moreover, a switching connection is inserted between the corresponding blocks of the two paths to strengthen their relationship. According to the results of a set of blood vessel segmentation experiments conducted on a public fetoscopic image dataset, our method has achieved higher scores than the current mainstream segmentation methods, raising the dice similarity coefficient, intersection over union, and pixel accuracy by 5.80%, 8.39% and 0.62%, respectively.

• Journal of the Korea Safety Management & Science
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• v.26 no.2
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• pp.93-105
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• 2024

In this study, based on previous research on personal broadcasting, we indirectly measured content quality, YouTuber attributes, user satisfaction, and behavioral intention, which are latent variables that cannot be directly measured, as measurement variables, and then measured theoretically between the latent variables. In order to analyze the causal relationship, we used a structural equation model to determine to what extent the content quality of the YouTube mukbang channel and the YouTuber's attributes influence behavioral intentions such as purchase, recommendation, and continued use according to viewers' satisfaction with use. We intend to analyze and verify the relationship between related variables. The research results are as follows. First, the value, relevance, timeliness, completeness, and data quantity of content quality were found to have no significant impact on user satisfaction. Second, the trustworthiness, expertise, attractiveness, and intimacy of YouTuber attributes were found to have a significant impact on user satisfaction, but the similarity of YouTuber attributes did not have a significant impact on usage. Third, user satisfaction was found to have a significant impact on behavioral intentions and purchase intentions. However, user satisfaction was not found to have a direct significant impact on recommendation intentions or continued usage intentions.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.5
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• pp.451-457
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• 2016

To improve security in the data communication field, many studies on the application of chaotic signals to encryption have been conducted in recent years. In this study, a new security protocol where the initial value sensitivity and noise similarity of chaotic signals have been applied to an RFID communication channel was designed. In the case of chaotic systems, if the initial values become identical, the same signals are generated at the same time after that point even though the two systems have been calculated independently. Therefore, an unpredictable security channel can be produced based on such characteristics. However, a security channel can be produced only when an initial value is shared in advance, and thus there is a potential problem of infringement during the transmission of the initial value. To resolve this problem, a method in which a certain proportion of new chaotic signals are applied to two chaotic systems for communication and are then synchronized after some time was proposed. This new method can conceal the initial value, and thus can resolve the problem of the existing communication method using chaotic signals. The designed method was verified with the encryption and decryption of images. It is expected that a more secure RFID system could be established by applying the communication protocol proposed in this study to insecure RFID communication channels.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.20-27
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• 2006

Multi-level thresholding is a method that is widely used in image segmentation. However most of the existing methods are not suited to be directly used in applicable fields and moreover expanded until a step of image segmentation. This paper proposes region-based multi-level thresholding as an image segmentation method. At first we classify pixels of each color channel to two clusters by using EWFCM(Entropy-based Weighted Fuzzy C-Means) algorithm that is an improved FCM algorithm with spatial information between pixels. To obtain better segmentation results, a reduction of clusters is then performed by a region-based reclassification step based on a similarity between regions existing in a cluster and the other clusters. The clusters are created using the classification information of pixels according to color channel. We finally perform a region merging by Bayesian algorithm based on Kullback-Leibler distance between a region and the neighboring regions as a post-processing method as many regions still exist in image. Experiments show that region-based multi-level thresholding is superior to cluster-, pixel-based multi-level thresholding, and the existing mettled. And much better segmentation results are obtained by the post-processing method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.70-77
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• 2008

This paper proposes a new text candidate region detection method that uses genetic algorithm based on information of the segmented regions. In image segmentation, a classification of the pixels at each color channel and a reclassification of the region-unit for reducing inhomogeneous clusters are performed. EWFCM(Entropy-based Weighted C-Means) algorithm to classify the pixels at each color channel is an improved FCM algorithm added with spatial information, and therefore it removes the meaningless regions like noise. A region-based reclassification based on a similarity between each segmented region of the most inhomogeneous cluster and the other clusters reduces the inhomogeneous clusters more efficiently than pixel- and cluster-based reclassifications. And detecting text candidate regions is performed by genetic algorithm based on energy and variance of the directional edge components, the number, and a size of the segmented regions. The region information-based detection method can singles out semantic text candidate regions more accurately than pixel-based detection method and the detection results will be more useful in recognizing the text regions hereafter. Experiments showed the results of the segmentation and the detection. And it confirmed that the proposed method was superior to the existing methods.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.5
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• pp.635-648
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• 1995

Macrobenthic fauna were collected seasonally using the van Veen Grab $(0,1/m^2)$ to investigate the benthic faunal assemblages on the soft-bottoms around the Youngjong Island during October 1991 to July 1992. A total of 266 species was identified. Of these polychaetes comprised 111 species $(41.7\%)$; crustaceans $75(28.2\%)$ molluscs, $59 (22.2\%)$ and others including echinoderms, $27(7.9\%)$. Mean density and biomass were estimated to be 498 $ind./m^2$ and 54.8$g./m^2$, respectively. Polychaetes were the most dominant faunal group in terms of abundance $(332\;ind./m^2)$ and number of species as well, whereas echinoderms were predominant in biomass $(332\;g./m^2)$. The dominant species were Mediomastus sp., Heteromastus sp., Nipponomysella oblongata, and Nephts polyranchia; the abundance of these species showed seasonal variations. The study area was divided into three regions by cluster analysis based on the similarity of species composition. The first region consisted of intertidal flat (G-I); the second, shallow subtital region of muddy sand (G-II); the third, channel region of mud sediments (G-III). The intertidal flat showed the highest density, and the channel was the lowest density, but the Highest in species diversity. Distribution of macrobenthic faunal assemblages of the study area seemed to be controlled by sedimentary facies and duration of tidal exposure.