• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.563-572
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• 2024

Image processing and computer vision technologies are becoming increasingly important in a variety of application fields that require techniques and tools for sophisticated image analysis. In particular, image segmentation is a technology that plays an important role in image analysis. In this study, in order to identify recent research trends on image segmentation techniques, we used the Web of Science(WoS) database to analyze the R&D topography based on the network structure of the author's keyword co-occurrence matrix. As a result, from 2015 to 2023, as a result of the analysis of the R&D map of research articles on image segmentation, R&D in this field is largely focused on four areas of research and development: (1) researches on collecting and preprocessing image data to build higher-performance image segmentation models, (2) the researches on image segmentation using statistics-based models or machine learning algorithms, (3) the researches on image segmentation for medical image analysis, and (4) deep learning-based image segmentation-related R&D. The scientometrics-based analysis performed in this study can not only map the trajectory of R&D related to image segmentation, but can also serve as a marker for future exploration in this dynamic field.

• Membrane and Water Treatment
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• v.14 no.4
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• pp.155-162
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• 2023

Global climate change and urbanization have various demerits, such as water pollution, flood damage, and deterioration of water circulation. Thus, attention is drawn to Nature-based Solution (NbS) that solve environmental problems in ways that imitate nature. Among the NbS, urban wetlands are facilities that perform functions, such as removing pollutants from a city, improving water circulation, and providing ecological habitats, by strengthening original natural wetland pillars. Frequent monitoring and maintenance are essential for urban wetlands to maintain their performance; therefore, there is a need to apply the Internet of Things (IoT) technology to wetland monitoring. Therefore, in this study, we attempted to develop a real-time wetland monitoring device and interface. Temperature, water temperature, humidity, soil humidity, PM1, PM2.5, and PM10 were measured, and the measurements were taken at 10-minute intervals for three days in both indoor and wetland. Sensors suitable for conditions that needed to be measured and an Arduino MEGA 2560 were connected to enable sensing, and communication modules were connected to transmit data to real-time databases. The transmitted data were displayed on a developed web page. The data measured to verify the monitoring device were compared with data from the Korea meteorological administration and the Korea environment corporation, and the output and upward or downward trend were similar. Moreover, findings from a related patent search indicated that there are a minimal number of instances where information and communication technology (ICT) has been applied in wetland contexts. Hence, it is essential to consider further research, development, and implementation of ICT to address this gap. The results of this study could be the basis for time-series data analysis research using automation, machine learning, or deep learning in urban wetland maintenance.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..

• Journal of Distribution Science
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• v.13 no.9
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• pp.81-93
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• 2015

Purpose - "Showrooming" refers to the phenomenon where a shopper visits a store to see and compare products but makes the purchase online at a lower price. Surveys on showrooming activities at home and abroad indicate that a significant number of consumers pursue showrooming activities. The advent of "showroomers," who engage in buying activities, hovering both on and offline, while selectively choosing sales channels to suit their needs, is powerful enough to erode the borders between channels and bring about seismic changes in the distribution industry. However, surprisingly, there has been no in-depth discussion on showrooming. This study seeks to theoretically investigate what impact personal characteristics have on showrooming preferences and attitudes in a multi-channel environment. Specifically, assumptions have been made that price perception, perceived performance risk, and trust in online shopping not only have a direct impact on showrooming attitudes but also indirectly affect it through the means of contact motivation. Research design, data, and methodology - To test the hypotheses, this study conducted a survey of male and female shoppers, ages 20 through 40s, who live in metropolitan areas, and have actively showroomed fashion items in the last six months. A clothing item usually purchased after a careful decision-making process was chosen as the target product of the study. The survey was conducted between October and November 2014, using a professional survey service provider. A total of 200 surveys were collected, of which 198 were used for analysis. Conceptual model Structural Equation Modeling (SEM) and Amos 18.0 were employed for data analysis and model verification. In addition, following the confirmatory factor analysis and measurement model analysis, the theoretical model that corresponds to the research model was analyzed. Results - Analysis results show that price perception, perceived performance risk, and trust in online shopping have a statistically significant and positive (+) impact on showrooming attitudes. In addition, in terms of the indirect influence of price perception and perceived performance risk on showrooming attitudes through means of contact motivation, price perception had a statistically significant and positive impact on means of contact motivation, whereas perceived performance risk did not have a statistically significant impact on it, with the relevant hypothesis rejected. Conclusions - These analysis results imply that the ultimate goal of consumers is to maximize their shopping benefits by selectively and strategically taking advantage of different channels in a complementary manner. This study presents many implications for distributors to encourage a deep understanding of showrooming consumers who have complicated consumption behaviors and to build channel integration strategies. This study has limitations in theoretical and practical implications. Therefore, subsequent studies need to focus on verifying that showrooming activities are based on reasonable and planned decisions by applying the theory of reasoned or planned behavior. In addition, the scope of the study should expand to include web showrooming, where consumers conduct product research online and purchase offline.

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.39-44
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• 2016

Due to the dazzling development of IT in this IT-oriented era, information delivering technology among objects, between objects and humans, and among humans has been actively performed. As information delivery technology has been actively performed, IoT became closely related to our daily lives and ubiquitous at any time and place. Therefore, IoT has become a part of our daily lives. CoAp, a web-based protocol, is mostly used in IoT environment. CoAp protocol is mostly used in the network where transmission speed is low along with the huge loss. Therefore, it is mostly used in IoT environment. However, there is a weakness on IoT that it is weak in security. If security issue occurs in IoT environment, there is a possibility for secret information of individuals or companies to be disclosed. If attackers infect the targeted device, and infected device accesses to the wireless frequently used in public areas, the relevant device sends arp spoofing to other devices in the network. Afterward, infected devices receive the packet sent by other devices in the network after occupying the packet flow in the internal network and send them to the designated hacker's server. This study suggests counter-attacks on this issues and a method of coping with them.

• Archives of Reconstructive Microsurgery
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• v.8 no.1
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• pp.77-83
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• 1999

The skin on the dorsum of the foot is a source of the reliable thin and sensory cutaneous free tissue transplantation with or without tendon, bone and joint. A composite flap with attached vascularized tendon grafts for the combined loss of skin and tendon on the dorsum of the hand and foot offers an immediate one stage solution to this problem. The flap provides a very durable innervated tissue cover for the heel of the foot and the dorsum of the hand and an osteocutaneous transfer combined with the second metatarsal. The major dorsalis pedis artery is constant in size, but the first dorsal metatarsal artery is variable in size and location. The dorsal surface of the foot receives sensory innervation through the superficial peroneal nerve and the first web through the deep peroneal nerve. Authors had performed 5 dorsalis pedis free flap transplantation in the foot and hand at Department of Orthopedic Surgery, Chonbuk National University Hospital from August 1993 through August 1997 and followed up for the period of between 19 and 67 months until March 1999. The results were as follows 1. 5 cases dorsalis pedis free flap transfer to the foot(4 cases) and the hand(1 case) were performed and the recipient was foot dorsum and heel 2 cases each and hand dorsum 1 case. 2 All of 5 cases(100%) were survived from free flap transfer and recipient artery was dorsalis pedis artery(2 cases), anterior tibial artery(1 case), posterior tibial artery(1 case) and ulnar artery(1 case) and recipient veins were 2 in number except in the hand. 3. Long term follow up of the exterior and maceration was good and sensory recovery was poor 4. Donor site was covered with full thickness skin graft obtained from one or both inguinal areas at postoperative 3rd week and skin graft was taken good and no morbidity was showed.

• Ocean and Polar Research
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• v.26 no.2
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• pp.287-298
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• 2004

As a part of Korea Deep Ocean Study program, we investigated the distribution of planktonic protists in the upper 200 m of the northeast Pacific from $5^{\circ}N$ to $17^{\circ}N$, along $131^{\circ}30'W$. Area of divergence was formed at $9^{\circ}N$ which is boundaries of the north equatorial counter current (NECC) and the north equatorial current (NEC) during this cruise. Chlorophyll-a concentration was higher in NECC than in NEC area. Pico chl-a(<$2\;{\mu}m$) to total chl-a accounted for average 89% in the study area. The contribution of pico chl-a to total chl-a was relatively high in NEC area than in NECC area. Biomass of planktonic protists, ranging from 635.3 to $1077.3\;mgC\;m^{-2}$(average $810\;mgC\;m^{-2}$), was most enhanced in NECC area and showed distinct latitudinal variation. Biomass of HNF ranged from 88.7 to $208.3\;mgC\;m^{-2}$ and comprised 15% of planktonic protists. Biomass of ciliates ranged from 123.6 to $393.0\;mgC\;m^{-2}$ and comprised 25% of planktonic protists. Biomass of HDF ranged from 407.2 to $607.8\;mgC\;m^{-2}$ and comprised 60% of planktonic protists. HDF was the most dominant component in both NECC and NEC areas. Nano-protist biomass accounted for more than 50% of total protists in the both areas. The contribution of nanoprotist to total protists biomass was relatively higher in NEC area than in NECC. The biomass of planktonic protists was significantly correlated with phytoplankton biomass in this study area. The size structure of phytoplankton biomass coincided with that of planktonic protists. This suggested that the structure of the planktonic protists community and the microbial food web were dependent on the size structure of the phytoplankton biomass. However, biomass and size structure of planktonic protist communities might be significantly influenced by physical characteristics of the water column and food concentration in this study area.

• Korean journal of communication and information
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• v.26
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• pp.45-82
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• 2004

The purpose of this study is to examine the concept of civic journalism and the contents of Korean Internet newspapers that might reflect the possibilities of this new medium for civic journalism practices. This study examined how far and deep civic journalism practices have extended into Korean Internet newspapers as journalism's new tradition. More specifically, this study analyzed news articles of Korean Internet newspapers to uncover any differences among civic journalism Internet newspapers with different citizen reporter systems. The composite measure based upon ten elements of civic journalism practices was used as indicator of civic journalism practices. To obtain systematic data on news offered by Korean Internet newspapers on the World Wide Web, four major Internet newspapers, including Ohmynews, Ngotimes, Netpinion and Pressian were examined by a content analysis in April and May of 2003. Findings of this study reveal that many Korean newspapers do not fully exploit the opportunities and advantages offered by the new medium for civic journalism practices in online environments. Both aggregate and individual level of analysis for the civic journalism index reveal that there are some differences between non-civic journalism and civic journalism Internet newspapers using citizen reporter systems. However, overall performances of civic journalism Internet newspapers are not good enough to support the argument that civic journalism is well practiced in Korean Internet newspapers. Nonetheless, it would not be fair to conclude that Korean Internet newspapers have totally ignored the Internet's potential to increase the civic journalism performance in online environments.

• Knowledge Management Research
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• v.18 no.1
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• pp.83-116
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• 2017

The purpose of this study is to analyze empirically analyze how the characteristics of participants in educational and social learning forums and the quality of events influence expectations and satisfaction of forums. The study also aims to provide strategic implications for forum organizers and give them suggestions on how to set up target audience, manage forum contents, speakers, and services, improve attendee satisfaction, and ultimately maximize overall outcomes. As exchanges among individuals, enterprises, and organizations, as well as countries are growing rapidly, the convention industry has become a key player in the market. Conventions have also become a venue for people to discuss a specific agenda or topic, exchange information and learn knowledge and insights. Especially, the forum - as part of the convention industry - plays a vital role providing educational and social learning opportunities as scholars and expertise come together to share their knowledge and experience through a variety of discussions. With its role, many of forums are taking place in recent years; however, there have been few empirical studies upon the forum itself. Also, there have been few attempts to research how the quality of forums affect participants' satisfaction along with their characteristics and how much of practical knowledge is provided throughout the events. This study is meaningful in that it is the first practical study that takes a deep understanding of the forum and sees how the quality of the forums influences participants' satisfaction and whether the characteristics of participants have a moderating effect in increasing the level of satisfaction. Forum organizers could also take a strategic approach as their major concerns are to increase the number of participants and raise degree of satisfaction by providing significant information. There are four key elements that determine success or failure of a social learning forum. The four elements are contents, speakers, services, and participants. Content plays an important role in providing rich information and knowledge for participants. Speakers are the main knowledge providers who contribute to the forum's social learning role. Also, the services provided by forum organizers such as simultaneous interpretation services, program brochures, lunch and refreshments, and the overall design of event hall can also influence the level of participants' satisfaction. Lastly, the participants and their characteristics are important since they are the ones who receive knowledge from the providers. The results of this study show that the quality of forum (content, speaker, and services) has a decisive effect on the participants' satisfaction and there are some differences in expectation among the participants in the forum. Also, some groups of participants were more likely to be stimulated by the quality of forum when determining their satisfaction. The study is modeled after MBN Y Forum 2016 and its participants' characteristics. The forum is one of the most representative social learning forums of South Korea and its audiences are mostly young people. It has analyzed how the participants' characteristics influence their satisfaction by grouping them into ${\Delta}participants$ who have invited for free and those who paid for the entrance fee, ${\Delta}first-time$ participants and returning participants, ${\Delta}voluntary$ and involuntary participants, ${\Delta}participants$ who registered through web and those who did through mobile, and ${\Delta}participants$ who registered during pre-sale opens and those who registered during general opens.