• Journal of the Korean Society of Clothing and Textiles
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• v.38 no.5
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• pp.611-626
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• 2014

Reading and foreseeing fashion trends is crucial and difficult in the fashion industry due to accelerated and diversified changes in fashion trends. We use network analysis to investigate fashion trends from 2004 to 2013 in order to find the inter-relevance among fashion trends. We extracted words from fashion trend info for women's wear provided by Samsung Design Net, created a 2-mode network of seasons and trend languages, and visualized this network using NodeXl program. Fashion trends repeated a unique pattern during the period. In the first half (2004-2008), retro modern, feminine modern, and ecological modern were dominant trends in consecutive order. The years 2009-2013 witnessed distinctive fashion trends in S/S seasons and in F/W seasons. 11F/W, 12F/W and 13F/W seasons were characterized by artistic creative style. From 2010, natural style dominated S/S seasons. 10S/S and 12S/S seasons were distinguished as a calm natural style that reflected a peaceful and simple life. In 11S/S and 13S/S seasons, soft natural style emerged as a sign of increased importance of inner spirit and natural energy. A seasonal reappearance of trends was observed every two years in S/S seasons that enabled the prediction that 14S/S will see another version of natural style. A macroscopic trend for the last 10 years was represented by the keywords 'modern' and 'natural'. 'Modern' involved the past styles such as 60's, Baroque and the origin of human life. 'Natural' was connected with design elements such as material, silhouette and color. Managerial implications and future study directions are discussed based on the results.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.73-85
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• 2013

This study examines the online social capital of local press using the website and social media. Moreover, the paper respectively visualizes web feature as Web 1.0 and social feature analysis as Web 2.0 by applying correspondence analysis. For data, the study analyzes 10 representative local press in Yeungnam areas. To collect the data, two coders coded web features from the websites and we employed NodeXL, an open-source software tool, for social media data. The results reveal that local websites expend online social capital using social media account. Especially, the social features of local presses attach importance to Twitter as the main press keep the well-balance use among all platforms.

• Journal of Digital Convergence
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• v.11 no.7
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• pp.185-192
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• 2013

The purpose of this study is to set the relationship between each parts that forms the engine of an automobile based on the 3D visualization technology which is able to be learned according to the skill of the operator in the industry field and to recommend the auto parts using a task ontology. A visualization method was proposed by structuring the complex knowledge by signifying the link and the node in forms of a network and using SOM which can be shown in the form of 3 dimension. In addition, by using is-a Relationship-based hierarchical Taxonomy setting the relationship between each of the parts that forms the engine of an automobile, to allow a recommendation using a weighted value possible. By providing and placing the complex knowledge in the 3D space to the user for an opportunity of more realistic and intuitive navigation, when randomly selecting the automobile parts, it allows the recommendation of the parts having a close relationship with the corresponding parts for easy assembly and to know the importance of usage for the automobile parts without any special expertise.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.493-496
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• 2011

Currently in the development of community wireless technology is gaining interest in location-based services and as a result, the importance of the location information is a growing trend. To calculate the location information is being suggested several ways, among them Trilateration is representative. Trilateration is three beacon nodes, the distance between the location in which you want to calculate with information. Beacon from a node to know where to get information when the distance between the obstacle and the distance error caused by the surrounding environment, which leads to the exact location can not be obtained. Currently due to distance error, location information has a variety of algorithms to reduce the error. However, a systematic analysis of these algorithms is not progress. This paper analyzes the location-aware technologies, and the error the distance of the location information to reduce errors in the various aspects of the algorithm for the systematic and empirical comparison was evaluated through the analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.670-675
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• 2018

There is a growing interest in EH-WSN (energy-harvesting wireless sensor networks) that can solve power problems in wireless sensor networks. In EH-WSN, on-off duty cycling is being studied in order to balance energy harvesting and consumption. However, the urgency of the sensed data and the energy harvesting rate in the environmental monitoring EH-WSN are important factors to determine the network performance. Therefore, it is necessary to control the duty-cycle period according to the importance of the sensed data and the energy harvesting rate in addition to simply maintaining the balance of the power. In this paper, we analyze the problem of on-off duty cycling in EH-WSN for environmental monitoring and propose an adaptive duty-cycle scheduling scheme considering the priority of sensed data and energy harvesting rate, where the priority of sensed data determined by sensed value and changing rate. The performance of scheduling scheme was analyzed by computer simulations.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.771-775
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• 2008

Concerns related to protecting, identifying, and isolating of subsystems of a water distribution network have led to the realization of the increased importance of valves in the system. The most important purpose of valves in water distribution systems is to isolate subsystems due to breakage, maintenance activities, or contamination. A subsystem called segment is isolated by the closure of adjacent valves and an efficient algorithm should identify the adjacent valves to minimize the pipe failure impact. In this paper, an algorithm to identify adjacent valves to be closed to isolate a subsystem from the remainder of a network in case of a pipe failure is presented. An application to the water distribution system in Ottawa, Canada demonstrates the developed algorithm efficiently locates the adjacent valves for the isolation of a broken pipe.

• Journal of Korean Society of Rural Planning
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• v.25 no.2
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• pp.15-21
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• 2019

The agricultural environment is changing and becoming more advanced due to the influence of the 4th Industrial Revolution. From the basic plan of Rural Informatics to the current level of 2nd generation smart farms aimed at improving productivity using Big data, cloud network and more IoT technology. We are continuing to provide support and research and development. However, many problems remain to be solved in order to supply and settle smart farms in Korea. The purpose of this study is to provide a method of collecting and sharing data on farming environment and to help improve the income and productivity of farmers based on collected data. In the case of hog farm, the multiple sensors for environmental data like temperature, humidity and gases and the network environment for connecting the internet were established. The environment sensor was made using the ESP8266 Node MCU board as micro-controller, DHT22 sensor for temperature and humidity, and MQ series sensors for various gases in the hog pens. The network sensor was applied experimentally for one month and the environmental data of the hog farm was stored on a web database. This study is expected to raise the importance of collecting and managing the agricultural and environmental data, for the next generation farmers to understand the smart farm more easily and to try it by themselves.

• Korean Journal of Clinical Pharmacy
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• v.31 no.2
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• pp.115-124
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• 2021

Background: With an increase in the number of breast cancer survivors, greater importance is attached to health-related quality of life, particularly pain and symptom control. This study aimed to identify the factors that are associated with pain in cancer patients based on the patterns of prescribing opioid, non-opioid, and adjuvant analgesics. Methods: This analysis included new patients who had developed breast cancer between 2003 and 2012. The degree of pain was analyzed based on the socio-demographic (age, income quintile, number of hospitalizations, and duration of disease), indicator (Body Mass Index; BMI, Charlson Comorbidity Index; CCI, Cumulative Analgesic Consumption Score; CACS), operation (mastectomy, lymph node dissection), and therapy (chemotherapy, radiation therapy), as well as complication-related variable (lymphedema). Results: As for the patterns of prescribing analgesics by stages, non-opioid and opioid analgesics constituted 30.7 and 69.3%, respectively. The mean value and variance of CACS were 5.596 and 12.567, respectively. The factors that significantly affected the degree of pain were age (≥50; IRR: 1.848, 95% CI 1.564-2.184, p=0.000), income quintile (IRR: 0.964, 95% CI 0.938-0.991, p=0.008), BMI (≥ 25; IRR: 1.479, 95% CI 1.222-1.795, p=0.000), CCI (≥ 4; IRR: 1.649, 95% CI 1.344-2.036, p=0.000), and lymphedema (yes; IRR: 1.267, 95% CI 1.006-1.610, p=0.047). Conclusions: It is necessary to develop systematic and comprehensive pain control measures to improve the quality of life for breast cancer survivors, especially for those who are 50 years or older, lie in the lower-income quintile, have BMI of ≥25 and CCI score ≥ 4, or have lymphedema.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.595-602
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• 2021

The robot is developing into a software-oriented shape that recognizes the surrounding situation and is given a task. Cloud Robotics Platform is a method to support Service Oriented Architecture shape for robots, and it is a cloud-based method to provide necessary tasks and motion controllers depending on the situation. As it evolves into a humanoid robot, the robot will be used to help humans in generalized daily life according to the three robot principles. Therefore, in addition to robots for specific individuals, robots as public goods that can help all humans depending on the situation will be universal. Therefore, the importance of information security in the Cloud Robotics Computing environment is analyzed to be composed of people, robots, service applications on the cloud that give intelligence to robots, and a cloud bridge that connects robots and clouds. It will become an indispensable element for In this paper, we propose a Security Scheme that can provide security for communication between people, robots, cloud bridges, and cloud systems in the Cloud Robotics Computing environment for intelligent robots, enabling robot services that are safe from hacking and protect personal information.