• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.817-826
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• 2024

Modern networks and the internet now handle an unprecedented volume of data packets, making efficient traffic management increasingly challenging. To address this issue, next-generation network architectures like Content-Centric Networks (CCN) have emerged. CCN focuses on content-centric data processing to minimize unnecessary traffic. Traditional internet architectures primarily use an end-to-end approach, where multiple users requesting content simultaneously can lead to a surge in traffic. In contrast, CCN optimizes traffic by utilizing in-network cache functions, reducing the time required to serve content to users. Therefore, in CCN, it is crucial to determine which caching policy to apply for effective content management. Existing research has proposed various caching methods based on factors like Hit count and producer popularity. However, these methods often fail to consider content type, resulting in situations where less popular videos are removed in favor of images, potentially increasing network traffic. This study proposes a caching strategy that distinguishes between content type and producer popularity to further reduce network traffic and response time.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1663-1676
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• 2022

Recently, a number of deep-learning based land cover segmentation studies have been introduced. Some studies denoted that the performance of land cover segmentation deteriorated due to insufficient training data. In this study, we verified the improvement of land cover segmentation performance through data augmentation. U-Net was implemented for the segmentation model. And 2020 satellite-derived landcover dataset was utilized for the study data. The pixel accuracies were 0.905 and 0.923 for U-Net trained by original and augmented data respectively. And the mean F1 scores of those models were 0.720 and 0.775 respectively, indicating the better performance of data augmentation. In addition, F1 scores for building, road, paddy field, upland field, forest, and unclassified area class were 0.770, 0.568, 0.433, 0.455, 0.964, and 0.830 for the U-Net trained by original data. It is verified that data augmentation is effective in that the F1 scores of every class were improved to 0.838, 0.660, 0.791, 0.530, 0.969, and 0.860 respectively. Although, we applied data augmentation without considering class balances, we find that data augmentation can mitigate biased segmentation performance caused by data imbalance problems from the comparisons between the performances of two models. It is expected that this study would help to prove the importance and effectiveness of data augmentation in various image processing fields.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.4
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• pp.569-579
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• 2013

Robot control and image processing using a smartphone and Wi-Fi communication is introduced. The robot has a wheel and quadruped mechanism that is transformed according to the environment and is mainly used for drug bottle delivery. The captured image on the camera is transmitted to the smartphone in the form of stream data, and the image data is processed in the smartphone to enable the robot to identify an object and to control the robot itself. A network was constructed so that only image data from the stream data was used, and an image processing scheme to identify the drug bottle and deliver it to a person using a robot arm is also presented. In this study, image processing techniques and algorithms were purely implemented on a smartphone with considerable computational power and multiple functions rather than a computer, which contributes to the intelligence and miniaturization of the robot system.

• The KIPS Transactions:PartA
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• v.13A no.4 s.101
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• pp.371-380
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• 2006

This paper utilizing model picture of finished clothes in fashion design field various material (textile fabrics) doing Draping directly can invent new design, and do not produce direction sample or poetic theme width and confirm clothes work to simulation. Also, construct database about model and material image and embodied system that can confirm Mapping result by real time. And propose clothes simulation system to dress to 3D human body model of imagination because using several cloth pieces first by process to do so that can do simulation dressing abstracted poetic theme width to 3D model here. Proposed system creates 3D model who put clothes by physical simulation that do fetters to mass-spring model after read 3D human body model file and 2D foundation pattern file. System of this treatise examines collision between triangle that compose human body model for realistic simulation and triangle that compose clothes and achieved reaction processing. Because number of triangle to compose human body is very much, this collision examination and reaction processing need much times. To solve this problem, treatise that see could create realistic picture by method to diminish collision public prosecutor and reaction processing number, and could dress clothes to imagination human body model within water plant taking advantage of Octree space sharing techniques.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1577-1585
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• 2022

As the interest in the 4th industrial revolution and metaverse increases, metaverse with multi edge structure is proposed and noted. Metaverse is a structure that can create digital doctor-like system through a large amount of image processing and data transmission in a multi edge system. Since metaverse application requires calculating performance, which can reconstruct 3-D space, edge hardware's insufficient calculating performance has been a problem. To provide streamable AI software in runtime, image processing, and data transmission, which is edge's loads, needs to be lightweight. Also lightweight at the edge leads to power consumption reduction of the entire metaverse application system. In this paper, we propose collaborative edge-cloud image processing with remote image processing method and Region of Interest (ROI) to overcome edge's power performance and build streamable and runtime executable AI software. The proposed structure was implemented using a PC and an embedded board, and the reduction of time, power, and network communications were verified.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.560-564
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• 2007

Korea Basic Science Institute(KBSI) has three general electron microscopes including High Voltage Electron Microscope(HVEM) which is the only one in Korea. Observed images through an electron microscope are what they are tilted by each step and saved, offering the more better circumstances for observers, a reconstruction to 3D could be a essential process. In this process, a warping method decreases distortions maximumly of avoided parts of a camera's focus. All these image treatment processes and 3D reconstruction processes are based on an accompaniment of a highly efficient computer, a number of Grid Node Personal computers share this process in a short time and dispose of it. Grid Node Personal computers' purpose is to make an owner can share different each other and various computing resources efficiently and also Grid Node Personal computers is applying to solve problems like a role scheduling needed for a constructing system, a resource management, a security, a capacity measurement, a condition monitoring and so on. Grid Node Personal computers accomplish roles of a highly efficient computer that general individuals felt hard to use, moreover, a image treatment using the warping method becomes a foundation for reconstructing to more closer shape with an real object of observation. Construction of the electron microscope volume 랜더링 system based on Grid Node Personal computer through the warping process can offer more convenient and speedy experiment circumstances to observers, and makes them meet with experiment outcome that is similar to real shapes and is easy to understand.

• Economic and Environmental Geology
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• v.49 no.5
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• pp.381-388
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• 2016

The purpose of this study is to integrate and visualize using mapping techniques based on precise seabed geomorphology, seafloor backscattering images and high-resolution underwater images of the nearshore area around the Dokdo, in the East Sea. We have been obtained the precise topography map using multibeam echosounder system around the nearshore area(~50 m) of the southern part of the Seodo. Side scan sonar survey for analysis seafloor backscattering images was carried out in the same area of topography data. High-resolution underwater images(zone(a), zone(b), zone(c)) were taken in significant habitat scope of the nearshore area of the southern part of the Seodo. Using the results of bathymetry, seafloor backscattering images, high-resolution underwater images, we performed an integrated visualization about the nearshore area of the Dokdo. The integrated visualizing techniques are possible to make the seabed characteristic mapping results of the nearshore area of the Dokdo. The integrated visualization results present more complex and reliable information than separate geological products for seabed environmental mapping study and it is useful to understand the relation between seafloor characteristics and topographic environments of the study area. The integrated visualizing techniques and mapping analysis need to study sustainably and periodically, for effective monitoring of the nearshore ecosystem of the Dokdo.

• Annual Conference of KIPS
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• 2001.04b
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• pp.731-734
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• 2001

우편물을 집배원이 배달하는 순서로 자동구분 처리하기 위한 요소기술 줌에서 4-state 바코드 시스템이 개발되고 있으며 우편번호, 배달순서코드, 고객정보 등이 적용될 예정이다. 기존의 고객 바코드 판독 시스템은 우편물상의 바코드 심볼로지가 존재하는 판독대상 영역의 기울기가 ${\pm}4.47^{\circ}$ 이하이고, 심볼의 훼손과 잡영이 없을 경우에 $79{\sim}100msec(35,000{\sim}45,000$통/시간)의 속도로 자동 구분 정보가 판독된다. 본 논문에서는 판독범위 및 판독성능을 개선을 위하여 CCD(Charge Coupled Device) 센서로부터 획득된 이미지상에서 존재하는 심볼로지 정보의 고속판독 방법을 제시한 것이다. 이 판독방법은 그레이(gray) 이미지 바탕면의 경계값(threshold) 기울기 분포를 기준으로 2개의 경계값을 설정하여 판독대상 정보를 획득하였다. 또한, 4-state 바코드 심볼로지의 존재 가능성 영역만을 탐색하고, 판독대상 영역에서 트래커(tracker)를 탐색하여 심볼로지의 기울기값, 심볼로지 경계값, 심볼위치 좌표값을 생성한 후 심볼값이 판독한 것이다. 판독시험 결과는 판독대상 영역의 심볼로지가 ${\pm}45^{\circ}$ 기울어지고, 잡영이 존재할 경우에도 $30{\sim}60msec(58,000{\sim}l16,000$통/시간) 이내에 판독되었다. 우편물 자동구분용 바코드 판독기로써 적용될 경우에 판독속도가 평균 57.25% 이상 개선되고, 판독범위의 확장으로 0.2%의 기계적인 오류(이송과정예서의 Jam 발생 비율)를 제외할 경우에 거의 99.8% 우편물을 판독하여 자동구분 처리할 수 있게 될 것으로 기대된다.onebook 엑세스 모들(Server Phonebook Access Module)로 구성되어 있다.외 보다 높았다(I/O ratio 2.5). BTEX의 상대적 함량도 실내가 실외보다 높아 실내에도 발생원이 있음을 암시하고 있다. 자료 분석결과 유치원 실내의 벤젠은 실외로부터 유입되고 있었고, 톨루엔, 에틸벤젠, 크실렌은 실외뿐 아니라 실내에서도 발생하고 있었다. 정량한 8개 화합물 각각과 총 휘발성 유기화합물의 스피어만 상관계수는 벤젠을 제외하고는 모두 유의하였다. 이중 톨루엔과 크실렌은 총 휘발성 유기화합물과 좋은 상관성 (톨루엔 0.76, 크실렌, 0.87)을 나타내었다. 이 연구는 톨루엔과 크실렌이 총 휘발성 유기화합물의 좋은 지표를 사용될 있고, 톨루엔, 에틸벤젠, 크실렌 등 많은 휘발성 유기화합물의 발생원은 실외뿐 아니라 실내에도 있음을 나타내고 있다.>10)의 $[^{18}F]F_2$를 얻었다. 결론: $^{18}O(p,n)^{18}F$ 핵반응을 이용하여 친전자성 방사성동위원소 $[^{18}F]F_2$를 생산하였다. 표적 챔버는 알루미늄으로 제작하였으며 본 연구에서 연구된 $[^{18}F]F_2$가스는 친핵성 치환반응으로 방사성동위원소를 도입하기 어려운 다양한 방사성의 약품개발에 유용하게 이용될 수 있을 것이다.었으나 움직임 보정 후 영상을 이용하여 비교한 경우, 결합능 변화가 선조체 영역에서 국한되어 나타나며 그 유의성이 움직임 보정 전에 비하여 낮음을 알 수 있었다. 결론: 뇌활성화 과제 수행시에 동반되는 피험자의 머리 움직임에 의하여 도파민 유리가 과대평가되었으며 이는 이 연구에서 제안한 영상정합을 이용한 움직임 보정기법에 의해서 개선되었다. 답이 없는 문제, 문제 만

• Korean Business Review
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• v.18 no.1
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• pp.59-79
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• 2005

recently, world is progressing large quantity consumption with continuous Innovation and economic growth and pollution is accelerated at these process. Increase of industry activity and service that is point of corporation activity is discharging environmental pollutants at whole process to manufacture of end product and exhaust process from acquisition of raw material for accompanied product production hereupon. At the same time, being promoting resources consumption by that use much raw material, As a result, is becoming obstacle factors in sustainable development. So, corporation's responsibility for environmental protection is emphasized. Corporation which must prepare in green round or environmental problems should consider environmental effects that is happened over whole life of products that include waste treatment after raw material acquisition and use as well as selling end product simply. A Life Cycle Assessment techniques is normalized and standardized in International Standard Organization for technical committee 207(TC 207) world widely, and effort to apply in corporation's activity because mastering LCA techniques in domestic several corporations is undergone actively. Coming into effect of Kyoto's Protocol and International Organization for Standard 14000 series revision are presenting new survival principle in competition between country or corporation. LCA technique may become very useful means to corporation which wish to attempt environment management in real condition that awareness for environment is important. Also, An LCA to each product is going to cause big effects in corporation's whole image as well as competitive power raising for single product. Therefore, this research wishes to examine some instances for the future competitive product development at the estimation of environmental friendliness using LCA techniques and more theoretical considerations of the LCA techniques that can dominate corporation's fate.