• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.411-418
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• 2020

In the field of information security, IDS(Intrusion Detection System) is normally classified in two different categories: signature-based IDS and anomaly-based IDS. Many studies in anomaly-based IDS have been conducted that analyze network traffic data generated in cyberspace by machine learning algorithms. In this paper, we studied pre-processing methods to overcome performance degradation problems cashed by rare classes. We experimented classification performance of a Machine Learning algorithm by reconstructing data set based on rare classes and semi rare classes. After reconstructing data into three different sets, wrapper and filter feature selection methods are applied continuously. Each data set is regularized by a quantile scaler. Depp neural network model is used for learning and validation. The evaluation results are compared by true positive values and false negative values. We acquired improved classification performances on all of three data sets.

• Journal of Sensor Science and Technology
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• v.5 no.1
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• pp.43-50
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• 1996

This paper presents a manufacture of the multiple subjects biotelemetry system using custom CMOS IC fabricated $1.5{\mu}m$ n-well process technology. The implantable circuits of the system except sensor interface circuits including FM transmitter are fabricated on a single chip with the sire of $4{\times}4mm^{2}$. It is possible to assemble the implantable system in a hybrid package as small as $3{\times}3{\times}2.5cm$ by using this chip, It's main function is to enable continuous measurement simultaneously up to 7-channel physiological signals from the selected one among 8 subjects. Another features of this system are to enable continuous measurement of physiological signals, and to accomplish ON/OFF switching of an implanted battery by subject selection signal with command signal from the external circuit. If this system is coupled with another appropriate sensors in medical field, various physiological parameters such as pressure, pH and temperature are to be measured effectively in the near future.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.33-40
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• 2003

Data mining plays an important role in a knowledge discovery process and various algorithms of data mining can be selected for the specific purpose. Most of traditional hierachical clustering methode are suitable for processing small data sets, so they difficulties in handling large data sets because of limited resources and insufficient efficiency. In this study we propose a hybrid neural networks clustering technique, called PPC for Pre-Post Clustering that can be applied to large data sets and find unknown patterns. PPC combinds an artificial intelligence method, SOM and a statistical method, hierarchical clustering technique, and clusters data through two processes. In pre-clustering process, PPC digests large data sets using SOM. Then in post-clustering, PPC measures Similarity values according to cohesive distances which show inner features, and adjacent distances which show external distances between clusters. At last PPC clusters large data sets using the simularity values. Experiment with UCI repository data showed that PPC had better cohensive values than the other clustering techniques.

• Journal of KIISE
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• v.42 no.8
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• pp.947-953
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• 2015

PRAM is being considered as a potential successor to DRAM because of its characteristics such as byte-addressability, non-volatility, and high density. To gain its benefits, buffer cache replacement algorithm based on PRAM has been actively studied. However, most of the previous studies on buffer cache replacement algorithm limitedly exploit the byte-level performance of PRAM by focusing its limited lifetime and slower access latency compared to DRAM. In this paper, we propose a novel buffer cache replacement algorithm that fully considers the byte-level performance of PRAM and the performance of secondary storage. To take advantage of small size write on PRAM, proposed scheme keeps pages, which are frequently accessed with a small size write, on PRAM and allows the selective page migration from DRAM to PRAM. As a result, our scheme significantly reduces the number of PRAM writes. Our experimental results indicate for real workloads that our scheme reduces the number of PRAM writes by up to 92% and improves its performance by up to 62% compared to CLOCK.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.113-124
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• 2010

In this study, the hybrid authorization quotation technique is based on the device ID for the integrity of the source region guarantee of user certificate, in order to improve the convenience and security for user in the hybrid PKI certificate Mechanism for authentication. The feature of the model in which it is presented from this paper is 5. First, because the user can select the policy himself in which it matches with each authentication situation and security level, the convenience can be improved. Second, the integrity of the source region of the user certificate can be guaranteed through the comparison of the DLDI Key, that is the hash-value of the device ID. Third, the security can be improved by continuously changing an encoding, and the value of the key in which it decodes through the EOTP Key. Fourth, the index value is added to a certificate, and the storage of a certificate is possible at the Multi-Device. Fifth, since the addi the inan aratus for the integrity of the source region guarantee of a certificate is not needed, the authentication process time can be reduced and the computational load of the certificate server can be reduced also.

• Prospectives of Industrial Chemistry
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• v.17 no.2
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• pp.8-20
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• 2014

나노세공체는 고표면적, 균일한 다공성, 분자크기의 세공구조, 높은 흡착용량, 이온교환 특성, 높은 촉매활성, 분자크기의 형상선택성 등의 특징을 갖기 때문에 촉매 및 흡착제로 나노소재 분야에서 가장 오랫동안 활용되어 왔던 중요한 물질 가운데 하나로 정유 및 석유화학 산업을 비롯한 화학산업과 환경 산업에 광범위하게 사용되고 있다. 본 고찰에서는 결정성 나노세공체 가운데 가장 중요한 제올라이트와 최근 연구가 활발한 하이브리드 나노세공체의 산업적 응용 및 기술개발 동향과 향후 발전 전망에 대해 간략히 기술하였다.

• Information and Communications Magazine
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• v.30 no.4
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• pp.23-28
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• 2013

개념적으로 존재하던 클라우드 컴퓨팅의 사용이 본격적으로 심화되면서 기업들의 클라우드 컴퓨팅에 대한 개념과 생각에도 많은 변화가 생기게 되었다. 클라우드 컴퓨팅 구축이 더욱 가속화되는 가운데, 그로 인한 비용절감이나 투자수익 창출 효과에 대한 검증이 활발해질 것으로 예측됐다. 시대를 뒤바꾸는 혁신적인 기술도 잇달아 등장할 것이란 기대도 높다. 2013년은 클라우드 컴퓨팅의 발전에 또 한번의 변곡점이 될 것으로 전망된다. 본 고에서는 2013년 클라우드 컴퓨팅에 관한 예측 몇 가지를 살펴보고자 한다. 첫째, 중소기업에서의 클라우드 컴퓨팅 사용 및 정착이 가속화될 것으로 보인다. 대기업의 경우에는 기존에 구축되어 있는 IT 시스템에 대한 거버넌스 체계를 완성해 나가고 있는 상황 속에서 중앙집중형과 사용자 편의성이 강화된 클라우드 컴퓨팅을 도입하기 위해서는 아직 추구 해야 할 과제가 많다. 하지만 중소기업은 예산을 문제로 대기업과 같은 수준의 IT 인프라를 갖추지 못하고 있기 때문에 클라우드 컴퓨팅 도입으로 비용대비 고효율의 IT 인프라를 갖출 수 있다. 둘째, CSP, CSB와 통합허브가 성장할 것이다. 플랫폼 전쟁이 시장 점유율 '횡령' 싸움임을 CSP들이 깨닫게 됨에 따라 이러한 가격 경쟁들은 더 잦아질 것으로 전망된다. 그리고 2013년 클라우드 벤더들은 클라우드 가격 책정이 비용-수익 관리(costyield management)의 연장선에 있음도 알게 될 것이다. 핵심은 효율적 설계와 저비용 운영, 그리고 무엇보다 높은 사용률에 있다. 또한 기업들이 점점 많은 애플리케이션을 소프트웨어 형태로 구매하고 있기 때문에 애플리케이션 자체를 통합하는 문제, 애플리케이션에 대한 보안과 감사 프로세스개발 등의 문제가 제기되고 있다. 시스템 통합 서비스와 통합 허브는 이런 문제를 해결하기 위해 노력하게 될 것이다 셋째, 2013년은 하이브리드 클라우드 컴퓨팅이 급부상할 것으로 전망된다. 앞으로는 클라우드 컴퓨팅 시장에서 공급업체끼리 경쟁하는 것은 의미가 없으며 기업들은 절대 한 가지 클라우드 기술이나 공급자에 안주하지 않을 것이다. 이것은 곧 2013년에는 하이브리드 및 이종 클라우드 컴퓨팅이 각광 받을 것을 의미한다. 이러한 하이브리드 클라우드 컴퓨팅을 도입하기 위해서는 클라우드 컴퓨팅의 단점과 문제 해결을 위해 하나의 목적을 가지고 다양한 산업체들이 모여 경쟁업체이면서도 협력관계를 이루는 것이 중요하다. 넷째, 멀티 디바이스를 이용한 클라우드 사용이 폭발적으로 증가될 것으로 보인다. 1인당 여러 대의 단말 보유, 이동성 향상 요구, 4G 확산 등 모바일 시장을 중심으로 한 환경 변화로 인해 이전보다 한층 진화된 클라우드 기반의 서비스가 다양하게 등장할 것으로 예상된다. 또한 4G 시대가 본격적으로 개막되면서 데이터 및 앱을 저장하는 것뿐만 아니라 앱을 실행하는 프로세싱까지도 모두 인터넷 상의 서버에서 이루어지는 방식인 클라우드 스트리밍(Cloud Streaming)이라는 신기술이 상용화될 것으로 기대된다. 다섯째, 2013년에는 'XaaS(Everything as a Service)' 개념이 보다 확장될 것이다. 클라우드 컴퓨팅의 사용이 확산됨에 따라 하드웨어의 도입, 소프트웨어 및 데이터베이스 개발과 구축, IT 서비스 등 IT 인프라 스트럭처의 토대에 많은 변화가 생겼다. 인프라스트럭처는 더 이상 고정불변의 자산으로서가 아니라 유연성과 확장성을 강조하는 서비스로서의 특징으로 점점 부각되고 있다. 따라서 모든 IT 인프라스트럭처가 Ondemand화 서비스로 제공되는 비즈니스 모델들이 부상하고 있으며 플랫폼, 하드웨어 데이터베이스 등 모든 IT 요소를 서비스 형태로 제공하는 XaaS가 2013년 새로운 개념으로 떠오를 것으로 기대된다. 여섯번째로 스토리지를 둘러싼 가격 경쟁이 더욱 심화될 것으로 보인다. 업체들의 가격 인하는 앞으로도 계속될 것이며 사용자들에게도 큰 혜택으로 돌아갈 것이지만 사람들은 가격만으로 서비스를 선택하지 않을 것이기 때문에 가격보다는 차별화된 기능 및 서비스 전략이 필요할 것이다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3389-3404
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• 2012

Based on the case study methods, the study analyzes emergence and ensuing typology of global ebook platforms such as Google eBook, Amazon Kindle, iBooks Store. Global ebook platforms show adaptation process responding to rapidly changing digital technological envirment and it's fitness landscape. The critical elements in its emerging process are the distinct selection criteria, the degree of resource abundance and the search process based on open innovation. Based on these critical elements, the global platforms show speciation process, so called niche creation and are evolving into a variety of the typology based on the initial condition of key resource which makes the platform emerge and grow. Each global ebook platforms is evolving into open platform, hybrid platform, closed platform. Google eBook has openness and extensibility due to a variety of devices based on Android and a direct involvement of actors. Amazon Kindle has developed from a online bookstore and into the hybrid platform which have not only closed quality but also openness with ebook devices and mobile network. iBooks Store has developed into the closed platform through the agency model based on competitive hardwares and closed quality with iphone and ipad.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.