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e-Lollapalooza: A Process-Driven e- Business Service Integration System for e-Logistics Services
( Kwanghoon Kim ),( Ilkyeun Ra ) 한국인터넷정보학회 2007 KSII Transactions on Internet and Information Syst Vol.1 No.1
There are two newly emerging research issues in the enterprise information systems literature. One is the scalability issue for rapidly increasing choreographic volumes between interrelated organizations. The other is the business intelligence issue for traceable and monitorable business processes and services interchanging e-Business data and applications across organizations. Based upon these emerging issues, through a functional extension of the ebXML technology we have developed a process-driven e- Business service integration (BSI) system, which is named `e-Lollapalooza`. It consists of three major components - the Choreography Modeler coping with the process-driven collaboration issue, the Runtime & Monitoring Client for coping with the business intelligence issue and the EJB-based BSI Engine coping with the scalability issue. This paper particularly focuses on the e-Lollapalooza`s development aspects for supporting the ebXML-based choreography and orchestration among the engaged organizations in a process-driven multiparty collaboration for e-Logistics and e- Commerce services. Here, it is fully deployed in an EJB-based middleware computing environment for e-Logistics process automation and B2B choreography.
하둡 분산파일시스템에서 안전한 쓰기, 읽기 모델과 평가
방세중 ( Sechung Pang ),나일균 ( Ilkyeun Ra ),김양우 ( Yangwoo Kim ) 한국인터넷정보학회 2012 인터넷정보학회논문지 Vol.13 No.5
요즘 클라우드 컴퓨팅이 활성화됨에 따라 분산파일시스템의 요구가 증대되고 있지만 클라우드 컴퓨팅 환경에서 민감한 개인정보의 악용을 방지하는 분산파일시스템의 프레임은 아직 없다. 그래서 이 논문에서는 비밀분산 방법을 이용하여 분산파일시스템을 위한 안전한 쓰기/읽기 모델을 제시하였다. 이 모델은 비밀분산 방법을 사용하여 분산파일시스템의 기밀성뿐만 아니라 가용성도 보장한다. 또 제안한 방법으로 비밀 분산, 복구를 실행하였고 이를 대표적 암호화 알고리즘인 SEED 알고리즘에 의한 것과 비교를 함으로써 제시한 방법의 우수성을 보였다. 이와 더불어 이 방법이 하둡 분산파일시스템에 쉽게 이식될 수 있도록 하둡 분산파일시스템의 구조에 의존적이지 않은 쓰기/읽기 모델을 제안하였으며, 비밀분산모델의 성능측정방법으로 제안모델에 대한 이론적 평가를 실시하였다. Nowadays, as Cloud computing becomes popular, a need for a DFS(distributed file system) is increased. But, in the current Cloud computing environments, there is no DFS framework that is sufficient to protect sensitive private information from attackers. Therefore, we designed and proposed a secure scheme for distributed file systems. The scheme provides confidentiality and availability for a distributed file system using a secret sharing method. In this paper, we measured the speed of encryption and decryption for our proposed method, and compared them with that of SEED algorithm which is the most popular algorithm in this field. This comparison showed the computational efficiency of our method. Moreover, the proposed secure read/write model is independent of Hadoop DFS structure so that our modified algorithm can be easily adapted for use in the HDFS. Finally, the proposed model is evaluated theoretically using performance measurement method for distributed secret sharing model.
A Hybrid Software Defined Networking Architecture for Next-Generation IoTs
( Ahyoung Lee ),( Xuan Wang ),( Hieu Nguyen ),( Ilkyeun Ra ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.2
Everything in the world is becoming connected and interactive due to the Internet. The future of interactive smart environments such as smart cities, smart industries, or smart farms demand high network bandwidth, high network flexibility, and self-organization systems without costly hardware upgrades, and they provide a sustainable, scalable, and replicable smart environment backbone infrastructure. This paper presents a new Hybrid Software-Defined architecture for integrating Internet-of-Things technologies that are essential technologies for smart environments. It combines a software-defined networking infrastructure and a real-time distributed network framework with an advanced optimization to enable self-configuration, self-management, and self-adaption for providing seamless communication and efficiently managing a vast number of smart heterogeneous devices.
A Study on the Mutual Cooperation for a Security Monitoring System based LBS
Fairuz Iqbal Maulana,Chang-Soo Kim,Ilkyeun Ra 한국정보통신학회 2016 2016 INTERNATIONAL CONFERENCE Vol.8 No.1
Development of the location-based services and the communication technique can not only provide the new information to the people but also inform the danger information. People can easily share the needed information of crime or dangerous places using the mobile devices through web portal. Our system can be applied to the website IDUN (Indonesia United) which is a national information disclosure to solve existing problems in society in mutual cooperation. This website is a pilot system which each user is given the same rights to the features provided, ranging from crime-prone location information through Google Map views to minimize the possibility of a victim of crime.
Fault Diagnosis of Bearing Based on Convolutional Neural Network Using Multi-Domain Features
( Xiaorui Shao ),( Lijiang Wang ),( Chang Soo Kim ),( Ilkyeun Ra ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.5
Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals’ macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.