• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.1-10
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• 2011

Recently, cloud computing which has provided enabling convenience that users can connect from anywhere and user friendly environment that offers on-demand network access to a shared pool of configurable computing resources such as smart-phones, net-books and PDA etc, is to be watched as a service that leads the digital revolution. Now, when business practices between departments being integrated through a cooperating system such as cloud computing, data streaming between departments is getting enormous and then it is inevitably necessary to find the solution that person in charge and find data they need. In previous studies the clustering simplifies the search process, but in this paper, it applies Hash Function to remove the de-duplicates in large amount of data in business firms. Also, it applies Bayesian Network of data mining for classifying the respect data and presents handling cloud computing based data. This system features improved search performance as well as the results Compared with conventional methods and CPU, Network Bandwidth Usage in such an efficient system performance is achieved.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.2
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• pp.133-144
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• 2006

In order to catch out such Bond Strength, the preceding researchers had ever examined the Bond Strength of FRP Plate through their experimentations by setting up of various fluent. However, since the experiment for research on such Bond Strength takes much of expenditure for equipment structure and time-consuming, also difficult to carry out, it is conducting limitedly. This Study purposes to develop the most suitable Artificial Neural Network Model by application of various Neural Network Model and Algorithm to the adhering experiment data of the preceding researchers. Output Layer of Artificial Neural Network Model, and Input Layer of Bond Strength were performed the learning by selection as the variable of the thickness, width, adhered length, the modulus of elasticity, tensile strength, and the compressive strength of concrete, tensile strength, width, respectively. The developed Artificial Neural Network Model has applied Back-Propagation, and its error was learnt to be converged within the range of 0.001. Besides, the process for generalization has dissolved the problem of Over-Fitting in the way of more generalized method by introduction of Bayesian Technique. The verification on the developed Model was executed by comparison with the resulted value of Bond Strength made by the other preceding researchers which was never been utilized to the learning as yet.

• ETRI Journal
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• v.35 no.1
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• pp.58-68
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• 2013

Five-phase reservation protocol (FPRP) is a contention-based media access control protocol for wireless ad hoc networks. FPRP uses a five-phase reservation process to establish slot assignments based on time division multiple access. It allows a node to reserve only one slot in an information frame. Once a node has reserved a slot, it will cease contending for other slots. As a result, there may be less contending nodes in the remaining slots, so the time slots in an information frame are not fully used by FPRP. To improve time slot utilization, this paper proposes an improved pseudo-Bayesian algorithm, based on which an improved contention access mechanism for FPRP is proposed, in which nodes are allowed to contend for more than one slot in a reservation frame according to a certain probability/priority. Simulation results indicate that the proposed mechanism performs better than FPRP in time slot utilization and hence the network throughput under various scenarios.

• International Journal of Advanced Culture Technology
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• v.2 no.2
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• pp.7-10
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• 2014

In order to develop smart grid communications infrastructure, a high level of interconnectivity and reliability among its nodes is required. Sensors, advanced metering devices, electrical appliances, and monitoring devices, just to mention a few, will be highly interconnected allowing for the seamless flow of data. Reliability and security in this flow of data between nodes is crucial due to the low latency and cyber-attacks resilience requirements of the Smart Grid. In particular, Artificial Intelligence techniques such as Fuzzy Logic, Bayesian Inference, Neural Networks, and other methods can be employed to enhance the security gaps in conventional IDSs. A distributed FPGA-based network with adaptive and cooperative capabilities can be used to study several security and communication aspects of the smart grid infrastructure both from the attackers and defensive point of view. In this paper, the vital issue of security in the smart grid is discussed, along with a possible approach to achieve this by employing FPGA based Radial Basis Function (RBF) network intrusion.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.305-308
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• 2005

시각 센서를 이용한 환경 및 상황 인식은 로봇의 자동화된 행동을 위해서 매우 중요하다. 실제 환경에서 사람은 주위를 인식할 때 여러 단계의 인식과정을 거친다. 효율적이고 정확한 환경 인식을 위해서는 지능형 로봇의 인식 또한 사람의 인식과정과 같이 다단계로 이루어져야 한다. 또한 실제 환경은 유동적이며 많은 불확실성을 가지고 있으므로 불확실한 상황에 강인한 인식 방법이 필요하다. 이러한 불확실성을 내포한 환경 및 상황 인식에는 베이지안 네트워크를 이용한 인식이 강인하나 복잡한 환경을 하나의 베이지안 네트워크로 인식하는 것은 어렵다. 이 논문에서는 복잡하고 불확실한 환경 인식을 위한 여러 베이지안 네트워크를 사람의 인식과 같은 다단계의 인식 과정으로 구성된 행동 네트워크 기반으로 결합하는 앙상블 기법을 제안한다. 불확실한 상황을 적용한 환경 실험과 로봇 시뮬레이터를 이용한 로봇 실험으로 베이지안 네트워크 앙상블 기법이 환경 인식에 효과적인 것을 확인할 수 있었다.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.185-192
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• 2005

There are many data mining techniques such as association rule, decision tree, neural network analysis, clustering, genetic algorithm, bayesian network, memory-based reasoning, etc. We analyze 2003 Gyeongnam social indicator survey data using k-means clustering technique for environmental information. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper, we used k-means clustering of several clustering techniques. The k-means clustering is classified as a partitional clustering method. We can apply k-means clustering outputs to environmental preservation and environmental improvement.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.2
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• pp.69-77
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• 2001

An artificial neural network model was developed to analyze and forecast daily steamflow flow a small watershed. Error Back propagation neural networks (EBPN) of daily rainfall and runoff data were found to have a high performance in simulating stremflow. The model adopts a gradient descent method where the momentum and adaptive learning rate concepts were employed to minimize local minima value problems and speed up the convergence of EBP method. The number of hidden nodes was optimized using Bayesian information criterion. The resulting optimal EBPN model for forecasting daily streamflow consists of three rainfall and four runoff data (Model34), and the best number of the hidden nodes were found to be 13. The proposed model simulates the daily streamflow satisfactorily by comparison compared to the observed data at the HS#3 watershed of the Baran watershed project, which is 391.8 ha and has relatively steep topography and complex land use.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.658-660
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• 2005

실세계의 시각정보로부터 식별된 물체정보를 이용하여 장면에 대해 설명하는 컨텍스트를 추론하는 시각 기반 장면 이해 문제에서는 변화가 많고 불확실한 환경을 극복해야 할 뿐만 아니라, 사용자의 요구 사항을 잘 반영해야 하고 궁극적으로는 지도(teaching)가 가능해야 한다. 본 논문에서는 불확실성 극복을 위해 확률적 접근 방법을 사용하고, 사용자의 요구를 실시간으로 반영하기 위해 논리 네트워크를 이용한 상호 작용 학습 방법을 제안한다. 몇 가지 테스트 환경에서 사용자에 의해 제공되는 논리적, 부분적, 실시간 정보를 이용하여 제안하는 상호작용 학습을 수행한 결과, 장면인식 에이전트의 기능 장 및 적응이 가능하고 새로운 기능의 지도가 가능함을 알 수 있었다.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.624-627
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• 1988

This paper describes the implementation of recognition of speaker - dependent Korean spoken continuous digits. The recognition system can be divided into two parts, acoustic - phonetic processor and lexical decoder. Acoustic - phonetic processor calculates the feature vectors from input speech signal and the performs frame labelling and phone labelling. Frame labelling is performed by Bayesian classification method and phone labelling is performed using labelled frame and posteriori probability. The lexical decoder accepts segments (phones) from acoustic - phonetic processor and decodes its lexical structure through phone network which is constructed from phonetic representation of ten digits. The experiment carried out with two sets of 4continuous digits, each set is composed of 35 patterns. An evaluation of the system yielded a pattern accuracy of about 80 percent resulting from a word accuracy of about 95 percent.

• Geomechanics and Engineering
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• v.1 no.3
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• pp.259-262
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• 2009

This paper examines the potential of relevance vector machine (RVM) in prediction of pullout capacity of small ground anchors. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network (ANN) model. Overall, the RVM showed good performance and is proven to be better than ANN model. It also estimates the prediction variance. The plausibility of RVM technique is shown by its superior performance in forecasting pullout capacity of small ground anchors providing exogenous knowledge.