• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.1-8
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• 2017

In this paper, we propose a new hybrid wear leveling technique for NAND Flash memory, called Time-Aware Wear Leveling (TAWL). Our proposal prolongs the lifetime of NAND Flash memory by using dynamic wear leveling technique which considers the wear level of hot blocks as well as static wear leveling technique which considers the wear level of the whole blocks. TAWL also reduces the overhead of garbage collection by separating hot data and cold data using update frequency rate. We showed that TAWL enhanced the lifetime of NAND flash memory up to 220% compared with previous wear leveling techniques and our technique also reduced the number of copy operations of garbage collections by separating hot and cold data up to 45%.

• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.339-350
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• 2004

A broadcast method in mobile computing environments which have very large client populations solves the problem of low bandwidth. But most of previous proposed concurrency control protocols are restricted to read-only transactions from mobile client. In this paper, we propose the UTSM-ARM method which uses caching and broadcast method, and allows update transactions in mobile client. The UTSM-ARM decides the validation of cache data consistency with the dynamic update pattern ratio of accessed cached data. Also, the timestamps of accessed cached data and transaction in mobile client guarantee the serializable execution of mobile transactions. As a result, UTSM-ARM makes efficiently use of the asymmetric bandwidth of broadcast environment and can Increase the transaction throughput.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.6B
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• pp.544-550
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• 2006

Next-generation wireless/mobile networks are envisioned to have an IP-based infrastructure. One of the research challenges for next-generation all IP-based networks is the design of intelligent mobility management technologies that have a seamless mobility and minimal signaling overhead. Recently, HMIPv6 was proposed by the IETF for efficient mobility management. HMIPv6 reduces the amount of signaling and improves the performance of MIPv6 in terms of handover latency. However the MAP can be a single point of performance bottleneck when there are a lot of local movements. HMIPv6 can cause signaling overhead due to the unnecessary location update of idle mobile nodes. Therefore, in this paper, we propose the dynamic paging Mobile IPv6 that reduces the signaling cost of the unnecessary location updates using IP paging and organizes dynamically optimal MAP area according to user's mobility and traffic. We show performance results that are obtained from the average total location update cost and packet delivery cost.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.65-80
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• 2012

Mobile IP is a simple and scalable global mobility solution. However, it may cause excessive signaling traffic and long signaling delay. So MN (Mobile Node) to the home network in order to reduce the number of location update signaling is necessary to reduce the delay. In this paper, the signaling overhead to be distributed evenly on the boundary of the regional network, each MN's dynamic mobility and traffic load is adjusted according to the PMIPv6 (Proxy Mobile IPv6) networks in the proposed dynamic regional mobility management scheme (dMMS). Each user in a distributed network system that offers the least amount of signaling traffic is tailored to the optimized system configuration. Signaling cost function in order to propose a new discrete analytical model is proposed, MN's mobility and packet arrival patterns. Location update and packet delivery costs to calculate the total average, the optimized area to determine the size of the network is proposed. The results of mathematical analysis, the proposed technique dMMS in terms of reducing the cost of the entire signaling were found to be excellent performance.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.67-74
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• 2012

In this paper, we propose an efficient dynamic background modelling method by using eigenbackground to extract moving objects from video stream. Even if a background model has been created, the model has to be updated to adapt to change due to several reasons such as weather or lighting. In this paper, we update a background model based on R-SVD method. At this time we define a change ratio of images and update the model dynamically according this value. Also eigenbackground need to be modelled by using sufficient training images for accurate models but we reorganize input images to reduce the number of images for training models. Through simulation, we show that the proposed method improves the performance against traditional eigenbackground method without background updating and a previous method.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.48-60
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• 2004

XML documents consist of elements that are basic units of information. When the structure of XML documents is changed dynamically, we need to update structure information efficiently without changing the information of the index structure for fast retrieval. In this paper, we propose a dynamic indexing model scheme that updates the index structure in real time as the structure of XML documents is changed by insertion and deletion of elements. Our dynamic indexing model consists of a structure information representation method and a dynamic index structure. The structure information representation method supports various types of structure retrievals. Our dynamic index structure processes various structural queries efficiently. We show through various experiments that our method outperforms existing ones in processing various types of queries such as content based queries, structural queries and hybrid queries.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.285-294
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• 2007

In this paper, we investigate a novel online estimation algorithm for dynamic Bayesian network(DBN) parameters, given as conditional probabilities. We sequentially update the parameter adjustment rule based on observation data. We apply our algorithm to two well known representations of DBNs: to a first-order Markov Chain(MC) model and to a Hidden Markov Model(HMM). A sliding window allows efficient adaptive computation in real time. We also examine the stochastic convergence and stability of the learning algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.314.2-314
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• 2002

It is necessary first to understand dynamic characteristics of a bus full BIW assembly for fatigue endurance analysis. FE model has been used usually for analyzing the dynamic behavior of structures. A lot of experience and effort, however, is necessary to make a credible FE model. Experimental modal analysis of structures has been performed to verify the credibility of initial FE model and to update the model. (omitted)

• Communications of the Korean Mathematical Society
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• v.24 no.1
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• pp.153-159
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• 2009

In this paper, we analyze a dynamic threshold decryption scheme proposed by Long et al. It was claimed that the scheme allows to renew the master key, or to add/remove or update a decryption server, without leaking any information on the master key and changing other decryption server's secret keys. We present an attack to Long et al's scheme by using the fact that it renews a decryption server's secret key without changing other decryption server's secret shares.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.109-118
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• 2008

We present two estimators for discrete non-Gaussian and nonstationary probability density estimation based on a dynamic Bayesian network (DBN). The first estimator is for off line computation and consists of a DBN whose transition distribution is represented in terms of kernel functions. The estimator parameters are the weights and shifts of the kernel functions. The parameters are determined through a recursive learning algorithm using maximum likelihood (ML) estimation. The second estimator is a DBN whose parameters form the transition probabilities. We use an asymptotically convergent, recursive, on-line algorithm to update the parameters using observation data. The DBN calculates the state probabilities using the estimated parameters. We provide examples that demonstrate the usefulness and simplicity of the two proposed estimators.