• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.48 no.4
• /
• pp.84-91
• /
• 2011

Recently, WBAN(Wireless Body Area Network) which has progressed standardization based on IEEE 802.15.6 standardization is a network for the purpose of the short-range wireless communications within around 3 meters from the inner or outer human body. Effective QoS control technique and data efficient management in limited bandwidth such as audio and video are important elements in terms of users and loads in short-range wireless networks. In this paper, for high-speed WBAN IEEE 802.15.6 standard, the dynamic allocation to give an efficient bandwidth management and weighted fair queueing algorithm have been proposed through the adjustment of the super-frame about limited data and Quality of Service (QoS) based on the queuing algorithm. Weighted Fair Queueing(WFQ) Algorithm represents the robust performance about elements to qualitative aspects as well as maintaining fairness and maximization of system performance. The performance results show that the dynamic allocation expanded transmission bandwidth five times and the weighted fair queueing increased maximum 24.3 % throughput and also resolved delay bound problem.

• Journal of KIISE:Information Networking
• /
• v.28 no.2
• /
• pp.251-261
• /
• 2001

The traffic on the Internet has been growing exponentially for some time. This growth is beginning to stress the current-day routers. However, switching technology offers much higher performance. So the label switching network which combines IP routing with switching technology, is emerged. EspeciaJJy in the data driven label switching, flow classification and cache table management are needed. Flow classification is to classify packets into switching and non-switching packets, and cache table management is to maintain the cache table which contains information for flow classification and label switching. However, the cache table management affects the performance of label switching network considerably as well as flowclassification because the bigger cache table makes more packet switched and maintains setup cost lower, but cache is restricted by local router resources. For that reason, there is need to study the cache replacement scheme for the efficient cache table management with the Internet traffic characterized by user. So in this paper, we propose several cache replacement schemes for label switching network. First, without the limitation at switching capacity in the router. we introduce FIFO(First In First Out). LFC(Least Flow Count), LRU(Least Recently Used! scheme and propose priority LRU, weighted priority LRU scheme. Second, with the limitation at switching capacity in the router, we introduce LFC-LFC, LFC-LRU, LRU-LFC, LRU-LRU scheme and propose LRU-weighted LRU scheme. Without limitation, weighted priority LRU scheme and with limitation, LRU-weighted LRU scheme showed best performance in this paper.

• The KIPS Transactions:PartA
• /
• v.10A no.3
• /
• pp.215-224
• /
• 2003

Since there are various networks and heterogeneity of nodes in Internet, the existing load-sharing algorithms are hardly adapted for use in Internet-based clustering systems. Therefore, in Internet-based clustering systems, a load-sharing algorithm must consider various conditions such as heterogeneity of nodes, characteristics of a network and imbalance of load, and so on. This paper has proposed an expanded-WF algorithm which is based on a WF (Weighted Factoring) algorithm for load-sharing in Internet-based clustering systems. The proposed algorithm uses an adaptive granularity strategy for load-sharing and duplicate execution of partial job for fault-tolerance. For the simulation, the to matrix multiplication using PVM is performed on the heterogeneous clustering environment which consists of two different networks. Compared to other algorithms such as Send, GSS and Weighted Factoring, the proposed algorithm results in an improvement of performance by 55%, 63% and 20%, respectively. Also, this paper shows that It can process the fault-tolerance.

• Journal of Internet Computing and Services
• /
• v.15 no.3
• /
• pp.109-116
• /
• 2014

This study proposed new instance selection using neural network with weighted fuzzy membership functions(NEWFM) based on Takagi-Sugeno(T-S) fuzzy model to improve the classification performance. The proposed instance selection adopted weighted average defuzzification of the T-S fuzzy model and an interval selection, same as the confidence interval in a normal distribution used in statistics. In order to evaluate the classification performance of the proposed instance selection, the results were compared with depending on whether to use instance selection from the case study. The classification performances of depending on whether to use instance selection show 77.33% and 78.19%, respectively. Also, to show the difference between the classification performance of depending on whether to use instance selection, a statistics methodology, McNemar test, was used. The test results showed that the instance selection was superior to no instance selection as the significance level was lower than 0.05.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.19 no.6
• /
• pp.1-8
• /
• 1982

In this paper a new analytic method for Ladder networks by weighted tree is proposed. In contrast to conventional tree concept that represents only information structure, in this paper, a tree with hierarchical structure is established by giving wei체t of impedance Z and admittance Y to branch and representing each node of its branch as a pair of voltage and current. Then, by defining generation level from tree structure and by parsing between standand level and arbitrary level, driving point impedance, transfer function and transfer impedance are simultaneously obtained instead of complex calculation method by inspection. The validity of this method is proved by the reciprocal theorem and this method is applied to four-terminal constants and the feedback network.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.11 no.4
• /
• pp.133-141
• /
• 1991

The empirical method is used to integration adjustment for the coordinates revision of a national control point but the existing values are not to be changed or changed with small variation by suitable datum selection (for example, fixed points). This paper treats the modified weighted station parameter adjustment by quasi-observations, and the method used only variance elements of existing coordinates which is substituted for all covariance elements. The movement detection of unstable points and the junction adjustment of new networks are successfully executed by the method, in integration of new secondary networks to old-secondary-triangulation points which are in the absence of the original observations in Korea. The investigation results reveal that the accuracy of old-secondary-triangulation points is ${\pm}16^{{\prime}{\prime}}$(${\pm}0.08m$), which results from the densification of test network and the analyses of old survey specifications. and is ${\pm}2.3^{{\prime}{\prime}}$ in fixing of old-secondary-triangulation points.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.05a
• /
• pp.113-115
• /
• 2015

In this paper, we proposed an algorithm based on movement prediction using rate of change of the attribute information of nodes what is called WRC(Weighted Rate Control) in delay tolerant networks(DTNs). Existing DTN routing algorithms based on movement prediction communicate by selecting relay nodes increasing connectivity with destination node. Thus, because the mobile nodes are in flux, the prediction algorithms that do not reflect the newest attribute information of node decrease reliability. In this paper, proposed algorithm approximate speed and direction of attribute information of node and analysis rate of change of attribute information of node. Then, it predict movement path of node using proposed weight. As the result, proposed algorithm show that network overhead and transmission delay time decreased by predicting movement path of node.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.48 no.6
• /
• pp.51-60
• /
• 2011

Researches on indoor localization using the wireless sensor network have been actively carried out to be used for indoor area where GPS signal is not received. Computationally efficient WCL(Weighted Centroid Localization) algorithm is shown to perform relatively well. However, to get the best performance for WCL all the anchor nodes must send signal with power to cover 96% of the network. The fact that outside the transmission range of the fixed nodes drastic localization error occurs results in large mean error and deviation. Due to these problems the WCL algorithm is not easily applied for use in the real indoor environment. In this paper we propose SRS(Succesive Region Setting) algorithm which sequentially reduces the estimated location area using the signal strength from the anchor nodes. The proposed algorithm does not show significant performance degradation corresponding to transmission range of the anchor nodes. Simulation results show that the proposed SRS algorithm has mean localization error 5 times lower than that of the WCL under free space propagation environment.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.2
• /
• pp.206-211
• /
• 2009

This paper presents a methodology to 1-day-forecast stock index using the automatic feature extraction method based on the neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by automatically removing the worst input features one by one. CPP$_{n,m}$(Current Price Position of the day n: a percentage of the difference between the price of the day n and the moving average from the day n-1 to the day n-m) and the 2 wavelet transformed coefficients from the recent 32 days of CPP$_{n,m}$ are selected as minimized features using bounded sum of weighted fuzzy membership functions (BSWFMs). For the data sets, from 1989 to 1998, the proposed method shows that the forecast rate is 60.93%.

• Animal Bioscience
• /
• v.35 no.6
• /
• pp.814-825
• /
• 2022

Objective: This study was conducted to identify the functional long non-coding RNAs (lncRNAs) for swine lactation by RNA-seq data of mammary gland. Methods: According to the RNA-seq data of swine mammary gland, we screened lncRNAs, performed differential expression analysis, and confirmed the functional lncRNAs for swine lactation by validation of genome wide association study (GWAS) signals, functional annotation and weighted gene co-expression network analysis (WGCNA). Results: We totally identified 286 differentially expressed (DE) lncRNAs in mammary gland at different stages from 14 days prior to (-) parturition to day 1 after (+) parturition, and the expressions of most of lncRNAs were strongly changed from day -2 to day +1. Further, the GWAS signals of sow milk ability trait were significantly enriched in DE lncRNAs. Functional annotation revealed that these DE lncRNAs were mainly involved in mammary gland and lactation developing, milk composition metabolism and colostrum function. By performing weighted WGCNA, we identified 7 out of 12 lncRNA-mRNA modules that were highly associated with the mammary gland at day -14, day -2, and day +1, in which, 35 lncRNAs and 319 mRNAs were involved. Conclusion: This study suggested that 18 lncRNAs and their 20 target genes were promising candidates for swine parturition and colostrum occurrence processes. Our research provided new insights into lncRNA profiles and their regulating mechanisms from colostrogenesis to lactogenesis in swine.