• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.22-30
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• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.8
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• pp.1998-2004
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• 2009

Recently, due to technical developments of various storage devices and networks, the amount of data increases rapidly. The large volume of data streams poses unique space and time constraints on the data mining process. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Most of the researches based on the support are concerned with the frequent itemsets, but ignore the infrequent itemsets even if it is crucial. In this paper, we propose an efficient method WSFI-Mine(Weighted Support Frequent Itemsets Mine) to mine all frequent itemsets by one scan from the data stream. This method can discover the closed frequent itemsets using DCT(Data Stream Closed Pattern Tree). We compare the performance of our algorithm with DSM-FI and THUI-Mine, under different minimum supports. As results show that WSFI-Mine not only run significant faster, but also consume less memory.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.43-49
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• 2023

Object tracking is being studied with various techniques such as Kalman filter and Luenberger tracker. Even in situations, such as the one in which the system model is not well specified, to which existing signal processing techniques are not successfully applicable, it is possible to design artificial neural networks to track objects. In this paper, we propose an artificial neural network, which we call 'maximum-likelihood weighted-average neural network', to continuously track unpredictably moving objects. This neural network does not directly estimate the locations of an object but obtains location estimates by making weighted average combining various results of maximum likelihood tracking with different data lengths. We compare the performance of the proposed system with those of Kalman filter and maximum likelihood object trackers and show that the proposed scheme exhibits excellent performance well adapting the change of object moving characteristics.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.287-290
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• 2000

This paper analyze link-sharing mechanisms in packet networks based on the hierarchical class based queueing. The CBQ outlines a set of flexible, efficiently implemented gateway mechanisms that can meet a range of service and link-sharing requirements. We have analyzed the Class level(B, C, D) using the EWMA (Exponential Weighted Moving Average) weight value and EWMA average limit value.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.202-202
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• 2006

• ETRI Journal
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• v.6 no.4
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• pp.3-8
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• 1984

Frequency division multiplexed 3ch. Color TV signals have been transmitted via optical fiber by employing $1. 3\mum$ InGaAsP DH-laser diode, graded index optical fiber and Ge-APD as optical components. Overall system margin of 20 dB was realized at weighted SNR of more than 49 dB. With this system margin, measured DG and DP were less than 10% and $5^{\circ}$respectively. Throughout this experiment, it was confirmed that multichannel TV signals could be economically transmitted over optical fiber in short haul networks. This paper describes system outlines and hardware implementation results.

• Proceedings of the IEEK Conference
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• 2000.06a
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• pp.275-278
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• 2000

Recently ATM(Asynchronous Transfer Mode) technology is facing challenges from Integrated Service IP(Internet Protocol), IP router, Gigabit Ethernet. Although ATM is approved by ITU-T as the standard technology in B-ISDN, its survival is still in question. In the ATM networks, the Guaranteed Frame Rate(GFR) service has been designed to accommodate non-real-time applications, such as TCP(Transmission Control Protocol)/IP based traffic. The GFR service not only guarantees a minimum throughput at the frame level, but also supports a fairshare of available resources. We have studied different discarding and scheduling schemes, and compared their throughput and fairness when TCP/IP Traffic is carried. Through simulations, we know that only per-VC queueing with weighted Round Robin(WRR) can guarantee Minimum Cell Rate Among all the Schemes that have been experimented, we recommend DT-EPD(Dynamic Threshold-Early Packet Discard) integrated with MCRplus(Minimum Cell Rate) to support the GFR service.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.315-326
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• 2007

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents a dynamic anomaly detection scheme that can effectively identify a group of especially harmful internal masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on the feature values, the use pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function with both the age of the user profile and weighted feature values. The performance of our scheme is evaluated by a simulation. Simulation results demonstrate that the anomalies are well detected by the proposed dynamic scheme that considers the age of user profiles.

• Journal of Communications and Networks
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• v.7 no.3
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• pp.385-393
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• 2005

In this paper, we propose a simple algorithm to adaptively adjust the value of dupthresh, the duplicate acknowledgement threshold that triggers the transmission control protocol (TCP) fast retransmission algorithm, to improve the TCP performance in a network environment with persistent packet reordering. Our algorithm uses an exponentially weighted moving average (EWMA) and the mean deviation of the lengths of the reordering events reported by a TCP receiver with the duplicate selective acknowledgement (DSACK) extension to estimate the value of dupthresh. We also apply an adaptive upper bound on dupthresh to avoid the retransmission timeout events. In addition, our algorithm includes a mechanism to exponentially reduce dupthresh when the retransmission timer expires. With these mechanisms, our algorithm is capable of converging to and staying at a near-optimal interval of dupthresh. The simulation results show that our algorithm improves the protocol performance significantly with minimal overheads, achieving a greater throughput and fewer false fast retransmissions.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.5
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• pp.79-84
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• 1989

Optical implementation of a quadratic associative memory model of neural networks is reported. Weighted $N^3$ interconnections between neurons are realized with an optical matrix-vector multiplier and interconnection holograms.