• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.34-45
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• 1997

In this paper, we present hardware implemcntation of self-organizing feature map (SOFM) neural networkwith constant adaptation gain and binary reinforcement function on FPGA. Whereas a tnme-varyingadaptation gain is used in the conventional SOFM, the proposed SOFM has a time-invariant adaptationgain and adds a binary reinforcement function in order to compensate for the lowered abilityof SOFM due to the constant adaptation gain. Since the proposed algorithm has no multiplication operation.it is much easier to implement than the original SOFM. Since a unit neuron is composed of 1adde $r_tracter and 2 adders, its structure is simple, and thus the number of neurons fabricated onFPGA is expected to he large. In addition, a few control signal: ;:rp sufficient for controlling !he neurons.Experimental results show that each componeni ot thi inipiemented neural network operates correctlyand the whole system also works well.stem also works well.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4B
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• pp.403-418
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• 2009

IP version 6 (IPv6) is a new If addressing scheme that has 128-bit address space. IPv6 is proposed to solve the address space problem of IP version 4 (IPv4) which has 32-bit address space. For a given IPv6 routing set, if a forwarding table is built using a trio structure, the trio has a lot more levels than that for IPv4. Hence, for IPv6 address lookup, the binary search on trio levels would be more appropriate and give better search performance than linear search on trio levels. This paper proposes a new IPv6 address lookup algorithm performing binary search on trio levels. The proposed algorithm uses a Bloom filter in pre-filtering levels which do not have matching nodes, and hence it reduces the number of off-chip memory accesses. Simulation has been performed using actual IPv6 routing sets, and the result shows that an IPv6 address lookup can be performed with 1-3 memory accesses in average for a routing data set with 1096 prefixes.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.12-19
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• 2015

In network-centric warfare, the communication network has played a significant role in defeating an enemy. Especially, the urgent and important data should be preferentially delivered in time. Thus, we proposed a priority MAC protocol based on CSMA/CA with Binary Exponential Back-off for tactical wireless networks. This MAC protocol suggested a PCW(Prioritized Contention Window) with differentiated back-off time by priority and a RBR(Repetitive Back-off Reset) to reset the remaining back-off time. The results showed that this proposed MAC has higher performance than those of DCF(Distributed Coordination Function) in the transmission success rate and the number of control packet transmission by reducing the packet collision. Thus, it produced more effective power consumption. In comparison with DCF, this proposed protocol is more suitable in high-traffic network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2121-2126
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• 2007

In this paper, the performance improvement of ultra-wideband(UWB) communications system to achieve high-data-rate using spatial diversity provided by multiple receive antennas is investigated. We derive the expression for the received SINR after spatially combining through multiple receive antennas and evaluate the bit error rate(BER) performance by numerical simulation. We also compare the performance results in the case of 2PPM systems with the theoretical performance results in the case of 2PAM THMA UWB systems. The impacts of spatial diversity on the performance of 2PPM and 2PAM THMA UWB systems are analyzed. It is shown that the BER performance is improved as the number of receive antennas increases. Also, it is observed that in the presence of multiple user interference signals, the performance of 2PAM THMA UWB systems is considerably superior to that of 2PPM THMA UWB systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.541-547
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• 2019

Recent developments in hacking technology are continuing to increase the number of new security vulnerabilities. Approximately 80,000 new vulnerabilities have been registered in the Common Vulnerability Enumeration (CVE) database, which is a representative vulnerability database, from 2010 to 2015, and the trend is gradually increasing in recent years. While security vulnerabilities are growing at a rapid pace, responses to security vulnerabilities are slow to respond because they rely on manual analysis. To solve this problem, there is a need for a technology that can automatically detect and patch security vulnerabilities and respond to security vulnerabilities in advance. In this paper, we propose the technology to extract the features of the vulnerability-discovery target binary through complexity analysis, and select a vulnerability-discovery strategy suitable for the feature and automatically explore the vulnerability. The proposed technology was compared to the AFL, ANGR, and Driller tools, with about 6% improvement in code coverage, about 2.4 times increase in crash count, and about 11% improvement in crash incidence.

• Journal of IKEEE
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• v.23 no.2
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• pp.529-533
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• 2019

Combined cycle power plants are often used to produce power. These days prediction of power plant output based on operating parameters is a major concern. This paper presents an approach to using computational intelligence technique to predict the output power of combined cycle power plant. Computational intelligence techniques have been developed and applied to many real world problems. In this paper, tree architectures of fuzzy neural networks are considered to predict the output power. Tree architectures of fuzzy neural networks have an advantage of reducing the number of rules by selecting fuzzy neurons as nodes and relevant inputs as leaves optimally. For the optimization of the networks, two-step optimization method is used. Genetic algorithms optimize the binary structure of the networks by selecting the nodes and leaves as binary, and followed by random signal-based learning further refines the optimized binary connections in the unit interval. To verify the effectiveness of the proposed method, combined cycle power plant dataset obtained from the UCI Machine Learning Repository Database is considered.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.543-550
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• 2000

An adaptive bit-reduced mean absolute difference (ABRMAD) is presented as a criterion for the block-matching algorithm (BMA) to reduce the complexity of the VLSI Implementation and to improve the processing time. The ABRMAD uses the lower pixel resolution of the significant bits instead of full resolution pixel values to estimate the motion vector (MV) by examining the pixels Ina block. Simulation results show that the 4-bit ABRMAD has competitive mean square error (MSE)results and a half less hardware complexity than the MAD criterion, It has also better characteristics in terms of both MSE performance and hardware complexity than the Minimax criterion and has better MSE performance than the difference pixel counting(DPC), binary block-matching with edge-map(BBME), and bit-plane matching(BPM) with the same number of bits.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.3
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• pp.296-302
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• 2013

The binary offset carrier (BOC) modulation has attracted much attention as a new physical layer implementation for satellite communication and global navigation satellite systems. In this paper, we propose a new correlation function for sine-phased BOC signals. Specifically, we separate a sub-carrier pulse into several rectangular pulses, and then, generate a number of partial correlation functions. Finally we combine the partial correlation functions to yield an unambiguous correlation function with no side-peak. From numerical results, it is demonstrated that the proposed correlation function provides a significant tracking performance improvement over the conventional correlation functions.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.710-715
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• 2016

Due to its simplicity and high reliability, the box-counting(BC) method is one of the most frequently used techniques to estimate the fractal dimensions of a binary image with a self-similarity property. The fractal calculation requires data sampling that determines the size of boxes to be sampled from the given image and directly affects the accuracy of the fractal dimension estimation. There are three non-overlapping regular grid methods: geometric-step method, arithmetic-step method and divisor-step method. These methods have some drawbacks when the image size M becomes large. This paper presents a BC algorithm for enhancing the accuracy of the fractal dimension estimation based on a new sampling method. Instead of using the geometric-step method, the new sampling method, called the coverage ratio-step method, selects the number of steps according to the coverage ratio. A set of experiments using well-known fractal images showed that the proposed method outperforms the existing BC method and the triangular BC method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3A
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• pp.184-194
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• 2005

One of the most important factors that decide the overall performance of RFID system is anti-collision algorithm. By enhancing the anti-collision algorithm, we can increase the number of RFID tags that can be processed in unit time. Two anti-collision algorithms are most widely prevailed: one is ALOHA-based protocol and the other is a binary tree walking method, but these are still under research. In this paper, we suggest an anti-collision algorithm named AAC(Address Allocating and Calling) using pre-distributed random address, which is much faster and more efficient than existing ones. Finally, we evaluate our scheme using mathematical analysis and computer simulation.