• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.393-398
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• 2007

In this paper, we propose the development of Artificial Hippocampus Algorithm(AHA) which remodels a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 4 steps system (EC, DG CA3, and CA1) and improve speed of teaming by addition of modulator to long-term memory teaming. In hippocampus system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labeled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CA1 region, convergence of connection weight which is used long-term memory is learned fast a by neural network which is applied modulator. To measure performance of Artificial Hippocampus Algorithm, PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) are applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by AHA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.11
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• pp.1872-1879
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• 1989

In this paper, we implement optical holographic associative memory based on the two-dimensional neural network model by using commercial LCTV as a spatial light modulator. The system consists of the simple Vander Lugt correlator utilizing plane Fourier hologram and the pinhole array. The associated output signal is detected on a two-dimensional CCD camera and electronically thresholded and fed back to the input plane of the system. Through the experimental demonstration of nonlinear holographic associative memory, it is suggested that commercial LCTV can be used as a new coherent spatial light modurator.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.6
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• pp.93-99
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• 1993

In previous paper we proposed a multi-layer bidirectional associative memory (MBAM) which is an extended model of the bidirectional associative memory (BAM) into a multilayer architecture. And we showed that the MBAM has the possibility to have binary storage for easy implementation. In this paper we present a MOdified MBAM(MOMBAM) with high performance compared to MBAM and multi-layer perceptron. The contents will include the architecture, the learning method, the computer simulation results for MOMBAM with MBAM and multi-layer perceptron, and the convergence properties shown by computer simulation examples.. And we will show that the proposed model can be used as classifier with a little restriction.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1395-1397
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• 1996

Many of the existing neural associative memories are trained and recalled in separate modes and are not suitable for temporal pattern storage and classification in that user must specify the time and length of input patterns. In this paper, a new on-line temporal associative memory model is presented. This memory is structured in layers of neurons and each neuron has limited number of weights so that calculation complexity can be considerably reduced and processing of patterns can be achieved in real time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.8
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• pp.667-677
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• 1990

In this paper, optical inplementation of the Hopfield neural network model for two-dimensinal associative memory is described For the real-time processing of two-dimensional images, the commercial LCTVs are used as a memory mask and an input spatical light modulator. A 4-D memory matrix is realized with a 2-D mask of a matrix arrangement and the inner-products between arbitrary input pattern and memory matrix are carried out by using the multifocus hololens. The output image is then electronically thresholded and fed back to the input of the associative memory system by 2-D CCd camera. From the good experimental results for the high error correction capability, the proposed system can be applied to practical pattern recognition and machine vision systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.8B
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• pp.541-548
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• 2005

IP lookup and forwarding process becomes the bottleneck of packet transmission as IP traffic increases. Previous hardware-based IP address lookup schemes using an index-based table are not memory-efficient due to sparse distribution of the routing prefixes. In this paper, we propose memory-efficient hardware based IP lookup scheme called set-associative IP address lookup scheme, which provides the same IP lookup speed with much smaller memory requirement. In the proposed scheme, an NHA entry stores the prefix and next hop together. The IP lookup procedure compares a destination IP address with eight entries in a corresponding set simultaneously and finds the longest matched prefix. The memory requirement of the proposed scheme is about $42\%$ of that of Lin's scheme. Thus, the set-associative IP address lookup scheme is a memory-efficient hardware based IP address lookup scheme.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.1-8
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• 2011

NAND type Flash memory has performing much researches for a hard disk substitution due to its low power consumption, cheap prices and a large storage. Especially, the NAND type flash memory is using general buffer systems of a cache memory for improving overall system performance, but this has shown a tendency to emphasize in terms of data. So, our research is to design a high performance instruction NAND type flash memory structure by using a buffer system. The proposed buffer system in a NAND flash memory consists of two parts, i.e., a fully associative temporal buffer for branch instruction and a fully associative spatial buffer for spatial locality. The spatial buffer with a large fetching size turns out to be effective serial instructions, and the temporal buffer with a small fetching size can achieve effective branch instructions. According to the simulation results, we can reduce average miss ratios by around 77% and the average memory access time can achieve a similar performance compared with the 2-way, victim and fully associative buffer with two or four sizes.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.51-55
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• 2020

In this paper, an improved FAM is proposed by adopting similarity learning in the existing FAM (Fuzzy Associative Memory) used in image restoration. Image restoration refers to the recovery of the latent clean image from its noise-corrupted version. In serious application like face recognition, this process should be noise-tolerant, robust, fast, and scalable. The existing FAM is a simple single layered neural network that can be applied to this domain with its robust fuzzy control but has low capacity problem in real world applications. That similarity measure is implied to the connection strength of the FAM structure to minimize the root mean square error between the recovered and the original image. The efficacy of the proposed algorithm is verified with significant low error magnitude from random noise in our experiment.

• ETRI Journal
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• v.34 no.5
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• pp.787-790
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• 2012

The objective of this research is to design a high-performance NAND flash memory system containing a buffer system. The proposed instruction buffer in the NAND flash memory consists of two parts, that is, a fully associative temporal buffer for temporal locality and a fully associative spatial buffer for spatial locality. A spatial buffer with a large fetching size turns out to be effective for serial instructions, and a temporal buffer with a small fetching size is devised for branch instructions. Simulation shows that the average memory access time of the proposed system is better than that of other buffer systems with four times more space. The average miss ratio is improved by about 70% compared with that of other buffer systems.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.860-863
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• 1997

FCM(Fuzzy Cognitive Map) is a fuzzy signed directed graph for representing causal reasoning which has fuzziness between causal concepts. Authors have already proposed FCM-based fault diagnostic scheme. However, the previously proposed scheme has the problem of lower diagnostic resolution. In order to improve the diagnostic resolution, a new diagnostic scheme based on extended FCM which incorporates the concept of fuzzy number into FCM is developed in this paper. Furthermore, an enhanced TAM(Temporal Associative Memory) recall procedure and pattern matching scheme are also proposed.