• JSTS:Journal of Semiconductor Technology and Science
• /
• v.16 no.4
• /
• pp.405-414
• /
• 2016

IC-implementations provide high performance for solving the high computational cost of pattern matching but have relative low flexibility for satisfying different applications. In this paper, we report an associative memory architecture for k nearest neighbor (KNN) search, which is one of the most basic algorithms in pattern matching. The designed architecture features reconfigurable vector-component parallelism enabled by programmable switching circuits between vector components, and a dedicated majority vote circuit. In addition, the main time-consuming part of KNN is solved by a clock mapping concept based weighted frequency dividers that drastically reduce the in principle exponential increase of the worst-case search-clock number with the bit width of vector components to only a linear increase. A test chip in 180 nm CMOS technology, which has 32 rows, 8 parallel 8-bit vector-components in each row, consumes altogether in peak 61.4 mW and only 11.9 mW for nearest squared Euclidean distance search (at 45.58 MHz and 1.8 V).

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.16 no.6
• /
• pp.545-555
• /
• 1991

In this paper, We discuss optical implementation of new optical adaptive patern recognition system based on single layer perception with learning capability and associative memory model having error corrective capability. The single layer perceptron is optically implemented by using 2 D LCTV spatial light modulators through the nonlinear quantization and polarization encoding methods, and 2 D hopfield associative memory is also implemented by using multifocus holographic lens. From some experimental results on classfication of Arabic numbers into even & edd numbers, it is shown that the proposed system can classify the patterns to the right classes correctly even for the partial and erronenous input patterns. Accordingly, the proposed optical adaptive pattern recognition system can be suggested for practical application in the fields of image processing and pattern recognition.

• Journal of Biomedical Engineering Research
• /
• v.21 no.5
• /
• pp.527-535
• /
• 2000

For a reliable patient set-up verification, better portal films are needed to track relevant features. Simulator films are compared with portal films as a reference image in radiotherapy planning. This shows some possibilities of the use of image information of simulator images for enhancement and restorations of portal images which are very poor in quality compared with the simulator images. This paper present an approach that combine an associative memory, a kind of artificial neural networks with fuzzy image enhancement technique using genetic algorithm which determines the fuzzy region of membership function by the use of maximum entropy principles. A higher portal image quality than conventional technique is achieved.

• Journal of Digital Convergence
• /
• v.14 no.8
• /
• pp.313-318
• /
• 2016

The recommendation service is changing from client-server based internet service to social networking. Especially in recent years, it is serving recommendations with personalization to users through crowdsourcing and social networking. The social networking based systems can be classified depending on methods of providing recommendation services and purposes by using memory and model based collaborative filtering. In this study, we proposed the social network based sensibility design recommendation using associative user. The proposed method makes {user - associative design} matrix through the social network and recommends sensibility design using the memory based collaborative filtering. For the performance evaluation of the proposed method, recall and precision verification are conducted. F-measure based on recommendation of social networking is used for the verification of accuracy.

• Korean Journal of Computational Design and Engineering
• /
• v.3 no.1
• /
• pp.57-67
• /
• 1998

Genetic programming (GP) is an extension of a genetic algoriths paradigm, deals with tree structures representing computer programs as individuals. In recent, there have been many research activities on applications of GP to various engineering problems including system identification, data mining, function approximation, and so forth. However, standard GP suffers from the lack of the estimation techniques for numerical parameters of the GP tree that is an essential element in treating various engineering applications involving real-valued function approximations. Unlike the other research activities, where nonlinear optimization methods are employed, I adopt the use of a weighted linear associative memory for estimation of these parameters under GP algorithm. This approach can significantly reduce computational cost while the reasonable accurate value for parameters can be obtained. Due to the fact that the GP algorithm is likely to fall into a local minimum, the GP algorithm often fails to generate the tree with the desired accuracy. This motivates to devise a group of additive genetic programming trees (GAGPT) which consists of a primary tree and a set of auxiliary trees. The output of the GAGPT is the summation of outputs of the primary tree and all auxiliary trees. The addition of auxiliary trees makes it possible to improve both the teaming and generalization capability of the GAGPT, since the auxiliary tree evolves toward refining the quality of the GAGPT by optimizing its fitness function. The effectiveness of this approach is verified by applying the GAGPT to the estimation of the principal dimensions of bulk cargo ships and engine torque of the passenger car.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.3
• /
• pp.448-459
• /
• 2012

It is important to understand how we can deal with elements for the modeling of neural networks when we are investigating the dynamical performance and the information processing capabilities. The information processing capabilities of model neural networks will change for different response, synaptic weights or learning rules. Using the statistical neurodynamics method, we evaluate the capabilities of neural networks in order to understand the basic concept of parallel distributed processing. In this paper, we explain the results of theoretical analysis of the effects of the hysteretic property on the performance of sequential associative neural networks.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.3
• /
• pp.41-49
• /
• 2009

Flash memory has become increasingly requestion for the importance and the demand as a storage due to its low power consumption, cheap prices and large capacity medium. This research is to design a high performance flash memory structure for the substitution of a hard-disk by dynamic prefetching of aggressive spatial locality from the spatial smart buffer system. The proposed buffer system in a NAND flash memory consists of three parts, i.e., a fully associative victim buffer for temporal locality, a fully associative spatial buffer for spatial locality, and a dynamic fetching unit. We proposed new dynamic prefetching algorithm for aggressive spatial locality. That is to use the flash memory instead of the hard disk, the proposed flash system can achieve better performance gain by overcoming many drawbacks of the flash memory by the new structure and the new algorithm. According to the simulation results, compared with the smart buffer system, the average miss ratio is reduced about 26% for Mediabench applications. The average memory access times are improved about 35% for Mediabench applications, over 30% for Spec2000 applications.

• Journal of Microbiology and Biotechnology
• /
• v.34 no.5
• /
• pp.1092-1100
• /
• 2024

The global elderly population, aged 65 and over, reached approximately 10% in 2020, and this proportion is expected to continue rising. Therefore, the prevalence of neurodegenerative diseases such as Parkinson's disease (PD), which are characterized by declining memory capabilities, is anticipated to increase. In a previous study, we successfully restored the diminished memory capabilities in a fruit fly model of PD by administering an omija extract. To identify functional ingredients that can enhance memory akin to the effects of the omija extract, we conducted screenings by administering halophyte extracts to the PD model. Halophytes are plants that thrive in high-salt environments, and given Korea's geographic proximity to the sea on three sides, it serves as an optimal hub for the utilization of these plants. Upon examining the effects of the oral administration of 12 halophyte extracts, Salicornia herbacea and Calystegia soldanella emerged as potential candidates for ameliorating memory loss in PD model flies. Moreover, our findings suggested that C. soldanella, but not S. herbacea, can mitigate oxidative stress in DJ-1β mutants.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.2
• /
• pp.161-167
• /
• 2000

In this paper, we propose a design method for BAMs(bidirectiona1 associative memories) which can perform the function of bidirectional association efficiently. Based on the theoretical investigation about the properties of BAMs, we first formulate the problem of finding a BAM that can store the given pattern pairs as stable states with high error correction ratio in the form of a constrained optimization problem. Next, we transform the constrained optimization problem into a GEVP(genera1ized eigenvalue problem), which can be solved by recently developed interior point methods. The applicability of the proposed method is illustrated via design examples.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.181-184
• /
• 2001

인간 두뇌의 연상과 기억 작용의 모델링을 통한 구현의 일부분으로, 본 논문에서는 Hebb 의 학습방법과 non-cloning template를 사용하여 discrete-time cellular neural networks의 연상메모리 기능을 구현한다. 본 논문에서 사용된 학습방법은 각 셀의 인접한 셀과의 연결상태에 따라 하중값 메트릭스를 구현한다. 이러한 방법은 새로운 패턴의 추가 학습과 삭제가 쉽고, 또한 쉽게 구현 할 수 있는 장점이 있다. 이 방법으로 모의 실험에서는 교통표지판의 분류에 사용한다.