• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.354-357
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• 1991

In the case of system operation, a line overload cause damage to spread an whole range of power system. Of the theorems on load shedding, this study applied power distribution theorem and load reduction theorem which are local load shedding method, which are not affected by the magnitude of the power system and need not a large memory capacity and computation time. In this paper, we treat the problem of overload when power system occurred to fatal fault. Especially, there is the special case that local load shedding theorem is not always solved. Therefore, we introduce a solved device of the problem and construct the expert system of expanded local load shedding. Because proposed method uses the merits of expert system, in the case of system operation, the system operator don't embarrass to fatal fault and promptly deals with.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.16-21
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• 2005

This paper considers the problem of state estimation in linear continuous-time systems with multi-sensor environment and observation uncertainties. We propose two suboptimal filtering algorithms for these types of systems. The filtering algorithms consist of two steps: The local optimal Kalman estimates are computed at the first step. And, these local estimates are lineally fused at the second step. The implementation of the two-step filtering algorithms needs a lower memory demand than the optimal Kalman and adaptive Lainiotis-Kalman filters. In consequence of parallel structure of the proposed filters, the parallel computers can be used for their design. The examples exhibit the effect of common noise on the performance of fusion of the local Kalman estimates based on observations from different sensors and in the presence of uncertainties.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.6
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• pp.680-687
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• 1988

When only global or local features of images are considered, the segmented results exhibit inevitable errors. To reduce these errors, first we divide the image into uniform and nonuniform regions by considering the local properties of the image. Next we obtain the segmented results by applying the Fuzzy C-means (FCM) algorithm to the picture and determining to which uniform reigons each pixel of the nonuniform regions belongs. To reduce the computational burden and memory required for the FCM algorithm, the equations used for FCM algorithm are modified. The performance of the proposed method is quantitatively compared to existing ones using only global or local features of the picture. Computer simualtion result shows that the segmented results obtained by applying the proposed method are superior to existing ones.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.804-806
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• 1995

This paper discusses Self-organized Distributed Networks(SODN) as identifier of nonlinear dynamical systems. The structure of system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. The learning with the proposed SODN is fast and precise. Such properties arc caused from the local learning mechanism. Each local networks learns only data in a subregion. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the SODN. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.242-252
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• 2021

Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance's importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.

• Korean Linguistics
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• v.76
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• pp.83-112
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• 2017

In this paper, newspaper articles were collected through '5.18' keyword search results and the news corpus was constructed from the collected data. In the articles of local newspapers 'Honam' and 'Yeongnam', the ideological differences regarding '5.18' were investigated. The ideological differences of local newspaper discourse through objective figures was analyzed.. The subjects of the newspaper articles, the frequency of nouns and predicates were analyzed. The use and meaning of the intended vocabulary were examined. As a result of analyzing the title of the newspaper article, the discourse written in 'Honam' emphasized the necessity of re - recognition of 5.18. In both regions, the word "Gwangju" is often used. However, 'Gwangju' in 'Honam' newspaper means spiritual space, not physical space. In Honam regional newspapers, there are many vocabularies describing the events such as 'shoot' and 'fire', this calls for recollection and memory of '5.18'. In the analysis of newspaper discourse, the analysis of the contrast between the local newspapers was very insignificant, but, this study was conducted to analyze the discourse among local newspapers.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.68-74
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• 2009

In this paper, we propose an effective memory reduction algorithm to reduce the amount of reference frame buffer and memory bandwidth in video encoder and decoder. In general video codecs, decoded previous frames should be stored and referred to reduce temporal redundancy. Recently, reference frames are recompressed for memory efficiency and bandwidth reduction between a main processor and external memory. However, these algorithms could hurt coding efficiency. Several algorithms have been proposed to reduce the amount of reference memory with minimum quality degradation. They still suffer from quality degradation with fixed-bit allocation. In this paper, we propose an adaptive block-based min-max quantization that considers local characteristics of image. In the proposed algorithm, basic process unit is $8{\times}8$ for memory alignment and apply an adaptive quantization to each $4{\times}4$ block for minimizing quality degradation. We found that the proposed algorithm can obtain around 1.7% BD-bitrate gain and 0.03dB BD-PSNR gain, compared with the conventional fixed-bit min-max algorithm with 37.5% memory saving.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9A
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• pp.924-933
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• 2008

This paper proposes a novel LDPC code decoder architecture to improve throughput for DVB-S2, a second generation standard of ETSI for satellite broad-band applications. The proposed architecture clusters 360 bitnodes and checknodes into groups utilizing the property of IRA-LDPC code. Functional modules which perform calculations for bitnode groups and checknode groups have local memories and store the messages from the other type of functional modules connected by edges at their local memories. The proposed architecture can avoid memory conflicts by accessing stored messages sequentially, hence, increases throughput in the proposed DVB-S2 LDPC code decoder architecture. The proposed architecture was synthesized using the TSMC 90nm technology. Synthesis results show that throughput of the proposed architecture is improved by 104% and 478%, respectively, when compared with those of the architectures proposed by F. Kienle and J. Dielissen.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.154-162
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• 2003

In this paper, Associative Memory with Cellular Neural Networks classifies and recognizes image patterns as an operator applied to image process. CNN processes nonlinear data in real-time like neural networks, and made by cell which communicates with each other directly through its neighbor cells as the Cellular Automata does. It is applied to the optimization problem, associative memory, pattern recognition, and computer vision. Image processing with CNN is appropriate to 2-D images, because each cell which corresponds to each pixel in the image is simultaneously processed in parallel. This paper shows the method for designing the structure of associative memory based on CNN and getting output image by choosing the most appropriate weight pattern among the whole learned weight pattern memories. Each template represents weight values between cells and updates them by learning. Hebbian rule is used for learning template weights and LMS algorithm is used for classification.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.542-546
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• 2008

Recently dual-port SDRAM (DPSDRAM) architecture tailored for dual-processor based mobile embedded systems has been announced where a single memory chip plays the role of the local memories and the shared memory for both processors. In order to maintain memory consistency from simultaneous accesses of both ports, every access to the shared memory should be protected by a synchronization mechanism, which can result in substantial access latency. We propose two optimization techniques by exploiting the communication patterns of target applications: lock-priority scheme and static-copy scheme. Further, by dividing the shared bank into multiple blocks, we allow simultaneous accesses to different blocks thus achieve considerable performance gain. Experiments on a virtual prototyping system show a promising result - we could achieve about 20-50% performance gain compared to the base DPSDRAM architecture.