• 전자공학회논문지C
• /
• 제34C권7호
• /
• pp.1-7
• /
• 1997

VHDL simulation requires both analysis and elaboration processes. Reducing the time taken by these processes shorten design cycles. We propose an incremental analysis and elaboration algorithm for VHDL, which minimizes the number of design units to be re-analyzed and re-elaborated after an incremental change, thereby reducing the desing cycle time. Experimental results show about four times performance improvement in analysis and 1.25 times in elaboration over the conventional method.

• 대한전자공학회논문지
• /
• 제25권6호
• /
• pp.614-628
• /
• 1988

In the original incremental Extended Horizon Control, the control inputs are computed recursively each step in the prediction horizon. But in this paper, we propose another incremental Extended Horizon Self-tuning Control version in which control inputs can be computed directly in any time interval. The effectiveness of this algorithm in a variable time delay or load disturbances environment is demonstrated by computer simulation. The controlled plant is a nonminimum phase system.

• ETRI Journal
• /
• 제32권4호
• /
• pp.603-606
• /
• 2010

In this letter, we propose a channel estimation algorithm based on incremental pilots. These are pilots additionally inserted after puncturing the modulated orthogonal frequency division multiplexing (OFDM) symbols to enhance channel estimation performance without lowering bandwidth efficiency. A low-density parity-check code puncturing scheme is also proposed to prevent the performance degradation due to the codeword bit loss caused by punctured OFDM symbols.

• Journal of Information Processing Systems
• /
• 제18권4호
• /
• pp.500-509
• /
• 2022

With the rapid development of educational informatization, teaching methods become diversified characteristics, but a large number of information data restrict the evaluation on teaching subject and object in terms of the effect of English education. Therefore, this study adopts the concept of incremental learning and eigenvalue interval algorithm to improve the weighted decision tree, and builds an English education effect evaluation model based on association rules. According to the results, the average accuracy of information classification of the improved decision tree algorithm is 96.18%, the classification error rate can be as low as 0.02%, and the anti-fitting performance is good. The classification error rate between the improved decision tree algorithm and the original decision tree does not exceed 1%. The proposed educational evaluation method can effectively provide early warning of academic situation analysis, and improve the teachers' professional skills in an accelerated manner and perfect the education system.

• 대한산업공학회지
• /
• 제27권4호
• /
• pp.394-405
• /
• 2001

Since most real-world application data involve continuous-valued attributes, properly addressing the discretization process for constructing a decision tree is an important problem. A continuous-valued attribute is typically discretized during decision tree generation by partitioning its range into two intervals recursively. In this paper, by removing the restriction to the binary discretization, we present a hybrid multi-interval discretization algorithm for discretizing the range of continuous-valued attribute into multiple intervals. On the basis of experiment using semiconductor etching machine, it has been verified that our discretization algorithm constructs a more efficient incremental decision tree compared to previously proposed discretization algorithms.

• Transactions on Control, Automation and Systems Engineering
• /
• 제3권3호
• /
• pp.159-163
• /
• 2001

An input constrained receding horizon predictive control algorithm for uncertain systems with nonzero set points is proposed. for constant nonzero set points, models with uncertainty can be converted into an augmented incremental system through the use of integrators and the problem is transformed into a zero-state regulation problem for the incremental system. But the original constraints on inputs are converted into constraints on the sum of control inputs at each time instants, which have not been dealt in earlier constrained robust receding horizon control problems. Recursive state bounding technique and worst case minimizing strategy developed in earlier works are applied to the augmented incremental system to yield an offset error free controller. The resulting algorithm is formulated so that it can be solved using LP.

• 융합신호처리학회논문지
• /
• 제2권2호
• /
• pp.111-118
• /
• 2001

본 연구에서는 패턴 인식용 다층 퍼셉트론 신경망을 순수 디지털 논리회로 모델로 구현할 수 있도록 새로운 논리뉴런의 구조, 디지털 정형 다층논리신경망 구조, 그리고 패턴인식의 응용을 위한 다단 다층논리 신경망 구조를 제안하고, 또한 제안된 구조는 매우 단순하면서도 효과적인 증가적인 가법적(Incremental Additive) 학습알고리즘이 존재함을 보였다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.502-502
• /
• 2000

An input constrained receding horizon predictive control algorithm for uncertain systems with nonzero set points is proposed. For constant nonzero set points, models with uncertainty can be converted into an augmented incremental system through the use of integrators and the problem is transformed into a zero-state regulation problem for the incremental system. But the original constraints on inputs are converted into constraints on the sum of control inputs at each time Instants, which have not been dealt in earlier constrained robust receding horizon control problems. Recursive state bounding technique and worst case minimizing strategy developed in earlier works are applied to the augmented incremental system to yield an of set error free controller. The resulting algorithm is formulated so that it can be solved using LP.

• 전기학회논문지P
• /
• 제67권1호
• /
• pp.47-51
• /
• 2018

This paper is concerned with an optimization design of Incremental Granular Model(IGM) based Genetic Algorithm (GA) as an evolutionary approach. The performance of IGM has been successfully demonstrated to various examples. However, the problem of IGM is that the same number of cluster in each context is determined. Also, fuzzification factor is set as typical value. In order to solve these problems, we develop a design method for optimizing the IGM to optimize the number of cluster centers in each context and the fuzzification factor. We perform energy analysis using 12 different building shapes simulated in Ecotect. The experimental results on energy efficiency data set of building revealed that the proposed GA-based IGM showed good performance in comparison with LR and IGM.

• Spatial Information Research
• /
• 제19권2호
• /
• pp.71-76
• /
• 2011

본 논문에서는 특정 지역의 전력 소비 데이터를 이용하여 safe와 non-safe 전력 부하 라인의 차이를 분석하여 정의하고, 출현패턴을 사용하여 잠재되어 있는 non-safe라인을 식별하기 위하여 제한된 메모리에서 효율적으로 패턴을 찾을 수 있는 TFP-tree 기반의 점진적 출현패턴 마이닝 알고리즘을 제안한다. 특히, 두 개의 다른 최소 지지도 값을 사용하여 전력 소비 데이터와 같은 대용량 데이터에서의 마이닝 문제를 해결한다.