• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.25-32
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• 2023

Decision-making refers to identifying the best alternative among a set of alternatives. When a set of criteria are involved, the decision-making is called multi-criteria decision-making (MCDM). In some cases, the involved criteria may be prioritized by the human decision-maker, which determines the importance degree for each criterion; hence, the decision-making becomes prioritized multi-criteria decision-making. The essence of prioritized MCDM is raking the different alternatives concerning the criteria and selecting best one(s) from the ranked list. This paper introduces a generic multi-level algorithm for ranking multiple alternatives in prioritized MCDM problems. The proposed algorithm is implemented by a decision support system for selecting the most critical short-road requests presented to the transportation ministry in the Kingdom of Saudi Arabia. The ranking results show that the proposed ranking algorithm achieves a good balance between the importance degrees determined by the human decision maker and the score value of the alternatives concerning the different criteria.

• 대한전자공학회논문지TC
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• 제44권4호
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• pp.1-9
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• 2007

본 논문에서는 RFID Tag 충돌방지를 위한 Adaptive Decision 알고리즘에 대해 연구 하였다. 이를 위해 기존의 RFID Tag 충돌방지 기법인 ALOHA기반의 기법과 이진 검색 충돌방지 기반의 알고리즘을 먼저 비교?분석하였다. 기존 알고리즘은 태그를 인식하기 위한 탐색횟수와 전송하는 데이터량을 감소시키는데 한계점을 가지고 있었다. 제안한 Adaptive Decision 알고리즘은 인식범위 내의 태그를 구별하기 위해, 호출에 응답한 모든 태그의 ID 비트 별 '1'의 개수를 계산하고, 개수가 작은 그룹의 태그를 우선적으로 식별한다. 각 태그 ID 비트의 '1'의 개수는 리더의 메모리에 저장하고, 식별된 태그 ID 비트의 ‘1’의 개수를 감산한다. 이와 같은 과정을 반복함으로써 인식범위 내의 모든 태그를 식별한다. 논문에서 제안한 능동적인 태그 선택기준과 간단한 가감 과정을 통해 불필요한 탐색횟수를 줄 일 수 있다. 알고리즘의 성능평가는 태그를 인식하기 위한 리더의 반복횟수와 전송 데이터 량으로 나타내었다. 성능평가 결과, 기존의 알고리즘과 비교하여 Adaptive Decision 알고리즘의 반복횟수가 16.8% 감소되었고, 전송 데이터 량도 ¼배 감소된 것을 확인할 수 있었다.

• 경영과학
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• 제23권1호
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• pp.63-74
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• 2006

Decision tree induction algorithm is one of the most widely used methods in classification problems. However, they could be trapped into a local minimum and have no reasonable means to escape from it if tree algorithm uses top-down search algorithm. Further, if irrelevant or redundant features are included in the data set, tree algorithms produces trees that are less accurate than those from the data set with only relevant features. We propose a hybrid algorithm to generate decision tree that uses genetic programming with sequentially selected features. Correlation-based Feature Selection (CFS) method is adopted to find relevant features which are fed to genetic programming sequentially to find optimal trees at each iteration. The new proposed algorithm produce simpler and more understandable decision trees as compared with other decision trees and it is also effective in producing similar or better trees with relatively smaller set of features in the view of cross-validation accuracy.

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

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권2호
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• pp.142-148
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• 2004

In this paper, we propose the color fuzzy decision algorithm to face segmentation in a color image. Our algorithm can segment without the user's interaction by fuzzy decision marking. And it removes small parts such as a noise using wavelet morphology in the image obtained by applying the fuzzy decision algorithm. Also, it merges and chooses the face region in each quantization image through rough sets. This video object division algorithm is shown to be superior to a conventional algorithm.

• 정보학연구
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• 제2권1호
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• pp.103-113
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• 1999

본 논문에서는 연판정지향 알고리즘에 Stop-and-Go 알고리즘의 개념을 결합한 연판정지향 Stop-and-Go 알고리즘을 제안한다. 제안한 알고리즘은 두 알고리즘보다 더 신뢰성 있는 오차신호를 사용함으로써 향상된 등화 성능을 가진다. 컴퓨터 모의실험을 통하여 제안한 알고리즘이 CMA, MCMA, Stop-and-Go 알고리즘, 단순화된 연판정지향 알고리즘에 비해 잔류 심벌간 간섭과 정상상태로의 수렴 속도면에서 우수한 성능을 가짐을 확인하였다.

• 전기학회논문지
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• 제61권8호
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• pp.1199-1203
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• 2012

H.264 adopted many compression tools to increase image data compression efficiency such as B frame bi-directional predictions, the direct mode coding and so on. Despite its high compression efficiency, H.264 can suffer from its long coding time due to the complicated tools of H.264. To realize a high performance H.264, several fast algorithms were proposed. One of them is adaptive fast direct mode decision algorithm using mode and Lagrangian cost prediction for B frame in H.264/AVC (MLP) algorithm which can determine the direct coding mode for macroblocks without a complex mode decision process. However, in this algorithm, macroblocks not satisfying the conditions of the MLP algorithm are required to process the complex mode decision calculation, yet suffering a long coding time. To overcome the problem, this paper proposes a fast direct mode prediction algorithm. Simulation results show that the proposed algorithm can determine the direct mode coding without a complex mode decision process for 42% more macroblocks and, this algorithm can reduce coding time by up to 23%, compared with Jin's algorithm. This enables to encode B frames fast with a less quality degradation.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(D)
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• pp.213-214
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• 2011

This paper presents a distributed relay selection algorithm for cooperative communication. The algorithm separates the decision making into two simple steps, decision making for employing cooperative communication and decision making for relay selection.

• 대한산업공학회지
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• 제33권2호
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• pp.183-190
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• 2007

We present a new decision tree classification algorithm using rough set theory that can induce classification rules, the construction of which is based on core attributes and relationship between objects. Although decision trees have been widely used in machine learning and artificial intelligence, little research has focused on improving classification quality. We propose a new decision tree construction algorithm that can be simplified and provides an improved classification quality. We also compare the new algorithm with the ID3 algorithm in terms of the number of rules.

• 정보처리학회논문지C
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• 제10C권1호
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• pp.27-32
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• 2003

본 논문에서는 선형 블록 부호에 대한 효율적인 연판정 복호법을 제안하였다. 제안된 연판정 복호법은 연판정 복호 과정을 반복하여 실현하는 방식이다. 경판정 복호 결과로부터 후보 부호버들을 효율적으로 탐색할 수 있는 방법을 개발하였다. 이 과정에서 후보 부호어가 선출되지 않는 경우의 발생을 억제할 수 있는 새로운 복호법을 제안하였다. 또한, 복잡도를 줄이는 방안도 개발하여 알고리듬 개선으로 인한 복잡도 증가가 거의 나타나지 않도록 하였다. 2진(63, 36) BCH 부호에 대한 시뮬레이션 결과로 이러한 사실들을 확인할 수 있었다.