• Spatial Information Research
• /
• 제16권1호
• /
• pp.113-127
• /
• 2008

최근 모바일 환경의 급속한 발달로 이동 중인 사용자의 위치에 기반한 다양한 서비스가 가능하게 되면서 모바일 기기에서 지도를 처리하는 모바일 소프트웨어의 개발이 증대되고 있다. 본 논문은 지도를 처리하는 모바일 소프트웨어에서 성능을 결정짓는 가장 중요한 요소인 공간 인덱스를 효율적으로 구성하는 새로운 방법을 제시한다. 본 논문에서 제시하는 $AR^*$-tree는 기존의 $R^*$-tree를 변형하여 2차원 공간 데이터의 x축 및 y축에 면적 (a) 축을 추가하여 3차원 데이터를 저장한다. 그리고, 검색 시에도 면적 축에 대한 조건을 추가함으로써 작은 화면을 갖는 모바일 기기에서의 지도 가독성을 증대시키고 시간 효율성도 동시에 향상시킨다.

• 한국전자통신학회논문지
• /
• 제6권6호
• /
• pp.849-854
• /
• 2011

시스템 이벤트들로부터 그 파급효과를 최소화하기 위해서는 실시간 조건에 기반한 규칙기반 전문가 시스템이 매우 효과적인데, 그 이벤트가 다양하고 부하조건이 매우 가변적이기 때문에 규칙 기반 전문가 시스템을 구축하기가 쉽지 않다. 따라서 본 연구에서는 CART 알고리즘 기반의 의사결정 트리 기법을 적용하여 상정사고 사례들로부터 규칙기반 전문가 시스템을 구축하는 방법론에 대해서 연구하고자 한다.

• The Journal of the Acoustical Society of Korea
• /
• 제17권4E호
• /
• pp.11-16
• /
• 1998

For large vocabulary speech recognition using HMMs, context-dependent subword units have been often employed. However, when context-dependent phone models are used, they result in a system which has too may parameters to train. The problem of too many parameters and too little training data is absolutely crucial in the design of a statistical speech recognizer. Furthermore, when building large vocabulary speech recognition systems, unseen triphone problem is unavoidable. In this paper, we propose the modified phonetic decision tree algorithm for the automatic prediction of unseen triphones which has advantages solving these problems through following two experiments in Japanese contexts. The baseline experimental results show that the modified tree based clustering algorithm is effective for clustering and reducing the number of states without any degradation in performance. The task experimental results show that our proposed algorithm also has the advantage of providing a automatic prediction of unseen triphones.

• Journal of Information Processing Systems
• /
• 제17권1호
• /
• pp.37-50
• /
• 2021

Query applications based on nested data, the most commonly used form of data representation on the web, especially precise query, is becoming more extensively used. MapReduce, a distributed architecture with parallel computing power, provides a good solution for big data processing. However, in practical application, query requests are usually concurrent, which causes bottlenecks in server processing. To solve this problem, this paper first combines a column storage structure and an inverted index to build index for nested data on MapReduce. On this basis, this paper puts forward an optimization strategy which combines query execution service tree and frequent sub-query trajectory to reduce the response time of frequent queries and further improve the efficiency of multi-user concurrent queries on large scale nested data. Experiments show that this method greatly improves the efficiency of nested data query.

• 디지털산업정보학회논문지
• /
• 제9권3호
• /
• pp.145-153
• /
• 2013

Multimedia contents' searching methods in existing mobile computer environment tend to be brought with many over heads when attempting to search data in large bulks. The MINT system has been studied to resolve such problem. However, MINT system delivers many over heads by searching multimedia contents through applying B-Tree. The suggested method is said to be CST-MCR searching system based on CST-Tree. The specific characteristic of this method is improved MINT system by applying CST-Tree's index. The result of capacity evaluation came out with improved capacity of 4.27% from MINT system and CST-MCR methods in average. Conclusively, it has been proven that the suggested method is superior.

• 한국데이터정보과학회:학술대회논문집
• /
• 한국데이터정보과학회 2005년도 춘계학술대회
• /
• pp.164-173
• /
• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, data reduction and variable screening, category merging, etc. We analyze waste database united with local information using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

• International Journal of Contents
• /
• 제13권2호
• /
• pp.57-65
• /
• 2017

Recently, due to the significance of Industry 4.0, the manufacturing industry is developing globally. Conventionally, the manufacturing industry generates a large volume of data that is often related to process, line and products. In this paper, we analyzed causes of defective products in the manufacturing process using the decision tree technique, that is a well-known technique used in data mining. We used data collected from the domestic manufacturing industry that includes Manufacturing Execution System (MES), Point of Production (POP), equipment data accumulated directly in equipment, in-process/external air-conditioning sensors and static electricity. We propose to implement a model using C4.5 decision tree algorithm. Specifically, the proposed decision tree model is modeled based on components of a specific part. We propose to identify the state of products, where the defect occurred and compare it with the generated decision tree model to determine the cause of the defect.

• Nuclear Engineering and Technology
• /
• 제50권6호
• /
• pp.854-859
• /
• 2018

Calculating minimal cut sets is a typical quantification method used to evaluate the top event probability for a fault tree. If minimal cut sets cannot be calculated or if the accuracy of the quantification result is in doubt, the Monte Carlo method can provide an alternative for fault tree quantification. The Monte Carlo method for fault tree quantification tends to take a long time because it repeats the calculation for a large number of samples. Herein, proposal is made to improve the quantification algorithm of a fault tree with circular logic. We developed a top-down iteration algorithm that combines the characteristics of the top-down approach and the iteration approach, thereby reducing the computation time of the Monte Carlo method.

• 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.

• 한국정보과학회논문지:데이타베이스
• /
• 제37권5호
• /
• pp.228-237
• /
• 2010

고차원 데이터에 대한 다양한 색인 구조가 제안되어 왔음에도 불구하고, 인터넷 서비스로서 이미지 및 동영상의 내용 기반 검색을 지원하기 위해서는 고확장성 지원 및 k-최근접점 검색 성능 향상을 지원하는 새로운 고차원 데이터의 색인 구조가 절실히 요구된다. 이에 우리는 다중 컴퓨팅 노드를 바탕으로 구축되는 분산 색인 구조로 분산 벡터 근사 트리(Distributed Vector Approximation-tree)를 제안한다. 분산 벡터 근사 트리는 대용량의 고차원 데이터로부터 추출한 샘플 데이터를 바탕으로 hybrid spill-tree를 구축하고, hybrid spill-tree외 말단 노드 각각에 분산 컴퓨팅 노드를 매핑하여 VA-file용 구축하는 두 레벨의 분산 색인 구조이다. 우리는 다중 컴퓨팅 노드들 상에 구축된 분산 벡터 근사 트리를 바탕으로 병렬 k-최근접점 검색을 수행함으로써 검씩 성능을 향상시킨다. 본 논문에서는 서로 다른 분포의 데이터 집합을 바탕으로 한 성능 시험 결과를 통하여, 분산 벡터 근사 트리가 기존의 고확장성을 지원하는 색인 구조와 비교하여 검색 정확도에 대한 손실 없이 더 빠른 k-최근접점 검색을 수행함을 보인다.