• The Journal of Korean Academic Society of Nursing Education
• /
• v.21 no.1
• /
• pp.129-140
• /
• 2015

Purpose: The purpose of this study was to analyze the types of attitudes of nursing students in Korea toward bucket lists. Methods: The Q-methodology, which provides a method for analyzing the subjectivity of each item was used. From each of the 37 subjects, 34 selected Q-statements were classified into the shape of a normal distribution, using a 9-point scale. The collected data were analyzed using a QUANL PC program. Results: Four types of attitudes toward the bucket list of subjects were identified in Korean nursing students. Type I is a pursuit of money type, Type II is a pursuit of relationship type, Type III is a pursuit of self-centered type, and TypeIV is a pursuit of others-centered type. Conclusion: The results of this study indicate that different approaches toward educational programs for students in the human service area are recommended based on the four types of nursing students' attitudes toward the bucket list.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.5
• /
• pp.506-512
• /
• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The FCS is based on the fuzzy controller system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. In this paper, the FCS modifies input message to fuzzified message and stores those in the message list. The FCS constructs rule-base through matching between messages of message list and classifiers of fuzzy classifier list. The FCS verifies the effectiveness of classifiers using Bucket Brigade algorithm. Also the FCS employs the Genetic Algorithms to generate new rules and modifY rules when performance of the system needs to be improved. Then the FCS finds the set of the effective rules. We will verifY the effectiveness of the poposed FCS by applying it to Autonomous Mobile Robot avoiding the obstacle and reaching the goal.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.37 no.5
• /
• pp.1-10
• /
• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The FCS is based on the fuzzy controller system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. In this paper, the FCS modifies input message to fuzzified message and stores those in the message list. The FCS constructs rule-base through matching between messages of message list and classifiers of fuzzy classifier list. The FCS verifies the effectiveness of classifiers using Bucket Brigade algorithm. Also the FCS employs the Genetic Algorithms to generate new rules and modify rules when performance of the system needs to be improved. Then the FCS finds the set of the effective rules. We will verify the effectiveness of the poposed FCS by applying it to Autonomous Mobile Robot avoiding the obstacle and reaching the goal.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.05a
• /
• pp.179-182
• /
• 2000

A Classifier System processes a discrete coded information from the environment. When the system codes the information to discontinuous data, it loses excessively the information of the environment. The Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies this ability of the machine learning to the concept of fuzzy controller. It is that the antecedent and consequent of classifier is same as a fuzzy rule of the rule base. In this paper, the FCS is the Michigan style and fuzzifies the input values to create the messages. The system stores those messages in the message list and uses the implicit Bucket Brigade Algorithms. Also the FCS employs the Genetic Algorithms(GAs) to make new rules and modify rules when performance of the system needs to be improved. We will verify the effectiveness of the proposed FCS by applying it to AMR avoiding the obstacle.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2003.11c
• /
• pp.1493-1496
• /
• 2003

데이터베이스 관계 연산자 중 프로젝션(projection)과 집단 연산(aggregate function)시 사용되는 GROUP BY절, 그리고 동등 조인(equi join)에 대한 질의 처리는 중복된 튜플 중복된 GROUP BY 필드, 조인 중 발생하는 임시결과에 대한 제거나 집단 연산, 임시 결과의 저장을 위해 정렬이나 해싱 기반 알고리즘을 적용하고 있다. 이 중 해싱 기반 알고리즘은 데이터에 대한 직접적인 접근 방법과 정렬비용이 없다는 장점으로 인해 자주 사용하게 된다. 그러나 이러한 해싱(extendible hashing)[1] 기반 알고리즘은 키 값이 저장되는 버켓(bucket) 페이지의 넘침(overflow)으로 인해 분할(split)이 발생하는 경우, 분할을 야기시킨 버켓 페이지에 대한 정보를 제외한 동일한 내용의 기존 디렉토리 구조를 배로 확장해야 하는 공간 확장과, 확장된 디렉토리 구조의 유지를 위해 많은 비용을 소모하게 된다. 본 논문에서는 다량의 데이터에 대한 접근 기법과 디렉토리 구조의 저장공간, 유지 비용 절감 및 중복 해시 값을 지니는 데이터를 처리하기위한 해시 색인인 가상 디렉토리 확장 해시 색인을 제안한다. 가상 디렉토리 확장 해시 색인은 디렉토리 구조를 다단계 구조로 유지함으로써, 넓은 저장 공간을 필요로 하는 다량의 데이터에 대한 접근경로 문제를 해결하였고, 가상 디렉토리 레벨이라는 새로운 구조를 통해, 기존 디렉토리 구조의 공간 낭비 및 유지 비용을 최소화 시켰으며, 버켓 페이지를 리스트(list) 구조로 유지함으로써 중복 해시 값에 의한 디렉토리 구조의 연쇄적 분할 문제를 해결하였다.

• KIPS Transactions on Computer and Communication Systems
• /
• v.8 no.6
• /
• pp.127-138
• /
• 2019

The data response speed is a critical issue of cloud services because it directly related to the user experience. As such, the in-memory database is widely adopted in many cloud-based applications for achieving fast data response. However, the current implementation of the in-memory database is mostly based on the linked list-based hash table which cannot guarantee the constant data response time. Thus, cuckoo hashing was introduced as an alternative solution, however, there is a disadvantage that only half of the allocated memory can be used for storing data. Subsequently, bucketized cuckoo hashing (BCH) improved the performance of cuckoo hashing in terms of memory efficiency but still cannot overcome the limitation that the insert overhead. In this paper, we propose a data management solution called Wall Cuckoo which aims to improve not only the insert performance but also lookup performance of BCH. The key idea of Wall Cuckoo is that separates the data among a bucket according to the different hash function be used. By doing so, the searching range among the bucket is narrowed down, thereby the amount of slot accesses required for the data lookup can be reduced. At the same time, the insert performance will be improved because the insert is following up the operation of the lookup. According to analysis, the expected value of slot access required for our Wall Cuckoo is less than that of BCH. We conducted experiments to show that Wall Cuckoo outperforms the BCH and Sorting Cuckoo in terms of the amount of slot access in lookup and insert operations and in different load factor (i.e., 10%-95%).