• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.47-61
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• 2001

Lazy loaming methods including CBR have relative advantages in comparison with eager loaming methods such as artificial neural networks and decision trees. However, they are very sensitive to irrelevant features. In other words, when there are irrelevant features, larry learning methods have difficulty in comparing cases. Therefore, their performance can be degraded significantly. To overcome this disadvantage, feature weighting methods for lazy loaming methods have been studied. Most of the existing researches, however, were focused on global feature weighting. In this research, we propose a new local feature weighting method, which we shall call CBDFW. CBDFW stores classification performance of randomly generated feature weight vectors. Then, given a new query case, CBDFW retrieves the successful feature weight vectors and designs a feature weight vector fur the query case. In the test on credit evaluation domain, CBDFW showed better classification accuracy when compared to the results of previous researches.

• Journal of KIISE:Software and Applications
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• v.29 no.5
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• pp.326-335
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• 2002

Recently there have been a number of researches to provide code mobility to lazy functional language (LFL) programs by translating LFL programs to Java programs. These approaches are basically baled on architectural similarities between abstract machines of LFLs and Java. The abstract machines of LFLs and Java programming language, Spineless Tagless G-Machine(STGM) and Java Virtual Machine(JVM) respectively, share important common features such as built- in garbage collector and stack machine architecture. Thus, we can provide code mobility to LFLs by translating LFLs to Java utilizing these common features. In this paper, we propose a new translation scheme which fully utilizes architectural common features between STGM and JVM. By redefining STGM as an eval-apply evaluation model, we have defined a new translation scheme which utilizes Java Virtual Machine Stack for function evaluation and totally eliminates stack simulation which causes array manipulation overhead in Java. Benchmark program translated to Java programs by our translation scheme run faster on JDK 1.3 than those translated by the previous schemes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2777-2783
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• 2015

Peak power reduction is becoming increasingly important not only for grid operators but also for residential users. The scheduling of electric loads tries to reduce the power peak by splitting the power-on period of an electric device into multiple smaller ones and by interleaving the on-periods of every device in a holistic way. This paper analyzes the performance of EDF, LSF, TCBM, and lazy scheduling algorithms and proposes the enhancement schemes. For analysis, we have implemented the scheduling policies in a simulation environment for distributed control systems. Through extensive experiments using real power traces, we discuss their performance characteristics in terms of power deviation, switch count, and temperature violation ratio. To prevent excessive switching, we propose to employ the concept of limited preemptibility and evaluate its effect on performance. It is found that the best performance is achieved when the scheduler capacity is dynamically adjusted to the actual power demand. The experiment results show that, by load scheduling, the probability of having a power deviation greater than 150W is reduced from 21.5% down to 3.2%.

• The KIPS Transactions:PartA
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• v.16A no.2
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• pp.101-112
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• 2009

Existing techniques for defragmentation of the file system need intensive disk operation for some periods at specific time such as disk defragmentation program. In this paper, for solving this problem, we design and implement the automatic and continuous defragmentation free system by distributing the disk operation. We propose the Automatic Layout Scoring(ALS) mechanism for measuring defragmentation degree and suggest the Lazy Copy mechanism that copies the defragmented data at idle time for scattering the disk operation. We search the defragmented file by Automatic Layout Scoring mechanism and then find for empty spaces for that searched file. After lazy copy of searched fils to empty space for preventing that file from being lost, the algorithm solves the defragmentation problem by updating the I-node of that file. We implement these algorithms in Linux and evaluate them for small and defragmented file to get the layout scoring. We outperform the Linux EXT2 file system by $2.4%{\sim}10.4%$ in layout scoring evaluation. And the performance of read and write for various file size is better than the EXT2 by $1%{\sim}8.5%$ for write performance and by $1.2%{\sim}7.5%$ for read performance. We suggest this system for solving the problem of defragmentation automatically without disturbing the I/O task and manual management.

• Journal of KIISE:Databases
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• v.35 no.4
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• pp.307-318
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• 2008

One of the primary issues confronting XML message brokers is the difficulty associated with processing a large set of continuous XPath queries over incoming XML seams. This paper proposes a novel system designed to present an effective solution to this problem. The proposed system transforms multiple XPath queries before their run-time into a new region-based data structure, called an XP-table, by sharing their common constraints. An XP-table is matched with a stream relation (SR) transformed from a target XML stream by a SAX parser. This arrangement is intended to minimize the runtime workload of continuous query processing. Also, system performance is estimated and verified through a variety of experiments, including comparisons with previous approaches such as YFilter and LazyDFA. The proposed system is practically linear- scalable and stable for evaluating a set of XPath queries in a continuous and timely fashion.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.4
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• pp.395-404
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• 1999

포인터 스위즐링 기법은 포인터 스위즐링과 언스위즐링으로 이루어지며, 포인터 스위즐링은 객체 접근 시 객체 식별자를 해당 객체의 메모리 주소로 교체하는 것을 말하며, 언스위즐링은 객체 교체 또는 객체 저장 시에 스위즐링된 포인터를 원래의 객체 식별자로 환원하는 것을 말한다. 본 연구에서는 시스템 버퍼 구조에 따라 여러 포인터 스위즐링 기법을 분류하여 장단점을 분석하였으며, 이중 버퍼 구조 상에서 적극/소극, 직접/간접 스위즐링, 언스위즐링 모듈을 설계, 구현하였다. 또한 제한된 크기의 객체 버퍼 상에서 각 포인터 스위즐링 모듈의 성능을 평가하였다. 이 성능 평가의 결과로는 사용하지 않는 포인터는 스위즐링하지 않으며 언스위즐링 부담이 적은 소극 간접 스위즐링 기법이 일반적으로 뛰어난 성능을 나타냄을 알 수 있게 되었다.Abstract The pointer swizzling methods consist of pointer swizzling and unswizzling. Pointer swizzling replaces the OID of a object to the memory address of the object at object access time and unswizzling replaces the swizzling pointer of the OID at object replacement time or object save time. In this research, the different techniques for pointer swizzling are classified according to the system buffer structure and analyzed the pros and cons. In addition, eager/lazy, direct/indirect swizzling, unswizzling modules are designed and implemented on a dual buffering structure. Also, we evaluate the performance of pointer swizzling modules on the restricted object buffer size. The results of performance evaluation show that the performance of lazy indirect pointer swizzling technique is generally good because unused pointers are not swizzled, and unswizzling overhead is minimized.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.1
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• pp.192-201
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• 2001

It has been a long standing problem in research of pure functional programming to program side-effect, input/output, exception handling, nondeterminism, and etc, preserving declarative property and lazy-evaluation of pure functional languages. Being introduced in 1989, Monadic approach has been suggested as a solution to this problem. In this paper, we introduce the principle of functional monadic programming and its various application techniques using the Pure functional language Haskell.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.7_8
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• pp.331-340
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• 2003

Low-cost commodity SMP(Symmetric Multiprocessor) is widely used as a node of cluster system. In this paper, we implement and evaluate the performance of SDSM system for SMP clusters. Our SDSM system provides HLRC(Home-based Lazy Release Consistency) memory consistency model. Our protocol utilize shared memory within same SMP node, so that page fetch and message passing through network can be reduced. It is implemented on 8 node of 2-way Pentium-III SMP interconnected with 100Mbps Fast Ethernet, and uses TCP/IP for transport/network layer protocol. The experiment with eight applications shows that our SMP protocol achieves maximum 33% speedup improvement and 13%-52% reduction of page fetch compared with uniprocessor protocol.

• Computers and Concrete
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• v.21 no.4
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• pp.407-417
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• 2018

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree-Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.