• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1803-1806
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• 2003

Metalworking fluids (MWFs) are fluids used during machining and grinding to prolong the lift of the tool, carry away debris, and protect the surfaces of work pieces. These fluids reduce friction between the cutting tool and the work surface. reduce wear and galling, protect surface characteristics, reduce surface adhesion or welding and carry away generated heat. Workers can be exposed to MWFs by inhaling aerosols (mists) and by skin contact with the fluid. Skin contact occurs by dipping the hands into the fluid, splashes, or handling workpieces coated with the fluids. The amount of mist generated (and the resulting level of exposure) depends on many factors. To reduce the environmental pollution wastes and the potential health risks associated with occupational exposures to MWFs, it is required to establish optimum MWFs supply method and condition with minimum quantity in all over the mechanical machining field including high-speed type heavy cutting process.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.851-852
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• 2006

This paper presents methods to reduce acoustic noise in interior permanent magnet (IPM) motor. Mechanical and magnetic sources are considered to reduce noise of the machine, and structural and electromagnetic designs are performed. In the structural design to reduce mechanical source, the structural resonances are moved to higher frequency for enhancement of stiffness. Then, the electromagnetic design to reduce magnetic source, the amplitudes of magnetic force harmonics are reduced by using objective function of response surface methodology (RSM). The validity of the design process and objective functions is confirmed with their calculated and experimental results.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1118-1120
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• 2012

컴퓨터 메모리의 용량이 커지고 기술이 발전하며 메모리와 저장장치의 데이터 처리속도 차이는 나날이 커지고 있다. 이를 보완하고자 데이터 처리를 가급적 메모리에서 해결하여 처리속도를 높이고자 하는 연구가 많이 있다. 그 중 MapReduce에 대한 연구는 현재 주목이 되고 있는 분야이다. MapReduce는 빅데이터를 클러스터 환경에서 처리하기에 대중적인 프로그래밍 모델이다. 본 논문은 MapReduce 기반의 Hadoop을 SSD를 적용하여 실행속도를 증진시키려 한다. 전통적인 MapReduce 모델은 데이터를 정렬하는데에 I/O가 크게 발생하는데, MapRedce가 사용하는 병합정렬의 I/O 병목현상을 개선하고자 SSD를 사용하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.121-122
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• 2009

인터넷 상에서 정보량이 급격히 증가함에 따라 ISP들은 데이터를 효과적으로 처리하고 분석하기 위한 방법을 연구하고 있다. 대표적으로 Google에서는 대용량의 분산 데이터 처리 기법인 MapReduce 모델을 개발하였다. 본 논문에서는 기존 MapReduce 모델에 Pipeline 방식을 적용하여 성능을 개선한 Pipeline-MapReduce 기법을 제안한다. 그리고 실험을 통해 제안 기법이 기존 기법에 비해 빠른 처리 결과를 나타냄을 보여준다.

• The Journal of Korean Physical Therapy
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• v.14 no.4
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• pp.423-440
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• 2002

The purpose of this study is to investigate the conception and developmental process of stretching in sports physical therapy. This study is to find conception of stretching, feature and effect, principles and fundamental rule, consideration of enforcement and developmental process in order to use the basic material which is very helpful in the every field and the scene of sports needing stretching. Flexibility is the ability to move muscles and joints through their full ranges of motion. Flexibility is developed by stretching. About player who insufficiency of flexibility, patient and disabled person who restrict of range of motion, older adult who reduce of flexibility, promote of flexibility for upgrading stability and efficiency of body on the based of scientific principles is completed by stretching. The method of stretching has been developed with passive stretching, CR, PNF stretching, PIC stretching, MET stretching in the order. The effects that we can get through stretching are as follows : 1. Enhance physical fitness. 2. Optimize learning, practice and performance of many types of skilled movement. 3. Increase mental and physical relaxation. 4. Promote development of body awareness. 5. Reduce risk of joint sprain or muscle strain. 6. Reduce risk of back problem. 7 Reduce muscular soreness. 8. Reduce the severity of painful menstruation for female athletes. 9. Reduce muscular tension. 10. Advance recognition of body.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.331-333
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• 2015

Data is increasing explosively with the spread of networks and mobile devices and there are problems in effectively processing the rapidly increasing data using existing recommendation techniques. Therefore, researches are being conducted on how to solve the scalability problem of the collaborative filtering technique. In this paper applies MapReduce, which is a distributed parallel process framework, to the collaborative filtering technique to reduce the scalability problem and heighten accuracy. The proposed technique applies MapReduce and the index technique to a user-based collaborative filtering technique and as a method which improves neighbor numbers which are used in similarity calculations and neighbor suitability, scalability and accuracy improvement effects can be expected.

• KIISE Transactions on Computing Practices
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• v.21 no.8
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• pp.561-566
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• 2015

Hadoop is becoming widely adopted in scientific and commercial areas as an open-source distributed data processing framework. Recently, for real-time processing and analysis of data, an attempt to apply high-performance computing technologies to Hadoop is being made. In this paper, we have expanded the Hadoop Filesystem library to support Lustre, which is a popular high-performance parallel distributed filesystem, and implemented the Hadoop MapReduce execution environment over the Lustre filesystem. We analysed Hadoop MapReduce over Lustre by using Hadoop standard benchmark tools. We found that Hadoop MapReduce over Lustre execution has a performance 2-13 times better than a typical Hadoop MapReduce execution.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.668-676
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• 2017

Recently, the importance of big data has been emphasized with the development of smartphone, web/SNS. As a result, MapReduce, which can efficiently process big data, is receiving worldwide attention because of its excellent scalability and stability. Since big data has a large amount, fast creation speed, and various properties, it is more efficient to process big data summary information than big data itself. Wavelet histogram, which is a typical data summary information generation technique, can generate optimal data summary information that does not cause loss of information of original data. Therefore, a system applying a wavelet histogram generation technique based on MapReduce has been actively studied. However, existing research has a disadvantage in that the generation speed is slow because the wavelet histogram is generated through one or more MapReduce Jobs. And there is a high possibility that the error of the data restored by the wavelet histogram becomes large. However, since the wavelet histogram generation system based on the MapReduce developed in this paper generates the wavelet histogram through one MapReduce Job, the generation speed can be greatly increased. In addition, since the wavelet histogram is generated by adjusting the error boundary specified by the user, the error of the restored data can be adjusted from the wavelet histogram. Finally, we verified the efficiency of the wavelet histogram generation system developed in this paper through performance evaluation.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.8
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• pp.586-590
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• 2009

The objective of this paper describes the design of MapReduce over Peer-to-Peer network for dynamic environments applications. MapReduce is a software framework used for Cloud Computing which processing large data sets in a highly-parallel way. Based on the Peer-to-Peer network character which node failures will happen anytime, we focus on using a DHT routing protocol which named Pastry to handle the problem of node failures. Our results are very promising and indicate that the framework could have a wide application in P2P network systems while maintaining good computational efficiency and scalability. We believe that, P2P networks and parallel computing emerge as very hot research and development topics in industry and academia for many years to come.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.196-204
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• 2012

Recently, many applications perform OLAP(On-Line Analytical Processing) over a very large volume of data. Multidimensional data cube is regarded as a core tool in OLAP analysis. This paper focuses on the method how to efficiently compute data cubes in parallel by using a popular parallel processing tool, MapReduce. We investigate efficient ways to implement PipeSort algorithm, a well-known data cube computation method, on the MapReduce framework. The PipeSort executes several (descendant) cuboids at the same time as a pipeline by scanning one (ancestor) cuboid once, which have the same sorting order. This paper proposed four ways implementing the pipeline of the PipeSort on the MapReduce framework which runs across 20 servers. Our experiments show that PipeMap-NoReduce algorithm outperforms the rest algorithms for high-dimensional data. On the contrary, Post-Pipe stands out above the others for low-dimensional data.