• The Journal of Information Technology and Database
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• v.1 no.2
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• pp.37-52
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• 1994

오늘날의 정보시스템 구축 방향은 대형 컴퓨터를 중심으로 수많은 단말기를 연결한 수직형네트워크 시스템에서 다운사이징과 네트워크 기술이 결합하여 분산적으로 자료를 처리하는 클라이언트/서버 시스템 구축으로 변화하고 있다. 본 연구는 모든 단말기를 통제하는 대형 컴퓨터를 운영하는 전산망에서 클라이언트/서버 전산망으로 전환하는 하나의 사례연구를 통하여 구축환경변화, 시스템구성, 경영환경변화를 분석함으로써 클라이언트/서버 시스템의 성공적인 사례를 제시하는 것이다. 클라이언트/서버 시스템으로 변환하기전에 재개발할 시스템의 목표와 방향을 설정하기 위해서 벤치마킹을 실시하였고, 이의 결과를 바탕으로 경영업무의 프로세스를 재설계 하였다. 그리고 기업의 내부와 외부자료에 관한 표준화를 실시하였다. 정보기술 투자의 경제성과 경쟁우위 확보를 위하여 클라이언트/서버 시스템을 구현하게 되었으며 판매물류, 생산물류, 그리고 재무회계에 관한 경영지원시스템으로 구성되는 종합물류시스템 이외에 영업소에 관련된 영업정보시스템과 자동창고시스템이 컴퓨터 네트워크와 관련해서 시스템을 구성하였다. 구성 전략 촛점은 리엔지니어링, 다운사이징, 그리고 벤치마킹이었다. 시스템을 재개발한 후 업무의 계획, 진행, 분석, 감리등에 관한 체계적 업무흐름이 정착될 정도로 변화하였으며, 통신방식의 확대 및 다양화로 의사소통의 원활화와 타 관련업무와 연계처리, 그리고 부서들 상호간에 정보를 공유할 수 있었다. 또한 정보기술에 의한 업무처리를 기본으로 하는 기능적 태도로 인하여 관리자와 실무자의 업무운영에 관한 사고와 의식이 변화하였다. 따라서 매출액의 증가와 종업원수가 감소되었다.

• The Korean Journal of Nuclear Medicine Technology
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• v.25 no.2
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• pp.29-34
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• 2021

Purpose Sample reception environment system in nuclear medicine has not changed much compared to 20 years ago. When preparing sample for in vitro test, there was no significant change because the test was carried out by generating an own specimen from the parent specimen. In this study, We would like to introduce a method that automatically removes the sample cap using the automated decapper equipment and enables automatic reception at the same time. In addition, including a provisional reception system. Materials and Methods In 2019, it was intended to get a device that automatically removes the cap of a patient's blood sample. This equipment is the same as the equipment used in the Department of Laboratory Medicine (Vacuette Ⓡ Unicap Belt Decapper, Greiner bio-one, Austria). However, the purchase was delayed due to differences in tube size, budget, and space. In January 2020, we borrowed domestic automatic decapper equipment and modified it to suit our laboratory environment. After 9 months, we were able to introduce a system that automatically removes the lid of a patient's blood sample and at the same time automatically accepts the test. And, through the provisional reception system, it was possible to know the arrival of the specimen in a short time. Results With the use of an automatic decapper device, the sample cap was automatically removed, and the reception proceeded at the same time. So, it was very efficient at work because it shortened the sample preparation time by about 20 minutes. In addition, it was possible to prevent the examiner's musculoskeletal disorders caused by repeated wrist use. After using the provisional reception system, patients were able to be discharged quickly, and the number of phone calls to confirm the arrival of samples was reduced. Conclusion Most hospitals have about four employees in the nuclear medicine in vitro laboratory. It is effective to use automatic decapper equipment and a provisional reception system for organizations that perform work with the minimum number of personnel.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.8
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• pp.1051-1057
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• 2013

In this study, an automatic surface-strain measurement system called "ASIAS-bio" has been developed. This system can be used even in cases in which it is very difficult to apply a regular grid pattern necessary for measuring surface-strain, such as curved or uneven surfaces; surfaces damaged by corrosion or contamination; or soft materials such as rubber, foam, and biological tissues. This system works independently of the measurement conditions including the material and its surface condition, grid pattern and size, grid marking method, and degree of deformation. A comparison between the strain distributions of the sheet metal parts measured by using this system and those obtained by a commercial system showed that this system was sufficiently reliable. In addition, the deformation of the swine joint capsule and human knee skin was measured by using this system to demonstrate its usefulness.

• Journal of Korea Multimedia Society
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• v.12 no.3
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• pp.451-459
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• 2009

This paper presents an approach to detect premature ventricular contraction(PVC) using discrete wavelet transform and fuzzy neural network. As the input of the algorithm, we use 14 coefficients of d3, d4, and d5, which are transformed by a discrete wavelet transform(DWT). This paper uses a neural network with weighted fuzzy membership functions(NEWFM) to diagnose PVC. The NEWFM discussed in this paper classifies a normal beat and a PVC beat. The size of the window of DWT is $-31/360{\sim}+32/360$ second(64 samples) whose center is the R wave. Using the seven records of the MIT-BIH arrhythmia database used in Shyu's paper, the classification performance of the proposed algorithm is 99.91%, which outperforms the 97.04% of Shyu's analysis. Using the forty records of the M1T-BIH arrhythmia database used in Inan's paper, the classification performance of the proposed algorithm is 98.01%, which outperforms 96.85% of Inan's one. The SE and SP of the proposed algorithm are 84.67% and 99.39%, which outperforms the 82.57% and 98.33%, respectively, of Inan's study.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.986-989
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• 2003

In the steel production line, the molten metal of a furnace is transformed into slab material and then move to the hot strip line, This paper describe about the real time recognition system of material management number, which is marked at the surface of a slab in the steel production line. This recognition processing should be performed before the slab is moved to the hot strip line. This system include following recognition steps. First, we remove noise from the captured slab image by use pre-filter. Second, we extract rough area, which is include slab number and then, we extract individual number area. Finally, we recognize material management number by use KLT(Karhunen-Loeve transform) algorithm. We applied our system to the real slave image, which was captured in the process line. In the results, we recognized slave number to the 94% accuracy.