• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.338-342
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• 2003

Hybrid machine is based on hardware technologies of machine tool and software technologies of open architecture controller. In machining technology, combination technology of turning, milling, and grinding and in machining energy technology. combination of mechanical, electrical, and chemical technology are developed. This paper describes hybrid machine technology for combination of machining, on-machine measurement, on-machine CAM, and on-machine remote monitoring and control in open architecture controller environment. For on-machine measurement, non-contact measurement technology based on CAD information is developed. For on-machine CAM, interactive CAM program for automatic NC program generation and tool path simulation is developed. For generation on-machine remote monitoring and control, suitable interface method between web program and CNC is proposed. The developed hybrid machine technology is implemented in 3 axes milling machine for evaluation of operablity.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 춘계학술대회 논문집
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• pp.345-346
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• 2006

In the future, a machine will be more improved in the form of advanced concept with collaborative ability in M2M(Machine to Machine, Mobile to Machine) environment for u-Manufacturing system. This paper tried to standardize M2M and design advanced concept machine. The M2M is front-end system for implementing autonomous ubiquitous environment. The advanced machine in M2M will be a collaborative machine with knowledge-evolutionary ability such as u-Machine(Ubiquitous machine), Vortal(Vertical Portal) machine and P2P(Peer to Peer) machine. Such advanced concept machines will be the key subject for M2M cooperation.

• 한국정보과학회논문지:소프트웨어및응용
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• 제27권7호
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• pp.759-768
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• 2000

ZG-machine은 태그옮김이라는 간단한 부호화 기법을 채택한 공간 효율적인 G-machine이다. 기억 장소 재활용 체계 없이 실험한 이전 실험에서 ZG-machine은 G-machine과 비교하여 30%의 힙 공간을 절약할 수 있었고 수행 시간 부담은 6%를 넘지 않았었다. 이 논문에서는 ZG-machine에 기억 장소 재활용 체계를 장착하여 추가로 실험한 결과를 설명한다. 결과에 따르면, G-machine과 비교할 때, ZG-machine의 수행 시간은 34% 증가하였지만 최소 힙 사용량은 평균 34% 감소하였다. 수행 시간 부담이 커진 이유는 기억 장소 재활용 체계때문이다. 그러나 힙 공간을 최소 힙 사용량의 7 배 정도로 늘렸을 경우에 G-machine에 대한 수행 시간 부담은 12%를 넘지 않았다. ZG-machine에서 최소 힙 사용량이 줄어든 특성은 ZG-machine이 내장 체계와 같은 기억 장소가 제한된 응용 분야에 사용될 수 있음을 의미한다. 또한 보다 효율적인 기억 장소 재활용 쳬계를 개발함으로써 수행 시간은 상당히 줄어들 것으로 예상 된다.

• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.412-419
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• 2022

The amount of digital text data is growing exponentially, and many machine learning solutions are being used to monitor and manage this data. Artificial intelligence and machine learning are used in many areas of our daily lives, but the underlying processes and concepts are not easy for most people to understand. At a time when many experts are needed to run a machine learning solution, no-code machine learning tools are a good solution. No-code machine learning tools is a platform that enables machine learning functions to be performed without engineers or developers. The latest No-Code machine learning tools run in your browser, so you don't need to install any additional software, and the simple GUI interface makes them easy to use. Using these platforms can save you a lot of money and time because there is less skill and less code to write. No-Code machine learning tools make it easy to understand artificial intelligence and machine learning. In this paper, we examine No-Code machine learning tools and compare their features.

• International Journal of Advanced Culture Technology
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• 제8권2호
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• pp.297-304
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• 2020

Recently, open source automatic machine learning solutions have been applied in many fields. To apply open source automated machine learning to real world problems, you need to write code with expertise in machine learning. Writing code without machine learning knowledge is challenging. To solve this problem, the automatic machine learning solutions provided by startups are made easy to use with a clean user interface. In this paper, we review automatic machine learning solutions of startups.

• 중소기업융합학회논문지
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• 제1권1호
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• pp.75-83
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• 2011

Unified Modeling Language (UML)는 산업계에서 소프트웨어 설계 표준 언어로서 인정되고 있으며 특히 UML State Machine Diagram은 클래스의 동적인 행위(behavior)를 묘사하는데 많이 사용되고 있다. 이 논문은 UML State Machine Diagram을를 이용하여 시스템의 데드락 (deadlock)을 찾는 방법에 대해서 논한다. 보통 State Machine Diagram는 개별의 클래스의 행위를 나타내는 데 사용되므로 시스템 범위의 행위를 알고 싶으면 시스템에 있는 클래스들 중 관심 있는 클래스들의 State Machine Diagram을 합하여 시스템의 행위를 나타낼 수 있는 State Machine Diagram이 필요하여진다. 일반적으로 이러한 시스템 수준의 State Machine Diagram은 매우 복잡하고 실제로는 타당하지 않은 State나 Transition들을 포함하게 된다. 실제 시스템의 행위를 나타내기 위해서 synchronization과 externalization을 적용하여 State Machine Diagram을 유효한 수준으로 줄이는 것이 필요하다. 이렇게 만들어진 State Machine Diagram은 시스템의 행위를 나타내는데 사용될 수 있으며 통상의 모델 체킹 방법이 적용될 수 있다. 이 논문은 데드락 탐지를 하는 방법을 간단한 예제를 통해서 보여준다. 모든 과정은 툴에서 자동으로 지원되며 필요한 알고리즘도 같이 설명된다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 추계학술대회
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• pp.865-867
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• 2014

IoT(Internet Of Things) 사업의 일종인 Machine Socialization은 각 기기가 지능을 가지고 M2M(Machine to Machine) 협업을 통하여 사용자의 상황을 인지하고 시나리오를 풀어나가는 것을 의미한다. 기존의 IoT는 단순한 센서 데이터를 통하여 1상황 1제어로 진행되었지만 Machine Socialization은 상황을 시나리오대로 풀어나가고 Machine Manager가 전체적 흐름을 통제하고 제어하는 것을 의미한다. 본 논문에서는 기존 H2H(Human to Human)의 SNS(Social Network Service)을 M2M에 적용하기 위한 스키마 제안하고 Machine Manager가 시나리오를 풀어나가야 하기 위한 각 기기의 정보를 스키마로 제안한다.

• 산업경영시스템학회지
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• 제42권2호
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• pp.94-103
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• 2019

Self Organizing Map (SOM) is a neural network that is effective in classifying patterns that form the feature map by extracting characteristics of the input data. In this study, we propose an algorithm to determine the cell formation and the machine layout within the cell for the cell formation problem with operation sequence using the SOM. In the proposed algorithm, the output layer of the SOM is a one-dimensional structure, and the SOM is applied to the parts and the machine in two steps. The initial cell is formed when the formed clusters is grouped largely by the utilization of the machine within the cell. At this stage, machine cell are formed. The next step is to create a flow matrix of the all machine that calculates the frequency of consecutive forward movement for the machine. The machine layout order in each machine cell is determined based on this flow matrix so that the machine operation sequence is most reflected. The final step is to optimize the overall machine and parts to increase machine layout efficiency. As a result, the final cell is formed and the machine layout within the cell is determined. The proposed algorithm was tested on well-known cell formation problems with operation sequence shown in previous papers. The proposed algorithm has better performance than the other algorithms.

• 한국생산제조학회지
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• 제22권3호
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• pp.349-355
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• 2013

Recently, there is an increasing need to produce more precise products with small deviations from defined target values. Machine capability is the ability of a machine tool to produce parts within a tolerance interval. Capability indices are a statistical way of describing how well a product is machined compared to defined target values and tolerances. Today, there is no standardized way to acquire a machine capability value. This paper describes a method for evaluating machine capability indices in machining centers. After the machining of specimens, the straightness, roundness, and positioning accuracy were measured by using CMM (coordinate measuring machine). These measured values and defined tolerances were used to evaluate the machine capability indices. It will be useful for the industry to have standardized ways to choose and calculate machine capability indices.

• 대한산업공학회지
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• 제31권1호
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• pp.20-26
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• 2005

This paper proposes the heuristic algorithm for the generalized GT problems to consider the restrictions which are given the number of machine cell and maximum number of machines in machine cell as well as minimum number of machines in machine cell. This approach is split into two phase. In the first phase, we use the similarity coefficient which proposes and calculates the similarity values about each pair of all machines and sort these values descending order. If we have a machine pair which has the largest similarity coefficient and adheres strictly to the constraint about birds of a different feather (BODF) in a machine cell, then we assign the machine to the machine cell. In the second phase, we assign parts into machine cell with the smallest number of exceptional elements. The results give a machine-part grouping. The proposed algorithm is compared to the Modified p-median model for machine-part grouping.