• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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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.

• Journal of KIISE:Software and Applications
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• v.27 no.7
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• pp.759-768
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• 2000

The ZG-machine is a space-efficient G-machine, which exploits a simple encoding method, called tag-forwarding, to compress the heap structure of graphs. Experiments on the ZG-machine without garbage collection shows that the ZG-machine saves 30% of heap space and the run-time overhead is no more than 6% than the G-machine. This paper presents the results of further experiments on the ZG-machine with the garbage collector. As a result, the heap-residency of the ZG-machine decreases by 34% on average although the run-time increases by 34% compared to the G-machine. The high rate of the run-time overhead of the ZG-machine is incurred by the garbage collector. However, when the heap size is 7 times the heap-residency, the run-time overhead of the ZG-machine is no more than 12% compared to the G-machine. With the aspect of reduced heap-residency, the ZG-machine may be useful in memory-restricted environments such as embedded systems. Also, with the development of a more efficient garbage collector, the run-time is expected to decrease significantly.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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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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• v.8 no.2
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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.

• Journal of Convergence Society for SMB
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• v.1 no.1
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• pp.75-83
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• 2011

Unified Modeling Language (UML) is widely accepted in industry and particularly UML State Machine Diagram is popular for describing the dynamic behavior of classes. This paper discusses deadlock detection of System using UML State Machine Diagram. Since a State Machine Diagram is used for indivisual class' behavior, all the State Machine Diagrams of the classes in the system are combined to make a big system-wide State Machine Diagram to describe system behavior. Generally this system-wide State Machine Diagram is very complex and contains invalid state and transitions. To make it a usable and valid State Machine Diagram, synchronization and externalization are applied. The reduced State Machine Diagram can be used for describing system behavior thus conventional model-checking technique can be applied. This paper shows how deadlock detection of system can be applied with simple examples. All the procedures can be automatically done in the tool.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.865-867
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• 2014

IoT is a kind of business means to maintain the scenario in aware of the situation with the user's through collaboration for M2M have intelligence to each appliance is of machine socializations. The existing IoT Progress from one situation one control of through a simple sensor data, mean from machine socialization is regulate and control on overall the flow of machine manager to solved a scenarios as the situation. In this paper, suggest schema for to apply the M2M of SNS of existing H2H, suggest is schema in information each appliance in solve scenario for machine manager.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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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.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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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.