• 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.

• 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.

• 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.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.

• 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 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 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 Mechanical Science and Technology
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• v.20 no.11
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• pp.1863-1872
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• 2006

In FMS (Flexible Manufacturing System) and CIM (Computer Integrated Manufacturing), machine-tools have been the target of integration in the last three decades. The conventional concept of integration is being changed into the autonomous manufacturing device based on the knowledge evolution by applying advanced information technology in which an open architecture controller, high-speed network and internet technology are included. In the advanced environment, the machine-tools is not the target of integration anymore, but has been the key subject of cooperation. In the near future, machine-tools will be more improved in the form of a knowledge-evolutionary intelligent device. The final goal of this study is to develop an intelligent machine having knowledge-evolution capability and a management system based on internet operability. The knowledge-evolutionary intelligent machine-tools is expected to gather knowledge autonomically, by producing knowledge, understanding knowledge, reasoning knowledge, making a new decision, dialoguing with other machines, etc. The concept of the knowledge-evolutionary intelligent machine is originated from the machine control being operated by human experts' sense, dialogue and decision. The structure of knowledge evolution in M2M (Machine to Machine) and the scheme for a dialogue agent among agent-based modules such as a sensory agent, a dialogue agent and an expert system (decision support agent) are presented in this paper, with intent to develop the knowledge-evolutionary machine-tools. The dialogue agent functions as an interface for inter-machine cooperation. To design the dialogue agent module in an M2M environment, FIPA (Foundation of Intelligent Physical Agent) standard platform and the ping agent based on FIPA are analyzed in this study. In addition, the dialogue agent is designed and applied to recommend cutting conditions and thermal error compensation in a tapping machine. The knowledge-evolutionary machine-tools are expected easily implemented on the basis of this study and shows a good assistance to sensory and decision support agents.

• 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.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.3
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• pp.175-180
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• 2004

The machine-part group formation is to group the sets of parts having similar processing requirements into part families, and the sets of machines needed to process a particular part family into machine cells using grid computing. It forms machine cells from the machine-part incidence matrix by means of Self-Organizing Maps(SOM) whose output layer is one-dimension and the number of output nodes is the twice as many as the number of input nodes in order to spread out the machine vectors. It generates machine-part group which are assigned to machine cells by means of the number of bottleneck machine with processing part. The proposed algorithm was tested on well-known machine-part grouping problems. The results of this computational study demonstrate the superiority of the proposed algorithm.