• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.6
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• pp.97-103
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• 2008

In this paper, the Post Process for the manifold complex processing using CAD/CAM Software of two and a half Dimensions(2.5D) has been developed to maximize the application of the manifold manufacturing machine. Many companies are currently making use of high price systems to improve manufacturing process using the multi-axial complex manufacturing machine. In accordance with the requirements, the utilization of CAD/CAM Software for the manifold complex manufacturing machine is earnestly demanded. However, the experts who have experience in manifold manufacturing machine are insufficient. Consequently the outcomes of the Post Process for 2.5D CAD/CAM Systems have been dealt in order to be smoothly operated by those who have basic skills and be understood in process drawings. CNC program functions can be specially used as they are, when drawn up. The Post Process for the original point designation and transformation of coordinates has been developed and applied. The results gave proof of practical manufacturing outcomes.

• Archives of design research
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• no.18
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• pp.177-186
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• 1996

With the change of patterns and the environment of the national standard of living the prohlem of environmental pollution became increasingly serious. Because of the enormous increase of various kinds of used and (after utilization) useless articles, efforts to save resources as well as the environment and the promotion of reprated utilization and recycling are inavoidable. The recognition of an environmental an health problem, and the desire for nonpollution created a desire for environment-friendly products in order to avoid an environmental consumptionism. Drink vending machines making use of vessels only once are closely related to the environmental problem. It is therefore necessary to develop an ecologically designed vending machine. In this study the backgrounds and concepts of green design, classification, construction and the environment of a drink vending machine arc analyzed. From this st1.rting-point a concept for the design of a drink vending machine is developed by two concepts : Type A (seperated-gathering type) and Type B (recycling type). Then three defferent types of vending-machines arc introduced a wall -adherable type, a center est1.blishable type and a desk top type. The conclusion of the text is threefold. There are needs for an ecological design of vending machines, ergonomIc considerations and a harmonization of the styldapperarance) of the machine and its circumferences.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.60-66
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• 2024

Host's data during transmission. Data tempering results in loss of host's sensitive information, which includes number of VM, storage availability, and other information. In the distributed cloud environment, each server (computing server (CS)) configured with Local Resource Monitors (LRMs) which runs independently and performs Virtual Machine (VM) migrations to nearby servers. Approaches like predictive VM migration [21] [22] by each server considering nearby server's CPU usage, roatative decision making capacity [21] among the servers in distributed cloud environment has been proposed. This approaches usage underlying server's computing power for predicting own server's future resource utilization and nearby server's resource usage computation. It results in running VM and its running application to remain in waiting state for computing power. In order to reduce this, a decentralized decision making hybrid model for VM migration need to be proposed where servers in decentralized cloud receives, future resource usage by analytical computing system and takes decision for migrating VM to its neighbor servers. Host's in the decentralized cloud shares, their detail with peer servers after fixed interval, this results in chance to tempering messages that would be exchanged in between HC and CH. At the same time, it reduces chance of over utilization of peer servers, caused due to compromised host. This paper discusses, an roatative decisive (RD) approach for VM migration among peer computing servers (CS) in decentralized cloud environment, preserving confidentiality and integrity of the host's data. Experimental result shows that, the proposed predictive VM migration approach reduces extra VM migration caused due over utilization of identified servers and reduces number of active servers in greater extent, and ensures confidentiality and integrity of peer host's data.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.19-24
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• 2019

Lung cancer is a chronic disease which ranks fourth in cancer incidence with 11 percent of the total cancer incidence in Korea. To deal with such issues, there is an active study on the usefulness and utilization of the Clinical Decision Support System (CDSS) which utilizes machine learning. Thus, this study reviews existing studies on artificial intelligence technology that can be used in determining the lung cancer, and conducted a study on the applicability of machine learning in determination of the lung cancer by comparison and analysis using Azure ML provided by Microsoft. The results of this study show different predictions yielded by three algorithms: Support Vector Machine (SVM), Two-Class Support Decision Jungle and Multiclass Decision Jungle. This study has its limitations in the size of the Big data used in Machine Learning. Although the data provided by Kaggle is the most suitable one for this study, it is assumed that there is a limit in learning the data fully due to the lack of absolute figures. Therefore, it is claimed that if the agency's cooperation in the subsequent research is used to compare and analyze various kinds of algorithms other than those used in this study, a more accurate screening machine for lung cancer could be created.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.147-158
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• 2022

Cloud Computing offers flexible, on demand, ubiquitous resources for cloud users. Cloud users are provided computing resources in a virtualized environment. In order to meet the growing demands for computing resources, data centres contain a large number of physical machines accommodating multiple virtual machines. However, cloud data centres cannot utilize their computing resources to their total capacity. Several policies have been proposed for improving energy proficiency and computing resource utilization in cloud data centres. Virtual machine placement is an effective method involving efficient mapping of virtual machines to physical machines. However, the availability of many physical machines accommodating multiple virtual machines in a data centre has made the virtual machine placement problem a non deterministic polynomial time hard (NP hard) problem. Metaheuristic algorithms have been widely used to solve the NP hard problems of multiple and conflicting objectives, such as the virtual machine placement problem. In this context, we presented essential concepts regarding virtual machine placement and objective functions for optimizing different parameters. This paper provides a taxonomy of metaheuristic algorithms for the virtual machine placement method. It is followed by a review of prominent research of virtual machine placement methods using meta heuristic algorithms and comparing them. Finally, this paper provides a conclusion and future research directions in virtual machine placement of cloud computing.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1335-1338
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• 2003

Fixed-size memory allocation is one of the most promising way to avoid external fragmentation in dynamic memory allocation problem. This paper presents an experimental result of applying the fixed- size memory allocation strategy to Java virtual machine for embedded system. The result says that although this strategy induces another memory utilization problem caused by internal fragmentation, the effect is not very considerable and this strategy is well-suited for embedded Java system. The experiment has been performed in a real embedded Java system called the simpleRTJ.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1553-1556
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• 2003

In this paper, We evaluate the learning ability of non-monotonic DBM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.275-278
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• 2001

In this paper, We evaluate the learning ability of non-monotonic DBM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.151-157
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• 1992

This paper presents a method for analyzing the transfer lines with finite storage buffers. Each machine spends a random amount of time processing each material. This transfer line can be modeled by the tandem queueing system with finite buffers. The great dimensionality of the state space renders the analysis of such system a formidable task. We propose an efficient algorithm to obtain the marginal state probabilities based on the exact algorithm for the two-machine system. Other performance measures, such as the utilization, the blocking probability, the average sojourn time, and the average queue length, can be easily calculated.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.05a
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• pp.52-56
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• 2003

In this paper, We evaluate the learning ability of non-monotonic DBM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.