• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1480-1493
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• 2018

Recently, computational intelligence has received a lot of attention from researchers due to its potential applications to artificial intelligence. In computer science, computational intelligence refers to a machine's ability to learn how to compete various tasks, such as making observations or carrying out experiments. We adopted a computational intelligence solution to monitoring residual resources in cloud computing environments. The proposed residual resource monitoring scheme periodically monitors the cloud-based host machines, so that the post migration performance of a virtual machine is as consistent with the pre-migration performance as possible. To this end, we use a novel similarity measure to find the best target host to migrate a virtual machine to. The design of the proposed residual resource monitoring scheme helps maintain the quality of service and service level agreement during the migration. We carried out a number of experimental evaluations to demonstrate the effectiveness of the proposed residual resource monitoring scheme. Our results show that the proposed scheme intelligently measures the similarities between virtual machines in cloud computing environments without causing performance degradation, whilst preserving the quality of service and service level agreement.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.167-183
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• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.161-169
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• 2011

Recent intelligent video surveillance system asks for development of more advanced technology for analysis and recognition of video data. Especially, machine learning algorithm such as Support Vector Machine (SVM) is used in order to recognize objects in video. Because SVM training demands massive amount of computation, parallel processing technique is necessary to reduce the execution time effectively. In this paper, we propose a parallel processing method of SVM training with a multi-core processor. The results of parallel SVM on a 4-core processor show that our proposed method can reduce the execution time of the sequential training by a factor of 2.5.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.115-123
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• 2021

Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.1
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• pp.38-42
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• 2002

Conditions are discussed for operating a dissymmetric human master-small (or micro) slave system in best (large position gain-small velocity gain) conditions allowing higher operator dexterity when real effects (joint compliance, link flexion delay and transmission distortion) are taken into account. It is shown that position PD feedback law advantage for ideal case no longer holds, and that more complicated feedback law depending on real effects has to be implemented with adapted transmission line. Drawback is slowdown of master slave interaction, suggesting to use more advanced predictive methods for the master and more intelligent control law for the slave.

• Proceedings of the KIEE Conference
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• 1986.07a
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• pp.97-109
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• 1986

The ultimate goal of an industrial robot is to make full use of its real ability to communicate with any given intelligent device, and to do so independently of hardware, architecture and languages. This paper describes the necessary functions of a robot used in an integrated manufacturing system, and the basic philosophy of organization as applied to the robot controller. An example of a machine vision system called MYVIS is reviewed in relation to MAP and LAN in a practical cell application.

• International journal of advanced smart convergence
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• v.2 no.2
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• pp.7-9
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• 2013

Current physics-based simulation is an important tool in the fluid animation. However some problems require a new change to current research trends which depend only on the simulation. The ultimate goal of this project is to obtain information of flow example, analyze an example through machine learning and the novel fluid animation reconfigure without physical simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.201-206
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• 1996

This paper presents a new information processing machine which is called artificial brain(ABrain) and considers the structure of artificial neural networks constructed in a RICOH neurocomputer RN-2000 in the ABrain, in order to track given trajectories which are produced in a micro-computer or a moving light by hand in a recognition and tracking system.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.265-270
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• 1993

In an intelligent man-machine interface, it is very effective to support human thinking and to be in communication in some intuitive fashion. For this, sharing experience between the party concerned, human operators(s) and the interface is essential. It is also necessary to keep mutual understanding in some conceptual levels. Here in the present paper, figures which are an aspect of concepts and form a basis of mental image are discussed.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1006-1011
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• 1993

In this study, a DNC(Distributed Numerical Control) management system is designed that can directly control and manage hybrid CNC machine tools on real-time. And management software is developed to inter-communicate field informations with CNC controllers using an interface processor(Intelligent Multi Communication Board, IMCB). Especially, IMCB supports that DNC system sends and receives part program with CNC controllers in the form of real-time multi-tasking.