• Journal of electromagnetic engineering and science
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• v.6 no.4
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• pp.244-252
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• 2006

A block Jacket transform and. its block inverse Jacket transformn have recently been reported in the paper 'Fast block inverse Jacket transform'. But the multiplication of the block Jacket transform and the corresponding block inverse Jacket transform is not equal to the identity transform, which does not conform to the mathematical rule. In this paper, new binary block Jacket transforms and the corresponding binary block inverse Jacket transforms of orders $N=2^k,\;3^k\;and\;5^k$ for integer values k are proposed and the mathematical proofs are also presented. With the aid of the Kronecker product of the lower order Jacket matrix and the identity matrix, the fast algorithms for realizing these transforms are obtained. Due to the simple inverse, fast algorithm and prime based $P^k$ order of proposed binary block inverse Jacket transform, it can be applied in communications such as space time block code design, signal processing, LDPC coding and information theory. Application of circular permutation matrix(CPM) binary low density quasi block Jacket matrix is also introduced in this paper which is useful in coding theory.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.505-508
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• 1997

In this paper, an auto-tuning method for fuzzy controller based on the neural network is presented. The backpropagated error of neural emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and used for speed control of induction motor. For the torque control method, an indirect vector control scheme with slip calculation is used because of its stable characteristics regardless of speed. Motor input current is regulated by a current controlled voltage source PWM inverter using space voltage vector technique. Also, the scheme of current control fuzzy controller is synchronous reference frame with decoupling term. DSP(TMS320C31) is used to achieve the high speed calculation of the space voltage vector PWM and to build the self-learning fuzz. control algorithm. An IPM is used to simplify hardware design.

• Coupled systems mechanics
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• v.5 no.2
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• pp.101-126
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• 2016

Seismic structural fragility constitutes an important step for performance based seismic design. Lateral load-resisting structural members are often analyzed under one component base excitation, while the effect of bi-directional shaking is accounted per simplified rules. Fragility curves are constructed herein under real bi-directional excitation by a simple extension of the conventional Incremental Dynamic Analysis (IDA) under uni-directional shaking. Simple SODF systems, parametrically adjusted to different periods, are examined under a set of near-fault and far-fault excitations. Consideration of bi-directional interaction appears important for stiff systems. Further, the study indicates that the peak ground accelertaion, velocity and displacement (PGA, PGV and PGD) of accelerogram are relatively stable and efficient intensity measures for short, medium and long period systems respectively. '30%' combination rule seems to reasonably predict the fragility under bi-directional shaking at least for first mode dominated systems dealt herein up to a limit state of damage control.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.10
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• pp.467-474
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• 2005

This paper presents a systematic method to compute a reliability measure for a short term electrical load forecasting system using neuro-fuzzy models. It has been realized that the reliability computation is essential for a load forecasting system to be applied practically. The proposed method employs a local reliability measure in order to exploit the local representation characteristic of the neuro-fuzzy models. It, hence, estimates the reliability of each fuzzy rule learned. The design procedure of the proposed short term load forecasting system is as follows: (1) construct initial structures of neuro-fuzzy models, (2) store them in the initial structure bank, (3) train the neuro-fuzzy model using an appropriate initial structure, and (4) compute load prediction and its reliability. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1996 and 1997 at KEPCO. Simulation results suggest that the proposed scheme extends the applicability of the load forecasting system with the reliably computed reliability measure.

• Smart Structures and Systems
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• v.26 no.4
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• pp.469-480
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• 2020

This is the first research on the smart control and vibration analysis of a Graphene nanoplatelets (GPLs) Reinforced Composite (GPLRC) porous cylindrical shell covered with piezoelectric layers as sensor and actuator (PLSA) in the framework of numerical based Generalized Differential Quadrature Method (GDQM). The stresses and strains are obtained using the First-order Shear Deformable Theory (FSDT). Rule of the mixture is employed to obtain varying mass density and Poisson's ratio, while the module of elasticity is computed by modified Halpin-Tsai model. The external voltage is applied to sensor layer and a Proportional-Derivative (PD) controller is used for sensor output control. Governing equations and boundary conditions of the GPLRC cylindrical shell are obtained by implementing Hamilton's principle. The results show that PD controller, length to radius ratio (L/R), applied voltage, porosity and weight fraction of GPL have significant influence on the frequency characteristics of a porous GPLRC cylindrical shell. Another important consequence is that at the lower value of the applied voltage, the influence of the smart controller on the frequency of the micro composite shell is much more significant in comparison with the higher ones.

• Journal of Internet Computing and Services
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• v.7 no.6
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• pp.21-40
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• 2006

As the knowledge-based society hot been constructed, the size of work process that has to be done grows big and the amount of the information that has to be analyzed increases. In the paper business process can be accurately executed by the rule agent to manage precise rules and definitions to be needed by execution of process for management of business process. And individuals con accurately measure and manage personal schedules to execute process through supporting the agent of schedule management. The model is designed and implemented for organization to improve process of control, flexibleness, agility, reliability, and reuse through business process automation and removing process overlapping and for personal to improve measuring personal process capacity and distinguishing process weak points.

• Journal of Korea Game Society
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• v.8 no.1
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• pp.3-13
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• 2008

We think that it is important to develop the metadata for educational game. Because they are applicable to game contents, separate learning sources and studying components in the game-based LCMS. But markets of eduainment and educational games are newborn field, so systematic development of metadata is not advanced yet. Therefore in this paper, we first established the design process of educational game metadata, and then according to the rule, we suggested this as a prototype. And we defined the extracted data as metadata for educational game through inspection of an expert group. If these metadata prototype are operated by adopting through standardization stage of public institutes, we can provide the convenience of searching, managing and recycling these metadata to learner, instructor and the developing institute. And we can also expect the prevention of overlapping investment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.479-482
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• 2001

IDS has been studied mainly in the field of the detection decision and collecting of audit data. The detection decision should decide whether successive behaviors are intrusions or not, the collecting of audit data needs ability that collects precisely data for intrusion decision. Artificial methods such as rule based system and neural network are recently introduced in order to solve this problem. However, these methods have simple host structures and defects that can't detect transformed intrusion patterns. So, we propose the method using data mining that can retrieve and estimate the patterns and retrieval of user's behavior in the distributed different hosts.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.619-622
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• 2002

IDS has been studied mainly in the field of the detection derision and collecting of audit data. The detection decision should decide whether successive behaviors are intrusions or not , the collecting of audit data needs ability that collects precisely data for intrusion decision. Artificial methods such as rule based system and neural network are recently introduced in order to solve this problem. However, these methods have simple host structures and defects that can't detect transformed intrusion patterns. So, we propose the method using data mining agent that can retrieve and estimate the patterns and retrieval of user's behavior in the distributed different hosts.

• Proceedings of the KIEE Conference
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• 2005.04a
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• pp.188-190
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• 2005

This paper is proposed HAI controller for high performance of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. The control performance of the HAI controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.