• Journal of the Korean Operations Research and Management Science Society
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• v.11 no.2
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• pp.37-50
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• 1986

In combining dispersed optimization models, either primal or dual(or both) decomposition method widely used as an organizing device. Interpreting the methods economically, the concepts of price and resource-directive coordination are generally well accepted. Most of deomposition/ integration methods utilize either primal information of dual information, not both, from subsystems, while some authors have developed mixed decomposition approaches employing two master problems dealing primal and dual proposals separately. In this paper a hybrid decomposition method is introduced, where one hybrid master problem utilizes the underlying relationships between primal and dual information from each subsystem. The suggested method is well justified with respect to the flexibility in information flow pattern choice (some prices and other quantities) and to the compatibility of subdivision's optimum to the systemwide optimum, that is often lacking in conventional decomposition methods such as Dantzig-Wolfe's. A numerical example is also presented to illustrate the suggested approach.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.1
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• pp.158-164
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• 2002

This paper proposes the optimization of ultrasonic signal recognition in ferrite carbon steel weld zone using neural networks. For these purposes, the ultrasonic signals for defects as porosity, incomplete penetration and slag inclusion in the weld zone are acquired in the type of time series data. And then their applications evaluated feature extraction based on the time-frequency-attractor domain(peak to peak, rise time, rise slope, fall time, fall slope, pulse duration, power spectrum, and bandwidth) and attractor characteristics (fractal dimension and attractor quadrant) etc. The proposed neural networks system in this study can enhances performance of ultrasonic signal recognition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1178-1191
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• 2014

The fault-tolerance routing problem is one of the most important issues in the application of the Internet of Things, and has been attracting growing research interests. In order to maintain the communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which alternate paths are created once the previous routing is broken. Then, we propose an improved efficient and intelligent fault-tolerance algorithm (IEIFTA) to provide the fast routing recovery and reconstruct the network topology for path failure in the Internet of Things. In the IEIFTA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by the immune mechanism, which can improve the ability of global search and improve the converging rate of the algorithm. The simulation results indicate that the IEIFTA-based fault-tolerance algorithm outperforms the EARQ algorithm and the SPSOA algorithm due to its ability of fast routing recovery mechanism and prolonging the lifetime of the Internet of Things.

• 한국IT서비스학회:학술대회논문집
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• 2007.05a
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• pp.330-335
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• 2007

국가 간 무역 활성화로 인하여, 국제 물류의 고도화가 요구되는 현 시점에서 보세창고의 운영 효율성을 제고할 필요성이 커지고 있다. 그러나 현재의 보세창고 프로세스 중 검수 및 장치화물관리 프로세스는 수작업으로 인한 오류발생률이 높아 효율성이 저하되는 문제점을 가지고 있다. 따라서 본 논문에서는 이에 대한 해결책으로 보세창고에 RFID 기술을 적용하여 검수작업을 자동화하는 방안을 제시하였다. RIFD 기술 도입은 검수 작업의 효율화뿐만 아니라, 향후 실시간 장치화물관리, 창고 내 Resource 관리 등 창고관리의 제 분야에 활용되어 보세창고관리의 효율성을 제고시킬 것으로 기대된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5592-5609
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• 2017

JPEG steganography detection is an active research topic in the field of information hiding due to the wide use of JPEG image in social network, image-sharing websites, and Internet communication, etc. In this paper, a new steganalysis method for content-adaptive JPEG steganography is proposed by integrating the evolutionary feature selection and classifier ensemble selection. First, the whole framework of the proposed steganalysis method is presented and then the characteristic of the proposed method is analyzed. Second, the feature selection method based on genetic algorithm is given and the implement process is described in detail. Third, the method of classifier ensemble selection is proposed based on Pareto evolutionary optimization. The experimental results indicate the proposed steganalysis method can achieve a competitive detection performance by compared with the state-of-the-art steganalysis methods when used for the detection of the latest content-adaptive JPEG steganography algorithms.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.282-284
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• 2001

This paper presents an efficient algorithm for the loss minimization in distribution systems. Ant colony algorithm is suitable for combinatorial optimization problem as network reconfiguration because it use the long term memory, called pheromone, and heuristic information with the property of the problem. The proposed methodology with some adoptions have been applied to improve the computation time and convergence property. Numerical examples demonstrate the validity and effectiveness of the proposed methodology using 32-bus system.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.681-683
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• 1998

Fuzzy Inference System is to trans late and be concrete with human expert in to mathematical equation. It is easy to be applied for Nonlinear System and the know ledge can be applied at that. With using the rule according to the Knowledge, when it is realized simulations must be required repeatedly and small vibration is generated in steady state, too. In this paper, we applied the system to the methodology of optimization with self-learn ing by us ing ANFIS(Adaptive Network-based Fuzzy Inference System) which makes use of back-propagation and least square method at a first order Sugeno Fuzzy System. In order to show the effect of Algorithm, we demonstrated it by us ing Rotary Inverted Pendulum.

• Journal of the Korean Institute of Telematics and Electronics
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• v.7 no.1
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• pp.9-17
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• 1970

The paper presents pole optimization in RC network of active RC filter using current inversion negative impedance converter. And also empais is placed on improving the stability of the active RC filter. Experimental results obtained with active RC low pass filter, having Chebyshev 2nd order response and modular angle 55$^{\circ}$, cutoff frequency 3.4KC, are shown and compared with theoretical curves.

• Smart Structures and Systems
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• v.5 no.4
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• pp.369-379
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• 2009

The sudden collapse of Interstate 35 Bridge in Minneapolis gave a wake-up call to US municipalities to re-evaluate aging bridges. In this situation, structural health monitoring (SHM) technology can provide the essential help needed for monitoring and maintaining the nation's infrastructure. Monitoring long span bridges such as cable-stayed bridges effectively requires the use of a large number of sensors. In this article, we introduce a probabilistic approach to identify optimal locations of sensors to enhance damage detection. Probability distribution functions are established using an artificial neural network trained using a priori knowledge of damage locations. The optimal number of sensors is identified using multi-objective optimization that simultaneously considers information entropy and sensor cost-objective functions. Luling Bridge, a cable-stayed bridge over the Mississippi River, is selected as a case study to demonstrate the efficiency of the proposed approach.

• Journal of the Korea Safety Management & Science
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• v.14 no.2
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• pp.185-192
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• 2012

It has been a huge amount of capacity of 10GB data base in a decade ago so far. Nowadays, however, 10TB is the common data base and even bigger capacities are available. So, new generation of Very Large Data Base (VLDB) has begun. Moving in to the new generation of VLDB has been caused major problems like backing up, restoring, and managing especially performance. It is very hard to export necessary data rapidly now due to the huge amount of data base. In the past, such kind of problems was out of the questions because of less data. As time goes on, however, optimization of performance became a big issue when the VLDB is common. Therefore, new professional technics are urgently required to maintain and optimize the data base that has become a VLDB or one that is in the progress of becoming one.