• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.585-597
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• 2008

The aim of this study was to find an analytic solution to the problem of determining the optimal capacity (lot-size) of a batch-storage network to meet demand for a finished product in a system undergoing random failures of operating time and/or batch material. The superstructure of the plant considered here consists of a network of serially and/or parallel interlinked batch processes and storage units. The production processes transform a set of feedstock materials into another set of products with constant conversion factors. The final product demand flow is susceptible to short-term random variations in the cycle time and batch size as well as long-term variations in the average trend. Some of the production processes have random variations in product quantity. The spoiled materials are treated through regeneration or waste disposal processes. All other processes have random variations only in the cycle time. The objective function of the optimization is minimizing the total cost, which is composed of setup and inventory holding costs as well as the capital costs of constructing processes and storage units. A novel production and inventory analysis, the PSW (Periodic Square Wave) model, provides a judicious graphical method to find the upper and lower bounds of random flows. The advantage of this model is that it provides a set of simple analytic solutions while also maintaining a realistic description of the random material flows between processes and storage units; as a consequence of these analytic solutions, the computation burden is significantly reduced.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.5
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• pp.56-63
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• 2009

The IEEE 802.11 standard has been used for wireless data networks such as wireless LAN, ad-hoc network, and vehicular ad-hoc network. Thus, the performance analysis of the IEEE 802.11 specification has been one of the hottest issues for network optimization and resource management. Most of the analysis studies were performed in a data plane of the IEEE 802.11 unicast. However, IEEE 802.11 broadcast is widely used for topology management, path management, and data dissemination. Thus, it is important to understand the performance of the broadcast scheme for the design of efficient wireless data network. In this contort, we analyze the IEEE 802.11 broadcast scheme in terms of the broadcast frame reception probability according to the distance from a sending node. Unlike the other works, our analysis framework includes not only the system parameters of the IEEE 802.11 specification such as transmission range, data rate, minimum contention window but also the networking environments such as the number of nodes, network load, and the radio propagation environments. Therefore, our analysis framework is expected to be used for the development of protocols and algorithms in a dynamic wireless data network.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.5
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• pp.26-35
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• 2009

OFDM (Orthogonal Frequency Division Multiplexing) system is robust to frequency selective fading and narrowband interference in high-speed data communications. However, an OFDM signal consists of a number of independently modulated subcarriers and the superposition of these subcarriers causes a problem that can give a large PAPR(Peak-to-Average Power Ratio). Phase rotation method can reduce the PAPR without nonlinear distortion by multiplying phase weighting factors. But computational complexity of searching phase weighting factors is increased exponentially with the number of subblocks and considered phase factor. Therefore, a new method, which can reduce computational complexity and detect phase weighting factors efficiently, should be developed. In this paper, a modeling process is introduced, which apply metaheuristic algerian in phase rotation method and optimize in PTS (Particle Swarm Optimization) scheme. Proposed algorithm can solve the computational complexity and guarantee to reduce PAPR We analyzed the efficiency of the PAPR reduction through a simulation when we applied the proposed method to telecommunication systems.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.8
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• pp.1105-1111
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• 2010

The Korea Atomic Energy Research Institutes has been developing the SMART (System integrated Modular Advanced ReacTor), an environment-friendly nuclear reactor for the generation of electricity and to perform desalination. SMART reactors can be exposed to various external and internal loads caused by seismic and coolant flows. The CRDM(control rod drive mechanism), one of structures of the SMART, is a component which is adjusting inserting amount of a control rod, controlling output of reactor power and in an emergency situation, inserting a control rod to stop the reactor. The purpose of this research is performing the analysis of dynamic characteristic to ensure safety and integrity of structure of CRDM. This paper presents two FE-models, 3-D solid model and simplified Beam model of the CRDM in the coolant, and then compared the results of the dynamic characteristic about the two FE-models using a commercial Finite Element tool, ABAQUS CAE V6.8 and ANSYS V12. Beam 4 and beam 188 of simplified-model were also compared each other. And simplified model is updated for accuracy compare to 3-D solid.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.7 no.3
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• pp.13-21
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• 1987

The object of this study was to evaluate the management alternatives with respect to the attainable water quality and total cost(construction+O & M cost) in order to devise a reasonable water quality management system. Joong Ryang-cheon stream located Seoul, Korea was taken as the study area, and dissolved oxygen concentration as the water quality index. Water quality simulation model QUAL2E and linear programming optimization technique were used for scientific and rational analyses. It was assumed that the improvement of water quality could be obtained by the treatment of major point sources where imaginary treatment plants were constructed. And by this, the relationship between total cost of the treatment plants and the stepwise improvement of water quality was studied. The result showed that 3.5mg/l of DO(=dissolved oxygen) level at best could be attained in Joong Ryang-cheon stream during summer. When the DO standard was set 3.0mg/l in the severely polluted regions, more than 5.0mg/l of DO level can be achieved by the construction of 4 treatment plants. Also, the cost comparisons showed that the uniform treatment method is economically inefficient(\$24.8{\times}10^8$) in comparison with the least cost method(\$22.9{\times}10^8$), and there is little difference between the least cost method and the the zoned treatment method(\$23.0{\times}10^8$) that is regarded as more equitable, which shows the characteristics of this basin.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.20 no.3
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• pp.97-102
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• 2014

It was mathematically analyzed at which steps to activate TTI in the cold chain for Alaska pollack, assuming that the performance of a commercial TTI product, and Fresh-check, could not always be optimized for the pollack. Three places were selected for the TTI activation, such as on fishing ship, Busan cooperative fish market, and mart. First, the kinetic and Arrhenius temperature dependent models were experimentally built under isothermal conditions. The color index of TTI and the level of Pseudomonas spp. of pollack were measured at time intervals. Second, the resultant models were used in the mathematical calculations for dynamic temperature conditions included in the cold chain. As a result, the TTI activated at the mart place showed the best agreement between the spoilage time of the pollack and the time for the TTI color to reach its end-point. It was therefore found that it is practically important to optimally select the TTI activation place or time when using a commercial TTI product.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

• Journal of the Microelectronics and Packaging Society
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• v.21 no.2
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• pp.43-51
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• 2014

In 3-dimensional (3D) integrated package, thermal management is one of the critical issues due to the high heat flux generated by stacked multi-functional chips in miniature packages. In this study, we used numerical simulation method to analyze the thermal behaviors, and investigated the thermal issues of 3D package using TSV (through-silicon-via) technology for mobile application. The 3D integrated package consists of up to 8 TSV memory chips and one logic chip with a interposer which has regularly embedded TSVs. Thermal performances and characteristics of glass and silicon interposers were compared. Thermal characteristics of logic and memory chips are also investigated. The effects of numbers of the stacked chip, size of the interposer and TSV via on the thermal behavior of 3D package were investigated. Numerical analysis of the junction temperature, thermal resistance, and heat flux for 3D TSV package was performed under normal operating and high performance operation conditions, respectively. Based on the simulation results, we proposed an effective integration scheme of the memory and logic chips to minimize the temperature rise of the package. The results will be useful of design optimization and provide a thermal design guideline for reliable and high performance 3D TSV package.