• Geomechanics and Engineering
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• v.13 no.2
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• pp.195-215
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• 2017

This paper proposes gob-side entry retaining by roof break and filling in thick-layer soft rock conditions based on the thick-layer soft rock roof strata migration law and the demand for non-pillar gob-side entry retaining projects. The functional expressions of main roof subsidence are derived for three break roof direction conditions: lateral deflection toward the roadway, lateral deflection toward the gob and vertically to the roof. These are derived according to the load-bearing boundary conditions of the main roadway roof stratum. It is concluded that the break roof angle is an important factor influencing the stability of gob-side entry retaining surrounding rock. This paper studies the stress distribution characteristics and plastic damage scope of gob-side entry retaining integrated coal seams, as well as the roof strata migration law and the supporting stability of caving structure filled on the break roof layer at the break roof angles of $-5^{\circ}$, $0^{\circ}$, $5^{\circ}$, $10^{\circ}$ and $15^{\circ}$ are studied. The simulation results of numerical analysis indicate that, the stress concentration and plastic damage scope to the sides of gob-side entry retaining integrated coal at the break roof angle of $5^{\circ}$ are reduced and shearing stress concentration of the caving filling body has been eliminated. The disturbance of coal mining to the roadway roof and loss of carrying capacity are mitigated. Field tests have been carried out on air-return roadway 5203 with the break roof angle of $5^{\circ}$. The monitoring indicates that the break roof filling section and compaction section are located at 0-45 m and 45-75 m behind the working face, respectively. The section from 75-100 m tends to be stable.

• Journal of Advanced Navigation Technology
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• v.15 no.6
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• pp.1015-1024
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• 2011

Mobile P2P protocols in ad-hoc networks have gained large attention recently. Although there has been much research on P2P algorithms for wired networks, existing P2P protocols are not suitable for mobile ad-hoc networks because they do not consider mobility of peers. This study proposes a new cluster-based P2P protocol for ad hoc networks which utilizes peer mobility. In typical cluster-based P2P algorithms, each cluster has a super peer and other peers of the cluster register their file list to the super peer. High mobility peers would cause a lot of file list registration traffic because they hand-off between clusters frequently. In the proposed scheme, while peers with low mobility behave in the same way as the peers of the typical cluster-based P2P schemes, peers with high mobility behave differently. They inform their entrance to the cluster region to the super peer but they do not register their file list to the super peer. When a peer wishes to find a file, it first searches the registered file list of the super peer and if fails, query message is broadcasted. We perform mathematical modeling, analysis and optimization of the proposed scheme regarding P2P traffic and associated routing traffic. Numerical results show that the proposed scheme performs much better than or similar to the typical cluster-based P2P scheme and flooding based Gnutella.

• Journal of Korea Water Resources Association
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• v.44 no.9
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• pp.695-710
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• 2011

Manning's roughness coefficients for the Han River, the Nakdong River and the Geum River were determined by the hydraulic models using their field measurements. The roughness coefficients of present study were compared with the ones of the conventional references. The hydraulic models, such as HEC-2, HEC-RAS and FLDWAV models, are usually applied to a river flow analysis. In order to compute the accurate flood level with the numerical models, accurate information about river sections, the upstream and downstream boundary conditions, and the appropriate roughness coefficients are indispensable. It is hard to obtain the reasonable roughness coefficient of the river, in the other hand the river cross sectional data and the boundary conditions are relatively easy to acquire. The coefficient values from the references are applied in many applications without considering the variation of locations and discharges of the river, or the values are unreasonably estimated. The final results from this study will give a reasonable and important data to perform the flood routing in the Korea river.

• 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 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.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.143-156
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• 2010

Faced with an increase of malicious threats from the Internet as well as local area networks, many companies are considering deploying a security system. To help a decision maker select a suitable security tool, this paper proposed a three-step integrated framework using linear fuzzy regression (LFR) and physical programming (PP). First, based on the experts' estimations on security criteria, analytic hierarchy process (AHP) and quality function deployment (QFD) are employed to specify an intermediate score for each criterion and the relationship among these criteria. Next, evaluation value of each criterion is computed by using LFR. Finally, a goal programming (GP) method is customized to obtain the most appropriate security tool for an organization, considering a tradeoff among the multi-objectives associated with quality, credibility and costs, utilizing the relative weights calculated by the physical programming weights (PPW) algorithm. A numerical example provided illustrates the advantages and contributions of this approach. Proposed approach is anticipated to help a decision maker select a suitable security tool by taking advantage of experts' experience, with noises eliminated, as well as the accuracy of mathematical optimization methods.

• Korean Journal of Food Science and Technology
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• v.45 no.2
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• pp.180-190
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• 2013

The purpose of this study was to determine the optimal amount of 2 ingredients, i.e., black barley flour ($X_1$), and olive oil ($X_2$), for the production of white pan bread from black barley flour. The experiment was designed according to the central composite design of response surface methodology, which showed 10 experimental points, including 2 replicates for black barley flour and olive oil. Significant differences were found in the results of the physical and mechanical properties analysis of each sample, including weight (p<0.05), volume (p<0.01), specific loaf volume (p<0.01), color L (p<0.01), color a (p<0.001), color b (p<0.05), hardness (p<0.001), springiness (p<0.01), cohesiveness (p<0.01), gumminess (p<0.001) and chewiness (p<0.05). Significant differences in the sensory measurements were observed in color (p<0.01), appearance (p<0.01), texture (p<0.05), taste (p<0.05) and overall quality (p<0.05). The optimum formulation, which was calculated using the numerical and graphical methods, was determined to be 18.00% black barley flour and 1.80% olive oil.

• Korean journal of food and cookery science
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• v.31 no.2
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• pp.175-184
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• 2015

The purpose of this study was to determine the optimal mixing ratio of Agaricus blazei Murill powder and butter in the preparation of cookies. The experimental design utilized herein was based on central composite design for response surface methodology, which included 10 experimental points, including 2 replicates for Agaricus blazei Murill and butter. The physical, mechanical, and sensory properties of the test were measured, and these values were applied to the mathematical models. A canonical form and perturbation plot showed the influence of each ingredient on the final mixed product. The spread ratio increased significantly with an increase in Agaricus blazei Murill powder and butter (p<0.05). The response surface methodology was applied to evaluate the effect of Agaricus blazei Murill powder and butter on cookie moisture and color (L, a) (p<0.001). Sensory evaluation showed significant values for color (p<0.05), flavor (p<0.05), texture (p<0.05) and overall quality (p<0.01) in the predicted model. The optimum formulation by numerical and graphical methods was calculated as follows: Agaricus blazei Murill powder 3.63 g, butter 55.37 g.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.25-36
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• 2016

The goal of the SmartGrid is to maximize energy efficiency by exchanging bi-directional real-time power information with the help of ICT(Information and Communication Technology). In this paper, we propose a "JRS-MS" (Joint Routing and Scheduling for Multi-channel SmartGrid) algorithm that uses numerical modeling methods in IEEE 802.11s based STDMA multi-channel SmartGrid NAN networks. The proposed algorithm controls the amount of data transmission adaptively at the link layer and finds a high data-rate path which has the least interference between traffic flows in multi-channel SmartGrid NAN networks. The proposed algorithm improve transmission performance by enhancing network utilization. By comparing the results of performance analysis between the proposed algorithm and the JRS-SG algorithm in the previous paper, we showed that the JRS-MS algorithm can improve transmission performance by maximally utilizing given network resources when the number of flows are increasing in the multi-hop NAN wireless mesh networks.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2
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• pp.21-32
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• 1996

The performance of the energy density control algorithm for controlling a broadband noise is evaluated in a one-dimensional enclosure. To avoid noncausality problem of a control filter, which often happens in a frequency domain optimization, analyses presented in this paper are undertaken in the time domain. This approach provides the form of the causally constrained optimal controller. Numerical results are presented to predict the performance of the active noise control system, and indicate that imp개ved global attenuation of the broadband noise can be achieved by minimizing the energy density, rather than the squared pressure. It is shown that minimizing the energy density at a single location yields global attenuation results that are comparable to minimizing the potential energy. Furthermore, unlike the squared pressure control, the energy density control does not demonstrate any dependence on the error sensor location for this one-dimensional field. A practical implementation of the energy-based control algorithm is presented. Results show that the energy density control can be implemented using the two sensor technique with a tolerable margin of performance degradation.