• The Korean Journal of Pain
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• v.29 no.2
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• pp.103-109
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• 2016

Background: The location and the number of lumbar sympathetic ganglia (LSG) vary between individuals. The aim of this study was to determine the appropriate level for a lumbar sympathetic ganglion block (LSGB), corresponding to the level at which the LSG principally aggregate. Methods: Seventy-four consecutive subjects, including 31 women and 31 men, underwent LSGB either on the left (n = 31) or the right side (n = 43). The primary site of needle entry was randomly selected at the L3 or L4 vertebra. A total of less than 1 ml of radio opaque dye with 4% lidocaine was injected, taking caution not to traverse beyond the level of one vertebral body. The procedure was considered responsive when the skin temperature increased by more than $1^{\circ}C$ within 5 minutes. Results: The median responsive level was significantly different between the left (lower third of the L4 body) and right (lower margin of the L3 body) sides (P = 0.021). However, there was no significant difference in the values between men and women. The overall median responsive level was the upper third of the L4 body. The mean responsive level did not correlate with height or BMI. There were no complications on short-term follow-up. Conclusions: Selection of the primary target in the left lower third of the L4 vertebral body and the right lower margin of the L3 vertebral body may reduce the number of needle insertions and the volume of agents used in conventional or neurolytic LSGB and radiofrequency thermocoagulation.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.

• Journal of the Korean Geotechnical Society
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• v.22 no.5
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• pp.27-37
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• 2006

Cemented Sand and Gravel (C.S.G) method has become increasingly popular in Japan and throughout the world as a construction method and material. This method is favorably used for cofferdam and large dam because a quarry and aggregate plant facility can be diminished. Also, this method can reduce construction cost, work duration and destruction of environment. In this paper, a methodology for C.S.G mix design based on so-called soil mechanics approach is proposed for trapezoid-shaped dam. The methodology consists of selection of a suitable aggregate, introduction of compaction method, processing to prepare standard specimens, and determination of mix portions. Also, unconfined compressive strength tests and large triaxial compression tests are performed. From the results of the test, correlation equation among strength, elastic modulus and unit cement is proposed.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.343-350
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• 2000

In this paper, we propose the Multi-FPNN(Fuzzy Polynomial Neural Networks) model based on FNN and PNN(Polyomial Neural Networks) for optimal system identifacation. Here FNN structure is designed using fuzzy input space divided by each separated input variable, and urilized both in order to get better output performace. Each node of PNN structure based on GMDH(Group Method of Data handing) method uses two types of high-order polynomials such as linearane and quadratic, and the input of that node uses three kinds of multi-variable inputs such as linear and quadratic, and the input of that node and Genetic Algorithms(GAs) to identify both the structure and the prepocessing of parameters of a Multi-FPNN model. Here, HCM clustering method, which is carried out for data preproessing of process system, is utilized to determine the structure method, which is carried out for data preprocessing of process system, is utilized to determance index with a weighting factor is used to according to the divisions of input-output space. A aggregate performance inddex with a wegihting factor is used to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of this aggregate abjective function which it is acailable and effective to design to design and optimal Multi-FPNN model. The study is illustrated with the aid of two representative numerical examples and the aggregate performance index related to the approximation and generalization abilities of the model is evaluated and discussed.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.557-567
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• 2005

The increasing complexity of the socio-economic environments makes it less and less possible for single decision-maker to consider all relevant aspects of problem. Therefore are, many organizations employ groups in decision making. In this paper, we present a multiperson decision making method using fuzzy logic with linguistic quantifier when each of group members specifies imprecise judgments possibly both on performance evaluations of alternatives with respect to the multiperson criteria and on the criteria. Inexact or vague preferences have appeared in the decision making literatures with a view to relaxing the burdens of preference specifications imposed to the decision-makers and thus taking into account the vagueness of human judgments. Allowing for the types of imprecise judgments in the model, however, makes more difficult a clear selection of alternative(s) that a group wants to make. So, further interactions with the decision-makers may proceed to the extent to compensate for the initial comforts of preference specifications. These interaction may not however guarantee the selection of the best alternative to implement. To circumvent this deadlock situation, we present a procedure for obtaining a satisfying solution by the use of linguistic quantifier guided aggregation which implies fuzzy majority. This is an approach to combine a prescriptive decision method via a mathematical programming and a well-established approximate solution method to aggregate multiple objects.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.280-285
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• 2022

In order to ensure maximum capacity at a given frequency resource, the number of antennas must be increased. The increase in antennas means that such guaranteed channel resources can be used as an increase in channel capacity by aquiring another channel resource. In order to aggregate antennas in such a situation where there are a plurality of antennas, a problem of miniaturizing and integrating antennas must be accompanied. In this situation, in order to efficiently allocate channel resources and antenna resources in limited device resources, the problem of antenna selection and user scheduling was considered and solved together. By numerical simulation results, the proposed algorithm was proven to effectively reduce 34 % power consumption in averagewith increase in antennas.

• The Journal of Fisheries Business Administration
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• v.41 no.2
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• pp.1-23
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• 2010

This study is to evaluate member fisheries cooperatives' self-effort for managerial improvement qualitatively and quantitatively. The impaired member cooperatives' got grant from National Federations of Fisheries Cooperatives for managerial improvement for 2003~2004 with establishment of MOU. This MOU describes a self-effort of memebr cooperatives' required fulfillment items for managerial improvement. From the various level of analyses, we conclude that per capita total return or ROA has direct and positive effect on the improvement of net capital ratio or profit ratio. However, other MOU items like human resource management or an investment increment did not have a correlation with it. In addition, an aggregate financial indicator, such as ROA, seems to have a positive effect on the improvement of net capital ratio or profit ratio for the group of well restructured member cooperatives, but does not for the bad performance group. This is because the good performance has leads to the improvement of net capital ratio for the well-restructured member cooperatives since there is little chance to have additional weakness. From this study we can check the proper selection of MOU items should be based on the analysis of its effect on the managerial improvement.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2002.05a
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• pp.111-116
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• 2002

The regularity effect for printed word recognition and naming depends on ambiguities between single letters (small grain-size) and their phonemic values. As a given word is repeated and becomes more familiar, letter-aggregate size (grain-size) is predicted to increase, thereby decreasing the ambiguity between spelling pattern and phonological representation and, therefore, decreasing the regularity effect. Lexical decision and naming tasks studied the effect of repetition on the regularity effect for words. The familiarity of a word from was manipulated by presenting low and high frequency words as well as by presenting half the stimuli in mixed upper- and lowercase letters (an unfamiliar form) and half in uniform case. In lexical decision, the regularity effect was initially strong for low frequency words but became null after two presentations; in naming it was also initially strong but was merely reduced (although still substantial) after three repetitions. Mixed case words were recognized and named more slowly and tended to show stronger regularity effects. The results were consistent with the primary hypothesis that familiar word forms are read faster because they are processed at a larger grain-size, which requires fewer operations to achieve lexical selection. Results are discussed in terms of a neurobiological model of word recognition based on brain imaging studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.1976-1997
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• 2016

This paper investigates the problem of co-tier interference mitigation for dynamic small- cell networks, in which the load of each small-cell varies with the number of active associated small-cell users (SUs). Due to the fact that most small-cell base stations (SBSs) are deployed in an ad-hoc manner, the problem of reducing co-tier interference caused by dynamic loads in a distributed fashion is quite challenging. First, we propose a new distributed channel allocation method for small-cells with dynamic loads and define a dynamic interference graph. Based on this approach, we formulate the problem as a dynamic interference graph game and prove that the game is a potential game and has at least one pure strategy Nash equilibrium (NE) point. Moreover, we show that the best pure strategy NE point minimizes the expectation of the aggregate dynamic co-tier interference in the small-cell network. A distributed dynamic learning algorithm is then designed to achieve NE of the game, in which each SBS is unaware of the probability distributions of its own and other SBSs' dynamic loads. Simulation results show that the proposed approach can mitigate dynamic co-tier interference effectively and significantly outperform random channel selection.