• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.415-424
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• 2004

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

• Journal of Technology Innovation
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• v.8 no.1
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• pp.73-94
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• 2000

The main objective of this research is to examine the spillover effects of technological knowledge from IT industry to other industrial sectors and, based on empirical findings, to draw policy implications and suggest policy directions. To this end, we divide IT industry into IT equipment and IT service, assuming that these two sub-sectors are considerably different each other in terms of technology knowledge flow. Other industries are classified into 17 different sectors based on the KSIC of 1990. As the proxy measure of technological knowledge, the notion of R&D stock is employed. The Input/output(I/O) Table is used to define the inter-industrial flow pattern and to draw the knowledge flow matrix. As the research methodology, cost function model is employed to gauge the spillover effects of technological knowledge of IT industry. Based on the results of analysis, it is found that the economic impact of technology diffusion also exhibits a different pattern between IT equipment and IT service. The diffusion of IT equipment tends to show labor-substitution effect whereas IT service displays labor-creation effect. This fact should be considered in devising industry, education, and labor policy. The expectations from this research are as follows. First, the sectoral pattern, difference between IT equipment and service in particular, identified from this research may shed light on the sector-specific policy direction. It is emphasized that a sector-specific approach, rather than an aggregate approach, is relevant for formulating IT policy. Second, it is expected that the importance of technology diffusion programs and policy measures are recognized among policy makers in IT industry.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1769-1777
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• 2018

This paper proposes a method for optimal placement of Phasor Measurement Units (PMUs) considering system configuration and its attributes during the planning phase of PMU deployment. Each bus of the system is assessed on four diverse attributes; namely, redundancy of measurements, rotor angle and frequency monitoring of generator buses, reactive power deficiency, and maximum loading limit under transmission line outage contingency, and a consolidated 'degree of criticality' is determined using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The major contribution of the proposed work is the development of modified objective function which incorporates values of the degree of criticality of buses. The problem is formulated as maximization of the aggregate degree of criticality of the system. The resultant PMU configuration extends complete observability of the system and majority of the PMUs are located on critical buses. As budgetary restrictions on utilities may not allow installation PMUs even at optimal locations in a single phase, multi-horizon deployment of PMUs is also addressed. The proposed approach is tested on IEEE 14-bus, IEEE 30-bus, New England (NE) 39-bus, IEEE 57-bus and IEEE 118-bus systems and compared with some existing methods.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.1
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• pp.8-12
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• 2005

Response in a modified reciprocal recurrent selection scheme for egg production was evaluated considering variable family sizes and genetic correlation between purebred and crossbred half sisters. The criteria of selection of purebred breeders included pullet's own performance, purebred full and half sisters and crossbred half sister's performance. Heritability of egg production of crossbreds (aggregate genotype) and purebred's was assumed to be 0.2 and genetic correlation between purebred and crossbred half sisters ($r_{pc}$) as 0.1, 0.2, 0.3, 0.4, 0.5, 1.0, -0.1, -0.2, -0.3, -0.4, -0.5 and -1.0. Number of dams per sire to produce purebred and crossbred progenies assumed to be 5, 6, 7, 8, while number of purebred female progeny ($N_p$) and crossbred progeny ($N_c$) per dam were considered to be 3, 4, 5 and 6 in each case. Considering phenotypic variance as unity, selection indices were constructed for different combinations of dams and progeny for each value of $r_{pc}$. Following selection index theory, response in crossbred and purebred for egg production was computed. Results indicated that response in crossbreds depended mainly on crossbred family size and also on magnitude of$r_{pc}$ irrespective of its direction, and response was greater with large crossbred family size than the purebred families. Correlated response in purebreds depends both on magnitude and direction of $r_{pc}$ and was expected to be greater with large purebred family size only. Inclusion of purebred information increased the accuracy of selection for crossbred response for higher magnitude of$r_{pc}$ irrespective of its direction. Present results indicate that desirable response in both crossbred and purebred performance is a function of $r_{pc}$ and family sizes. The ratio of crossbred and purebred family sizes can be optimized depending on the objective of improving the performance of crossbreds and/or of purebreds.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.81-86
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informally speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality Granulation of information with the aid of Hard C-Means (HCM) clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method (LSM). An aggregate objective function with a weighting factor is also used in order to achieve a balance between performance of the fuzzy model. The proposed model is evaluated with using a numerical example and is contrasted with the performance of conventional fuzzy models in the literature.

• Journal of the Korean Recycled Construction Resources Institute
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• v.4 no.2
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• pp.165-171
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• 2016

The objective of this study is to evaluate the compressive strength development and ecological characteristics of mortars using expanded vermiculite absorbing bacteria as a fundamental investigation to develop precast eco-concrete products. For bacterial growth under the high-alkalinity and high-dried environments within hardened mortars and for creating plant growth function to mortars, Bacillus alcalophilus and Rhodoblastus acidophilus were separated and cultured. The cultured bacteria were absorbed into expanded vermiculite selected for bacteria shelter. The expanded vermiculite absorbing bacteria was then added into mortar mixture as a volumetric replacement of fine aggregate. Test results showed that the developed technology is very effective in enhancing the plant growth onto the hardened mortars and reducing the COD and T-N concentration in raw water. The optimum replacement level of expanded vermiculite absorbing bacteria can be recommended to be less than 10% considering the compressive strength development and cost of mortars along with the ecological effectiveness.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.183-193
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• 2009

Both On-Line Analytical Processing (OLAF) data cubes and Statistical Databases (SDBs) deal with multidimensional data sets. and both are concerned with statistical summarizations over the dimensions of the data sets. However, there is a distinction between the two that can be made. While SDBs are usually derived from other base data, OLAF data cubes often represent directly the base data. In other word, the base data of SDBs are the macro-data, whereas the core cubiod data in OLAF data cubes are the micro-data. The base table in OLAF is used to populate the data cube with values of the measure attribute, and each record in the base tables is used to populate a cell of the core cuboid. The fact that OLAF data cubes mostly represent the micro-data may make some records be absent in the base table. Some cells of the core cuboid remain empty, if corresponding records are absent in the base table. Wang and others proposed a method for securing OLAF data cubes against privacy breaches. They assert that the proposed method does not depend on specific types of aggregation functions. In this paper, however, it is found that their assertion on aggregate functions is wrong whenever any cell of the core cuboid remains empty. The objective of this study is to design an inference control process in OLAF data cubes which rectifying Wang's error.