• Journal of Computing Science and Engineering
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• v.2 no.1
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• pp.44-73
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• 2008

There is general agreement that the problem of data semantics has to be addressed for XML data to become machine-processable. This problem can be tackled by defining a semantic mapping between an XML schema and an ontology. Unfortunately, creating such mappings is a tedious, time-consuming, and error-prone task. To alleviate this problem, we present a solution that heuristically discovers semantic mappings between XML schemas and ontologies. The solution takes as input an initial set of simple correspondences between element attributes in an XML schema and class attributes in an ontology, and then generates a set of mapping formulas. Once such a mapping is created, it is important and necessary to maintain the consistency of the mapping when the associated XML schema and ontology evolve. In this paper, we first offer a mapping formalism to represent semantic mappings. Second, we present our heuristic mapping discovery algorithm. Third, we show through an empirical study that considerable effort can be saved when discovering complex mappings by using our prototype tool. Finally, we propose a mapping maintenance plan dealing with schema evolution. Our study provides a set of effective solutions for building sustainable semantic integration systems for XML data.

• Journal of Communications and Networks
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• v.11 no.2
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• pp.134-147
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• 2009

Tracking and exploiting instantaneous spectrum opportunities are fundamental challenges in opportunistic spectrum access (OSA) in presence of the bursty traffic of primary users and the limited spectrum sensing capability of secondary users. In order to take advantage of the history of spectrum sensing and access decisions, a sequential decision framework is widely used to design optimal policies. However, many existing schemes, based on a partially observed Markov decision process (POMDP) framework, reveal that optimal policies are non-stationary in nature which renders them difficult to calculate and implement. Therefore, this work pursues stationary OSA policies, which are thereby efficient yet low-complexity, while still incorporating many practical factors, such as spectrum sensing errors and a priori unknown statistical spectrum knowledge. First, with an approximation on channel evolution, OSA is formulated in a multi-armed bandit (MAB) framework. As a result, the optimal policy is specified by the wellknown Gittins index rule, where the channel with the largest Gittins index is always selected. Then, closed-form formulas are derived for the Gittins indices with tunable approximation, and the design of a reinforcement learning algorithm is presented for calculating the Gittins indices, depending on whether the Markovian channel parameters are available a priori or not. Finally, the superiority of the scheme is presented via extensive experiments compared to other existing schemes in terms of the quality of policies and optimality.

• Computers and Concrete
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• v.8 no.2
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• pp.163-176
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• 2011

By virtue of chord-length density function from the field of statistical physics, this paper introduced a quantitative approach to estimate the distribution of cement paste thickness between aggregates in concrete. Dynamics mixing method based on molecular dynamics was employed to generate one model structure, then image analysis algorithm was used to obtain the distribution of thickness of cement paste in model structure for the purpose of verification. By comparison of probability density curves and cumulative probability curves of the cement paste thickness among neighboring aggregates, it is found that the theoretical results are consistent with the simulation. Furthermore, for the model mortar and concrete mixtures with practical volume fraction of Fuller-type aggregate, this analytical formula was employed to predict the influence of aggregate volume fraction and aggregate fineness. And evolution of its mean values were also investigated with the variation of volume fraction of aggregate as well as the fineness of aggregates in model mortars and concretes.

• Structural Engineering and Mechanics
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• v.16 no.6
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• pp.713-730
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• 2003

This paper contains the results of the study on the development of fracture and crack propagation in quasi-brittle materials, such as concrete or rocks, using the Discrete Element Method (DEM). A new discrete element numerical model is proposed as the basis for analyzing the inelastic evolution and growth of cracks up to the point of gross material failure. The model is expected to predict the fracture behavior for the quasi-brittle material structure using the elementary aggregate level, the interaction between aggregate materials, and bond cementation. The algorithms generate normal and shear forces between two interfacing blocks and contains two kinds of contact logic, one for connected blocks and the other one for blocks that are not directly connected. The Mohr-Coulomb theory has been used for the fracture limit. In this algorithm the particles are moving based on the connected block logic until the forces increase up to the fracture limit. After passing the limit, the particles are governed by the discrete block logic. In setting up a discrete polygon element model, two dimensional polygons are used to investigate the response of an assembly of different shapes, sizes, and orientations with blocks subjected to simple applied loads. Several examples involving assemblies of particles are presented to show the behavior of the fracture and the failure process.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.4
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• pp.9-19
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• 2014

The objective of this study was to evaluate of the Tank model in simulating runoff discharge from rural watershed in comparison to the SWAT (Soil and Water Assessment Tool) model. The model parameters of SWAT was calibrated by the shuffled complex evolution-university Arizona (SCE-UA) method while Tank model was calibrated by genetic algorithm (GA) and validated. Four dam watersheds were selected as the study areas. Hydrological data of the Water Management Information System (WAMIS) and geological data were used as an input data for the model simulation. Runoff data were used for the model calibration and validation. The determination coefficient ($R^2$), root mean square error (RMSE), Nash-Sutcliffe efficiency index (NSE) were used to evaluate the model performances. The result indicated that both SWAT model and Tank model simulated runoff reasonably during calibration and validation period. For annual runoff, the Tank model tended to overestimate, especially for small runoff (< 0.2 mm) whereas SWAT model underestimate runoff as compared to observed data. The statistics indicated that the Tank model simulated runoff more accurately than the SWAT model. Therefore the Tank model could be a good tool for runoff simulation considering its ease of use.

• Proceedings of the KSME Conference
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• 2001.06e
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• pp.474-481
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• 2001

This paper addresses a numerical method for predicting transient temperature distributions in the wall of a curved pipe subjected to internally thermal stratification flow. A simple and convenient numerical method of treating the unsteady conjugate heat transfer in the non-orthogonal coordinate systems is presented. The proposed method is implemented in a finite volume thermal-hydraulic computer code based on a cell-centered, non-staggered grid arrangement, the SIMPLEC algorithm, a higher-order bounded convection scheme, and the modified version of momentum interpolation method. Calculations are performed for the transient evolution of thermal stratification in two curved pipes, where the one has thick wall and the other has so thin wall that its presence can be negligible in the heat transfer analysis. The predicted results show that the thermally stratified flow and transient conjugate heat transfer in a curved pipe with a finite wall thickness can be satisfactorily analyzed by the present numerical method, and that the neglect of wall thickness in the prediction of pipe wall temperature distributions can provide unacceptably distorted results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.679-682
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• 2003

In this paper, we propose a Genetic Algorithm (GA) using symbiotic evolutionary viruses. Our GA is based on both the building block hypothesis and the virus theory of evolution. The proposed GA aims to control a destruction of building blocks by discovering, keeping, and propagating of building blocks based on virus operation. Concretely, we prepare the group of individuals and the group of viruses. In our GA, the group of individuals searches solutions and the group of viruses searches building blocks. These searches done based on the symbiotic relation of both groups. Also, our GA has two types of virus evaluation techniques. One is that each virus is evaluated by the difference of the fitness of an individual between before and after infection of virus. Another is that all viruses aye evaluated by the difference of the fitness of an individual between before and after infection of all viruses. Furthermore, we applied the proposed GA to the minimum value search problem of a test function which has some local solutions far from the optimal solution. And, we discuss a difference of behaviors of the proposed GA based on each virus evaluation techniques.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.3
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• pp.248-254
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• 2009

The worn crater wear geometry of coated tools after machining has been configured by using Confocal Laser Scanning Microscopy(CLSM) and the Wavelet-based filtering technique. The CLSM can be well suited to construct the three-dimensional crater wear on the rake surfaces of coated tips. However, The raw heightness data of HEI(height encoded image) acquired by CLSM must be filtered due to the electronic and imaging noise occurring in constructing the crater image. So the Wavelet-based filtering algorithm is necessary to denoise the shape features in a micro scales so as to realize accurate crater wear topography analysis. The crater wear patterns filtered enable us to predict the crater wear shape in order to study the tool wear evolution. The study shows that the technique by combining the CLSM and Wavelet-based filtering is an excellent one to obtain the geometries of worn tool rake surfaces over a wide range of surface resolution in a micro scale.

• Proceedings of the KIEE Conference
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• 2007.04c
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• pp.107-109
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• 2007

Pancake windings have merits that are easier to make high field magnets and replace windings when an accident happens and windings are aged. The current of the whole magnet consisted of windings connected in series was limited by the minimum current of the top and bottom pancake windings where maximum perpendicular magnetic field was applied. This paper propose a optimal design of a HTS magnet excited by a single source, where evolution strategy was adopted for optimal design algorithm. A magnet consisted of 8 BSCCO pancakes was chosen to prove the effectiveness of this optimal design method. Magnetic field at the center of a magnet was chosen as the object function and it was used maximized. Results of the optimal design shows that the increment of the number of turns of the pancake winding make the magnetic field of the center of the magnet increase but the current of the winding decrease for the larger perpendicular magnetic field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5489-5511
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• 2019

The performance of evolutionary algorithms can be seriously weakened when constraints limit the feasible region of the search space. In this paper we present a constrained multi-objective optimization algorithm based on adaptive ε-truncation (ε-T-CMOA) to further improve distribution and convergence of the obtained solutions. First of all, as a novel constraint handling technique, ε-truncation technique keeps an effective balance between feasible solutions and infeasible solutions by permitting some excellent infeasible solutions with good objective value and low constraint violation to take part in the evolution, so diversity is improved, and convergence is also coordinated. Next, an exponential variation is introduced after differential mutation and crossover to boost the local exploitation ability. At last, the improved crowding density method only selects some Pareto solutions and near solutions to join in calculation, thus it can evaluate the distribution more accurately. The comparative results with other state-of-the-art algorithms show that ε-T-CMOA is more diverse than the other algorithms and it gains better in terms of convergence in some extent.