• Smart Structures and Systems
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• v.21 no.3
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• pp.321-333
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• 2018

Simplified Dolphin Echolocation (SDE) algorithm is a recently developed meta-heuristic algorithm. This algorithm is an improved and simplified version of the Dolphin Echolocation Optimization (DEO) method, based on the baiting behavior of the dolphins. The main advantage of the SDE algorithm is that it needs no empirical parameter. In this paper, the SDE algorithm is applied for optimization of three well-studied frame structures. The designs are then compared with those of other meta-heuristic methods from the literature. Numerical results show the efficiency of the SDE algorithm and its competitive ability with other well-established meta-heuristics methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3594-3607
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• 2017

The traditional feature extraction methods such as principal component analysis (PCA) cannot obtain the local structure of the samples, and locally linear embedding (LLE) cannot obtain the global structure of the samples. However, a common drawback of existing PCA and LLE algorithm is that they cannot deal well with the sparse problem of the samples. Therefore, by integrating the globality of PCA and the locality of LLE with a sparse constraint, we developed an improved and unsupervised difference algorithm called Sparse Difference Embedding (SDE), for dimensionality reduction of high-dimensional data in small sample size problems. Significantly differing from the existing PCA and LLE algorithms, SDE seeks to find a set of perfect projections that can not only impact the locality of intraclass and maximize the globality of interclass, but can also simultaneously use the Lasso regression to obtain a sparse transformation matrix. This characteristic makes SDE more intuitive and more powerful than PCA and LLE. At last, the proposed algorithm was estimated through experiments using the Yale and AR face image databases and the USPS handwriting digital databases. The experimental results show that SDE outperforms PCA LLE and UDP attributed to its sparse discriminating characteristics, which also indicates that the SDE is an effective method for face recognition.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.5
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• pp.553-563
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• 1999

The Spatial Database Engine(SDETM) developed by Environmental Systems Research Institute, Inc. is a spatial database that employs a client-server architecture incorporated with a set of software services to perform efficient spatial operations and to manage large, shared and geographic data sets. It currently supports a wide variety of spatial search methods and spatial relationships determined dynamically. Spatial objects in the space world can be changed by either non-spatial operations or spatial operations. Conventional geographical information systems(GISs) did not manage their historical information, however, because they handle the snapshot images of spatial objects in the world. In this paper we propose a spatio-temporal data model and an algorithm for temporal relationship macro which is able to manage and retrieve the historical information of spatial objects. The proposed spatio-temporal data model and its operations can be used as a software tool for history management of time-varying objects in database without any change.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.22-24
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• 2001

A new GIS software Spatial Database Engine(SDE) has been developed to integrated with spatial database that combines conventional and spatially related data. As we known well in the traditional relation database system, the query evaluation techniques are a well-researched subject, many useful and efficient algorithms have been proposed, but in the spatial database system, it is a litter difference with the traditionally ones. Based on the Query Graph Model(QGM), we implemented our own query evaulation plan in the SDE, which can deal with the full functionality query statement SELECT-FROM-WHERE_GROUPBY-HAVING, and treat the spatial data and non-spatial data seamlessly. We proposed a novel multi way join algorithm base on nest loop that may be attractive.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.7
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• pp.82-91
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• 1997

In this paper, we analyze the dynamics of langevine competitive learning neural network based on its fokker-plank equation. From the viewpont of the stochastic differential equation (SDE), langevine competitive learning equation is one of langevine stochastic differential equation and has the diffusin equation on the topological space (.ohm., F, P) with probability measure. We derive the fokker-plank equation from the proposed algorithm and prove by introducing a infinitestimal operator for markov semigroups, that the weight vector in the particular simplex can converge to the globally optimal point under the condition of some convex or pseudo-convex performance measure function. Experimental resutls for pattern recognition of the remote sensing data indicate the superiority of langevine competitive learning neural network in comparison to the conventional competitive learning neural network.

• Smart Structures and Systems
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• v.18 no.5
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• pp.983-1004
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• 2016

Nowadays, there are two classes of methods for damage detection in structures consisting of static and dynamic. The dynamic methods are based on studying the changes in structure's dynamic characteristics. The theoretical basis of this method is that damage causes changes in dynamic characteristics of structures. The dynamic methods are divided into two categories: signal based and modal based. The modal based methods utilize the modal properties consisting of natural frequencies, modal damping and mode shapes. As the modal properties are sensitive to changes in the structure, these can be used for detecting the damages. In this study, using dynamic method and modal based approach (natural frequencies and mode shapes), the objective function is formulated. Then, detection of damages of truss structures is addressed by using Simplified Dolphin Echolocation algorithm and solving inverse optimization problem. Many scenarios are used to simulate the damages. To demonstrate the ability of the algorithm, different truss structures with several multiple elements scenarios are tested using a few runs. The influence of the two different levels of noise in the modal data for these scenarios is also considered. The last example of this article is investigated using a different mutation. This mutation obtains the exact answer with fewer loops and population by limited computational effort.

• Structural Engineering and Mechanics
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• v.39 no.6
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• pp.767-793
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• 2011

An efficient edge-based smoothed finite element method (ES-FEM) has been recently developed for solving solid mechanics problems. The ES-FEM uses triangular elements that can be generated easily for complicated domains. In this paper, the complexity study of the ES-FEM based on triangular elements is conducted in detail, which confirms the ES-FEM produces higher computational efficiency compared to the FEM. Therefore, the ES-FEM offers an excellent platform for adaptive analysis, and this paper presents an efficient adaptive procedure based on the ES-FEM. A smoothing domain based energy (SDE) error estimate is first devised making use of the features of the ES-FEM. The present error estimate differs from the conventional approaches and evaluates error based on smoothing domains used in the ES-FEM. A local refinement technique based on the Delaunay algorithm is then implemented to achieve high efficiency in the mesh refinement. In this refinement technique, each node is assigned a scaling factor to control the local nodal density, and refinement of the neighborhood of a node is accomplished simply by adjusting its scaling factor. Intensive numerical studies, including an actual engineering problem of an automobile part, show that the proposed adaptive procedure is effective and efficient in producing solutions of desired accuracy.