• Structural Engineering and Mechanics
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• v.65 no.6
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• pp.707-719
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• 2018

In this article, a new method is introduced to improve the local search capability of meta-heuristic algorithms using the projection of the path on the border of constraints. In a mathematical point of view, the Gradient Projection Method is applied through a new approach, while the imposed limitations are removed. Accordingly, the gradient vector is replaced with a new meta-heuristic based vector. Besides, the active constraint identification algorithm, and the projection method are changed into less complex approaches. As a result, if a constraint is violated by an agent, a new path will be suggested to correct the direction of the agent's movement. The presented procedure includes three main steps: (1) the identification of the active constraint, (2) the neighboring point determination, and (3) the new direction and step length. Moreover, this method can be applied to some meta-heuristic algorithms. It increases the chance of convergence in the final phase of the search process, especially when the number of the violations of the constraints increases. The method is applied jointly with the authors' newly developed meta-heuristic algorithm, entitled Star Graph. The capability of the resulted hybrid method is examined using the optimal design of truss and frame structures. Eventually, the comparison of the results with other meta-heuristics of the literature shows that the hybrid method is successful in the global as well as local search.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5A
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• pp.267-275
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• 2003

In UMTS terminal modem uses 3 step search procedure for initial cell search, which comprises 1) slot synchronization, 2) code group identification and frame synchronization, and 3) scrambling-code identification. The performance of initial cell search procedure depends on search parameters like observation time and threshold. The purpose of this paper is to get the optimal observation time and threshold of each step for minimum mean acquisition time. In this paper we induce mean detection time of each step and mean acquisition timefrom the model of 3 step search procedure using state diagram. Also we propose initial cell search algorithm which utilize window search method against initial oscillator error, and select an appropriate observation time and threshold of each step by the analysis of simulation and induced result. It is shown that the mean acquisition time in multipath fading channel can be shorter than 500ms by using the determined observation time and threshold of each step.

• Smart Structures and Systems
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• v.19 no.4
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• pp.449-461
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• 2017

One of the most recent methods of structural damage identification is using the difference between structures responses after and before damage occurrence. To do this one can formulate the damage detection problem as an inverse optimization problem where the extents of damage in each element are considered as the optimizations variables. To optimize the objective function, heuristic methods such as GA, PSO etc. are widely utilized. In this paper, inspired by animals such as bat, dolphin, oilbird, shrew etc. that use echolocation for finding food, a new and efficient method, called Echolocation Search Algorithm (ESA), is proposed to properly identify the site and extent of multiple damage cases in structural systems. Numerical results show that the proposed method can reliably determine the location and severity of multiple damage cases in structural systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.575-594
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• 2010

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with "conventional" evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polyno.mial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using four representative numerical examples such as No.n-linear function, gas furnace, NO.x emission process data, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some "conventional" fuzzy models already encountered in the literature.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.384-386
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• 2009

This paper introduces an effectively optimized Fuzzy model identification by means of complex and nonlinear system applying PSO algorithm. In other words, we use PSO(Particle Swarm Optimization) for identification of Fuzzy model structure and parameter. PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. This paper identifies the premise part parameters and the consequence structures that have many effects on Fuzzy system based on PSO. In the premise parts of the rules, we use triangular. Finally we evaluate the Fuzzy model that is widely used in the standard model of gas data and sew data.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.64-71
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• 2022

Various types of attendance management systems are being introduced in a remote working environment and research on using face recognition is in progress. To ensure accurate worker's attendance, a face recognition-based attendance management system must analyze every frame of video, but face recognition is a heavy task, the number of the task should be minimized without affecting accuracy. In this paper, we proposed a search model using time interval variation to minimize the number of face recognition task of recorded videos for attendance management system. The proposed model performs face recognition by changing the interval of the frame identification time when there is no change in the attendance status for a certain period. When a change in the face recognition status occurs, it moves in the reverse direction and performs frame checks to more accurate attendance time checking. The implementation of proposed model performed at least 4.5 times faster than all frame identification and showed at least 97% accuracy.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.289-300
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• 2003

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of "If...., then... " statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environment realizes parameter identification through a synergistic usage of genetic optimization and complex search method. The hybrid identification algorithm is carried out by combining both genetic optimization and the improved complex method in order to guarantee both global optimization and local convergence. An aggregate objective function with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. The proposed model is experimented with using two nonlinear data. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other models.er models.

• Korean Journal of Microbiology
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• v.44 no.4
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• pp.333-338
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• 2008

Computational analysis of partial rpoB gene sequence (777 bp) was done in this study to identify B. anthracis and its closely related species B. cereus and B. thuringiensis. Sequence data including 17 B. anthracis strains, 9 B. cereus strains, and 7 B. thuringiensis strains were obtained by searching databases. Those sequences were aligned and used for other computational analysis. B. anthracis strains were identificated by in silico restriction enzyme digestion. B. cereus and B. thuringiensis were not segregated by this method. Those sequencing and BLAST search were required to distinguish the two. In actual identification tests, B. anthracis strains could be identified by PCR-RFLP, and B. cereus and B. thuringiensis strains were distinguished by BLAST search with reliable e-value. In this study fast and accurate method for identifying three Bacillus species, and flow chart of identification were developed.

• Smart Structures and Systems
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• v.25 no.3
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• pp.345-368
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• 2020

The present work proposes a self-controlled multi-stage optimization method for damage identification of structures utilizing standard particle swarm optimization (PSO) algorithm. Damage identification problem is formulated as an inverse optimization problem where damage severity in each element of the structure is considered as optimization variables. An efficient objective function is formed using the first few frequencies and mode shapes of the structure. This objective function is minimized by a self-controlled multi-stage strategy to identify and quantify the damage extent of the structural members. In the first stage, standard PSO is utilized to get an initial solution to the problem. Subsequently, the algorithm identifies the most damage-prone elements of the structure using an adaptable threshold value of damage severity. These identified elements are included in the search space of the standard PSO at the next stage. Thus, the algorithm reduces the dimension of the search space and subsequently increases the accuracy of damage prediction with a considerable reduction in computational cost. The efficiency of the proposed method is investigated and compared with available results through three numerical examples considering both with and without noise. The obtained results demonstrate the accuracy of the present method can accurately estimate the location and severity of multi-damage cases in the structural systems with less computational cost.

• Journal of the Korean Wood Science and Technology
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• v.18 no.2
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• pp.49-66
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• 1990

In order to identify an unknown wood sample native to Korea. the softwood databases(KSWCHUN; Korean SoftWood CHUN) and the hardwood databases(KHWCHUN; Korean HardWood CHUN) had been built. and the new computer searching programs(IDINEX; IDentification INformation EXpress) has been written in Turbo Pascal(V.5.0) and in Macro Assembly(V.5.0). The characters of the data were based on the 74 features of softwood and on the 148 features of hardwood which are a part of new "IAWA list of microscopic features for hardwood identification" published in 1989. For the purpose of this investigation the wood anatomical nature of 25 species of softwood(13 genera of 5 families) and of 112 species of hardwood(57 genera of 31 families) were observed under a scanning electron microscope and light microscope. and a lot of literature used. The IDINEX programs are based on edge-punched card keys. with several improvements. The maximum number of features in the IDINEX is 229. but that is fixed for a given database. Large numbers of taxa are handled efficiently and new taxa easily added. A search may be based on sequence numbers of features. Comparisons are made sequentially by feature and taxon using the entire suite of features specified to produce the list of possible matching taxa. The results are followings. (1) The databases of Korean wood and the searching programs(IDINEX) had been built. (2) The databases of Korean wood could be an information to search an unknown wood. (3) The databases would be valuable. for the new features, which were not mentioned in Korean wood up to the present. were observed in details. (4) The ultrastructures of the cell walls(warty layer) and crystals observed under a scanning electron microscope will be helpful to search an unknown wood in particular. (5) The searching process is more quick and accurate than the others. 6) We can obtain the information on the differences of a species from the other and search an unknown wood using probability. in IDINEX, (7) The IDINEX will be utilized to identify and classify an animal life, vegetable world, mineral kingdom, and so on.