• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.329-337
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• 2017

This paper proposes a search method that explores parameters C and σ values of support vector machines (SVM) to improve performance while maintaining search accuracy. A traditional grid search method requires tremendous computational times because it searches all available combinations of C and σ values to find optimal combinations which provide the best performance of SVM. To address this issue, this paper proposes a deep search method that reduces computational time. In the first stage, it divides C-σ- accurate metrics into four regions, searches a median value of each region, and then selects a point of the highest accurate value as a start point. In the second stage, the selected start points are re-divided into four regions, and then the highest accurate point is assigned as a new search point. In the third stage, after eight points near the search point. are explored and the highest accurate value is assigned as a new search point, corresponding points are divided into four parts and it calculates an accurate value. In the last stage, it is continued until an accurate metric value is the highest compared to the neighborhood point values. If it is not satisfied, it is repeated from the second stage with the input level value. Experimental results using normal and defect bearings show that the proposed deep search algorithm outperforms the conventional algorithms in terms of performance and search time.

• Interaction and multiscale mechanics
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• v.5 no.3
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• pp.211-228
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• 2012

This paper reports the development of a generalized inverse analysis formulation for the parameter estimation of four-parameter Burger model. The analysis is carried out by formulating the problem as a mathematical programming formulation in terms of identification of the design vector, the objective function and the design constraints. Thereafter, the formulated constrained nonlinear multivariable problem is solved with the aid of fmincon: an in-built constrained optimization solver module available in MatLab. In order to gain experience, a synthetic case-study is considered wherein key issues such as the determination and setting up of variable bounds, global optimality of the solution and minimum number of data-points required for prediction of parameters is addressed. The results reveal that the developed technique is quite efficient in predicting the model parameters. The best result is obtained when the design variables are subjected to a lower bound without any upper bound. Global optimality of the solution is achieved using the developed technique. A minimum of 4-5 randomly selected data-points are required to achieve the optimal solution. The above technique has also been adopted for real-time settlement of four oil refineries with encouraging results.

• Smart Structures and Systems
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• v.18 no.6
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• pp.1233-1250
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• 2016

Cracks in plate-like structures are some of the main reasons for destruction of the entire structure. In this study, a novel two-stage methodology is proposed for damage detection of flexural plates using an optimized artificial neural network. In the first stage, location of damages in plates is investigated using curvature-moment and curvature-moment derivative concepts. After detecting the damaged areas, the equations for damage severity detection are solved via Bat Algorithm (BA). In the second stage, in order to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, multiple damage location assurance criterion index based on the frequency change vector of structures are evaluated using properly trained cascade feed-forward neural network (CFNN) as a surrogate model. In order to achieve the most generalized neural network as a surrogate model, its structure is optimized using binary version of BA. To validate this proposed solution method, two examples are presented. The results indicate that after determining the damage location based on curvature-moment derivative concept, the proposed solution method for damage severity detection leads to significant reduction of computational time compared with direct finite element method. Furthermore, integrating BA with the efficient approximation mechanism of finite element model, maintains the acceptable accuracy of damage severity detection.

• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.315-322
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• 2009

Penetration and installation of a new dynamic technology known as Flexible AC Transmission Systems (FACTS) in a practical and dynamic network requires and force expert engineer to develop robust and flexible strategy for planning and control. Unified Power Flow Controller (UPFC) is one of the recent and effective FACTS devices designed for multi control operation to enhance the power system security. This paper presents a dynamic strategy based on Particle Swarm Optimization (PSO) for optimal parameters setting of UPFC to enhance the system loadability. Firstly, we perform a multi power flow analysis with load incrementation to construct a global database to determine the initial efficient bounds associated to active power and reactive power target vector. Secondly a PSO technique applied to search the new parameters setting of the UPFC within the initial new active power and reactive power target bounds. The proposed approach is implemented with Matlab program and verified with IEEE 30-Bus test network. The results show that the proposed approach can converge to the near optimum solution with accuracy, and confirm that flexible multi-control of this device coordinated with efficient location enhance the system security of power system by eliminating the overloaded lines and the bus voltage violation.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.85-93
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• 2011

Previous existing vocabulary recognition programs calculate general vector values from a database, so they can not process phonemes that form during a search. And because they can not create a model for phoneme data, the accuracy of the Gaussian model can not secure. Therefore, in this paper, we recommend use of the Bhattacharyya distance measurement method based on the features of the phoneme-thus allowing us to improve the recognition rate by picking up accurate phonemes and minimizing recognition of similar and erroneous phonemes. We test the Gaussian model optimization through share continuous probability distribution, and we confirm the heighten recognition rate. The Bhattacharyya distance measurement method suggest in this paper reflect an average 1.9% improvement in performance compare to previous methods, and it has average 2.9% improvement based on reliability in recognition rate.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1945-1954
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• 2017

In recent years, several LCL filter design methods for different converter topologies have been published, many of which use analytical expressions to calculate the ideal converter AC voltage harmonic spectrum. This paper presents the LCL filter design methodology but the focus is on presentation and validation of the non-iterative filter design method for a grid-connected three-phase two-level PWM-VSC. The developed method can be adapted for different converter topologies and PWM algorithms. Furthermore, as a starting point for the design procedure, only the range of PWM carrier frequencies is required instead of an exact value. System nonlinearities, usually omitted from analysis have a significant influence on VSC AC voltage harmonic spectrum. In order to achieve better accuracy of the proposed procedure, the system nonlinear model is incorporated into the method. Optimal filter parameters are determined using the novel cost function based on higher frequency losses of the filter. An example of LCL filter design for a 40 kVA grid-connected PWM-VSC has been presented. Obtained results have been used to construct the corresponding laboratory setup and measurements have been performed to verify the proposed method.

• Journal of Microbiology and Biotechnology
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• v.21 no.8
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• pp.854-857
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• 2011

POMT7, which is an O-methyltransferase from poplar, transfers a methyl group to several flavonoids that contain a 7-hydroxyl group. POMT7 has been shown to have a higher affinity toward quercetin, and the reaction product rhamnetin has been shown to inhibit the formation of beta-amyloid. Thus, rhamnetin holds great promise for use in therapeutic applications; however, methods for mass production of this compound are not currently available. In this study, quercetin was biotransformed into rhamnetin using Escherichia coli expressing POMT7, with the goal of developing an approach for mass production of rhamnetin. In order to maximize the production of rhamnetin, POMT7 was subcloned into four different E. coli expression vectors, each of which was maintained in E. coli with a different copy number, and the best expression vector was selected. In addition, the S-adenosylmethionine biosynthesis pathway was engineered for optimal cofactor production. Through the combination of optimized POMT7 expression and cofactor production, the production of rhamnetin was increased up to 111 mg/l, which is approximately 2-fold higher compared with the E. coli strain containing only POMT7.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.440-448
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• 2006

Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantization, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve more efficient genetic process. Every offspring is transformed into an isomorphic neural network with more desirable shape for genetic search. This helps genes with strong epistasis to stay close together in the chromosome. Experimental results showed considerable improvement over previous results.

• Archives of Pharmacal Research
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• v.29 no.9
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• pp.795-799
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• 2006

Peptides, although high efficacy and specificity in their physiological function, usually have low therapeutical activities due to their poor bioavailability when administrated orally. Nanoparticles have been regarded as a useful vector for targeted drug delivery system because they can protect drug from being degraded quickly and pass the gastrointestinal barriers. Here we described a novel oral N-trimethyl chitosan nanoparticles formulation containing thymopentin (Tp5-TMC-NP). N-trimethyl chitosan (TMC) was synthesized and then used to prepare Tp5-TMC-NP by ionotropic gelation. A three-factor, five-level CCD (Central Composite Design) design was used in the optimization procedure, with HPLC as the analyzing method. The resulting Tp5-TMC-NP had a regular spherical surface and a narrow particle size range with a mean diameter of 110.6 nm. The average entrapment efficiency was 78.8%. The lyophilized Tp5-TMC-NP formulation was stable in $4^{\circ}C\;or\;-20^{\circ}C$ after storage of 3 months without obvious changes in morphology, particle size, pH and entrapment ratio. The results of the flow cytometer determination showed that the ratio of $CD4^+/CD8^+$ of Wistar female rat given Tp5-TMC-NP (ig) was 2.59 time that of the group given Tp5 (ig).

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.3
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• pp.119-127
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• 2020

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.