• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.39-48
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• 2023

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

• Journal of Electrical Engineering and Technology
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• v.11 no.5
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• pp.1404-1411
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• 2016

• Proceedings of the KIEE Conference
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• 2008.04c
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• pp.44-46
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• 2008

This paper Proposes a new couple particle swarm optimization (CPSO) for multimodal functions. In this method, main particles are generated uniformly using Faure-sequences, and move accordingly to cognition only model. If any main particle detects the movement direction which has local optimum, this particle would create a new particle beside itself and make a couple. After that, all couples move accordingly to conventional particle swarm optimization (PSO) model. If these couples tend toward the same local optimum, only the best couple would be kept and the others would be eliminated. We had applied this method to some analytic multimodal functions and successfully locate all local optima.

• Speech Sciences
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• v.14 no.1
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• pp.163-174
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• 2007

All observation has uncertainty due to noise or channel characteristics. This uncertainty should be counted in the modeling of observation. In this paper we propose a modified optimization object function of a GMM training considering inexact observation. The object function is modified by introducing the concept of observation confidence as a weighting factor of probabilities. The optimization of the proposed criterion is solved using a common EM algorithm. To verify the proposed method we apply it to the speaker recognition domain. The experimental results of text-independent speaker identification with VidTimit DB show that the error rate is reduced from 14.8% to 11.7% by the modified GMM training.

• Proceedings of the Korean Information Science Society Conference
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• 2010.11a
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• pp.147-148
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• 2010

최근 IT 산업 전반에 걸쳐 모바일에 대한 중요도가 높아짐에 따라 인터넷 브라우저의 성능이 중요하게 되었다. 자바스크립트 언어의 수행은 인터넷 브라우저의 사용에 있어 상당히 비중이 높다. 이 논문에서는 자바스크립트 언어를 수행하는 엔진 중 하나인 TraceMonkey엔진이 트레이스를 하는 과정에서 생기는 오버헤드를 줄이는 최적화를 구현, 적용하고 이를 실험하여 평가한다.

• ETRI Journal
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• v.33 no.1
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• pp.39-49
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• 2011

A new structure learning approach for Bayesian networks based on asexual reproduction optimization (ARO) is proposed in this paper. ARO can be considered an evolutionary-based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter, the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem: This leads to the fitter individual. The convergence measure of ARO is analyzed. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulations. Results of simulations show that ARO outperforms genetic algorithm (GA) because ARO results in a good structure and fast convergence rate in comparison with GA.

• Journal of Power Electronics
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• v.9 no.6
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• pp.919-928
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• 2009

Microgrids (MGs) are typically comprised of distributed generators (DGs) including renewable energy sources (RESs), storage devices and controllable loads, which can operate in either interconnected or isolated mode from the main distribution grid. This paper introduces a novel dynamic programming (DP) approach to MG optimization which takes into consideration the coordination of energy supply in terms of heat and electricity. The DP method has been applied successfully to several cases in power system operations. In this paper, a special emphasis is placed on the uncontrollability of RESs, the constraints of DGs, and the application of demand response (DR) programs such as directed load control (DLC), interruptible/curtaillable (I/C) service, and/or demand-side bidding (DSB) in the deregulated market. Finally, in order to illustrate the optimization results, this approach is applied to a couple of examples of MGs in a certain configuration. The results also show the maximum profit that can be achieved.

• Journal of Korean Society for Quality Management
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• v.9 no.2
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• pp.31-36
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• 1981

This paper deals with the reliability optimization of parallel - in - series system subject to several linear constraints. The model of nonlinear constrained optimization is transformed to a saddle point problem by using Lagrange multipliers. Then Newton - Raphson method is used to solve the resulting problem and these step - by - step solution procedures are programmed in Basic Level II of micro - computer TRS-80. An example which has two linear constraints is solved and the results are analyzed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.35-43
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• 2001

In this paper, we propose a design method for GBSB (generalized brain-state-in-a-box) based associative memories. Based on the theoretical investigation about the properties of GBSB, we parameterize the solution space utilizing the limited number of parameters sufficient to represent the solution space and appropriate to be searched. Next we formulate the problem of finding a GBSB that can store the given pattern as stable states in the form of constrained optimization problems. Finally, we transform the constrained optimization problem into a SDP(semidefinite program), which can be solved by recently developed interior point methods. The applicability of the proposed method is illustrated via design examples.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.3B no.1
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• pp.23-31
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• 2003

A novel 3D shape optimization algorithm is presented for electromagnetic devices carry-ing eddy current. The algorithm integrates the 3D finite element performance analysis and the steepest descent method with design sensitivity and mesh relocation method. For the design sensitivity formula, the adjoint variable vector is defined in complex form based on the 3D finite element method for eddy current problems. A new 3D mesh relocation method is also proposed using the deformation theory of the elastic body under stress to renew the mesh as the shape changes. The design sensitivity f3r the sur-face nodal points is also systematically converted into that for the design variables for the parameterized optimization application. The proposed algorithm is applied to the optimum design of the tank shield model of the transformer and the effectiveness is proved.