• Journal of Applied Biological Chemistry
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• v.63 no.4
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• pp.421-428
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• 2020

This study investigated a mixture of Astragalus membranaceus (AM) and Lithospermum erythrorhizon (LE) extracts (ALM16), exerts anti-inflammatory effects in lipopolysaccharide (LPS)-induced RAW264.7 macrophage cells, and its underlying mechanism. ALM16 was prepared by mixing AM and LE extracts in a ratio of 7:3 (w/w). Cytotoxicity of ALM16 in RAW264.7 cells was not shown up to 200 ㎍/mL of ALM16. The results of this study showed that ALM16 does-dependently inhibits the production of nitric oxide, prostaglandin E2 and pro-inflammatory cytokines (interleukin-1β, interleukin-6, and tumor necrosis factor-α) in LPS-induced RAW264.7 cells. ALM16 not only markedly reduced the protein expression levels of inducible nitric oxide synthase and cyclooxygenase-2 (COX-2) in LPS-stimulated RAW264.7 cells, but also inhibited the nuclear translocation and DNA-binding activity of nuclear factor-kappa B (NF-κB). In addition, ALM16 specifically inhibited the phosphorylation of c-Jun N-terminal kinase and extracellular signal-regulated kinases in LPS-stimulated RAW264.7 cells. In conclusion, these results suggest that ALM16 may exert anti-inflammatory effect by modulating mitogen-activated protein kinase and NF-κB signaling pathways.

• Journal of Biomedical Engineering Research
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• v.36 no.1
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• pp.22-30
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• 2015

Melanoma is originated from the melanocyte producing the melanin which determines the complexion, and it has the highest mortality among skin cancers. Acral lentiginous melanoma(ALM) arises from extremities such as hands, feet or fingernails. Since the appearance of ALM is different from melanoma on the body, conventional auto diagnosis systems for melanoma is inappropriate to detect ALM. Therefore, ALM is typically difficult to distinguish from general nevus, resulting in delayed diagnosis and bad prognosis. In this paper, we firstly introduce a determination method for ALM by dermatologists and propose a method to rotate dermoscopic images automatically as a pre-processing for facilitating the easy determination of ALM and to select the optimal value of the Gaussian differentiation filter parameter which is significant for precise pattern extraction using the scale space analysis. From experimental results, it is shown that there exists the consistency between empirical values of the Gaussian differential filter parameter and optimal values derived from the scale space analysis to distinguish nevus and ALM.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.10
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• pp.47-57
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive teaming mechanism-fuzzy neural network(ALM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $^i{_d}$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.21
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• pp.9555-9556
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• 2014

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.1
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• pp.197-205
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• 1992

For the optimal design of composite breakwaters, a computer program PROCOBRA is developed using the combined ALM-BFGS algorithm. A model formulation for the section design optimization problem of composite breakwaters is proposed where a concept of subsectional weighting factors is introduced in the objective function. Usability of the program is verified through a numerical example. From the study, it is found that the ALM-BFGS method is reliable and can be effectively applied for the design optimization of coastal structures. Compared with conventional design process, it is proved that the economical design of composite breakwaters is possible.

• Journal of the korean Society of Automotive Engineers
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• v.11 no.2
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• pp.24-33
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• 1989

본 고에서는 이러한 추세에 따라서, 보다 효율적인 optimization program에 대해서 소개하고자 한다. 사용한 최적화 알고리즘은 ALM(augmented lagrange multiplier) 방법을 적용해서 구속조건이 있는 문제를 구속조건이 없는 문제로 변환한 후, self-scaling BFGS(broydon-flecher-goldfarb-schanno)를 적용한다. BFGS의 각 descent 방향에서의 step 길이는, sequential search로 unimodal point를 구해서, golden section 방법으로 refine을 한후, cubic approximation을 적용해서 구한다.

• Journal of Teachers' Pension
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• v.5
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• pp.97-133
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• 2020

전략적 자산배분에서 가장 중요한 것은 목표를 결정하는 것이다. 여기서 목표는 자산운용에서 발생하는 향후 포트폴리오의 예상되는 분포와 이에 영향을 받는 조직의 기대효용을 극대화하는 것으로 정의하는 것이 과학적이고 학술적인 방식이다. 실무는 대체로 이러한 방식과 다르다. 예를 들어 기금운용평가의 가이드라인에서는 목표 수익률에 초점을 두고 있다. 특히 기금운용평가에서는 ALM 기반의 목표 수익률 산출을 제안한다. 하지만, 비현실적인 목표 수익률의 산출과 그 의미의 모호성으로 많은 기금들은 이를 적용하지 않고 있다. 본 연구는 이러한 ALM 기반의 목표수익률 설정 및 자산배분의 문제점을 확인하고, ALM 기반 자산배분에 대한 두 가지 대안을 제시한다. 첫 번째, 기금 자산운용의 목표 설정에 대해서는 Doran(1981)의 SMART (Specific, Measurable, Achievable, Relevant, Time-limited) 기준에 따른 목표수립을 제안한다. 두 번째, 목표 수익률 산출에서는 목표기반투자 (Goal based Investing, GBI) 에 따른 목표 수익률 산출 방법을 제시한다.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.26-28
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• 2009

This paper proposes maximum torque control of IPMSM drive using adaptive teaming mechanism-fuzzy neural network (ALM-FNN) controller, model reference adaptive fuzzy tonal(MFC) and artificial neural network(ANN). This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using ALM-FNN, MFC and ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN, MFC and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, MFC and ANN controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.8
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• pp.57-66
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper proposes hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme, The validity of the proposed controller is verified by results at different dynamic operating conditions.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.155-157
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive learning mechanism-fuzzy neural network(ALM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.