• Journal of Families and Better Life
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• v.21 no.2
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• pp.145-154
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• 2003

As the life-expectancy is ever-increasing, and the proportion of the elderly population is growing steadily in every society of the world, it is ever more important to establish what factors allow certain elderly people to age successfully and remain relatively independent while others grow old less successfully and require extensive intervention. However, there is no consensus yet as to what successful aging means. Researchers have defined successful aging in a variety of ways. This study attempted to define the concept of successful aging and to clarify some dimensions of it through literature review. Previous approaches of studying successful aging and related themes were examined. Early perspectives including activity, disengagement, and continuity theories, Selective Optimization with Compensation (SOC) model by Baltes and Baltes, three different conceptions of successful aging, that is, psychological well-being, physical health, and wisdom, and MacArthur research on successful aging have been reviewed for this study. The definition derived from the review is: Keeping up continuous developmental processes to achieve wisdom or ego-integrity, without suffering any major disabilities in either physical or mental functioning, while maintaining psychological well-being and employing SOC strategies, and participating in positive relationships with significant others. The dimensions of successful aging are 1) personal resources, including physical health, cognitive competences, self esteem, and social support 2) adaptation process of SOC, and 3) psychological aspects, including psychological well-being and wisdom.

• Journal of Magnetics
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• v.20 no.2
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• pp.176-185
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• 2015

We present the design optimization of the magnetic pole and slot design options that minimize the cogging torque of permanent-magnet (PM) brushless generators for small wind turbine generators. Most small wind-turbines use direct-driven PM generators which have the characteristics of low speed and high efficiency. Small wind-turbines are usually self-starting and require very simple controls. The cogging torque is an inherent characteristic of PM generators, and is mainly caused by the generator's geometry. The inherent the cogging torque can cause problems during turbine start-up and cut-in in order to start softly and to run a power generator even when there is little wind power during turbine start-up. Thus, to improve the operation of small turbines, it is important to minimize the cogging torque. To determine the effects of the cogging torque reductions, we adjust the slot opening width, slot skewing, mounting method of magnets, magnet shape, and the opening and combinations of different numbers of slots per pole. Of these different methods, we combine the methods and optimized the design variables for the most significant design options affecting the cogging torque. Finally, we apply to the target design model and compare FEA simulation and measured results to validate the design optimization.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1724-1731
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• 2018

The conventional polynomial neural network (PNN) is a classical flexible neural structure and self-organizing network, however it is not free from the limitation of overfitting problem. In this study, we propose a space search-optimized polynomial neural network (ssPNN) structure to alleviate this problem. Ranking selection is realized by means of ranking selection-based performance index (RS_PI) which is combined with conventional performance index (PI) and coefficients based performance index (CPI) (viz. the sum of squared coefficient). Unlike the conventional PNN, L2-norm regularization method for estimating the polynomial coefficients is also used when designing the ssPNN. Furthermore, space search optimization (SSO) is exploited here to optimize the parameters of ssPNN (viz. the number of input variables, which variables will be selected as input variables, and the type of polynomial). Experimental results show that the proposed ranking selection-based polynomial neural network gives rise to better performance in comparison with the neuron fuzzy models reported in the literatures.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.6
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• pp.381-387
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• 2020

According to the increase of concern for environmental problems, the energy saving becomes an important issue because it is one of the most effective methods of decreasing CO2 which is major environmental problem. In the present study, the post device after propeller related with rudder has been focussed. Recently the full-spade twisted rudder has been frequently used not only to increase the efficiency but also to remove the cavitation risk on leading edge. In addition to that the rudder bulb is also applied to the rudder to increase the propulsion efficiency as well as to minimize the cavitation erosion risk around twisting part. The parametric study has been conducted for investigating the optimum configuration of twisting rudder with bulb by CFD. The present optimization has been applied to the KVLCC2 full-body ship. The verification of the computed results is also expected to be conducted by the comparison with experimental results in the near future.

• Journal of Korean Association for Spatial Structures
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• v.21 no.4
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• pp.49-56
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• 2021

This study aims to develop a form-finding algorithm for a single-layered pneumatic membrane. The initial shape of this pneumatic membrane, which is an air-supported type pneumatic membrane, is to find a state in which a given initial tension and internal pneumatic pressure are in equilibrium. The algorithm developed to satisfy these conditions is that a nonlinear optimization problem based on the force method considering the deformed shape is formulated, and, it's able to find the shape by iteratively repeating the process of obtaining a solution of the governing equations. An computational technique based on the Gauss-Newton method was used as a method for obtaining solutions of nonlinear equations. In order to verify the validity of the proposed form-finding algorithm, a single-curvature pneumatic membrane example and a double-curvature air pneumatic membrane example were adopted, respectively. In the results of these examples, it was possible to well observe the step-by-step convergence process of the shape of the pneumatic membrane, and it was also possible to confirm the change in shape according to the air pressure. In addition, the calculation results of the shape and internal force after deformation due to initial tension, air pressure, and self-weight were obtained.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2852-2859
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• 2020

This paper deals with the simulation-based design optimization and experimental validation of the characteristics of an in-core silver Self-Powered Neutron Detector (SPND). Optimized dimensions of the SPND are determined by combining Monte Carlo simulations and analytical methods. As a first step, the Monte Carlo transport code MCNPX is used to follow the trajectory and fate of the neutrons emitted from an external source. This simulation is able to seamlessly integrate various phenomena, including neutron slowing-down and shielding effects. Then, the expected number of beta particles and their energy spectrum following a neutron capture reaction in the silver emitter are fetched from the TENDEL database using the JANIS software interface and integrated with the data from the first step to yield the origin and spectrum of the source electrons. Eventually, the MCNPX transport code is used for the Monte Carlo calculation of the ballistic current of beta particles in the various regions of the SPND. Then, the output current and the maximum insulator thickness to avoid breakdown are determined. The optimum design of the SPND is then manufactured and experimental tests are conducted. The calculated design parameters of this detector have been found in good agreement with the obtained experimental results.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.382-388
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• 2011

Being aware of the risk in advance necessitates intricate processes but is feasible. Although previous studies have demonstrated high accuracy, their performance still leaves room for improvement. A self-organizing feature map (SOM) based neurofuzzy model is developed in this study to provide another alternative for forecasting corporate financial distress. The model is designed to yield high prediction accuracy, as well as reference rules for evaluating corporate financial status. As a database, the study collects all financial reports from listed construction companies during the latest decade, resulting in over 1000 effective samples. The proportion of "failed" and "non-failed" companies is approximately 1:2. Each financial report is comprised of 25 ratios which are set as the input variable s. The proposed model integrates the concepts of pattern classification, fuzzy modeling and SOM-based optimization to predict corporate financial distress. The results exhibit a high accuracy rate at 85.1%. This model outperforms previous tools. A total of 97 rules are extracted from the proposed model which can be also used as reference for construction practitioners. Users may easily identify their corporate financial status by using these rules.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.223-230
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• 2001

Since self-organizing map (SOM) preserves the topology of ordering in input spaces and trains itself by unsupervised algorithm, it is Llsed in many areas. However, SOM has a shortcoming: structure cannot be easily detcrmined without many trials-and-errors. Structure-adaptive self-orgnizing map (SASOM) which can adapt its structure as well as its weights overcome the shortcoming of self-organizing map: SASOM makes use of structure adaptation capability to place the nodes of prototype vectors into the pattern space accurately so as to make the decision boundmies as close to the class boundaries as possible. In this scheme, the initialization of weights of newly adapted nodes is important. This paper proposes a method which optimizes SASOM with genetic algorithm (GA) to determines the weight vector of newly split node. The leanling algorithm is a hybrid of unsupervised learning method and supervised learning method using LVQ algorithm. This proposed method not only shows higher performance than SASOM in terms of recognition rate and variation, but also preserves the topological order of input patterns well. Experiments with 2D pattern space data and handwritten digit database show that the proposed method is promising.

• Smart Structures and Systems
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• v.17 no.6
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• pp.957-980
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• 2016

Structural damage detection (SDD) is a challenging task in the field of structural health monitoring (SHM). As an exploring attempt to the SDD problem, a hybrid self-adaptive Firefly-Nelder-Mead (SA-FNM) algorithm is proposed for the SDD problem in this study. First of all, the basic principle of firefly algorithm (FA) is introduced. The Nelder-Mead (NM) algorithm is incorporated into FA for improving the local searching ability. A new strategy for exchanging the information in the firefly group is introduced into the SA-FNM for reducing the computation cost. A random walk strategy for the best firefly and a self-adaptive control strategy of three key parameters, such as light absorption, randomization parameter and critical distance, are proposed for preferably balancing the exploitation and exploration ability of the SA-FNM. The computing performance of the SA-FNM is evaluated and compared with the basic FA by three benchmark functions. Secondly, the SDD problem is mathematically converted into a constrained optimization problem, which is then hopefully solved by the SA-FNM algorithm. A multi-step method is proposed for finding the minimum fitness with a big probability. In order to assess the accuracy and the feasibility of the proposed method, a two-storey rigid frame structure without considering the finite element model (FEM) error and a steel beam with considering the model error are taken examples for numerical simulations. Finally, a series of experimental studies on damage detection of a steel beam with four damage patterns are performed in laboratory. The illustrated results show that the proposed method can accurately identify the structural damage. Some valuable conclusions are made and related issues are discussed as well.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.4
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• pp.62-67
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• 2024

In this paper, we developed and optimized a chitosan-based polymer microbial bead carrier that is cell-friendly, has a high moisture absorption rate, and effectively provides the conditions for microbial biomineral formation as an optimal microbial carrier that protects microorganisms in concrete, and evaluated the self-healing performance of mortar using it. In order to incorporate circular-shaped microbial endospores, a circular-shaped microbial bead carrier was developed by combining chitosan and alginate polymers, and the amount of calcium carbonate produced could be actively controlled by adjusting the composition of the carrier. The amount of biominerals formed and the size of crystals were maximized in the hydrogel bead carrier containing chitosan, and in the case of mortar cracks using this, it was confirmed that self-healing of cracks with a maximum crack width of 0.3mm was achieved within 96 hours after crack generation.