• Journal of the Korean Home Economics Association
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• v.48 no.5
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• pp.61-72
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• 2010

This study examined the effects of maternal parenting, children's self-esteem and emotion regulation strategy on emotion regulation. Data were collected from 493 5th and 6th graders. The results were as follows: Firstly, maternal authoritarian and permissive parenting directly affected children's maladaptive emotion regulation, while maternal affectionate and permissive parenting directly affected children's adaptive emotion regulation. Secondly, children's selfesteem directly affected both their maladaptive and adaptive emotion regulation, while also acting as a mediator between maternal parenting and children's maladaptive and adaptive emotion regulation. Children's cognitive reappraiser strategy positively affected adaptive emotion regulation, but emotion suppressive strategy negatively affected adaptive emotion regulation. These emotion regulation strategies played a mediating role between maternal parenting or children's self-esteem and adaptive emotion regulation.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.1-11
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• 2021

Estimation of distribution algorithm (EDA) is a popular stochastic metaheuristic algorithm. EDA has been widely utilized in various optimization problems. However, it has been shown that the diversity of the population gradually decreases during the iterations, which makes EDA easily lead to premature convergence. This article introduces a hybrid estimation of distribution algorithm (EDA) with differential evolution (DE) based on self-adaptive strategy, namely HEDADE-SA. Firstly, an alternative probability model is used in sampling to improve population diversity. Secondly, the proposed algorithm is combined with DE, and a self-adaptive strategy is adopted to improve the convergence speed of the algorithm. Finally, twenty-five benchmark problems are conducted to verify the performance of HEDADE-SA. Experimental results indicate that HEDADE-SA is a feasible and effective algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.111-120
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• 2015

Recently, self-adaptive system researches have been done to solve the problems occurred in the dynamic environment. Designing requirement in the self-adaptive system is necessary to recognize and solve the problem for the system, and if a developer reuses existing adaptation strategy to design the requirement, the designing time and cost would be reduced. Therefore, this paper proposes the system which extracts reusable adaptation strategy from the existing self-adaptive system. For the proposal, this paper conceptualizes the self-adaptation elements, defines the adaptation strategy ontology and target system ontology, and presents the process of extracting reusable strategy. This paper also implements proposed system and evaluates the reuse rate of the extracted strategy. As a result, the adaptation strategies extracted by proposed system are exactly operated, and the extraction method of proposed system shows higher reuse rate than a previous method.

• Research in Community and Public Health Nursing
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• v.32 no.4
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• pp.457-466
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• 2021

Purpose: The purpose of this study was to examine the correlations among emotional perception clarity, emotion regulation, family relationship, non-suicidal self-injury, and depression, and to determine associated factors of non-suicidal self-injury and depression for senior elementary school students. Methods: Data were collected from 150 early adolescences in K region, Korea. A self-report questionnaire consisted of Trait Meta-Mood Scale, Cognitive Emotion Regulation Questionnaire, Family Relationship Assessment Scale, Functional Assessment of Self-Mutilation, and Children's Depression Inventory. The data were analyzed using t-test, Pearson's correlation coefficient, logistic regression, and multiple regression analysis. Results: Non-suicidal self-injury and depression were positively associated with maladaptive emotion regulation strategy and family conflict, but negatively related to emotional perception clarity and family support. Adaptive emotion regulation strategy and family togetherness were only significantly correlated with depression. In logistic regression analysis, significant predictors of non-suicidal self-injury were emotional perception clarity, maladaptive emotion regulation strategy, and family support. Multiple regression analysis found that significant factors of depression were adaptive and maladaptive emotion regulation strategies, which explained 38.0% of the variance. Conclusion: Our study findings suggest that targeted intervention to reinforce the adaptive emotion regulation strategy and family relationship may prevent non-suicidal self-injury, and depression for senior elementary school students.

• 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.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.43-55
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• 2005

In this paper, a predictive control method using self-recurrent wavelet neural network (SRWNN) is proposed for chaotic systems. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system though the SRWNN has less mother wavelet nodes than the wavelet neural network (WNN). Thus, the SRWNN is used as a model predictor for predicting the dynamic property of chaotic systems. The gradient descent method with the adaptive learning rates is applied to train the parameters of the SRWNN based predictor and controller. The adaptive learning rates are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the predictive controller. Finally, the chaotic systems are provided to demonstrate the effectiveness of the proposed control strategy.

• Journal of Digital Convergence
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• v.10 no.1
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• pp.349-355
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• 2012

Today, the computing environments is very complex, so researches that endow a system with the self-healing's ability that recognizes problem arising in a target system are being an important issues. However, the existing methodology, the goal for the new requirements for self-healing system developers to model and analyze the constraints that must be greater efforts. Therefore, in this paper are aware of problems detected by the system to solve the problem is the analysis of goal-based scenarios. In addition, there is a pre and post applying a strategy to be dynamically reconfigured to show you how to self-healing. These proposed new requirements for methodology, self-healing reduces the load on the developer's analysis.

• Journal of Power Electronics
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• v.14 no.3
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• pp.421-431
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• 2014

A DSP-based self-adaptive proportional-integral (PI) controller to control a DC-DC converter is proposed in this paper. The full-bridge topology is adopted here to obtain higher power output capability and higher conversion efficiency. The converter adopts the zero-voltage-switching (ZVS) technique to reduce the conduction losses. A parallel secondary active clamp circuit is added to deal with the voltage overshoot and ringing effect on the transformer's secondary side. A self-adaptive PI controller is proposed to replace the traditional PI controller. Moreover, the designed converter adopts the constant-current and constant-voltage (CC-CV) output control strategy. The secondary active clamp mechanism is discussed in detail. The effectiveness of the proposed converter was experimentally verified by an IGBT-based 10kW prototype.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.9
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• pp.1463-1468
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• 2003

This paper presented the real-time self-tuning learning control based on evolutionary computation, which proves its superiority in finding of the optimal solution at the off-line learning method. The individuals of the populations are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations is proposed. It is possible to control the control object slightly varied as time changes. As the state value of the control object is generated, evolutionary strategy is applied each sampling time because the learning process of an estimation, selection, mutation is done in real-time. These algorithms can be applied; the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

• Advances in Computational Design
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• v.1 no.3
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• pp.275-296
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• 2016

This article presents an adaptive directional differential evolution (ADDE) algorithm and its application in solving discrete sizing truss optimization problems. The algorithm is featured by a new self-adaptation approach and a simple directional strategy. In the adaptation approach, the mutation operator is adjusted in accordance with the change of population diversity, which can well balance between global exploration and local exploitation as well as locate the promising solutions. The directional strategy is based on the order relation between two difference solutions chosen for mutation and can bias the search direction for increasing the possibility of finding improved solutions. In addition, a new scaling factor is introduced as a vector of uniform random variables to maintain the diversity without crossover operation. Numerical results show that the optimal solutions of ADDE are as good as or better than those from some modern metaheuristics in the literature, while ADDE often uses fewer structural analyses.