• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.106-116
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• 1998

A position control algorithm of a flexible manipulator is studied. The proposed algorithm is based on an ACFAC(Automatic Constructed Fuzzy Adaptive Controller) system based on the neural network learning algorithms. The proposed system learns membership functions for input variables using unsupervised competitive learning algorithm and output information using supervised outstar learning algorithm. ACFAC does not need a dynamic modeling of the flexible manipulator. An ACFAC is designed that the end point of the flexible manipulator tracks the desired trajectory. The control input to the process is determined by error, velocity and variation of error. Simulation and experiment results show a robustness of ACFAC compared with the PID control and neural network algorithms.

• Nuclear Engineering and Technology
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• v.52 no.2
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• pp.287-295
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• 2020

Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems. In this study, multi-layer GMDH networks are used to perform uncertainty quantification (UQ) and sensitivity analysis (SA) of nuclear reactor simulations. GMDH is utilized as a surrogate/metamodel to replace high fidelity computer models with cheap-to-evaluate surrogate models, which facilitate UQ and SA tasks (e.g. variance decomposition, uncertainty propagation, etc.). GMDH performance is validated through two UQ applications in reactor simulations: (1) low dimensional input space (two-phase flow in a reactor channel), and (2) high dimensional space (8-group homogenized cross-sections). In both applications, GMDH networks show very good performance with small mean absolute and squared errors as well as high accuracy in capturing the target variance. GMDH is utilized afterward to perform UQ tasks such as variance decomposition through Sobol indices, and GMDH-based uncertainty propagation with large number of samples. GMDH performance is also compared to other surrogates including Gaussian processes and polynomial chaos expansions. The comparison shows that GMDH has competitive performance with the other methods for the low dimensional problem, and reliable performance for the high dimensional problem.

• Journal of the Korea Computer Graphics Society
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• v.15 no.2
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• pp.9-17
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• 2009

Animating multiple characters to compete with each other is an important problem in computer games and animation films. However, it remains difficult to simulate strategic competition among characters because of its inherent complex decision process that should be able to cope with often unpredictable behavior of opponents. We adopt a reinforcement learning method in Markov games to action models built from captured motion data. This enables two characters to perform globally optimal counter-strategies with respect to each other. We also extend this method to simulate competition between two teams, each of which can consist of an arbitrary number of characters. We demonstrate the usefulness of our approach through various competitive scenarios, including playing-tag, keeping-distance, and shooting.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.587-594
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• 2001

Multi-agent system fits to the distributed and open internet environments. In a multi-agent system, agents must cooperate with each other through a coordination procedure, when the conflicts between agents arise. Where those are caused by the point that each action acts for a purpose separately without coordination. But previous researches for coordination methods in multi-agent system have a deficiency that they cannot solve correctly the cooperation problem between agents, which have different goals in dynamic environment. In this paper, we suggest the automatic coordination model for multi-agent system using neural network and reinforcement learning in dynamic environment. We have competitive experiment between multi-agents that have complexity environment and diverse activity. And we analysis and evaluate effect of activity of multi-agents. The results show that the proposed method is proper.

• Earthquakes and Structures
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• v.18 no.6
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• pp.719-730
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• 2020

Recently, population based optimization algorithms are developed to deal with a variety of optimization problems. In this paper, the salp swarm algorithm (SSA) is dramatically enhanced and a new algorithm is named Enhanced Salp Swarm Algorithm (ESSA) which is effectively utilized in optimization problems. To generate the ESSA, an opposition-based learning and merit function methods are added to standard SSA to enhance both exploration and exploitation abilities. To have a clear judgment about the performance of the ESSA, firstly, it is employed to solve some mathematical benchmark test functions. Next, it is exploited to deal with engineering problems such as optimally designing the benchmark buildings equipped with multiple tuned mass damper (MTMD) under earthquake excitation. By comparing the obtained results with those obtained from other algorithms, it can be concluded that the proposed new ESSA algorithm not only provides very competitive results, but also it can be successfully applied to the optimal design of the MTMD.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.313-315
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• 2021

Timely and accurate diagnosis of lung diseases using Chest X-ray images has been gained much attention from the computer vision and medical imaging communities. Although previous studies have presented the capability of deep convolutional neural networks by achieving competitive binary classification results, their models were seemingly unreliable to effectively distinguish multiple disease groups using a large number of x-ray images. In this paper, we aim to build an advanced approach, so-called Ensemble Knowledge Distillation (EKD), to significantly boost the classification accuracies, compared to traditional KD methods by distilling knowledge from a cumbersome teacher model into an ensemble of lightweight student models with parallel branches trained with ground truth labels. Therefore, learning features at different branches of the student models could enable the network to learn diverse patterns and improve the qualify of final predictions through an ensemble learning solution. Although we observed that experiments on the well-established ChestX-ray14 dataset showed the classification improvements of traditional KD compared to the base transfer learning approach, the EKD performance would be expected to potentially enhance classification accuracy and model generalization, especially in situations of the imbalanced dataset and the interdependency of 14 weakly annotated thorax diseases.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.2
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• pp.109-123
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• 2024

The purpose of this study is to identify strategic insights for cyber universities to secure a competitive advantage based on market analysis grounded in customer needs and motivations. As a research method, we surveyed and analyzed college students using conjoint analysis, identified the importance of cyber university components, estimated the utility of each detailed level, and identified the configuration of cyber universities most preferred by potential customers. In the study results, the importance of attributes that appeared by analyzing all respondents was in the order of 'expected ourcoms after graduation', 'department characteristic', 'cyber university name', and 'learning management style'. Cluster analysis was performed, divided into two groups, and conjoint analysis was performed. For Cluster 1, the importance values of the components were 'expected outcomes after graduation,' 'learning management style,' 'cyber university name,' and 'department characteristics,' in that order. For Cluster 2, the importance values were 'expected outcomes after graduation,' 'department characteristics,' 'cyber university name,' and 'learning management style,' in that order. As an application of the research, As an application of the study, it is suggested that analyzing the preferences of potential customers in the entire group is not accurate; therefore, segmenting the groups for analysis and strategy formulation can be useful.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.6
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• pp.1-13
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• 2015

There are various performance factors for SMEs in order to survive in the rapid changing market and it is discussing the importance of entrepreneurs' entrepreneurial orientation based on many researches. Thus, it is worth to analyze factors of seize new opportunity and firm's performance to build sustainable competitive advantages, which provide the directions to SMEs. This study investigates through exploratory research that the important factors of entrepreneurial orientation and the influence factors on firm's performance confirmed by empirical study. This study was conducted to explore the relationship between entrepreneurial orientation of SME CEO, learning orientation and corporate performance was verified following section. First, entrepreneurial orientation (pro-activeness, competitive aggressiveness, risk taking, innovativeness) was to examine the effect of learning orientation; Second, entrepreneurial orientation was to examine the impact on firm's performance; and in the last, validated learning orientation affect factors that are mediated between entrepreneurship orientation and firm's performance through empirical research. The results of this study, each SME have shown that they have a different impact on firm's performance based on a variety of entrepreneurial orientation. This result shows that the need for a separate independent study on entrepreneurial orientation of SMEs. In conclusion, this study implicates that entrepreneurial orientation is important role for firm's performance, entrepreneurs of SMEs are innovative rather than competitive aggressive, and risk taking activities positively affect firm's activity. The conclusions of this study would be utilized to develop the entrepreneurial orientation when necessary for entrepreneurs of SMEs.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.765-777
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• 2022

The purpose of this study is to present a digital transformation-based learning model that can be used in universities based on learning digital transformation in order f to be competitive in a rapidly changing environment. Literature review, case study, and focus group interview were conducted and the implications for the learning model from these are as follows. Universities that stand out in related fields are actively using learning analysis to implement dashboards, develop predictive models, and support adaptive learning based on big data, They also have actively introduced advanced edutech to classes. In addition, problems and difficulties faced by other universities and K University when implementing digital transformation were also confirmed. Based on these findings, a digital transformation-based learning model of K University was developed. This model consists of four dimensions: diagnosis, recommendation, learning, and success. It allows students to proceed with learning by diagnosing and recommending various learning processes necessary for individual success, and systematically managing learning outcomes. Finally, academic and practical implications about the research results were discussed.

• Culinary science and hospitality research
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• v.8 no.3
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• pp.123-146
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• 2002

Outsourcing is procuring of outside resources, other than core resources for core competence, by a contract, from which a corporate can focus its core resources on core business. The outsourcing strategies of Korea tourist hotel business are in a rudimentary stage, which has been limited in simple work areas such as housekeeping services, room maid services, parking control services, security services, janitor services, laundry services, facility management, shuttle bus services, and sterilization services and their purposes are mainly to retrench a burden of employment or firm-fixed expenses. Therefore, the outsourcing strategies of Korea tourist hotel business have the following problems. First, their outsourcing has introduced only for the purpose of retrenching expenses. Second, it tends to deteriorate service quality, due to lack of pre-training. Third, it tends to concentrate their attentions only on simple repetition works. Fourth, their outsourcing is slow adjusted to the needs of business cultures. Outsourcing services in Korea tourist hotel business have never contributed to their basic concepts such as 1) maintenance or enhancement of core competences, 2) promotion of business efficiency through service quality improvement and expense retrenchment, and 3) achievement or enhancement of competitive advantage through enlarging their specialties, cultivating their market, learning new knowledge, and developing their asset. Therefore, this study is to insist on fife necessity of overcoming simple repetitive service outsourcing in tourist hotel business. In order to build a core competence and/or achieve a competitive advantage, the scopes of outsourcing services should be enlarged in Korea tourist hotel business.