• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1297-1300
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• 1993

Machine learning in an uncertain or unknown environment is of vital interest to those working with intelligent systems. The ability to garner new information, process it, and increase the understanding/ capability of the machine is crucial to the performance of autonomous systems. The field of artificial intelligence provides two major approaches to the problem of knowledge engineering-expert systems and neural networks. Harnessing the power of these two techniques in a hybrid, cooperating system holds great promise.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1037-1041
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• 1996

Input-output linearization technique in nonlinear control does not guarantee the robustness in the presence of parameter uncertainty or unmodeled dynamics, etc. However, it has been used as an important preliminary step in achieving additional control objectives, for instance, robustness to parameter uncertainty and disturbance attenuation. An indirect adaptive control scheme based on input-output linearization is proposed in this paper. The scheme consists of a Hopfield network for process parameter identification and an adaptive sliding mode controller based on input-output linearization, which steers the system response into a desired configuration. A numerical example is presented for the trajectory tracking of uncertain nonlinear dynamic systems with slowly time-varying parameters.

• Asian Journal of Innovation and Policy
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• v.8 no.2
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• pp.180-207
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• 2019

This article explores the historical evolutionary process of the biopharmaceutical industry of Korea, and how intentional and unintentional policy interventions have triggered the creation of the industry's system dynamics and paved the way for the generation of a few global leading products, including biosimilar, as well as next-generation therapeutics of gene and cell. The policies cover the simple technology transfer of API synthesis to overcome the endemic parasitic disease, new substance patent adoption and new drug development consortia, human resource development, various national initiatives influenced by the Human Genome Project, and venture promotion schemes. The scope and implementation tools under these policies have been aligned and refined to transform traditional fine chemical-based pharmaceuticals, to stimulate large companies' participation and to create technology-based venture companies in the biopharma business of Korea.

• Advances in aircraft and spacecraft science
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• v.6 no.5
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• pp.371-389
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• 2019

The implementation of a robust $H_{\infty}$ Control, which is numerically efficient for uncertain nonlinear dynamics, on longitudinal and lateral autopilots is realised for a quarter scale Piper J3-Cub model accepted as an unmanned aerial vehicle (UAV) under the condition of sensor noise and disturbance effects. The stability and control coefficients of the UAV are evaluated through XFLR5 software, which utilises a vortex lattice method at a predefined flight condition. After that, the longitudinal trim point is computed, and the linearization process is performed at this trim point. The "${\mu}$-Synthesis"-based robust $H_{\infty}$ control algorithm for roll, pitch and yaw displacement autopilots are developed for both longitudinal and lateral linearised nonlinear dynamics. Controller performances, closed-loop frequency responses, nominal and perturbed system responses are obtained under the conditions of disturbance and sensor noise. The simulation results indicate that the proposed control scheme achieves robust performance and guarantees stability under exogenous disturbance and measurement noise effects and model uncertainty.

• Structural Engineering and Mechanics
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• v.70 no.1
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• pp.81-96
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• 2019

In this paper, a hybrid uncertain model is applied to system reliability based design optimization (RBDO) of trusses. All random variables are described by random distributions but some key distribution parameters of them which lack information are defined by variation intervals. For system RBDO of trusses, the first order reliability method, as well as monotonicity analysis and the branch and bound method, are utilized to determine the system failure probability; and Improved (${\mu}+{\lambda}$) constrained differential evolution (ICDE) is employed for the optimization process. System reliability assessment of several numerical examples and system RBDO of different truss structures are proposed to verify our results. Moreover, the effect of different classes of interval distribution parameters on the optimum weight of the structure and the reliability index are also investigated. The results indicate that the weight of the structure is increased by increasing the uncertainty level. Moreover, it is shown that for a certain random variable, the optimum weight is more increased by the translation interval parameters than the rotation ones.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.56-63
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• 2022

Cyber-physical systems (CPSs) can solve real problems by utilizing closely connected resources in the cyber world. Most problems arise because the physical world is uncertain and unpredictable. To address this uncertainty, information pouring from numerous devices must be collected in real-time, and each interconnected device must share the information. At this time, CPS must meet timing-related techniques and strict timing constraints that can deliver accurate information within predefined deadlines in order to interact closely beyond simply connecting the cyber and physical worlds. Timing errors in safety-critical systems, such as automobiles, aviation, and medical systems, can lead to catastrophic disasters. In this paper, we classify timing problems into two types: real-time delay and synchronization problems. The results of this study can be used in the entire process of CPS system design, implementation, operation, verification, and maintenance. As a result, it can contribute to securing the safety and reliability of CPS.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.622-624
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• 2015

As uncertain factors are latent in a construction project by nature, a change order occurs frequently. The occurrence of change orders in construction projects conducted during construction phase is known to cause unexpected negative impacts such as cost overrun, schedule delay, quality problem, and claims in the post-process. Thus, an efficient management method is necessary to prevent and minimize change orders during construction phase when they occur frequently. This paper analyzed the causes of change orders and the impact factors that occur during the construction phase of a construction project and suggested a direction for change order database building.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.121-130
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• 1999

The objective of this paper is to apply Extended AND-OR Graph (EAOG)-related techniques to extract knowledge from a specific problem-domain and perform analysis in complicated decision making area. Expert systems use expertise about a specific domain as their primary source of solving problems belonging to that domain. However, such expertise is complicated as well as uncertain, because most knowledge is expressed in causal relationships between concepts or variables. Therefore, if expert systems can be used effectively to provide more intelligent support for decision making in complicated specific problems, it should be equipped with real-time inference mechanism. We develop two kinds of EAOG-driven inference mechanisms(1) EAOG-based forward chaining and (2) EAOG-based backward chaining. and The EAOG method processes the following three characteristics. 1. Real-time inference : The EAOG inference mechanism is suitable for the real-time inference because its computational mechanism is based on matrix computation. 2. Matrix operation : All the subjective knowledge is delineated in a matrix form, so that inference process can proceed based on the matrix operation which is computationally efficient. 3. Bi-directional inference : Traditional inference method of expert systems is based on either forward chaining or backward chaining which is mutually exclusive in terms of logical process and computational efficiency. However, the proposed EAOG inference mechanism is generically bi-directional without loss of both speed and efficiency.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.761-771
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• 2020

This study discusses the role of Board Monitoring Effectiveness (BME) on managers' decisions regarding the business strategies that fit the external business environmental conditions by using a contingency analysis approach. Furthermore, this study will examine how fit strategies affect Sustainability Reporting (SR) of listed companies on the Indonesia Stock Exchange (IDX) from 2014 to 2017. This study uses Conditional Mixed Process (CMP) technique. This CMP method is claimed to be more efficient in analyzing the TSL models. This study found that in highly uncertain conditions, BME had a positive influence on the probability of managers to choose prospector and defender strategies rather than analyzers. These results indicate that BME shows positive impact on the contingency fit between business strategies and environmental uncertainty. In addition, the study documents that only prospectors have a positive impact on SR, however this study failed to document that defenders have positive impact on SR. Meanwhile the unexpected result is analyzers have a significantly positive effect on SR. This study is the first study to investigate the role of BME in contingency fit between business strategies and environmental uncertainties and how it produces effects up to the level of SR.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.131-136
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• 1997

This paper represent the variable structure adaptive mode control technique which is new approach to implement the robust control of industrial robot manipulator with external disturbances and parameter uncertainties. Sliding mode control is a well-known technique for robust control of uncertain nonlinear systems. The robustness of sliding model controllers can be shown in contiuous time, but digital implementation may not preserve robustness properties because the sampling process limits the existence of a true sliding mode. the sampling process often forces the trajectory to oscillate in the neighborhood of the sliding surface. Adaptive control technique is particularly well-suited to robot manipulators where dynamic model is highly complex and may contain unknown parameters. Adaptive control algorithm is designed by using the principle of the model reference adaptive control method based upon the hyperstability theory. The proposed control scheme has a simple sturcture is computationally fast and does not require knowledge of the complex dynamic model or the parameter values of the manipulator or the payload. Simulation results show that the proposed method not only improves the performance of the system but also reduces the chattering problem of sliding mode control, Consequently, it is expected that the new adaptive sliding mode control algorithm will be suited for various practical applications of industrial robot control system.