• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.27-28
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.395-396
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• 2011

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.394-399
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• 2010

This paper presents to study the velocity control method of AGV for heavy material transport. Generally, in the industries, fork-type AGV using path tracking requires high stop-precision with performing operations for 20 hours. To obtain the high stop-precision of AGV for heavy material transport, AGV requires driving technic during low speed. Hence, we use encoder with keeping the speed of AGV and study the velocity control method to improve for the stop-precision of AGV. To experiment the proposed the velocity control method, we performed the experiments engaging the pallet located 4m in front of the AGV. In the experimental result, the maximum error of stop-precision was less than 18.64mm, and we verified that the proposed method is able to control stable.

• Journal of Korean Society for Quality Management
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• v.29 no.4
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• pp.38-53
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• 2001

In resolving industrial quality control problems, a vector of multiple quality characteristic variables is involved rather than a single variable. However, it is not guaranteed that a multivariate control chart based on statistical methods can monitor abnormal signal in case that small changes of relationship between each variables causes abnormal production process. Hence a quality control system for real-time monitoring of the multi-dimensional quality characteristic vector under a multivariate normal process is needed to enhance tile production system quality performance. A pattern analysis approach based on self-organizing map (SOM), an unsupervised learning technique of neural network, is applied to the design of such a quality control system. In this study we present a new material quality control system based on pattern analysis approach and illustrate the effectiveness of proposed system using actual electronic company material data.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.2059-2063
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• 2004

Most power plants have operated many independent computerize systems for maintenance. Independence of systems have caused complexity of business process and inconvenience of computer system management. Because the equipment and material master data is not standardize and structurize, it is difficult to manage equipment maintenance history and material delivery. Especially equipment classification criterion is important for standardization of every maintenance information. It is necessary to integrate function of independent systems for business process simplification and rapid work flow. this paper provides equipment classification criterion design and system integration method with the case of live system development.

• Computers and Concrete
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• v.30 no.2
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• pp.85-97
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• 2022

Using a combination of nonlocal Eringen as well as classical beam theories, this research explores the thermal buckling of a bidirectional functionally graded nanobeam. The formulations of the presented problem are acquired by means on conserved energy as well as nonlocal theory. The results are obtained via generalized differential quadrature method (GDQM). The mechanical properties of the generated material vary in both axial and lateral directions, two-dimensional functionally graded material (2D-FGM). In nanostructures, porosity gaps are seen as a flaw. Finally, the information gained is used to the creation of small-scale sensors, providing an outstanding overview of nanostructure production history.

• Advances in nano research
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• v.12 no.3
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• pp.281-290
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• 2022

In the current research, the thermal buckling characteristics of the bi-directional functionally graded nano-scale tapered beam on the basis of a couple of nonlocal Eringen and classical beam theories are scrutinized. The nonlocal governing equation and associated nonlocal boundary conditions are constructed using the conservation energy principle, and the resulting equations are solved using the generalized differential quadrature method (GDQM). The mechanical characteristics of the produced material are altered along both the beam length and thickness direction, indicating that it is a two-dimensional functionally graded material (2D-FGM). It is thought that the nanostructures are defective because to the presence of porosity voids. Finally, the obtained results are used to design small-scale sensors and make an excellent panorama of developing the production of nanostructures.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.22 no.3
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• pp.41-47
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• 2016

This paper provides an overview of kimchi packaging technology, focusing on packaging materials, package design, and active/intelligent packaging technology for kimchi. From a packaging-material standpoint, although various materials have been used to ensure customer satisfaction and convenience, plastic is the most widely used material, in the form of bags, trays, pouches, and rigid containers. Additionally, recent efforts in the kimchi packaging industry have allowed companies to differentiate their products by using different packaging materials and technologies, while simultaneously improving product safety and quality. On the other hand, the biggest problem in kimchi packaging is excess $CO_2$ production, leading to package expansion and leakage. To alleviate this problem, the use of $CO_2$ absorbers, high $CO_2$-permeable films, and degassing valves, in addition to the use of different packaging systems, has been investigated. Active and/or intelligent packaging systems have been developed, to include active functions beyond simply inert, passive containment and protection of the kimchi product. However, most such approaches are not yet adequately effective to be useful on a commercial scale. Therefore, further studies are needed to resolve the limitations of each technology.

• Smart Structures and Systems
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• v.11 no.3
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• pp.315-329
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• 2013

The construction of an experimental nonlinear structural model with little cost and unlimited repeatability for vibration control study represents a challenging task, especially for material nonlinearity. This paper reports the design, analysis and vibration control of a nonlinear structural experiment platform with adjustable hinges. In our approach, magnetorheological rotary brakes are substituted for the joints of a frame structure to simulate the nonlinear material behaviors of plastic hinges. For vibration control, a separate magnetorheological damper was employed to provide semi-active damping force to the nonlinear structure. A dynamic neural network was designed as a state observer to enable the feedback based semi-active vibration control. Based on the dynamic neural network observer, an adaptive fuzzy sliding mode based output control was developed for the magnetorheological damper to suppress the vibrations of the structure. The performance of the intelligent control algorithm was studied by subjecting the structure to shake table experiments. Experimental results show that the magnetorheological rotary brake can simulate the nonlinearity of the structural model with good repeatability. Moreover, different nonlinear behaviors can be achieved by controlling the input voltage of magnetorheological rotary damper. Different levels of nonlinearity in the vibration response of the structure can be achieved with the above adaptive fuzzy sliding mode control algorithm using a dynamic neural network observer.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.251-256
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• 2005

Up to now, a number of models have been proposed and discussed to describe a wide range of inelastic behaviors of materials. The fetal problem of using such models is however the existence of model errors, and the problem remains inevitably as far as a material model is written explicitly. In this paper, the authors define the implicit constitutive model and propose an implicit viscoplastic constitutive model using neural networks. In their modeling, inelastic material behaviors are generalized in a state space representation and the state space form is constructed by a neural network using input output data sets. A technique to extract the input-output data from experimental data is also described. The proposed model was first generated from pseudo-experimental data created by one of the widely used constitutive models and was found to replace the model well. Then, having been tested with the actual experimental data, the proposed model resulted in a negligible amount of model errors indicating its superiority to all the existing explicit models in accuracy.