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Design of basic chamber of the Main Control Valve

Lee S., Shin Y., Byun C.

Journal of Achievements in Materials and Manufacturing Engineering

2008

Vol. 28, nr 2

175-178

Purpose: The development of a control valve for closed circuit requires comprehensive technologies in the overall precision machinery industry, from the development of casting materials for the housing to various types of parts. The development of a new type of control valve would have great advantage with a long lifecycle. Therefore, it is necessary to secure the MCV (Main Control Valve) development technology that applies various sensors. This paper aims at providing a fundamental base for the establishment of design systems including the flow chamber design database of the MCV for wheel loaders, strength and rigidity design system, and the system for energy efficiency improvement. Particularly, this study set up the basic design database for the flow chamber design to establish the flow chamber design database, and secured the stability of the flow chamber from the basic design stage. In addition, major design variables were determined by utilizing a statistical technique in order to design such flow chamber. Design/methodology/approach: This study uses the I-DEAS to analyze the MCV structure characteristics. In addition, it uses the factorial design and sensitivity analysis to select important factors for the MCV design. Findings: This study establishes the unit flow chamber database for the MCV housing unit and the governing equation for the flow chamber. Research limitations/implications: Since the MCV damage often occurs due to the problem with the material itself and in the manufacturing process, it is difficult to tell clearly whether it occurred as the MCV reached the failure pressure. Practical implications: The basic data needed to design the MCV can be provided, and the required time for the design and the reliability of the design can be reduced and improved respectively. Originality/value: The verification of the design factors obtained from the flow analysis and structural analysis as well as the DOE was made by fabricating a sample MCV and performing tests on it.

Facial expression recognition based on dimension model using automated feature extraction

Shin Y., Lee S., Lee Y.

Machine Graphics and Vision

2000

Vol. 9, No. 3

573-585

This paper presents a facial expression recognition based on dimension model of internal states that uses automated feature extraction. We apply this approach mostly for the frontal pose. Features of facial expressions are extracted automatically in three steps. In the first steo, Gabor wavelet representation can provide edges extraction of major face components using the average value of the image's 2D Gabor wavelet coefficient fistogram. In the second step, sparse features of facial expressions are extracted using fuzzy C-means clustering (FCM) algorithm for neural faces, and in the third step, using the dynamic model (DM) for expression images. The result of facial expression recognition is compared with dimensional values of internal states derived from semantic ratings of words related to emotion by experimental subjects. The dimensional model can recognize not only 6 facial expressions related to Ekman's basic emotions, but also expressions of various internal states. A facial expression in the dimension model includes two dimensions which are pleasure-upleasure and arousal-sleep. We show the result of expression recognition in the dimension model. In this paper, with the dimension model we have improved the limitations of expression recognition based on basic emotions, and have extracted features automatically with a new approach using the FCM algorith and the Dynamic Model.