• Structural Engineering and Mechanics
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• v.32 no.2
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• pp.351-370
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• 2009

This paper presents a computational study for the structural response of blast loaded metallic sandwich panels, with the emphasis placed on their failure behaviours. The fully-clamped panels are square, and the honeycomb core and skins are made of the same aluminium alloy. A material model considering strain and strain rate hardening effects is used and the blast load is idealised as either a uniform or localised pressure over a short duration. The deformation/failure procedure and modes of the sandwich panels are identified and analysed. In the uniform loading condition, the effect of core density and face-sheets thicknesses is analysed. Likewise, the influence of pulse shape on the failure modes is investigated by deriving a pressure-impulse (P-I) diagram. For localised loading, a comparative study is carried out to assess the blast resistant behaviours of three types of structures: sandwich panel with honeycomb core, two face-sheets with air core and monolithic plate, in terms of their permanent deflections and damage degrees. The finding of this research provides a valuable insight into the engineering design of sandwich constructions against air blast loads.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.6000-6017
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• 2018

This paper presents a new descriptor, named Histograms of Prominent Edge Directions (HPED), for the recognition of facial expressions in a person-independent environment. In this paper, we raise the issue of sampling error in generating the code-histogram from spatial regions of the face image, as observed in the existing descriptors. HPED describes facial appearance changes based on the statistical distribution of the top two prominent edge directions (i.e., primary and secondary direction) captured over small spatial regions of the face. Compared to existing descriptors, HPED uses a smaller number of code-bins to describe the spatial regions, which helps avoid sampling error despite having fewer samples while preserving the valuable spatial information. In contrast to the existing Histogram of Oriented Gradients (HOG) that uses the histogram of the primary edge direction (i.e., gradient orientation) only, we additionally consider the histogram of the secondary edge direction, which provides more meaningful shape information related to the local texture. Experiments on popular facial expression datasets demonstrate the superior performance of the proposed HPED against existing descriptors in a person-independent environment.

• Science of Emotion and Sensibility
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• v.17 no.4
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• pp.61-70
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• 2014

Verbal communication has limits in the interaction between robot and man, and therefore nonverbal communication is required for realizing smoother and more efficient communication and even the emotional expression of the robot. This study derived 7 pieces of nonverbal information based on shopping behavior using the robot designed to support shopping, selected facial expression as the element of the nonverbal information derived, and coded face components through 2D analysis. Also, this study analyzed the significance of the expression of nonverbal information using 3D animation that combines the codes of face components. The analysis showed that the proposed expression method for nonverbal information manifested high level of significance, suggesting the potential of this study as the base line data for the research on nonverbal information. However, the case of 'embarrassment' showed limits in applying the coded face components to shape and requires more systematic studies.

• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.504-511
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• 2010

In this paper, we propose the face region recognition system for City-Security CCTV(Closed Circuit Television) using hippocampal neural network which is modelling of human brain's hippocampus. This system is composed of feature extraction, learning and recognition part. The feature extraction part is constructed using PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis). In the learning part, it can label the features of the image-data which are inputted according to the order of hippocampal neuron structure to reaction-pattern according to the adjustment of a good impression in a dentate gyrus and remove the noise through the auto-associative memory in the CA3 region. In the CA1 region receiving the information of the CA3, it can make long-term memory learned by neuron. Experiments confirm the each recognition rate, that are shape change and light change. The experimental results show that we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to existing methods.

• Journal of Mechanical Science and Technology
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• v.17 no.11
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• pp.1801-1811
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• 2003

An experimental study was conducted to investigate the effects of air-side fouling and cleaning on the performances of various condenser coils used in unitary air-conditioning systems. A total of six condenser coils with different fin geometry and row number were tested. Performance tests were performed at three different conditions: clean-as-received, after fouling, and after cleaning. In all cases, it was observed that the fouling was mostly confined to the frontal face of the heat exchanger as reported in the previous investigations. The amount of deposited dust was more dependent on fin geometry for the single-row heat exchangers than for the double-row heat exchangers. The predominant effect of fouling was to cause a more significant increase in air-side pressure drop than a degradation in heat transfer performance. For the single-row heat exchangers, the pressure drop increased by 28 to 31%, while the heat transfer performance decreased by 7 to 12% at the standard air face velocity of 1.53 m/s depending on fin shape. For the double-row heat exchangers, the pressure drop increased by 22 to 37%, and heat transfer performance decreased by only 4-5% at the same air face velocity. Once the contaminated coils were cleaned according to the given cleaning procedure the original performance of the heat exchangers could almost be recovered completely. The pressure drop could be restored within 1 to 7% and the heat transfer performance could be recovered to within 1 to 5% of the originally clean heat exchangers. Therefore, it is concluded that a periodic application of the specified cleaning technique will be effective in maintaining the thermal performance of the condenser coils.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.1961-1968
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• 2010

The Face Stimulus Assessment is an efficient projective drawing test developed by Betts. This paper categorizes scales which Betts suggested into the following five groups: accuracy of painting, color fit, perception of shape, precision of drawing, and space usage. In this paper, a computer algorithm which objectively evaluates these five scales is suggested. The proposed algorithm defines the areas of the lip, eyes, hair, etc. which take on significant roles in the evaluation of the FSA and based on these factors, it calculates the grade of each scale through the main color and color ratio. The consistency of evaluations between the computer algorithm and the art therapist is measured by the Quadratic Weighted Kappa. By providing objectivity and consistency, the computer algorithm is expected to solve the problem of uncertainty found in art therapists' evaluations of projective drawing tests caused by their subjective judgment, experience, and intuition.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1202-1205
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• 2005

This paper describes a method for vision-based person identification that can detect, track, and recognize person from video using multiple cues: height and dressing colors. The method does not require constrained target's pose or fully frontal face image to identify the person. First, the system, which is connected to a pan-tilt-zoom camera, detects target using motion detection and human cardboard model. The system keeps tracking the moving target while it is trying to identify whether it is a human and identify who it is among the registered persons in the database. To segment the moving target from the background scene, we employ a version of background subtraction technique and some spatial filtering. Once the target is segmented, we then align the target with the generic human cardboard model to verify whether the detected target is a human. If the target is identified as a human, the card board model is also used to segment the body parts to obtain some salient features such as head, torso, and legs. The whole body silhouette is also analyzed to obtain the target's shape information such as height and slimness. We then use these multiple cues (at present, we uses shirt color, trousers color, and body height) to recognize the target using a supervised self-organization process. We preliminary tested the system on a set of 5 subjects with multiple clothes. The recognition rate is 100% if the person is wearing the clothes that were learned before. In case a person wears new dresses the system fail to identify. This means height is not enough to classify persons. We plan to extend the work by adding more cues such as skin color, and face recognition by utilizing the zoom capability of the camera to obtain high resolution view of face; then, evaluate the system with more subjects.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.104-113
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• 2000

In this study, we propose facial image recognition based on wavelet transform and neural network. This algorithm is proposed by following processes. First, two gray level images is captured in constant illumination and, after removing input image noise using a gaussian filter, differential image is obtained between background and face input image, and this image has a process of erosion and dilation. Second, a mask is made from dilation image and background and facial image is divided by projecting the mask into face input image Then, characteristic area of square shape that consists of eyes, a nose, a mouth, eyebrows and cheeks is detected by searching the edge of divided face image. Finally, after characteristic vectors are extracted from performing discrete wavelet transform(DWT) of this characteristic area and is normalized, normalized vectors become neural network input vectors. And recognition processing is performed based on neural network learning. Simulation results show recognition rate of 100 % about learned image and 92% about unlearned image.

• Steel and Composite Structures
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• v.26 no.6
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• pp.771-791
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• 2018

This paper deals with the low velocity impact response and dynamic stresses of composite sandwich truncated conical shells (STCS) with compressible or incompressible core. Impacts are assumed to occur normally over the top face-sheet and the interaction between the impactor and the structure is simulated using a new equivalent three-degree-of-freedom (TDOF) spring-mass-damper (SMD) model. The displacement fields of core and face sheets are considered by higher order and first order shear deformation theory (FSDT), respectively. Considering continuity boundary conditions between the layers, the motion equations are derived based on Hamilton's principal incorporating the curvature, in-plane stress of the core and the structural damping effects based on Kelvin-Voigt model. In order to obtain the contact force, the displacement histories and the dynamic stresses, the differential quadrature method (DQM) is used. The effects of different parameters such as number of the layers of the face sheets, boundary conditions, semi vertex angle of the cone, impact velocity of impactor, trapezoidal shape and in-plane stresses of the core are examined on the low velocity impact response of STCS. Comparison of the present results with those reported by other researchers, confirms the accuracy of the present method. Numerical results show that increasing the impact velocity of the impactor yields to increases in the maximum contact force and deflection, while the contact duration is decreased. In addition, the normal stresses induced in top layer are higher than bottom layer since the top layer is subjected to impact load. Furthermore, with considering structural damping, the contact force and dynamic deflection decrees.

• Journal of KIISE
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• v.44 no.7
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• pp.692-702
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• 2017

Face detection using the LBP based feature descriptor has issues in that it can not represent spatial information between facial shape and facial components such as eyes, nose and mouth. To address these issues, in previous research, a facial image was divided into a number of square sub-regions. However, since the sub-regions are divided into different numbers and sizes, the division criteria of the sub-region suitable for the database used in the experiment is ambiguous, the dimension of the LBP histogram increases in proportion to the number of sub-regions and as the number of sub-regions increases, the sensitivity to facial orientation rotation increases significantly. In this paper, we present a novel facial region segmentation method that can solve in-plane rotation issues associated with LBP based feature descriptors and the number of dimensions of feature descriptors. As a result, the proposed method showed detection accuracy of 99.0278% from a single facial image rotated in orientation.