• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.5
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• pp.628-638
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• 1998

This study, the second in two part series, focuses on effect of product involvement and consumer knowledge on consumers' perceptions of six dimensions of clothing price: price -quality schema, prestige sensitivity: sale proneness, price mavenism, value consciousness, and price consciousness. Additionally, correlation between product involvement and consumer knowledge was examined. Multi-item measures of six dimensions of price, product involvement, and consumer knowledge were included in the questionnaires. The subjects were 264 college students living in Seoul, Korea. The data were collected by self-administered questionnaires and analyzed by factor analysis, regression analysis, and correlation analysis using SPSS PC. In addition, two focus group interviews were conducted to obtain qualitative data for the result interpretation. Results of this study showed that the degree of consumers' product involvement significantly affects consumers' perceptions of each price dimension except price consciousness dimension. Consumers' knowledge about clothing has a significant effect on consumers' perceptions of all price dimensions. Dimensions of product involvement and consumer knowledge were partially correlated. Marketing implications based on these results were suggested.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1784-1786
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• 2004

PD(Partial Discharge) signal emitted from PD sources has their intrinsic features in the region of time and frequency. STFT(Short Time Fourier Transform) shows time-frequency distribution at the same time. 2-Dimensional matrices(33${\times}$77) from STFT for PD pulse signals are a good feature vectors and can be decreased in dimension by wavelet 2D data compression technique. Decreased feature vectors(13${\times}$24) were used as inputs of Back-propagation ANN(Artificial Neural Network) for discrimination of Multi-PD sources(air discharge sources(3), surface discharge(1)). They are a good feature vectors for discriminating Multi-PD sources.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.604-610
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• 2004

Ultraviolet-nanoimprint lithography (UV-NIL) is a promising method for cost-effectively defining nanoscale structures at room temperature and low pressure. Since the resolution of transferred nanostructures depends strongly upon that of nanostamps, the nanostamp fabrication technology is a key technology to UV-NIL. In this paper, a $5\times5\times0.09$ in. quartz stamp whose critical dimension is 377 nm was fabricated using the etching process in which a Cr film was employed as a hard mask for transferring nanostructures onto the quartz plate. To effectively apply the fabricated 5-in. stamp to UV-NIL on a 4-in. Si wafer, we have proposed a new UV-NIL process using a multi-dispensing method as a way to supply resist on a wafer. Experiments have shown that the multi-dispensing method can enable UV-NIL using a large-area stamp.

• Journal of Korea Multimedia Society
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• v.19 no.10
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• pp.1747-1758
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• 2016

In this paper, we propose a new fast Hough transform to improve the processing time and line detection of Hough transform that is widely used in various vision systems. First, for the fast processing time, we reduce the number of features by using multi-statistical methods and also reduce the dimension of angle through six separate directions. Next, for improving the line detection, we effectively detect the lines of various directions by designing the line detection method which detects line in proportion to the number of features in six separate directions. The proposed method was evaluated with previous methods and obtained the excellent results. The processing time was improved in about 20% to 50% and line detection was performed better in various directions than conventional methods with experimental images.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.397-400
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• 2004

The direct extrusion with porthole die can produce condenser tube which has the competitive power in costs and qualities compared with the existing conform extrusion. In general, porthole die extrusion has a great advantage in the forming that produces the hollow sections difficult to produce by conventional extrusion with a mandrel on the stem. Especially, condenser tube manufactured by porthole die belongs to sophisticated part and demands tighter dimension tolerance and higher surface finish than any other part. In order to confirm the general of porthole die extrusion, we perform the 3D FE analysis of hot porthole extrusion in non-steady state by using DEFORM 3D and investigate a pattern of elastic deformation for porthole die through the stress analysis using ANSYS 5.5 during extrusion process.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.9
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• pp.857-862
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• 2011

This paper studies the output consensus problem for a class of heterogeneous linear multi-agent systems under a fixed directed communication network. The dynamics, as well as its dimension, of each agent can widely differ from the others, but all the agents are assumed to have the same transfer matrix. In addition, only the system outputs are constrained to be delivered through the network. Under these conditions, we show that the output consensus is reached by a group of identical controllers, which is designed to achieve the state consensus for the homogeneous multi-agent system obtained from the minimal realization of the transfer matrix. Finally, an example is given to demonstrate the proposed result.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.3
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• pp.222-226
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• 2010

In this paper we study the consensus problem of SISO (Single-Input Single-Output) linear multi-agent systems under a fixed weighted undirected network communication. The dynamics of each agent can be of any order, while it is constrained to be neutrally stable. Based on a simple root locus argument, we show that the problem at hand is always solvable if the gain and zeros of the dynamic compensator, of which dimension is the same as the number of plant poles on the imaginary axis, are chosen suitably, finally the result is successfully verified by an example.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3186-3203
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• 2021

In this paper, a multi-scale local difference directional number (MLDDN) pattern is proposed for pig identification. Firstly, the color images of individual pig are converted into grey images by the most significant bits (MSB) quantization, which makes the grey values have better discrimination. Then, Gabor amplitude and phase responses on different scales are obtained by convoluting the grey images with Gabor masks. Next, by calculating the main difference of local edge directions instead of traditionally edge information, the directional numbers of Gabor amplitude and phase responses are encoded. Finally, the block histograms of the encoded images are concatenated on each scale, and the maximum pooling is adopted on different scales to avoid the high feature dimension. Experimental results on two pigsties show that MLDDN impressively outperforms the other widely used local descriptors.

• Journal of Multimedia Information System
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• v.9 no.3
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• pp.183-190
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• 2022

Image-based 3D object detection is one of the important and difficult problems in autonomous driving and robotics, and aims to find and represent the location, dimension and orientation of the object of interest. It generates three dimensional (3D) bounding boxes with only 2D images obtained from cameras, so there is no need for devices that provide accurate depth information such as LiDAR or Radar. Image-based methods can be divided into three main categories: monocular, stereo, and multi-view 3D object detection. In this paper, we investigate the recent state-of-the-art models of the above three categories. In the multi-view 3D object detection, which appeared together with the release of the new benchmark datasets, NuScenes and Waymo, we discuss the differences from the existing monocular and stereo methods. Also, we analyze their performance and discuss the advantages and disadvantages of them. Finally, we conclude the remaining challenges and a future direction in this field.

• Journal of Auto-vehicle Safety Association
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• v.16 no.1
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• pp.29-34
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• 2024

This paper presents a multi-label lane detection method for autonomous vehicles based on deep learning. The proposed algorithm can detect two types of lanes: center lane and normal lane. The algorithm uses a convolution neural network with an encoder-decoder architecture to extract features from input images and produce a multi-label heatmap for predicting lane's label. This architecture has the potential to detect more diverse types of lanes in that it can add the number of labels by extending the heatmap's dimension. The proposed algorithm was tested on an OpenLane dataset and achieved 85 Frames Per Second (FPS) in end to-end inference time. The results demonstrate the usability and computational efficiency of the proposed algorithm for the lane detection in autonomous vehicles.