• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.121-127
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• 2009

The Multistage Feature-based Classification Model(MFCM) is proposed in this paper. MFCM does not use whole feature vectors extracted from the original data at once to classify each data, but use only groups related to each feature vector to classify separately. In the training stage, the contribution rate calculated from each feature vector group is drew throughout the accuracy of each feature vector group and then, in the testing stage, the final classification result is obtained by applying weights corresponding to the contribution rate of each feature vector group. In this paper, the proposed MFCM algorithm is applied to the problem of music genre classification. The results demonstrate that the proposed MFCM outperforms conventional algorithms by 7% - 13% on average in terms of classification accuracy.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.36-41
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• 2010

An advanced form of Multistage Feature-based Classification Model(AMFCM), called AMFCM, is proposed in this paper. AMFCM like MFCM does not use the concatenated form of available feature vectors extracted from original data to classify each data, but uses only groups related to each feature vector to classify separately. The prpposed AMFCM improves the contribution rate used in MFCM and proposes a confusion table for each local classifier using a specific feature vector group. The confusion table for each local classifier contains accuracy information of each local classifier on each class of data. The proposed AMFCM is applied to the problem of music genre classification on a set of music data. The results demonstrate that the proposed AMFCM outperforms MFCM by 8% - 15% on average in terms of classification accuracy depending on the grouping algorithms used for local classifiers and the number of clusters.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.10
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• pp.807-821
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• 1991

A mulitpath MIN(Multistage Interconnection Network), CSMP(Chained Shuffle Multi-Path) network, is proposed, having fault-tolerance and dynamic reroutability. The number of stages and the number of links between adjacent stagges are the same as in single path MINs, so the overall hardware complexity is considerably reduced in comparison with other multipath MINs. The CSMP networks feature links between switches belonging to the same state, forming loops of switches. The network can tolerate multiple faults, up to (N/4)*(log$_2$N-1), having occured in any stages including the first and the last ones(N:NO. of input). To analyze reliability, terminal reliability (TR) and mean time to failure( MTTE) age given for the networks, and the TR figures are compared to those of other static and dynamic rerouting multipath MINs. Also the MTTE figures are compared. The performance of the proposed network with respect to its bandwidth (BW) and probability of acceptance(PA) is analyzed and is compared to that of other more complex multipath MINs. The cost efficiency analysis of reliability and performance shows that the network is more cost-effective than other previously proposed fault-tolerant multipath MINs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2068-2082
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• 2023

With the rapid development of deep learning, Depth Map Super-Resolution (DMSR) method has achieved more advanced performances. However, when the upsampling rate is very large, it is difficult to capture the structural consistency between color features and depth features by these DMSR methods. Therefore, we propose a color-image guided DMSR method based on iterative depth feature enhancement. Considering the feature difference between high-quality color features and low-quality depth features, we propose to decompose the depth features into High-Frequency (HF) and Low-Frequency (LF) components. Due to structural homogeneity of depth HF components and HF color features, only HF color features are used to enhance the depth HF features without using the LF color features. Before the HF and LF depth feature decomposition, the LF component of the previous depth decomposition and the updated HF component are combined together. After decomposing and reorganizing recursively-updated features, we combine all the depth LF features with the final updated depth HF features to obtain the enhanced-depth features. Next, the enhanced-depth features are input into the multistage depth map fusion reconstruction block, in which the cross enhancement module is introduced into the reconstruction block to fully mine the spatial correlation of depth map by interleaving various features between different convolution groups. Experimental results can show that the two objective assessments of root mean square error and mean absolute deviation of the proposed method are superior to those of many latest DMSR methods.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.9
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• pp.1117-1125
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• 2013

A finite element based approach that can be used to investigate the plane-view shape and crop loss of a material during plate rolling is presented. We employed a three-dimensional finite element model to continuously simulate the shape change of the head and tail of a plate as the number of rolling passes increases. The main feature of the proposed model lies in the fact that the multistage rolling can be simulated without a break because the rolling direction of the material is reversibly controlled as the roll gap sequentially decreases. The material constants required in the finite element analysis were experimentally obtained by hot tensile tests. We also performed a pilot hot plate rolling test to verify the usefulness of the proposed finite element model. Results reveal that the computed plane-view shapes as well as crop losses by the proposed finite element model were in good agreement with the measured ones. The crop losses predicted by the proposed model were within 5% of those measured from the pilot hot plate rolling test.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.10
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• pp.68-77
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• 1990

In this paper, a neural patten recognition method for the automatic circuit diagram reading system is proposed. The proposed procedure to recognize a deformed logic symbols is composed of three stages: feature detection, log mapping, and pattern classification. In the feature detection stage, a modified competitive learning algorithm where each pattern has the inhibition weight as well as the activation weight is developed. The global information of hand-written logic symbols is obtained by the feature detection neural network having both the inhibition and activation weights. The obtained global data is then transformed into a log space by the conformal mapping where according to the Schwartz's theory about the human visual signal process-ing, the degree of rotation and the scale change are mapped into the translation change. Logic symbols are finally classified by a three layer perceptron trained by the error back propagation algorithm. The computer simulation demonstrates that the proposed multistage neural network system can recognize well the deformed patterns of hand-written logic circuit diagrams.