• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.3 s.246
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• pp.231-240
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• 2006

Immersive virtual reality (VR) for the manufacturing planning helps to shorten the planning times as well as to improve the quality of planning results. However, VR equipment is expensive, both in terms of development efforts and device. Engineers also spend time to manually repair erroneous 3-D shape because of imperfect translation between 3-D engineering CAD model and VR system format. In this paper a method is proposed to link 3-D engineering CAD model to a multichannel visualization system with PC clusters. The multichannel visualization module enables distributed computing for PC clusters, which can reduce the cost of VR experience while offering high performance. Each PC in a cluster renders a particular viewpoint of a scene. Scenes are synchronized by reading parameters from the master scene control module and passing them to client scenes.

• ETRI Journal
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• v.39 no.6
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• pp.832-840
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• 2017

Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection. The detection of temporally overlapping sound events in realistic environments is much more challenging than in monophonic detection problems. In this paper, we present an approach to improve the accuracy of polyphonic sound event detection in multichannel audio based on gated recurrent neural networks in combination with auditory spectral features. In the proposed method, human hearing perception-based spatial and spectral-domain noise-reduced harmonic features are extracted from multichannel audio and used as high-resolution spectral inputs to train gated recurrent neural networks. This provides a fast and stable convergence rate compared to long short-term memory recurrent neural networks. Our evaluation reveals that the proposed method outperforms the conventional approaches.

• Proceedings of the Korean Nuclear Society Conference
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• 2003.05a
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• pp.340.2-340.2
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• 2003

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8A
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• pp.569-580
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• 2003

One of the important advantages of multichannel switches is the incorporation of inherent fault tolerance into the switching fabric. For example, if a link which belongs to the multichannel group fails, the remaining links can assume responsibility for some of the traffic on the failed link. On the other hand, if faults occur in the switching elements, it can lead to erroneous routing and sequencing in the multichannel switch. We investigate several fault localization algorithms in multichannel crossbar ATM switches with a view to early fault recovery, The optimal algorithm gives the best performance in terms of time to localization but is computationally complex which makes it difficult to implement. We develop an on-line algorithm which is computationally mote efficient than the optimal algorithm. We evaluate its performance through simulation. The simulation results show that performance of the on line algorithm is only slightly sub-optimal for both random and bursty traffic. Finally a fault recovery algorithm is described which utilizes the information provided by the fault localization algorithm.

• Information Systems Review
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• v.19 no.3
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• pp.179-199
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• 2017

As users' cross-over shopping behaviors become more popular, many studies have attempted to describe a theoretical mechanism in multichannel environments. Apart from explaining a simplified multichannel user behavior, relevant researchers must deeply understand the mechanism of users' cross-over shopping behavior, which cannot be discovered by employing either existing theories or traditional research methods. Thus, this study explores why, how, and when users conduct cross-over shopping behaviors in multichannel environments by employing a grounded theory approach. In this study, we have interviewed 25 participants who have prior experiences in cross-over shopping. By analyzing the interview manuscripts using the grounded theory approach, we have extracted 118 codes in the coding steps and ultimately presented 28 categories by incorporating similar concepts from those codes. In this qualitative grounded theory study, we have discussed why, how, and when users do cross-over shopping behavior based on our selected codes and categories as well as by listening to the stories of our interviewees. By grounding our proposed framework, which can capture both dynamic information search and purchasing behavior, this study provides an alternative research approach to explain user behavior, thereby bolstering our current understanding of the cross-over shopping behavior of users in multichannel environments.

• 한국초전도학회:학술대회논문집
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• v.11
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• pp.10-10
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• 2001

• Proceedings of the KSLP Conference
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• 2015.03a
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• pp.16-16
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• 2015

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.884-892
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• 1996

An adaptive blind equalizer is used to combat the distortions caused by a nonideal channel without resorting to a training sequence, given the received signal and statistical information of the transmitted signal. Incidentally, a multipath channel may result in a fade which produces intersymbol interference in the received signal. Therefore, a new type of algorithm which can compenste the effects of this fade is required in the multipath channel environment. In this paper, a modified form of adaptive blind equalization algorithm using stop-and-go algorithm for multichannel system is proposed. It is demonstrated via computer simulations that the performance of the proposed multichannel stop-and-go algorithm is much better than that of the conventional multichannel algorithms.

• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.328-338
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• 2005

Technology for compressing low-bitrate multichannel audio coding is being standardized owing to the increasing need of consumer for multichannel audio contents. In this paper we propose the sound source location cue coding (SSLCC) for extremely compressing multichannel audio to be suitable at the narrow bandwidth transmission environment. To improve the compression capability of the conventional binaural cue coding(BCC), the SSLCC adopts the virtual source location information (VSLI) as a spatial cue parameter, a symmetric uniform quantizer, and Huffman coder. The objective and subjective assessment results show that the SSLCC provides lower bitrate and better audio quality than conventional BCC method.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1486-1495
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• 2020

The analysis of digital microscopy images plays a vital role in computer-aided diagnosis (CAD) and prognosis. The main purpose of this paper is to develop a machine learning technique to predict the histological grades in prostate biopsy. To perform a multiclass classification, an AI-based deep learning algorithm, a multichannel convolutional neural network (MCCNN) was developed by connecting layers with artificial neurons inspired by the human brain system. The histological grades that were used for the analysis are benign, grade 3, grade 4, and grade 5. The proposed approach aims to classify multiple patterns of images extracted from the whole slide image (WSI) of a prostate biopsy based on the Gleason grading system. The Multichannel Convolution Neural Network (MCCNN) model takes three input channels (Red, Green, and Blue) to extract the computational features from each channel and concatenate them for multiclass classification. Stain normalization was carried out for each histological grade to standardize the intensity and contrast level in the image. The proposed model has been trained, validated, and tested with the histopathological images and has achieved an average accuracy of 96.4%, 94.6%, and 95.1%, respectively.