• Korean Journal of Bronchoesophagology
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• v.2 no.1
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• pp.46-56
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• 1996

Perichondrium is generally used for reconstruction of airway and successful regeneration of cartilage framework using perichondrium are reported by several authors. It has many advantages for airway reconstruction. It can maintain the stable framework and it has higher flexibility so it's easy to design according to the shape of defects. It resist strongly against infection process. Its airtightness and rapid mucosalization enables to predict good postoperative recovery and results. To investigate the differences in cartilage regeneration between avascular and vascularized perichondrial flap, this study was designed with vascularized flaps composed of vascularized perichondrium and central auricular artery and vein. Morphologic study was performed to determine the fate of the grafted perichondrium at regular intervals using light microscopy with H & E stain. Chondrogenic potential and formation of cartilaginous plate of experimental group was superior than in the control group. Grafted perichondrium was fed by consistent feeding vessel. At 6 weeks, the regenerated cartilage could hardly be distinguished form the normal cartilage framework but in control group, regenerated cartilaginous tissue was generally immature in same period. In conclusion, this study indicated that consistent vasculature to grafed tissue is the essential factor for successful reconstruction of cartilaginous framework.

• IE interfaces
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• v.25 no.1
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• pp.40-51
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• 2012

Today, many manufacturing companies realize that collaboration is crucial for their survival. Especially, in the perspective of quality, the importance of collaboration is emphasized because economic loss increases exponentially while defective parts go through the process in supply chain. However, the manufacturing companies are facing two main difficulties in implementing collaborative relationships with their suppliers. First, it is difficult for the suppliers to produce reliable products due to their obsolete facilities. The problem gets worse for second- or third-tire vendors. Second, the companies experience the lack of universally understandable set of terminology and effective methodologies for knowledge representation. Ontology is one of the best approaches to expressing and processing a domain knowledge. In this paper, we propose the manufacturing ontology-based quality prediction framework to represent and share the knowledge of industrial environment and to predict product quality in manufacturing processes. In addition, we develop the ontology-based quality prediction system based on the proposed framework. We carried out a series of experiments for an injection molding process at an automotive part supplier. The experimental results demonstrated that the proposed framework and system can be successfully applicable in manufacturing industry.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1909-1918
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• 2016

This paper presents a unified framework for joint Convolutional Neural Network (CNN) based vehicle detection by leveraging multi-spectral image pairs. With the observation that under challenging environments such as night vision and limited light source, vehicle detection in a single color image can be more tractable by using additional far-infrared (FIR) image, we design joint CNN architecture for both RGB and FIR image pairs. We assume that a score map from joint CNN applied to overall image can be considered as confidence of vehicle existence. To deal with various scale ratios of vehicle candidates, multi-scale images are first generated scaling an image according to possible scale ratio of vehicles. The vehicle candidates are then detected on local maximal on each score maps. The generation of overlapped candidates is prevented with non-maximal suppression on multi-scale score maps. The experimental results show that our framework have superior performance than conventional methods with a joint framework of multi-spectral image pairs reducing false positive generated by conventional vehicle detection framework using only single color image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.1909-1928
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• 2020

Predicting human mobility has always been an important task in Location-based Social Network. Previous efforts fail to capture spatial dependence effectively, mainly reflected in weakening the location topology information. In this paper, we propose a neural network-based method which can capture spatial-temporal dependence to predict the next location of a person. Specifically, we involve a graph convolutional network (GCN) based on a seq2seq framework to capture the location topology information and temporal dependence, respectively. The encoder of the seq2seq framework first generates the hidden state and cell state of the historical trajectories. The GCN is then used to generate graph embeddings of the location topology graph. Finally, we predict future trajectories by aggregated temporal dependence and graph embeddings in the decoder. For evaluation, we leverage two real-world datasets, Foursquare and Gowalla. The experimental results demonstrate that our model has a better performance than the compared models.

• Journal of the Korean Society for Library and Information Science
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• v.41 no.1
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• pp.185-214
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• 2007

This study intends to design the bibliographic framework based on IFLA-FRBR model for television program materials and to evaluate this in terms of effectiveness of retrieval and usability of the system. The FRBR model supplies mote suitable bibliographic framework of audio-visual material which has a sufficient hierarchical relations and relative bibliographical records. The followings are research methods designed by this study; (1) The experimental metadata system named it FbCS based on FRBR was developed by using the entity-related database and composed of multi-layed and hierarchy. FbCS is developed through benchmarking of a case study for iMMix model in Netherlands based on FRBR. (2) To evaluate effectiveness of retrieval and usability of FbCS, this study made a experiment and survey by user groups of professionals.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1214-1222
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• 2020

Pervasive augmented reality (AR) technology can be used to efficiently search for the required information regarding products in stores through text augmentation in an Internet of Things (IoT) environment. The evolution of context awareness and image processing technologies are the main driving forces that realize this type of AR service. One of the problems to be addressed in the service is that augmented objects are fixed and cannot be replaced efficiently in real time. To address this problem, a real-time mobile AR framework is proposed. In this framework, an optimal object to be augmented is selected based on object similarity comparison, and the augmented objects are efficiently managed using distributed metadata servers to adapt to the user requirements, in a given situation. To evaluate the feasibility of the proposed framework, a prototype system was implemented, and a qualitative evaluation based on questionnaires was conducted. The experimental results show that the proposed framework provides a better user experience than existing features in smartphones, and through fast AR service, the users are able to conveniently obtain additional information on products or objects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3904-3917
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• 2017

IP multimedia subsystem (IMS) is an open standardized architecture for delivering multimedia service over IP network in a route-agnostic manner. With the increasing popularity of conversational class service, the delivery of a traffic flow with a certain bandwidth demand over a single network path is either not possible or not cost-effective. Multipath transmission is considered to be a promising solution to provide high-quality delivery service. This paper proposes a software defined service overlay network (SDSON) based multipath transmission framework for IMS, which is complementary to existing network architecture. The framework transforms original two-party session negotiation into three-party session negotiation that supports participants to negotiate multipath transmission capacity and path information by signaling message. Based on existing IETF standards, SIP and SDP are scalable to support these functions. Finally, the proposed framework is fully implemented on open source platform and examined by experiments. Experimental results show that multipath-enabled IMS is an effective way to improve the delivery performance of conversational class service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.86-109
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• 2019

Cloud computing systems' performance is still a central focus of research for determining optimal resource utilization. Running several existing benchmarks simultaneously serves to acquire performance information from specific cloud system resources. However, the complexity of monitoring the existing performance of computing systems is a challenge requiring an efficient and interactive user directing performance-monitoring system. In this paper, we propose an effective multivariate control framework for monitoring cloud systems performance. The proposed framework utilizes the hardware cloud systems performance metrics, collects and displays the performance measurements in terms of meaningful graphics, stores the graphical information in a database, and provides the data on-demand without requiring a third party software. We present performance metrics in terms of CPU usage, RAM availability, number of cloud active machines, and number of running processes on the selected machines that can be monitored at a high control level by either using a cloud service customer or a cloud service provider. The experimental results show that the proposed framework is reliable, scalable, precise, and thus outperforming its counterparts in the field of monitoring cloud performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4024-4041
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• 2022

Robust reversible watermarking has attracted widespread attention in the field of information hiding in recent years. It should not only have robustness against attacks in transmission but also meet the reversibility of distortion-free transmission. According to our best knowledge, the most recent robust reversible watermarking methods adopt a single image as the carrier, which might lead to low efficiency in terms of carrier utilization. To address the issue, a novel dual-image robust reversible watermarking framework is proposed in this paper to effectively utilize the correlation between both carriers (namely dual images) and thus improve the efficiency of carrier utilization. In the dual-image robust reversible watermarking framework, a two-layer robust watermarking mechanism is designed to further improve the algorithm performances, i.e., embedding capacity and robustness. In addition, an optimization model is built to determine the parameters. Finally, the proposed framework is applied in different domains (namely domain-independent), i.e., Slantlet Transform and Singular Value Decomposition domain, and Zernike moments, respectively to demonstrate its effectiveness and generality. Experimental results demonstrate the superiority of the proposed dual-image robust reversible watermarking framework.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.471-485
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• 2023

As data sharing increases explosively, such information encoded in QR code is completely public as private messages are not securely protected. This paper proposes a new 'PROMISE' framework for hiding information based on the QR code projection matrix by using image segmentation without modifying the essential QR code characteristics. Projection matrix mapping, matrix scrambling, fusion image segmentation and steganography with SEL(secret embedding logic) are part of the PROMISE framework. The QR code could be mapped to determine the segmentation site of the fusion image as a binary information matrix. To further protect the site information, matrix scrambling could be adopted after the mapping phase. Image segmentation is then performed on the fusion image and the SEL module is applied to embed the secret message into the fusion image. Matrix transformation and SEL parameters should be uploaded to the server as the secret key for authorized users to decode the private message. And it was possible to further obtain the private message hidden by the framework we proposed. Experimental findings show that when compared to some traditional information hiding methods, better anti-detection performance, greater secret key space and lower complexity could be obtained in our work.