• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1620-1634
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• 2023

This paper studies the task of embedding nodes with multiple graphs representing multiple information channels, which is useful in a large volume of network clustering tasks. By learning a node using multiple graphs, various characteristics of the node can be represented and embedded stably. Existing studies using multi-channel networks have been conducted by integrating heterogeneous graphs or limiting common nodes appearing in multiple graphs to have similar embeddings. Although these methods effectively represent nodes, it also has limitations by assuming that all networks provide the same amount of information. This paper proposes a method to overcome these limitations; The proposed method gives different weights according to the source graph when embedding nodes; the characteristics of the graph with more important information can be reflected more in the node. To this end, a novel method incorporating a multi-channel gate layer is proposed to weigh more important channels and ignore unnecessary data to embed a node with multiple graphs. Empirical experiments demonstrate the effectiveness of the proposed multi-channel-based embedding methods.

• 융합신호처리학회논문지
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• 제12권3호
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• pp.176-180
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• 2011

Even though algorithms for watermark embedding and extraction step are important issue for digital watermarking, watermark selection and post-processing can give us an opportunity to improve our algorithms and achieve higher performance. For this reason, we summarized the possibilities of improvements for digital watermarking by referring to the watermark merging techniques rather than embedding and extraction algorithms in this paper. We chose Cox's function as main embedding and extraction algorithm, and multiple barcode watermarks as a watermark. Each bit of the multiple copies of barcode watermark was embedded into a gray-scale image with Cox's embedding function. After extracting the numbers of watermark, we applied the watermark merging techniques; including the simple merging, N-step iterated merging, recover merging and combination of iterated-recover merging. Main consequence of our paper was the fact of finding out how multiple barcode watermarks and merging techniques can give us opportunities to improve the performance of algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4144-4165
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• 2018

Software-defined networking (SDN) has emerged as a promising technology for network programmability and experiments. In this work, we focus on virtual network embedding in multiple controllers SDN network. In SDN virtualization environment, virtual SDN networks (vSDNs) operate on the shared substrate network and managed by their each controller, the placement and load of controllers affect vSDN embedding process. We consider controller placement, vSDN embedding, controller adjustment as a joint problem, together considering different quality of service (QoS) requirement for users, formulate the problem into mathematical models to minimize the average time delay of control paths, the load imbalance degree of controllers and embedding cost. We propose a heuristic method which places controllers and partitions control domains according to substrate SDN network, embeds different QoS constraint vSDN requests by corresponding algorithms, and migrates switches between control domains to realize load balance of controllers. The simulation results show that the proposed method can satisfy different QoS requirement of tenants, keep load balance between controllers, and work well in the acceptance ratio and revenue to cost ratio for vSDN embedding.

• ETRI Journal
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• 제46권1호
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• pp.35-47
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• 2024

This study introduces CR-M-SpanBERT, a coreference resolution (CR) model that utilizes multiple embedding-based span bidirectional encoder representations from transformers, for antecedent recognition in natural language (NL) text. Information extraction studies aimed to extract knowledge from NL text autonomously and cost-effectively. However, the extracted information may not represent knowledge accurately owing to the presence of ambiguous entities. Therefore, we propose a CR model that identifies mentions referring to the same entity in NL text. In the case of CR, it is necessary to understand both the syntax and semantics of the NL text simultaneously. Therefore, multiple embeddings are generated for CR, which can include syntactic and semantic information for each word. We evaluate the effectiveness of CR-M-SpanBERT by comparing it to a model that uses SpanBERT as the language model in CR studies. The results demonstrate that our proposed deep neural network model achieves high-recognition accuracy for extracting antecedents from NL text. Additionally, it requires fewer epochs to achieve an average F1 accuracy greater than 75% compared with the conventional SpanBERT approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권1호
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• pp.145-164
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• 2014

Network virtualization provides a promising tool to allow multiple heterogeneous virtual networks to run on a shared substrate network simultaneously. A long-standing challenge in network virtualization is the Virtual Network Embedding (VNE) problem: how to embed virtual networks onto specific physical nodes and links in the substrate network effectively. Recent research presents several heuristic algorithms that only consider single topological attribute of networks, which may lead to decreased utilization of resources. In this paper, we introduce six complementary characteristics that reflect different topological attributes, and propose three topology-aware VNE algorithms by leveraging the respective advantages of different characteristics. In addition, a new KS-core decomposition algorithm based on two characteristics is devised to better disentangle the hierarchical topological structure of virtual networks. Due to the overall consideration of topological attributes of substrate and virtual networks by using multiple characteristics, our study better coordinates node and link embedding. Extensive simulations demonstrate that our proposed algorithms improve the long-term average revenue, acceptance ratio, and revenue/cost ratio compared to previous algorithms.

• International Journal of Oral Biology
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• 제40권4호
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• pp.183-187
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• 2015

The present study was aimed to evaluate the influence of glutaraldehyde (GA) concentration on multiple electron microscopic (EM) immunostaining using pre-embedding peroxidase and post-embedding immunogold method. Influence of various concentrations of GA included in the fixative on immuoreactivity was assessed in the multiple immunostaining using antisera against anti-transient receptor potential vanilloid 1 (TRPV1) for peroxidase staining and anti-GABA for immunogold labeling in the rat trigeminal caudal nucleus. Anti-TRPV1 antiserum had specificity in pre-embedding peroxidase staining when tissues were fixed with fixative containing paraformaldehyde (PFA) alone. Immunoreactivity for TRPV1 was specific in tissues fixed with fixative containing 0.5% GA at both perfusion and postfixation steps, though the immunoreactivity was weaker than in tissues fixed with fixative containing PFA alone. Tissues fixed with fixative containing 0.5% GA at the perfusion and postfixation steps showed specific immunogold staining for GABA. The results of the present study indicate that GA concentration is critical for immunoreactivity to antigens such as TRPV1 and GABA. This study also suggests that the appropriate GA concentration is 0.5% for multiple immunostaining with peroxidase labeling for TRPV1 and immunogold labeling for GABA.

• 지식경영연구
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• 제21권1호
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• pp.99-116
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• 2020

Text is the most widely used means of exchanging or expressing knowledge and information in the real world. Recently, researches on structuring unstructured text data for text analysis have been actively performed. One of the most representative document embedding method (i.e. doc2Vec) generates a single vector for each document using the whole corpus included in the document. This causes a limitation that the document vector is affected by not only core words but also other miscellaneous words. Additionally, the traditional document embedding algorithms map each document into only one vector. Therefore, it is not easy to represent a complex document with interdisciplinary subjects into a single vector properly by the traditional approach. In this paper, we introduce a multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. After introducing the previous study on multi-vector document embedding, we visually analyze the effects of the multi-vector document embedding method. Firstly, the new method vectorizes the document using only predefined keywords instead of the entire words. Secondly, the new method decomposes various subjects included in the document and generates multiple vectors for each document. The experiments for about three thousands of academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the multi-vector based method, we ascertained that the information and knowledge in complex documents can be represented more accurately by eliminating the interference among subjects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.522-541
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• 2016

Network virtualization provides an effective way to overcome the Internet ossification problem. As one of the main challenges in network virtualization, virtual network embedding refers to mapping multiple virtual networks onto a shared substrate network. However, existing heuristic embedding algorithms evaluate the embedding potential of the nodes simply by the product of different resource attributes, which would result in an unbalanced embedding. Furthermore, ignoring the hops of substrate paths that the virtual links would be mapped onto may restrict the ability of the substrate network to accept additional virtual network requests, and lead to low utilization rate of resource. In this paper, we introduce and extend five node attributes that quantify the embedding potential of the nodes from both the local and global views, and adopt the technique for order preference by similarity ideal solution (TOPSIS) to rank the nodes, aiming at balancing different node attributes to increase the utilization rate of resource. Moreover, we propose a novel two-stage virtual network embedding algorithm, which maps the virtual nodes onto the substrate nodes according to the node ranks, and adopts a shortest path-based algorithm to map the virtual links. Simulation results show that the new algorithm significantly increases the long-term average revenue, the long-term revenue to cost ratio and the acceptance ratio.

• 소성∙가공
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• 제14권9호통권81호
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• pp.756-763
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• 2005

The applicability and productivity of hydroforming process can be increased by combining pre- and post-forming processes such as the bending, piercing and embedding process. For the fabrication of automotive parts, the hollow bodies with connecting nuts are widely used to connect parts together. Hollow body with connecting nuts has been conventionally fabricated by welding nuts or screwing in autobody screws. It requires multiple steps and devices fur the welding and/or screwing Therefore in this study, hydro-embedding process that combines the hydraulic embedding of connecting element(nut) with hydroforming process is investigated. Studies on the hydro-embedding technology have been performed to optimize the shape of the connecting element by analyzing the deformed mode of the embedded tube The effects of the shape of the screw tip, screw thread and shape of thread on the connection force between the tube and the connecting element have been investigated to optimize the shape of connecting element. Finite element analysis has also been performed to provide deformation behaviors of the tube surrounding a hole produced by hydro-embedding.

• 지능정보연구
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• 제25권3호
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• pp.19-41
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• 2019

텍스트 데이터에 대한 다양한 분석을 위해 최근 비정형 텍스트 데이터를 구조화하는 방안에 대한 연구가 활발하게 이루어지고 있다. doc2Vec으로 대표되는 기존 문서 임베딩 방법은 문서가 포함한 모든 단어를 사용하여 벡터를 만들기 때문에, 문서 벡터가 핵심 단어뿐 아니라 주변 단어의 영향도 함께 받는다는 한계가 있다. 또한 기존 문서 임베딩 방법은 하나의 문서가 하나의 벡터로 표현되기 때문에, 다양한 주제를 복합적으로 갖는 복합 문서를 정확하게 사상하기 어렵다는 한계를 갖는다. 본 논문에서는 기존의 문서 임베딩이 갖는 이러한 두 가지 한계를 극복하기 위해 다중 벡터 문서 임베딩 방법론을 새롭게 제안한다. 구체적으로 제안 방법론은 전체 단어가 아닌 핵심 단어만 이용하여 문서를 벡터화하고, 문서가 포함하는 다양한 주제를 분해하여 하나의 문서를 여러 벡터의 집합으로 표현한다. KISS에서 수집한 총 3,147개의 논문에 대한 실험을 통해 복합 문서를 단일 벡터로 표현하는 경우의 벡터 왜곡 현상을 확인하였으며, 복합 문서를 의미적으로 분해하여 다중 벡터로 나타내는 제안 방법론에 의해 이러한 왜곡 현상을 보정하고 각 문서를 더욱 정확하게 임베딩할 수 있음을 확인하였다.