• 전자공학회논문지B
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• 제32B권8호
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• pp.1057-1063
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• 1995

This paper describes an approach for constucting a Rectilinear Steiner Tree (RST) derivable from a Minimum Spanning Tree (MST), using Associative Processor (AP). We propose a fast parallel algorithm using AP's basic algorithms which can be realized by the processing capability of rudimentary logic and the selective matching capability of Content- Addressable Memory (CAM). The main idea behind the proposed algorithm is to maximize the overlaps between the consecutive edges in MST, thus minimizing the cost of a RST. An efficient parallel linear algorithm with O(n) complexity to construct a RST is proposed using an algorithm to find a MST, where n is the number of nodes. A node insertion method is introduced to allow the Z-type layout. The routing process which only depends on the neighbor edges and the no-rerouting strategy both help to speed up finding a RST.

• Journal of Mechanical Science and Technology
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• 제20권11호
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• pp.1881-1890
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• 2006

In this paper, a Petrov-Galerkin natural element method (PG-NEM) based upon the natural neighbor concept is presented for the free vibration and dynamic response analyses of two-dimensional linear elastic structures. A problem domain is discretized with a finite number of nodes and the trial basis functions are defined with the help of the Voronoi diagram. Meanwhile, the test basis functions are supported by Delaunay triangles for the accurate and easy numerical integration with the conventional Gauss quadrature rule. The numerical accuracy and stability of the proposed method are verified through illustrative numerical tests.

• 경영과학
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• 제18권2호
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• pp.25-38
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• 2001

The well-known back-propagation algorithm for multi-layered neural network has successfully been applied to pattern c1assification problems with remarkable flexibility. Recently. the multi-layered neural network is used as a powerful data mining tool. Nevertheless, in many cases with complex boundary of classification, the successful learning is not guaranteed and the problems of long learning time and local minimum attraction restrict the field application. In this paper, an Improved learning procedure of multi-layered neural network is proposed. The procedure is based on the generalized delta rule but it is particular in the point that the architecture of network is not fixed but enlarged during learning. That is, the number of hidden nodes or hidden layers are increased to help finding the classification boundary and such procedure is controlled by entropy evaluation. The learning speed and the pattern classification performance are analyzed and compared with the back-propagation algorithm.

• 보건교육건강증진학회지
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• 제33권3호
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• pp.1-11
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• 2016

Objectives: The challenging issue of public health program is to strengthen partnership and network between health resources. This study identified the structure and characteristics of school health program network. Methods: In this paper we collected data from schools and organizations in 4 local communities in 2014 that participated to school health program. Using social network analysis techniques we measured the number of component, diameter, density, average degree, node centralization for each network. Results: We determined that networks shared some common organizational structure such as less density, low average degree, and short diameter. Networks were dominated by the health center, and directions of collaborations between nodes were mostly one-way. Conclusions: These findings can help to depict the network of school health program. The further research is necessary to define causal relationship between network effectiveness and public health outcomes.

• Journal of electromagnetic engineering and science
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• 제11권1호
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• pp.5-10
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• 2011

We propose an adaptive cooperative scheme with a multi-relay node that achieves high bandwidth efficiency and achieves better SEP performance. In the proposed protocol, if the quality of the direct channel is better than that of the all channels from relays to destination, the source will transmit directly to the destination. Otherwise, the source broadcasts the signal and then a potential relay will be chosen to help the source. A re-transmission will also occur if the potential relay cannot be detected. The spectral efficiency is first derived by calculating the probability of each mode, i.e., direct and cooperation transmission. Subsequently, the SEP performance of M-PSK modulation for the scheme is analyzed by considering each event where the source transmits data to the destination. Finally, the obtained analytical results are verified through computer simulations.

• International Journal of Contents
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• 제17권3호
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• pp.1-14
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• 2021

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, a lot of efforts have been made in the field of data science to help combat against this disease. Among them, forecasting the number of cases of infection is a crucial problem to predict the development of the pandemic. Many deep learning-based models can be applied to solve this type of time series problem. In this research, we would like to take a step forward to incorporate spatial data (geography) with time series data to forecast the cases of region-level infection simultaneously. Specifically, we model a single spatio-temporal graph, in which nodes represent the geographic regions, spatial edges represent the distance between each pair of regions, and temporal edges indicate the node features through time. We evaluate this approach in COVID-19 in a Korean dataset, and we show a decrease of approximately 10% in both RMSE and MAE, and a significant boost to the training speed compared to the baseline models. Moreover, the training efficiency allows this approach to be extended for a large-scale spatio-temporal dataset.

• International journal of thyroidology
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• 제11권2호
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• pp.99-108
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• 2018

Most thyroid cancers are well-differentiated cancers and have a very good prognosis. About 10% of thyroid cancer, however, invades the surrounding tissues, causing local recurrence and distant metastasis, and eventually affecting survival rate. In locally advanced thyroid cancers, the invasion of trachea, larynx and esophagus, can be occurred by primary tumor and may also result in lymph nodes metastasis. Surgical resection is still mainstay for the treatment of locally advanced thyroid cancer. The main purpose of the surgical resection is to eliminate the cancer completely, therefore, it can cause many complications such as dysfunction of the larynx, trachea and esophagus. It can have a serious effect on the quality of life, therefore there is still controversy on the extent of the surgery. The authors compare and analyze the opinions which were already discussed in the literatures published so far. These will help to select the surgical method.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.59-66
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• 2018

Data Distribution Service(DDS) provides the communications service programmers need to distribute time-critical data between embedded and/or enterprise devices or nodes. In this paper, I propose efficient methods for transmitting and receiving messages of various characteristics in real-time using DDS middleware. For high-frequency characteristic data, I describe several DDS packet types and various default and extended DDS QoS policies. In particular, the batching method is probably the best solution when considering several performance aspects. For large-capacity characteristic data. I will show a method using extended DDS QoS policies, a segmentation and reassembly method, and transmitting and receiving a large-capacity data with low priority method considering network conditions. Finally, I simulate and compare the result of performance for each methods. This results will help determine efficient methods for transmitting and receiving messages of various characteristics using DDS middleware.

• ETRI Journal
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• 제44권5호
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• pp.780-793
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• 2022

Large-scale ontology management is one of the main issues when using ontology data practically. Although many approaches have been proposed in relational database management systems (RDBMSs) or object-oriented DBMSs (OODBMSs) to develop large-scale ontology management systems, they have several limitations because ontology data structures are intrinsically different from traditional data structures in RDBMSs or OODBMSs. In addition, users have difficulty using ontology data because many terminologies (ontology nodes) in large-scale ontology data match with a given string keyword. Therefore, in this study, we propose a (graph database-based ontology management system (GOMS) to efficiently manage large-scale ontology data. GOMS uses a graph DBMS and provides new query templates to help users find key concepts or instances. Furthermore, to run queries with multiple joins and path conditions efficiently, we propose GOMS encoding as a filtering tool and develop hash-based join processing algorithms in the graph DBMS. Finally, we experimentally show that GOMS can process various types of queries efficiently.

• 전자공학회논문지CI
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• 제48권2호
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• pp.86-95
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• 2011

무선 센서 네트워크에서의 센서 노드는 제한적인 하드웨어 성능과 네트워크 토폴로지가 수시로 변하는 무선 통신을 이용하기 때문에 유선 네트워크보다 보안이 취약하다. 보안을 강화하는 기법 중 노드 인증 메커니즘은 노드의 ID를 이용한 데이터 위변조 공격이나 네트워크의 라우팅을 방해하는 라우팅 공격을 방어하는 데 이용한다. 본 논문에서는 베이스 스테이션이 인증 요청을 하는 노드의 시간에 따른 에너지 값을 이용해서 인증키를 생성하고, 다른 노드와의 데이터 전송을 위한 통신 절차를 수행하는 AM-E 메커니즘을 제안한다. 노드의 에너지 값은 시간에 따라 변하므로, 인증 요청을 할 때마다 인증키가 바뀌는 특징을 갖는다. 이러한 특징은 센서 네트워크의 보안성을 강화하여 보다 안전한 WSN을 구성하는데 일조할 것이다.