• 한국전자거래학회지
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• 제20권3호
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• pp.29-46
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• 2015

정부의 연구개발에 대한 기획 정책이 변화함에 따라 국가 R&D 지식 네트워크가 어떻게 변동하였는지에 대해 살펴보고자 한다. 이를 위해 본 연구는 산업통상자원부의 산업융합원천 기술개발사업에 참여하는 각 주체들 간의 네트워크를 시기별로 분석하였다. 산업융합원천기술개발사업의 기획 정책은 2012년 전후를 기점으로 바뀌게 되는데 2012년 이전에는 '기획위원' 중심으로 기획 과제를 선정하다가 2012년 이후에는 'PD' 중심으로 과제를 기획하는 시스템으로 바뀐다. 이에 따라 우선, '기획위원' 제도에 따른 R&D 네트워크를 분석하고자 2009년부터 2011년까지의 현황을 살펴보고, 이후 'PD' 제도도입에 따른 R&D 네트워크 변동을 분석하고자 2012년부터 2013년까지의 현황으로 시기를 나누어 분석하였다. 분석 결과 PD 제도 도입 이후 셀프관계(기획자가 본인이 기획한 과제를 과제 참여자로 직접 수행하는 형태)가 대폭 개선되는 등 효과가 나타났음을 파악할 수 있었다. 셀프관계가 많을수록 신진학자에게는 기획의 불평등이 존재한다는 점을 고려할 때 PD 제도의 도입은 긍정적인 효과를 가져왔다고 볼 수 있다. 이러한 연구는 정부의 기획 정책 변화에 따른 효과를 계량적으로 분석하여 성과를 제고하고 향후 R&D정책의 방향을 제시한다는 점에서 의미가 있다고 본다.

• 지식경영연구
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• 제19권4호
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• pp.19-37
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• 2018

Knowledge transfer among employees integrates individual knowledge scattered within a firm, thus increases organizational effectiveness. In particular, the role of knowledge broker, which enables knowledge sharing across multiple teams or subunits, is critical for the success of knowledge management. This study classified the types of knowledge broker that facilitates knowledge flows among team, and examined the influences of various intra- and inter-team social networks. Survey responses from 128 employees of four R&D teams were gathered and analyzed using partial least square structural equation modeling. The results of analysis showed that all types of inter-team networks(i.e., emotional closeness network, frequency of interaction network, and perceived expertise network) had significant influences on related knowledge brokerage behaviors. In case of intra-team networks, only the emotional closeness network showed significant influence. These results proved the necessity of managing various types of intra- and inter-team networks to encourage knowledge brokerage behaviors within a firm.

• ETRI Journal
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• 제34권3호
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• pp.352-360
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• 2012

This paper proposes a low-cost, low-power, and high-capacity optical-electrical-optical-type reach extender that can provide 3R frame regeneration and remote management to increase the reach and split ratio with no change to a legacy time division multiple access passive optical network. To provide remote management, the extender gathers information regarding optical transceivers and link status per port and then transmits to a service provider using a simple network management protocol agent. The extender can also apply to an Ethernet passive optical network (E-PON) or a gigabit-capable PON (G-PON) by remote control. In a G-PON, in particular, it can provide burst mode signal retiming and burst-to-continuous mode conversion at the upstream path through a G-PON transmission convergence frame adaptor. Our proposed reach extender is based on the quad-port architecture for cost-effective design and can accommodate both the physical reach of 60 km and the 512 split ratios in a G-PON and the physical reach of 80 km and the 256 split ratios in an E-PON.

• JSTS:Journal of Semiconductor Technology and Science
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• 제9권4호
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• pp.216-224
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• 2009

The endless scaling down of the semiconductor process made the impact of the power network noise on the performance of the state-of-the-art chip a serious design problem. This paper compares the performances of two popular circuit reduction approaches used to improve the efficiency of power network noise analysis: moment matching-based model order reduction (MOR) and node elimination-based MOR. As the benchmarks, we chose PRIMA and R2Power as the matching-based MOR and the node elimination-based MOR. Experimental results indicate that the accuracy, efficiency, and memory requirement of both methods very strongly depend on the structure of the given circuit, i.e., numbers of the nodes and sources, and the number of moments to preserve for PRIMA. PRIMA has higher accuracy in general, while the error of R2Power is also in the acceptable range. On the other hand, PRIMA has the higher efficiency than R2Power, only when the numbers of nodes and sources are small enough. Otherwise, R2Power clearly outperforms PRIMA in efficiency. In the memory requirement, the memory size of PRIMA increases very quickly as the numbers of nodes, sources, and preserved moments increase.

• 대한의용생체공학회:의공학회지
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• 제12권3호
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• pp.191-202
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• 1991

This paper describes ECG data compression algorithm using neural network. As a learning method, we use back error propagation algorithm. ECG data compression is performed using learning ability of neural network. CSE database, which is sampled 12bit digitized at 500samp1e/sec, is selected as a input signal. In order to reduce unit number of input layer, we modify sampling ratio 250samples/sec in QRS complex, 125samples/sec in P & T wave respectively. hs a input pattern of neural network, from 35 points backward to 45 points forward sample Points of R peak are used.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.273-275
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• 2022

Deep Learning is used nowadays in Nuclei segmentation. While recent developments in theory and open-source software have made these tools easier to implement, expert knowledge is still required to choose the exemplary model architecture and training setup. We compare two popular segmentation frameworks, U-Net and Mask-RCNN, in the nuclei segmentation task and find that they have different strengths and failures. we compared both models aiming for the best nuclei segmentation performance. Experimental Results of Nuclei Medical Images Segmentation using U-NET algorithm Outperform Mask R-CNN Algorithm.

• Smart Structures and Systems
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• 제32권2호
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• pp.83-99
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• 2023

The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.93-98
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• 2023

Instagram is one of the fastest-growing online photo social web services where users share their life images and videos with other users. Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. Hashtags can be used on just about any social media platform, but they're most popular on Twitter and Instagram. Using hashtags is essentially a way to group together conversations or content around a certain topic, making it easy for people to find content that interests them. Practically on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stophashtags, that are used across totally different images just for gathering clicks and for search ability enhancement. Hence in this work, Sorting instagram hashtags all the way through mass tagging using HITS (Hyperlink-Induced Topic Search) algorithm is presented. The hashtags can sorted to several groups according to Jensen-Shannon divergence between any two hashtags. This approach provides an effective and consistent way for finding pairs of Instagram images and hashtags, which lead to representative and noise-free training sets for content-based image retrieval. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowd tagging task and then to identify the right hashtags per image.

• 융합보안논문지
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• 제21권2호
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• pp.3-10
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• 2021

데이터 기반의 과학연구가 추세인 요즘 대용량의 데이터 전송은 연구 생산성에 많은 영향을 미친다. 이러한 문제를 해결하기 위해서 대용량 과학 빅데이터를 전송하기 위한 별도의 네트워크 구조가 필요하다. ScienceDMZ는 이러한 과학 빅데이터를 전송하기 위해서 고안된 네트워크 구조이다. 이러한 네트워크 구성에서는 사용자 및 자원에 대한 접근제어정책(ACL, access control list) 수립이 필수적이다. 본 논문에서는 실제 ScienceDMZ 네트워크 구조로 구현된 R&E Together 프로젝트와 네트워크 구조를 설명하고, 안전한 데이터 전송 및 서비스 제공을 위해 접근제어정책을 적용할 사용자 및 서비스를 정의한다. 또한 네트워크 관리자가 전체 네트워크 자원 및 사용자에 대해 일괄적으로 접근제어정책을 적용할 수 있는 방법을 제시하며, 이를 통해 접근제어정책 적용에 대한 자동화를 이룰 수 있었다.

• Computers and Concrete
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• 제15권2호
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• pp.167-181
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• 2015

"Mortar or concrete pneumatically projected at high velocity onto a surface" is called Shotcrete. Models that predict shotcrete design parameters (e.g. compressive strength, slump etc) from any mixing proportions of admixtures could save considerable experimentation time consumed during trial and error based procedures. Artificial Neural Network (ANN) has been widely used for similar purposes; however, such models have been rarely applied on shotcrete design. In this study 19 samples of shotcrete test panels with varying quantities of water, steel fibers and silica fume were used to determine their slump, cost and compressive strength at different ages. A number of 3-layer Back propagation Neural Network (BPNN) models of different network architectures were used to train the network using 15 samples, while 4 samples were randomly chosen to validate the model. The predicted compressive strength from linear regression lacked accuracy with $R^2$ value of 0.36. Whereas, outputs from 3-5-3 ANN architecture gave higher correlations of $R^2$ = 0.99, 0.95 and 0.98 for compressive strength, cost and slump parameters of the training data and corresponding $R^2$ values of 0.99, 0.99 and 0.90 for the validation dataset. Sensitivity analysis of output variables using ANN can unfold the nonlinear cause and effect relationship for otherwise obscure ANN model.