• The Journal of Society for e-Business Studies
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• v.20 no.3
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• pp.29-46
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• 2015

Examined how it varied the knowledge network of the country along with R&D changes in planning policy for the research and development of government. Therefore, in this study, chronological Analysis analyzed separately the network between each entity of participate in the industry fusion source technology development business of industry trade and Energy. Planning policy of industrial fusion source technology development business, to change the starting point before and after 2012, before 2012 from selected planning issues at the center "planning committee" and in 2012 'PD' changes to a system for planning issues around. First of all, an attempt to analyze the R&D network based on the "planning committee" current situation of 2009~2011, from 2012 to analyze the variation of the R&D network with the introduction of the 'PD' system after it was analyzed by dividing the time in the current state of up to 2013. The results of the analysis, since the PD system was introduced, such as self-relationship (the form of planning user to run directly challenges the person was planning to challenge participants)is greatly improved, I was able to grasp the effect became clear. The more the self-relation, and the budding scholars considering that there is inequality of the planning, the introduction of the PD scheme, it can be seen to have resulted in a positive effect. These studies, quantitatively analyzed to improve the results to the effects associated with changes in the planning policy of the government, I think that there is a meaning in terms of presenting the future direction of R&D policy.

• Knowledge Management Research
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• v.19 no.4
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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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• v.34 no.3
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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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• v.9 no.4
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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.

• Journal of Biomedical Engineering Research
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• v.12 no.3
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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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• v.22 no.3
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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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• v.32 no.2
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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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• v.23 no.11
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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.

• Convergence Security Journal
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• v.21 no.2
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• pp.3-10
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• 2021

Nowadays, data-based scientific research is a trend, and the transmission of large amounts of data has a great influence on research productivity. To solve this problem, a separate network structure for transmitting large-scale scientific big data is required. ScienceDMZ is a network structure designed to transmit such scientific big data. In such a network configuration, it is essential to establish an access control list(ACL) for users and resources. In this paper, we describe the R&E Together project and the network structure implemented in the actual ScienceDMZ network structure, and define users and services to which access control policies are applied for safe data transmission and service provision. In addition, it presents a method for the network administrator to apply the access control policy to all network resources and users collectively, and through this, it was possible to achieve automation of the application of the access control policy.

• Computers and Concrete
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• v.15 no.2
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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.