• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.2
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• pp.151-158
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• 2012

This paper proposes a H-plane 8-way rectangular waveguide power divider using Y-junction. A general N-way power divider can be composed of multi-stage T-junctions. However, if the distances of output ports are close, the matching characteristic is not improved by using only T-junctions because of space limitation. In this case, since other types of 3-port junctions should be used to final output stage, Y-junctions are used with T-junctions in this paper. The proposed Y-junction uses the tapered-line impedance transformer and inductive irises to improve impedance matching characteristic. The 8-way power divider using Y-junction is fabricated and measured. The measured return loss and insertion loss from input port to output port are -30.8 dB and -9.3 dB at operating frequency, respectively. The measured maximum phase difference is about $1^{\circ}$. Therefore, the proposed power divider will be useful to apply to various microwave systems, which need to divide the input power equally, such as feed networks for array antennas.

• The korean journal of orthodontics
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• v.52 no.4
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• pp.287-297
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• 2022

Objective: To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent two-jaw orthognathic surgery. Methods: A total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed. Results: The total mean error was 1.17 mm without significant difference among the four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups. Conclusions: The CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.

• Korean Journal of Radiology
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• v.22 no.2
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• pp.168-178
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• 2021

Objective: To provide an automatic method for segmentation and diameter measurement of type B aortic dissection (TBAD). Materials and Methods: Aortic computed tomography angiographic images from 139 patients with TBAD were consecutively collected. We implemented a deep learning method based on a three-dimensional (3D) deep convolutional neural (CNN) network, which realizes automatic segmentation and measurement of the entire aorta (EA), true lumen (TL), and false lumen (FL). The accuracy, stability, and measurement time were compared between deep learning and manual methods. The intra- and inter-observer reproducibility of the manual method was also evaluated. Results: The mean dice coefficient scores were 0.958, 0.961, and 0.932 for EA, TL, and FL, respectively. There was a linear relationship between the reference standard and measurement by the manual and deep learning method (r = 0.964 and 0.991, respectively). The average measurement error of the deep learning method was less than that of the manual method (EA, 1.64% vs. 4.13%; TL, 2.46% vs. 11.67%; FL, 2.50% vs. 8.02%). Bland-Altman plots revealed that the deviations of the diameters between the deep learning method and the reference standard were -0.042 mm (-3.412 to 3.330 mm), -0.376 mm (-3.328 to 2.577 mm), and 0.026 mm (-3.040 to 3.092 mm) for EA, TL, and FL, respectively. For the manual method, the corresponding deviations were -0.166 mm (-1.419 to 1.086 mm), -0.050 mm (-0.970 to 1.070 mm), and -0.085 mm (-1.010 to 0.084 mm). Intra- and inter-observer differences were found in measurements with the manual method, but not with the deep learning method. The measurement time with the deep learning method was markedly shorter than with the manual method (21.7 ± 1.1 vs. 82.5 ± 16.1 minutes, p < 0.001). Conclusion: The performance of efficient segmentation and diameter measurement of TBADs based on the 3D deep CNN was both accurate and stable. This method is promising for evaluating aortic morphology automatically and alleviating the workload of radiologists in the near future.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.11
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• pp.2305-2315
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• 2015

Frequency resource value is growing more and more with the development of the wireless communication. With the advent of the current information society comes a serious shortage of frequency resource, as the amount of supply is far from meeting its demands. Thus, cognitive radio (CR) technique is receiving more attention as a way to make use of the temporarily unoccupied frequency resource. In this paper we propose a novel out-of-band spectrum sensing and dynamic channel access scheme for frequency hopping-based cognitive radio ad-hoc networks. At the beginning of each current channel hopping time, member nodes perform spectrum sensing for the next hopping channel. Based on the proposed collision free primary detection notification, member nodes can determine whether they should execute a hopping time extension procedure of the current channel or not. When the primary detected hopping channel is re-idled, the hopping pattern recovery procedure is performed. In this paper we evaluated the performance of the proposed dynamic sensing and hopping channel extension mechanism for the various wireless network conditions. As a result, we show that the proposed method can increase channel utilization and provide reliable channel management operation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.35-46
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• 2017

This study analyzed the role of innovation intermediaries on the performance of technology-based firms in Korea. Technology-based firms are important to the economy because they contribute to regional economic development and national competitiveness. In Korea, various types of intermediaries, such as Techno-parks and incubators have been established to foster technology-based firms. Researchers analyzed various factors influencing the performance of technology-based firms. On the other hand, there have been few studies on the relationship between the innovation intermediaries and the performance of technology-based firms in Korea. This study identified the firms' capabilities, institutional and environmental factors in the light of the literature. A total of 2,313 technology-based firms in Techno-parks, business incubator of public institutes and universities were surveyed. Of these, 110 respondents were used for empirical analysis. OLS techniques were applied to analyze the data. The empirical results showed that the marketing competence, R&D capacity, which is a firms' innovation capacity, have a positive effect on the performance. The support of intermediaries positively affects the performance of technology-based firms. The economic aspects of regional innovation infrastructure, and cooperation with the customer has a positive effect on the performance of technology-based firms.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.13-22
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• 2014

This study analyzed the globalization policy of ICT SMEs and ventures using cognitive map analysis and derived the priority to importance about action plans using AHP model, while the globalization paradigm has been rapidly changing in the ICT industry. Empirical results showed that policy tool variables should be needed to develop because policy goal variables are generally presented more than policy tool variables. In addition, this cognitive map could be characterized by a scarcity of feedback loops which means policy landscape for ICT SMEs and ventures' globalization is unilateral rather than cyclical to reach policy goal from policy tools. Another finding is that creative economy policy variable was not observed as policy tool variable but as policy goal variable. This means creative economy can be implemented through support for ICT SMEs and ventures' globalization. Finally, for detailed policy measures, installation of global start-up center, recruiting and utilization of global specialists, revitalization of ICT R&D international collaboration study, enlargement of global investment network, accompanied overseas advance of large enterprises and SMEs are presented in order in terms of the importance of policy priority.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.1
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• pp.269-284
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• 2020

This study explored the core management competencies of ventures formed by the entrepreneur's incubator organization and startup experience in the biomedical industry in Korea. An in-depth interview was conducted with 13 entrepreneurs of biomedical ventures. Based on the previous literature, the core management competencies of the ventures, which are influenced by the incubator organization and startup experience, are classified into 'technical competency', 'organization management competency', 'network competency' and 'market pioneering competency'. Analysis of the in-depth interview has revealed 18 factors influencing the formation of the core management competencies of ventures. Qualitative factors that were not addressed by the previous empirical studies were identified in this study. These include 'confidence in technology development', 'way of performing R&D', 'organizational culture' etc. This study is characterized by its scarcity as a qualitative study that deals with the entrepreneurs' prior experience. In addition, this study categorize the core management competencies which are formed by entrepreneurs' incubator organization and startup experience as four factors. This result is expected to be useful in future research.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.2
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• pp.187-200
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• 2014

Industry Cooperation is not the choice of national development but the inevitable component in the world. Industry cooperation results of the reconstruction of the country is an important place as an essential element of the economic development of the national policy in the major economies. Despite several changes in the international economic environment, United States, Canada, Finland, Sweden, Israel settled and maintaining the sustainable development of the countries which successfully established Industry-University Cooperation or Industry-Acaemy Cooperation system in history. In this study, delivered to the realistic ways of Industry cooperation through comparison study on domestic and international cooperation programs. The new activation programs of industry academic cooperation are delivered, that is, The bonus payments system of technology development patent and free technology transfer for joint development, bonus points system and evaluation indicators for joint capacity building program which participate student, industry and academic sector, step-by-step training. system for total employment and entrepreneurship at the same time strengthening management training programs and education opportunity gives to the benefits for the community members. Finally, Intellectual property expert matching program which develops basis of technology trader and expert maps in the smallest unit by administrative area. practice the internet information search services in national wide network for this matching program and government office dedicated to staffing for technology transfer.

• Korean Chemical Engineering Research
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• v.53 no.2
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• pp.236-242
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• 2015

We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations.