• Smart Media Journal
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• v.8 no.2
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• pp.58-65
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• 2019

Automatic text summarization is a process of shortening a text document by either extraction or abstraction. The abstraction approach inspired by deep learning methods scaling to a large amount of document is applied in recent work. Abstractive text summarization involves utilizing pre-generated word embedding information. Low-frequent but salient words such as terminologies are seldom included to dictionaries, that are so called, out-of-vocabulary(OOV) problems. OOV deteriorates the performance of Encoder-Decoder model in neural network. In order to address OOV words in abstractive text summarization, we propose a copy mechanism to facilitate copying new words in the target document and generating summary sentences. Different from the previous studies, the proposed approach combines accurate pointing information and selective copy mechanism based on bidirectional RNN and bidirectional LSTM. In addition, neural network gate model to estimate the generation probability and the loss function to optimize the entire abstraction model has been applied. The dataset has been constructed from the collection of abstractions and titles of journal articles. Experimental results demonstrate that both ROUGE-1 (based on word recall) and ROUGE-L (employed longest common subsequence) of the proposed Encoding-Decoding model have been improved to 47.01 and 29.55, respectively.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1399-1414
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• 2018

Natural forests are un-manned forests where the artificial forces of people are not applied to the formation of forests. On the other hand, artificial forests are managed by people for their own purposes such as producing wood, preventing natural disasters, and protecting wind. The artificial forests enable us to enhance economical benefits of producing more wood per unit area because it is well-maintained with the purpose of the production of wood. The distinction surveys have been performed due to different management methods according to forests. The distinction survey between natural forests and artificial forests is traditionally performed via airborne remote sensing or in-situ surveys. In this study, we suggest a classification method of forest types using satellite imagery to reduce the time and cost of in-situ surveying. A classification map of natural forest and artificial forest were generated using KOMPSAT-3, 3A, 5 data by employing artificial neural network (ANN). And in order to validate the accuracy of classification, we utilized reference data from 1/5,000 stock map. As a result of the study on the classification of natural forest and plantation forest using artificial neural network, the overall accuracy of classification of learning result is 77.03% when compared with 1/5,000 stock map. It was confirmed that the acquisition time of the image and other factors such as needleleaf trees and broadleaf trees affect the distinction between artificial and natural forests using artificial neural networks.

• Journal of Technology Innovation
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• v.27 no.1
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• pp.45-77
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• 2019

The balance between exploration for new possibility and exploitation for existing certainty is an important issue in strategy, innovation, R&D as well as organization learning. Among the convergence trends of technologies, many firms seek to have the wider technological knowledge assets and the deeper technology capabilities for the sustainable competitive advantage at the same time. While firms plan technology portfolio strategies, they should consider the attribute of the technology. Nanotechnology, a cutting-edge technology, is a general purpose technology, unlike conventional product-oriented technologies. This empirical study was focused on how multi-national firms' exploration and exploitation strategies for nanotechnology affect their innovative and financial performance. It uses multiple regression analysis on panel data. This result shows that the more diversified and specialized nanotechnology as platform technology is positively related to their innovative and financial performance, unlike the research results for product-oriented technologies. In addition, exploratory innovation is more effective to firm performance than exploitation. This implies how global firms can manage effectively platform technology strategies under the constraints of resources.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.213-227
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• 2019

In this paper, we propose an end-to-end deep learning model combining Bayesian neural network with Korean speech recognition. In the past, Korean speech recognition was a complicated task due to the excessive parameters of many intermediate steps and needs for Korean expertise knowledge. Fortunately, Korean speech recognition becomes manageable with the aid of recent breakthroughs in "End-to-end" model. The end-to-end model decodes mel-frequency cepstral coefficients directly as text without any intermediate processes. Especially, Connectionist Temporal Classification loss and Attention based model are a kind of the end-to-end. In addition, we combine Bayesian neural network to implement the end-to-end model and obtain Monte Carlo estimates. Finally, we carry out our experiments on the "WorimalSam" online dictionary dataset. We obtain 4.58% Word Error Rate showing improved results compared to Google and Naver API.

• Journal of the Korean earth science society
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• v.40 no.3
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• pp.300-312
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• 2019

The purpose of this study is to develop an astronomical observing education program that enables high school students to inquire the distance of astronomical bodies based on the research methods (observing open clusters and exploring collected big data) using small telescopes and DSLR cameras. After analyzing the 2015 revised science curriculum, we developed science club activity materials and teacher-student learning contents suitable for high school earth science education. A panel of six teachers and researchers of earth science education and astronomy, participated in developing the educational materials. The validity of the program was verified through establishing the agreement among the panels after in-depth discussions and clarifications. The program, developed with 10 lessons in total, showed high satisfactory content validity (CVI, .89) and conformity of school class (Likert's 5 point scales, 4.17). The feedback of the panels and the Delphi analysis continued to improve the quality of the program. The pilot testing result with high school students (N=9) showed that the students' satisfaction rate was high as 4.48. Using the astronomical observational education program of this study is expected to contribute in improving the convergence educational activity, interest, curiosity, and inquiry ability of students in the universe and the astronomical bodies.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.1-16
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• 2019

Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.3
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• pp.13-26
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• 2019

There is a growing interest in the entrepreneurial activity that has long been considered essential for sustainable economic development and value creating. Although it is strongly encouraged by focusing on the positive aspects of venturing, less has been paid attention to entrepreneurial failure, which is the biggest cause of hesitation in starting a business. The uncertain and risky nature of entrepreneurship implies a considerable possibility of failure. Even if it fails, the experience and knowledge of entrepreneurs acquired through entrepreneurship indeed offers valuable lessons for the re-venturing, which can serve as an important social asset that should not be lost. It has been argued that re-entering the same industry for the subsequent venture maximizes the learning effect through utilizing potential benefits from industry-specific knowledge. Although the re-startup after entrepreneurial failure is a very important topic in the studies on serial entrepreneurs, there is a paucity of systematic empirical investigation. This study responds to calls for more research on the re-startup after entrepreneurial failure, and specifically complements existing studies on serial entrepreneurs. Focusing on the entrepreneurs' attribution for the failure, we conducted an empirical analysis of how this affects the re-startup process. Moreover, we also examined the moderating effects of entrepreneurial self-efficacy and resilience. For the analyses, we surveyed the entrepreneurs who tried to re-start the subsequent business after the entrepreneurial failure through the "Revitalization Center for Strained Entrepreneur". The results found that failed entrepreneurs who blamed internal factors for their previous venture failures were likely to keep the same industry for their subsequent business. In addition, the positive effect of internal attribution on maintaining the same industry for the re-startup was found to be stronger when entrepreneurial self-efficacy and resilience were high.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.41-51
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• 2018

Recently, in the field of video surveillance, deep learning based learning method is applied to intelligent video surveillance system, and various events such as crime, fire, and abnormal phenomenon can be robustly detected. However, since occlusion occurs due to the loss of 3d information generated by projecting the 3d real-world in 2d image, it is need to consider the occlusion problem in order to accurately detect the object and to estimate the pose. Therefore, in this paper, we detect moving objects by solving the occlusion problem of object detection process by adding depth information to existing RGB information. Then, using the convolution neural network in the detected region, the positions of the 14 keypoints of the human joint region can be predicted. Finally, in order to solve the self-occlusion problem occurring in the pose estimation process, the method for 3d human pose estimation is described by extending the range of estimation to the 3d space using the predicted result of 2d keypoint and the deep neural network. In the future, the result of 2d and 3d pose estimation of this research can be used as easy data for future human behavior recognition and contribute to the development of industrial technology.

• The Journal of Engineering Geology
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• v.28 no.4
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• pp.673-686
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• 2018

The physical properties of rocks constituting the rock mass were analyzed by using various methods such as 7 kinds of physical properties of about 2,400 data. The correlation equation was derived from the correlation equation with the dependent variables by screening independent variables through the significance level using multiple regression analysis. In order to verify the reliability of this equation, verification was performed through comparison with actual data using artificial neural network learning. The analysis results by petrogenesis and strength confirmed that the elastic wave velocity (compressional wave) and elastic modulus as the main influence factors for the independent variables affecting the dependent variables. This proves that most of the correlation equations using the above items are found in existing studies. And through this study, it is confirmed whether the rock classification is based on the above items in various standards. In addition, the analysis results of representative rocks showed a high correlation as the equation for estimating unconfined compressive strength and elastic modulus exceeds the coefficient of determination 0.8.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.441-447
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• 2021

Construction sites are complex and dangerous because heavy equipment and workers perform various operations simultaneously within limited working areas. Solitary works of heavy equipment in complex job sites can cause fatal accidents, and thus they should interact with spotters and obtain information about surrounding environments during operations. Recently, many computer vision technologies have been developed to automatically monitor construction equipment and detect their interactions with other resources. However, previous methods did not take into account the interactions between equipment and spotters, which is crucial for identifying solitary works of heavy equipment. To address the drawback, this research develops a computer vision-based solitary work detection model that considers interactive operations between heavy equipment and spotters. To validate the proposed model, the research team performed experiments using image data collected from actual construction sites. The results showed that the model was able to detect workers and equipment with 83.4 % accuracy, classify workers and spotters with 84.2 % accuracy, and analyze the equipment-to-spotter interactions with 95.1 % accuracy. The findings of this study can be used to automate manual operation monitoring of heavy equipment and reduce the time and costs required for on-site safety management.