• Journal of The Korean Association For Science Education
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• v.33 no.5
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• pp.995-1006
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• 2013

The purposes of this study were to develop an instrument to measure high school students' systems thinking and to validate the scale. The scale of systems thinking was made up for 5 factors - systems thinking, mental model, shared vision, personal mastery, and team learning through analyses of related literature. Six items per factor were constructed and the scale consisted of a total of 30 items for the pretest. After exploratory factor analysis, the number of total items was reduced to 20 items. For the main test, 280 students were sampled from high school and analyzed valid cases were 260 students. The finding of the exploratory factor analysis indicated 5 factors in the model, and 4 items per single factor. The result of confirmatory factor analysis was generally appropriate and acceptable (5 factor model: $x^2/df$=1.275, TLI=.946, CFI=.959, RMSEA=.033). The reliability for 20 items turned out to be reliable because the Cronbach's alphas were .840 and .604~.723 per each factor. This study should be expanded to various school levels and should be standardized for further research. The subsequent studies regarding diverse learning program development and implementation and the verification on the students' impact within the developed program can be recommended.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.1045-1058
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• 2008

Drought is a natural phenomena caused by long time lack of precipitation causing varying damages in several regions which increases yearly. Specifically, in 1994$\sim$1995 and 2001 severe drought occurred in almost every region of Korea. Small and medium sized water supply reservoirs exposed their bottoms and also raised considerable economic losses. In spite of this fact, damages and impacts from the drought can still be minimized by well defined drought management plans with optimal management of water supply facilities. Throughout this research, integrated drought information system is proposed to used in monitoring the drought of Korea in real time. And the expert system for the management of water supply facilities has developed using Shared Vision Model (SVM) to enable the Virtual Drought Exercise (VDE). To find a better way to manage water during drought and to develop the enhanced abilities to respond to drought, virtual drought exercise is the most effective approach and process. The proposed process of virtual drought exercise using integrated drought information system can be used as an effective tool to prepare the optimal water supply plans during the drought.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.520-527
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• 2019

In this study, the technical level and competence of Chungbuk region manufactures were diagnosed and implications for efficiency improvement of cooperation with local universities were derived. The results are as follow. First, in Chungbuk area, 75% of the skilled workers are medium-skilled and high skilled workers. And the life cycle of production products was found to have entered middle/old age. In addition, the industries were overestimating its technology capabilities, including marketing and sales technology, and management technology. Therefore, local universities should develop differentiated program such as technology transfer and commercialization support so that companies can nurture new industries and it is necessary to improve understanding of reality and future prediction ability through various education and seminars. Second, universities in Chungbuk province have failed to meet the practical demands of industry by providing general educational programs such as lifelong education curriculum, rather than the practical training required by industry. First of all, industries needed the practical training programs such as human resource empowerment, technical education and workers' retraining for local industry development. In addition, industries were expected to provide relevant knowledge and infrastructure such as testing, analysis, participation in technology development such as commissioning and joint research. Therefore, universities should prepare customized Industry-Academia Cooperation Programs through industry demand survey in planning. Also, it is necessary to establish various connection points with industry to ensure that industry-academia cooperation will continue and achieve results. Third, the technology of the industries in Chungbuk province was found to be very unrelated to the next generation regional strategic industries. This is not shared vision between industry and local government, Industry-Academia Cooperation Programs will serve as a platform to organize various community entities. Universities will be able to play a key role in between industries and local governments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.620-626
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• 2021

The purpose of this study is to examine the characteristics and industrial structure and to present a future vision through analysis of industrial agglomeration, growth, and industrial efficiency targeting the Chungnam display industry nurtured by regional industrial policies since 2002. The industrial scale and aggregate status were analyzed through the business scale quotient and location quotient, and the growth and pace of structural change in the industry were analyzed through the industrial growth rate and change of industrial structure. Analysis results found the display industry shows its solid status based on industrial agglomeration. The RLQ, added value ratio, and employment coefficient have a relatively high comparative advantage. However, the growth rate has declined, and the pace of structural change has become lower. The regional competitiveness has been deteriorating. It is necessary to develop strategies to help the industry evolve into a convergence display industry to secure competitiveness by forming value chains. The regional industrial policy 'Regional Innovation Growth Plan' should be utilized to foster local industries that consider the regional characteristics and development directions and to promote the shared growth of related subsidiary industries through fostering specialized complexes for materials, parts, and equipment.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.81-89
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• 2023

In modern society, blind people face difficulties in navigating common environments such as sidewalks, elevators, and crosswalks. Research has been conducted to alleviate these inconveniences for the visually impaired through the use of visual and audio aids. However, such research often encounters limitations when it comes to practical implementation due to the high cost of wearable devices, high-performance CCTV systems, and voice sensors. In this paper, we propose an artificial intelligence fusion algorithm that utilizes low-cost video sensors integrated into smartphones to help blind people safely navigate their surroundings during walking. The proposed algorithm combines motion capture and object detection algorithms to detect moving people and various obstacles encountered during walking. We employed the MediaPipe library for motion capture to model and detect surrounding pedestrians during motion. Additionally, we used object detection algorithms to model and detect various obstacles that can occur during walking on sidewalks. Through experimentation, we validated the performance of the artificial intelligence fusion algorithm, achieving accuracy of 0.92, precision of 0.91, recall of 0.99, and an F1 score of 0.95. This research can assist blind people in navigating through obstacles such as bollards, shared scooters, and vehicles encountered during walking, thereby enhancing their mobility and safety.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.203-219
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• 2011

Previously common users just want to watch the video contents without any specific requirements or purposes. However, in today's life while watching video user attempts to know and discover more about things that appear on the video. Therefore, the requirements for finding multimedia or browsing information of objects that users want, are spreading with the increasing use of multimedia such as videos which are not only available on the internet-capable devices such as computers but also on smart TV and smart phone. In order to meet the users. requirements, labor-intensive annotation of objects in video contents is inevitable. For this reason, many researchers have actively studied about methods of annotating the object that appear on the video. In keyword-based annotation related information of the object that appeared on the video content is immediately added and annotation data including all related information about the object must be individually managed. Users will have to directly input all related information to the object. Consequently, when a user browses for information that related to the object, user can only find and get limited resources that solely exists in annotated data. Also, in order to place annotation for objects user's huge workload is required. To cope with reducing user's workload and to minimize the work involved in annotation, in existing object-based annotation automatic annotation is being attempted using computer vision techniques like object detection, recognition and tracking. By using such computer vision techniques a wide variety of objects that appears on the video content must be all detected and recognized. But until now it is still a problem facing some difficulties which have to deal with automated annotation. To overcome these difficulties, we propose a system which consists of two modules. The first module is the annotation module that enables many annotators to collaboratively annotate the objects in the video content in order to access the semantic data using Linked Data. Annotation data managed by annotation server is represented using ontology so that the information can easily be shared and extended. Since annotation data does not include all the relevant information of the object, existing objects in Linked Data and objects that appear in the video content simply connect with each other to get all the related information of the object. In other words, annotation data which contains only URI and metadata like position, time and size are stored on the annotation sever. So when user needs other related information about the object, all of that information is retrieved from Linked Data through its relevant URI. The second module enables viewers to browse interesting information about the object using annotation data which is collaboratively generated by many users while watching video. With this system, through simple user interaction the query is automatically generated and all the related information is retrieved from Linked Data and finally all the additional information of the object is offered to the user. With this study, in the future of Semantic Web environment our proposed system is expected to establish a better video content service environment by offering users relevant information about the objects that appear on the screen of any internet-capable devices such as PC, smart TV or smart phone.

• The Journal of Korean Philosophical History
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• no.31
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• pp.7-32
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• 2011

Cheng Yi(程?, 1033~1107) understood that nature is full of "changes(易)". And he noted that human being as part of nature also exists only in a series of changes, i.e. birth, growth, extinction and death. All things including human being arise from the same principle, or "Heavenly Principle." Hence human being can fundamentally be one with all other beings, or nature. It is called "Unity of all things(萬物一體)" and "Unity of heaven and human(天人合一)." This philosophical perspective cannot be regarded as being unique to Cheng only; neo-Confucian predecessors called "the five masters of the Northern Song(北宋五子)" anticipated Cheng's vision already. Nevertheless, Cheng elaborated on the shared vision, revealing his philosophical uniqueness. Cheng maintains that only human being receives the principle in the unstained form, and thereby is capable of being one with nature. The one who realizes her/his potential to be one with nature is a sage(聖人); for Cheng, the order and pattern found in nature is nothing other than moral principle that human beings have to live up to and vice versa. Cheng's idea on the principle which human being receives from Heaven no doubt relates to Mencian notion of the innate goodness of human nature(性善); the innate goodness of human nature is no other than Heavenly Principle, and to become a sage depends on whether one can realize her/his potential - human nature, i.e. Heavenly Principle in her/himself. For Cheng, human nature tantamount to Heavenly Principle has no evil quality; all the evil in the world comes from imperfect "physical endowment(氣質)," or "capacity(才)" which is various from person to person, making various personalities. Accordingly, the task of moral cultivation in Cheng's theory can translate into the matter of rectification of one's physical endowment.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.4
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• pp.64-73
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• 2021

The purpose of this study is to present a method to assess the landscape aesthetic value of Bukhansan National Park using geotagged images that have been shared on social media sites. The method presented in this study consisted mainly of collecting geotagged image data, identifying landscape images, and analyzing the cumulative visibility by applying a target probability index. Ramblr is an application that supports outdoor activities with many users in Korea, from which a total of 110,954 geotagged images for Bukhansan National Park were collected and used to assess the landscape aesthetics. The collected geotagged images were interpreted using the Google Vision API, and were subsequently were divided into 11 landscape image types and 9 non-landscape image types through cluster analysis. As a result of analyzing the landscape types of Bukhansan National Park based on the extracted landscape images, landscape types related to topographical characteristics, such as peaks and mountain ranges, accounted for the largest portion, and forest landscapes, foliage landscapes, and waterscapes were also commonly found as major landscape types. In the derived landscape aesthetic value map, the higher the elevation and slope, the higher the overall landscape aesthetic value, according to the proportion and characteristics of these major landscape types. However, high landscape aesthetic values were also confirmed in some areas of lowlands with gentle slopes. In addition, the Bukhansan area was evaluated to have higher landscape aesthetics than the Dobongsan area. Despite the high elevation and slope, the Dobongsan area had a relatively low landscape aesthetic value. This shows that the aesthetic value of the landscape is strongly related not only to the physical environment but also to the recreational activities of visitors who are viewing the scenery. In this way, the landscape aesthetics assessment using the cumulative visibility of geotagged images is expected to be useful for planning and managing the landscape of Bukhansan National Park in the future, through allowing the geographical understanding of the landscape values based on people's perceptions and the identification of the regional deviations.

• Journal of Korea Technology Innovation Society
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• v.20 no.1
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• pp.1-33
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• 2017

In the last decades, technology-oriented small firms, i.e. venture businesses, have been increasingly engaged in R&D collaborations with external parties as strategic means for technological innovation. Despite ample evidence on the benefit of such collaborations for the firms, there has been less attention to examining whether and how the firms' social interactions with cooperating partners and their managerial characteristics contribute to that benefit. Drawing on the theories of social capital and entrepreneurial orientation, this study is to remedy this gap. The theory of social capital, referring to a sum of the value and potential resources embedded in social relationships of collectives, provides an integrated view of social factors among cooperating partners, e.g. strong ties, network stability, trust, reciprocity, shared vision and value. It categorizes these factors into structural, relational, and cognitive dimensions of social capital. Entrepreneurial orientation theory captures firms' managerial characteristics as a combination of innovativeness, proactiveness, and risk-taking. This addresses firms' managerial process to utilize and combine internal and external resources for wealth creation and opportunity realization. Against this background, this study investigates what roles social capital among cooperating R&D partners and entrepreneurial orientation of the collaborating firms play for collective performance improvement in R&D collaborations. In terms of the collective performance, this study adopts two indicators: technological competitiveness and business performance. Technological competitiveness refers to the contribution of a technology developed by a cooperative R&D project to competitive advantage of a firm while business performance is defined as the financial and economic outcome of a collaboration. Using a sample of 218 Korean ventures engaging in R&D collaboration with external parties, the author finds the significant effects of social capital (i.e. structural, relational, and cognitive dimensions) and entrepreneurial orientation (i.e. innovativeness, proactiveness, and risk-taking) on both of the technological competitiveness and the business performance. Further, the higher the social capital among R&D partners, the more likely it is to foster the entrepreneurial orientation at firm-level. Most importantly, the entrepreneurial orientation at firm-level is an significant mediator of the relationship between social capital and collective performance. Beyond these novel empirical findings, this study contributes to the literature on R&D collaboration. The findings' implications for management and policy are deeply discussed in the conclusion.