• Journal of the Korean Institute of Landscape Architecture
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• v.35 no.4
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• pp.57-70
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• 2007

As a basic element of Jeju landscape, Oreum offers a beautiful and aesthetic view. Considering topographical and geological research achievements, however, an effort to discover implicit value in terms of landscape characteristics and value has been ignored. This paper has investigated the characteristics and value of landscape by Oreum focusing on Jeju landscape characteristics and eco-touristic value and discussed a scheme to maximize the values. Under a theme of 'Sustainable Development' of the RIO Declaration, tour industry has recently changed its focus from eco-tourism to gee-tourism. Fortunately, Jeju Oreum has very distinctive and unique landscape with depressed crater at a crest. Nevertheless, it's very difficult to see a true aspect of Oreum from the street or over the car window. Therefore, it's urgent to begin a research on how to make advantage of and preserve Oreum landscape in order to maximize its landscape values and improve its potential as a tourist attraction. Through diverse programs such as sky leisure sports(ex: light airplane and helicopter riding, paragliding), sky watching, and mountain hiking, in particular, a possibility that Oreum can succeed as LBD(Learning by Doing)-based tour program with volcanic features needs to be examined. Besides, it's also a good idea to develop Oreum tour program or Oreum Museum as an alternative plan. Above all, however, it's most urgent to protect the existing Oreum and restore ecological and landscape beauty of Oreum through proper land use.

• Journal of KIISE
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• v.45 no.1
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• pp.94-98
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• 2018

We propose Wide Inception ResNet (WIR Net) an optimized neural network architecture as a real-time semantic segmentation method for autonomous driving. The neural network architecture consists of an encoder that extracts features by applying a residual connection and inception module, and a decoder that increases the resolution by using transposed convolution and a low layer feature map. We also improved the performance by applying an ELU activation function and optimized the neural network by reducing the number of layers and increasing the number of filters. The performance evaluations used an NVIDIA Geforce GTX 1080 and TX1 boards to assess the class and category IoU for cityscapes data in the driving environment. The experimental results show that the accuracy of class IoU 53.4, category IoU 81.8 and the execution speed of $640{\times}360$, $720{\times}480$ resolution image processing 17.8fps and 13.0fps on TX1 board.

• Korean Journal of Cognitive Science
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• v.21 no.4
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• pp.603-625
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• 2010

Neural representation of a visual object is distributed across visual cortex and overlapped with those of many other objects. Thus repeating an object facilitates the recognition of the object while it impairs the recognition of other objects. These effects are called repetition priming and antipriming, respectively. Two experiments investigated a new phenomenon of repetition antipriming, in which a repeated object itself is antiprimed. The learning stage presented object pictures which were degraded at various levels. Participants determined how recognizable each object was. Then, the test stage presented the intact version of the object pictures and made participants to perform a categorization task. Both Experiment 1 and 2 found that the processing of the objects that had been recognized were facilitated (repetition priming) while the processing of the objects that had been perceptually ambiguous were impaired (repetition antipriming). These findings suggest that experiencing a perceptually ambiguous object might enhance the connection between feature-level representations and multiple object-level representations, which impairs the subsequent recognition of the repeated object.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.15-27
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• 2020

Accurate knowledge of land use/land cover (LULC) features and their relative changes over upon the time are essential for sustainable urban management. Urban sprawl growth has been always also a worldwide concern that needs to carefully monitor particularly in a developing country where unplanned building constriction has been expanding at a high rate. Recently, remotely sensed imageries with a very high spatial/spectral resolution and state of the art machine learning approaches sent the urban classification and growth monitoring to a higher level. In this research, we classified the Quickbird satellite imagery by object-based image analysis of Dempster-Shafer (OBIA-DS) for the years of 2002 and 2015 at Karbala-Iraq. The real LULC changes including, residential sprawl expansion, amongst these years, were identified via change detection procedure. In accordance with extracted features of LULC and detected trend of urban pattern, the future LULC dynamic was simulated by using land transformation model (LTM) in geospatial information system (GIS) platform. Both classification and prediction stages were successfully validated using ground control points (GCPs) through accuracy assessment metric of Kappa coefficient that indicated 0.87 and 0.91 for 2002 and 2015 classification as well as 0.79 for prediction part. Detail results revealed a substantial growth in building over fifteen years that mostly replaced by agriculture and orchard field. The prediction scenario of LULC sprawl development for 2030 revealed a substantial decline in green and agriculture land as well as an extensive increment in build-up area especially at the countryside of the city without following the residential pattern standard. The proposed method helps urban decision-makers to identify the detail temporal-spatial growth pattern of highly populated cities like Karbala. Additionally, the results of this study can be considered as a probable future map in order to design enough future social services and amenities for the local inhabitants.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.8
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• pp.801-806
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• 2015

Journal bearings support rotors using fluid film between the rotor and the stator. Generally, journal bearings are used in large rotor systems such as turbines in a power plant, because even in high-speed and load conditions, journal bearing systems run in a stable condition. To enhance the reliability of journal-bearing systems, in this paper, we study health-diagnosis algorithms that are based on the supervised learning method. Specifically, this paper focused on defining the unit of features, while other previous papers have focused on defining various features of vibration signals. We evaluate the features of various lengths or units on the separable ability basis. From our results, we find that one cycle datum in the time-domain and 60 cycle datum in the frequency domain are the optimal datum units for real-time journal-bearing diagnosis systems.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.859-872
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• 2016

Land cover classification is an important tool for preventing natural disasters, collecting environmental information, and monitoring natural resources. Hyperspectral imaging is widely used for this task thanks to sufficient spectral information. However, the curse of dimensionality, spatiotemporal variability, and lack of labeled data make it difficult to classify the land cover correctly. We propose a novel classification framework for land cover classification of hyperspectral data based on convolutional neural networks. The proposed framework naturally incorporates full spectral features with the information from neighboring pixels and has advantages over existing methods that require additional feature extraction or pre-processing steps. Empirical evaluation results show that the proposed framework provides good generalization power with classification accuracies better than (or comparable to) the most advanced existing classifiers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2484-2498
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• 2017

Various types of social media make the people share their personal experience in different ways. In some social networking sites. Some users post their reviews, some users can support these reviews with comments, and some users just rate the reviews as kind of support or not. Unfortunately, there is rare explicit negative comments towards other reviews. This means if there is a link between two users, it must be positive link. Apparently, the negative link is invisible in these social network. Or in other word, the negative links are redundant to positive links. In this work, we first discuss the feature extraction from social media data and propose new method to compute the distance between each pair of comments or reviews on social media. Then we investigate whether we can predict negative links via regression analysis when only positive links are manifested from social media data. In particular, we provide a principled way to mathematically incorporate multi-facet data in a novel framework, Constructing Negative Links, CsNL to predict negative links for discovering the hidden information. Additionally, we investigate the ways of solution to general negative link predication problems with CsNL and its extension. Experiments are performed on real-world data and results show that negative links is predictable with multi-facet of social media data by the proposed framework CsNL. Essentially, high prediction accuracy suggests that negative links are redundant to positive links. Further experiments are performed to evaluate coefficients on different kernels. The results show that user generated content dominates the prediction performance of CsNL.

• The Journal of the Korea Contents Association
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• v.8 no.9
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• pp.234-241
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• 2008

The feature of the hotel business as a labor intensive industry and its heavy dependence on man power is relatively bigger than other industries. the important factors influencing the customer`s decision making are tangible facilities and intangible service qualities. however, the changes in economic situation are also seriously influencing them. So hotels are started to find other IT(information technology) systems. BSC which has been recognized as one of barometers to establish management performance is one of them. The purpose of this study was to develop KPI(key performance indicator) by using the BSC(Balanced Scorecard) for evaluating hotel management performance. This thesis presents customer performance, inner process performance, learning and growing performance as non-financial factors and tries to examine the cause and effect in the hotel industry. Hotels have to know nonfinancial performance which has positively relate to financial performance. To introduce BSC system is not to lead increasing income and bettermenting service quality, satisfacting customer needa for hotels, But to lead developing value enhancement to hotel enterprises and present process.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.117-125
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• 2015

In the modern age, it is important for people to maintain a good sitting posture because they spend long hours sitting. Posture correction treatment requires a great deal of time and expenses with continuous observation by a specialist. Therefore, there is a need for a system with which users can judge and correct their postures on their own. In this study, we collected users' postures and judged whether they are normal or abnormal. To obtain a user's posture, we propose a four-joint motion capture system that uses inertial sensors. The system collects the subject's postures, and features are extracted from the collected data to build a database. The data in the DB are classified into normal and abnormal postures after posture learning using the K-means clustering algorithm. An experiment was performed to classify the posture from the joints' rotation angles and positions; the normal posture judgment reached a success rate of 99.79%. This result suggests that the features of the four joints can be used to judge and help correct a user's posture through application to a spinal disease prevention system in the future.

• Journal of the Korean Chemical Society
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• v.46 no.5
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• pp.450-468
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• 2002

As the computer is popularized in individual and society, it is using in a many of area. In particular, there are many materials to learn a science knowledge using multimedia through computer. Many of them are web-based learning materials, which are developed by Java or Flash. Since the technology of the representation, storage, com-putation and communication in computer make progress, the environment of education is also developed. Especially, the internet and VR technology will cause the education to change. A key feature of VR is real-time interactivity, in that the computer is able to detect student input and instantaneously modify the virtual world. It is reported that using the VR simulation in chemistry education can increase student engagement in class, promote understanding of basic chem-ical principles, and augment laboratory experience. In this study, application way of the virtual reality technology in chemistry education is examined.