• World Technopolis Review
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• v.5 no.2
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• pp.108-118
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• 2016

There are very few studies on governance structure for the collaboration between HEIs and science and technology parks until today. Major activities between science parks and HEIs are R&D activities, collaborative researches, technology transfer, space provision for BIs and Technology BIs in the science parks, provision of technical, legal and financial services for start-ups and venture firms. Governance structure for the collaboration of high education institutes with science and technology parks is the handling of complexity and management of dynamic flows of collaboration between two groups. Three models on the governance structure for the collaboration are suggested in this study. The first model is a governance structure that links R&D system such as universities, public research institutes and private research institutes with industrial production cluster such as a group of companies and industrial parks. The second model is a governance structure that has four layers of hierarchy. This hierarchical governance model is composed of four levels of organizations such as central government, three actors, one center for collaboration and many individual research performers. The third model is a governance structure that networks all the stakeholders horizontally. Under this structure, governance is conducted by the network members with no separate and unique governance entity.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3095-3111
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• 2018

Spectrum sensing (SS) is one of the fundamental tasks for cognitive radio. In SS, decisions can be made via comparing the test statistics with a threshold. Conventional adaptive algorithms for SS usually adjust their thresholds according to the radio environment. This paper concentrates on the issue of adaptive SS whose threshold is adjusted based on the Markovian behavior of primary user (PU). Moreover, Bayesian cost is adopted as the performance metric to achieve a trade-off between false alarm and missed detection probabilities. Two novel adaptive algorithms, including Markov Bayesian energy detection (MBED) algorithm and IMBED (improved MBED) algorithm, are proposed. Both algorithms model the behavior of PU as a two-state Markov process, with which their thresholds are adaptively adjusted according to the detection results at previous slots. Compared with the existing Bayesian energy detection (BED) algorithm, MBED algorithm can achieve lower Bayesian cost, especially in high signal-to-noise ratio (SNR) regime. Furthermore, it has the advantage of low computational complexity. IMBED algorithm is proposed to alleviate the side effects of detection errors at previous slots. It can reduce Bayesian cost more significantly and in a wider SNR region. Simulation results are provided to illustrate the effectiveness and efficiencies of both algorithms.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.12 no.1
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• pp.32-43
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• 2009

The purpose of this study is to suggest the visual landscape plan for Shinan province with ecological landscape resources, which is comprised of more than 1,000 islands. The plan was done by the order of image plan, landscape structure plan and detained landscape plan. The image of Shinan province was elicited as 'nature', 'complexity' and 'connectivity', by the aspects of planning, cognitive and strategic sides. The landscape zones are planned, such as leisure zone, rural & marine ecological zone and marine tourism zone, and the landscape axes are also set, such as marine axis, ecological axis and circular axis. Especially to conserve the ecological resources, some conservation zones are proposed and design guidelines for each landscape type are also provided, which are not commonly included in the urban landscape plan. Consequently, the landscape plan and ecological environmental plan were complementary to each other. In the detailed landscape plan, more specific plans and design guidelines are suggested for coastal scenery, village and forest scenery, historical and cultural landscape management and promotion. To improve the visual landscape in terms of planning and administrative aspects, the visual landscape plan has become increasingly important for the local governments. The establishment of visual landscape plan may hopefully help to make Shinan province more beautiful and attractive. The landscape plan and ecological environment plan should be integrated, and the further discussion and research are necessary.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.38-44
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• 2018

In this paper, we propose Intelligent Driver Assistance System (I-DAS) for driver safety. The proposed system recognizes safety and danger status by analyzing blind spots that the driver cannot see because of a large angle of head movement from the front. Most studies use image pre-processing such as face detection for collecting information about the driver's head movement. This not only increases the computational complexity of the system, but also decreases the accuracy of the recognition because the image processing system dose not use the entire image of the driver's upper body while seated on the driver's seat and when the head moves at a large angle from the front. The proposed system uses a convolutional neural network to replace the face detection system and uses the entire image of the driver's upper body. Therefore, high accuracy can be maintained even when the driver performs head movement at a large angle from the frontal gaze position without image pre-processing. Experimental result shows that the proposed system can accurately recognize the dangerous conditions in the blind zone during operation and performs with 95% accuracy of recognition for five drivers.

• Journal of Communications and Networks
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• v.16 no.2
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• pp.217-226
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• 2014

In this research, we study the problem of opportunistic spectrum access (OSA) in a time-varying environment with fading channels, where the channel state is characterized by both channel quality and the occupancy of primary users (PUs). First, a finite-state Markov channel model is introduced to represent a fading channel. Second, by probing channel quality and exploring the activities of PUs jointly, a two-dimensional partially observable Markov decision process framework is proposed for OSA. In addition, a greedy strategy is designed, where a secondary user selects a channel that has the best-expected data transmission rate to maximize the instantaneous reward in the current slot. Compared with the optimal strategy that considers future reward, the greedy strategy brings low complexity and relatively ideal performance. Meanwhile, the spectrum sensing error that causes the collision between a PU and a secondary user (SU) is also discussed. Furthermore, we analyze the multiuser situation in which the proposed single-user strategy is adopted by every SU compared with the previous one. By observing the simulation results, the proposed strategy attains a larger throughput than the previous works under various parameter configurations.

• The Research Journal of the Costume Culture
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• v.23 no.1
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• pp.145-160
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• 2015

The purpose of this study is to review the concept and thinking structure of deconstruction theoretically and thereupon, analyze the visual beauty and wearable comfort of the clothing and further, discuss the aesthetic characteristics and values of the decentering phenomenon in the 21st century fashion. Deconstruction provides for an cognitive framework whereby we could comprehensively review the difficult-to-understand and imprudent creativity unravelling in the name of the post-modernism as well as the ambiguous visual beauty and wearable comfort of our contemporary fashion. In particular, deconstruction refuses such concepts involving the relationship between the conventional clothing and its components as order, symmetry, balance, harmony, perfection and simplicity and instead, attaches some sense of value to such relatively inferior concepts as disorder, asymmetry, unbalance, disharmony, imperfection and complexity, and thus, reflects them in the modes of aesthetic representations to create new aesthetics and expand the expressive potential.

• Sleep Medicine and Psychophysiology
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• v.15 no.1
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• pp.5-11
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• 2008

Sleep is an essential process maintaining the life cycle of the human. In parallel with physiological, cognitive, subjective, and behavioral changes that take place during the sleep, there are remarkable changes in the electroencephalogram (EEG) that reflect the underlying electro-physiological activity of the brain. However, analyzing EEG and relating the results to clinical observations is often very hard due to the complexity and a huge data amount. In this article, I introduce several linear and non-linear tools, developed to analyze a huge time series data in many scientific researches, and apply them to EEG to characterize various sleep states. In particular, the spectral analysis, detrended fluctuation analysis (DFA), and synchrony analysis are administered to EEG recorded during nocturnal polysomnography (NPSG) processes and daytime multiple sleep latency tests (MSLT). I report that 1) sleep stages could be differentiated by the spectral analysis and the DFA ; 2) the gradual transition from Wake to Sleep during the sleep onset could be illustrated by the spectral analysis and the DFA ; 3) electrophysiological properties of narcolepsy could be characterized by the DFA ; 4) hypnic jerks (sleep starts) could be quantified by the synchrony analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.1080-1099
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• 2019

A better tradeoff between robustness and invisibility will be realized by using the just noticeable (JND) model into the quantization-based watermarking scheme. The JND model is usually used to describe the perception characteristics of human visual systems (HVS). According to the research of cognitive science, HVS can adaptively extract the structure features of an image. However, the existing JND models in the watermarking scheme do not consider the structure features. Therefore, a novel JND model is proposed, which includes three aspects: contrast sensitivity function, luminance adaptation, and contrast masking (CM). In this model, the CM effect is modeled by analyzing the direction features and texture complexity, which meets the human visual perception characteristics and matches well with the spread transform dither modulation (STDM) watermarking framework by employing a new method to measure edge intensity. Compared with the other existing JND models, the proposed JND model based on structural regularity is more efficient and applicable in the STDM watermarking scheme. In terms of the experimental results, the proposed scheme performs better than the other watermarking scheme based on the existing JND models.

• Journal of Korean Academy of Nursing
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• v.49 no.5
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• pp.601-612
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• 2019

Purpose: The purpose of this study was to construct and test a hypothetical model of clinical decision-making ability of nurses based on the Decision Making Process model and the Cognitive Continuum theory. Methods: The data were collected from nurses working at 11 hospitals in Busan, Daejeon, and South Gyeongsang Province from June 30 to August 1, 2017. Finally, the data from 323 nurses were analyzed. Results: The goodness-of-fit of the final model was at a good level ($x^2/df=2.46$, GFI=.87, AGFI=.84, IFI=.90, CFI=.90, SRMR=.07, RMSEA=.07) and 6 out of 10 paths of the model were supported. The clinical decision-making ability was both directly and indirectly affected by task complexity and indirectly affected by experiences, autonomy, and work environment. Specifically, it was strongly directly affected by analytical competency but was insignificantly affected by intuitive competency. These variables accounted for 66.0% of clinical decision-making ability. Conclusion: The nurses' clinical decision-making ability can be improved by improving their analytical competency. Therefore, it is necessary to organize nursing work, create a supportive work environment, and develop and implement various education programs.