• Journal of Communications and Networks
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• v.14 no.5
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• pp.471-480
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• 2012

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1179-1191
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• 2019

We design an ingenious view-pooling method named learning-based multiple pooling fusion (LMPF), and apply it to multi-view convolutional neural network (MVCNN) for 3D model classification or retrieval. By this means, multi-view feature maps projected from a 3D model can be compiled as a simple and effective feature descriptor. The LMPF method fuses the max pooling method and the mean pooling method by learning a set of optimal weights. Compared with the hand-crafted approaches such as max pooling and mean pooling, the LMPF method can decrease the information loss effectively because of its "learning" ability. Experiments on ModelNet40 dataset and McGill dataset are presented and the results verify that LMPF can outperform those previous methods to a great extent.

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.122-130
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• 2019

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

• Journal of Animal Science and Technology
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• v.64 no.5
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• pp.830-841
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• 2022

Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.273-283
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• 2023

Education has recently witnessed a rapid increase in the use of edutech worldwide. This study focuses on Korean workers and explores an edutech-based learning model for vocational education and training. Based on analyses of edutech cases and interviews with edutech experts, a draft edutech model was designed and the validity was evaluated based on two Delphi surveys with a panel of experts in the field. The study's findings suggest that edutech-based employee education and training should prioritize LXP orientation (last CVR=1, last Mean=4.70) , implement adaptive learning through learning analytics (last CVR=1, last Mean=4.90), enhance the human touch effect using edutech (last CVR=1, last Mean=4.90), and emphasize the importance of designing curricula that apply edutech in a step-by-step learning process while incorporating suitable instructional design for the key technologies involved in vocational training programs. In addition, it was revealed that there is a strong need to implement a method that makes each stage of the learning process more effective (before, during, and after). Edutech-based vocational training program should consider the interests of all stakeholders, including learners, instructors, vocational training institutions, and government agencies. Given the promotion of government-sponsored vocational training projects in Korea, the findings of this research are likely to have significant implications for the future of Korea's education and training policies.

• Korean Journal of Radiology
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• v.22 no.10
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• pp.1697-1707
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• 2021

The recent introduction of various high-dimensional modeling methods, such as radiomics and deep learning, has created a much greater diversity in modeling approaches for survival prediction (or, more generally, time-to-event prediction). The newness of the recent modeling approaches and unfamiliarity with the model outputs may confuse some researchers and practitioners about the evaluation of the performance of such models. Methodological literacy to critically appraise the performance evaluation of the models and, ideally, the ability to conduct such an evaluation would be needed for those who want to develop models or apply them in practice. This article intends to provide intuitive, conceptual, and practical explanations of the statistical methods for evaluating the performance of survival prediction models with minimal usage of mathematical descriptions. It covers from conventional to deep learning methods, and emphasis has been placed on recent modeling approaches. This review article includes straightforward explanations of C indices (Harrell's C index, etc.), time-dependent receiver operating characteristic curve analysis, calibration plot, other methods for evaluating the calibration performance, and Brier score.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.727-732
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• 2007

This paper is to investigate how types of presenting e-learning contents affect learning. The study examines learning outcome such as students' learning achievement, satisfaction, and perceived learning outcome of a course in liberal arts by three different types: VOD, WBI, and Text. The findings of this study will present the effectiveness of contents presentation type in e-learning. It is anticipated that the study will offer some suggestions to develop e-learning contents types for similar courses and further to develop effective contents types for various courses.

• Journal of Korean Medical classics
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• v.31 no.2
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• pp.1-16
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• 2018

Objective : The college of Korean medicine uses a variety of Korean medical classics. Thus, in order to overcome the natural difference of the details of their curriculum, this paper aims to study the usage of Flipped Learning as a way of standardizing the curriculum that teach Korean medical classics. Method : One effective teaching and learning methodology, which is called for by the changing educational paradigm, is Flipped Learning. To introduce this learning method, which is actively applied to different curriculum, the paper revises the goal of learning objectives and introduces a teaching model of Flipped Learning in order to suggest the standardization of Korean medical educations through the re-design of the curriculum for Korean medical classics. Result : The professors of the Korean medical classics must work together to use the revised learning objectives and teaching model and create a set of lectures to serve as a basis of educational standardization. Conclusion : The standardization of the education of Korean medical classics through the Flipped Learning method could pre-emptively deal with the Korean medical doctor's capacity model that is in development now.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.687-690
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• 2007

Opening and participation, blog that mean public ownership, 2.0 series with UCC etc.. etc. is popularized to supply spread of latest web 2.0 technologies. While e-Learning was speaker initiative, lean much to elder brother knowledge last month and real condition that participation of learners is inactive in this running race meantime, e-Learning 2.0 that apply web 2.0 know-hows is offering environment that can spring this boundary. Because e-Learning applies well web 2.0, necessity of development of this running race 2.0 systems is risen with participation of learners and public ownership. e-Learning 2.0 may improve each other efficiency of study epochally exchanging studying method or know-how, information, summary notebook etc. with participation and public ownership.

• Journal of Engineering Education Research
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• v.16 no.5
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• pp.24-35
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• 2013

This study aims to explore self-directed learning strategies of high academic achievers majoring in engineering. The research participants were 21 fourth-year students who had attained the first or second highest cumulative grade point average in each department during the past three-year and were asked to write an essay on "my successful learning methods or techniques." The essays were analyzed by theme analysis method which is one of the qualitative methods to extract the self-directed learning strategies used by high performing students. According to the results of this study, the self-directed learning strategies of excellent students could be categorized into fundamental strategies to induce self-directed learning, preparatory strategies, implementation strategies and management strategies for marinating self-directed learning. Detail information on each category is as follow: 1) fundamental strategies refer to positive and pleasant mind, academic confidence and effort attribution, 2) preparatory strategies refer to concrete and challenging goal setting, establishment of learning strategies adjusted courses characteristics and practical learning planning, 3) implementation strategies refer to intensive learning in class, knowledge exploration, knowledge acquisition, social networking and exhaustive preparation for exams and 4) management strategies refer to time management and learning environment management.