• Journal of Communications and Networks
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• 제14권5호
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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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• 제15권5호
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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.

• 로봇학회논문지
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• 제14권2호
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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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• 제64권5호
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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.

• 실천공학교육논문지
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• 제15권2호
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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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• 제22권10호
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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.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2007년도 추계 종합학술대회 논문집
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• pp.727-732
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• 2007

본 연구는 e-Learning 콘텐츠 제시 유형이 학습결과에 어떠한 영향을 미치는지를 살펴보기 위해 수행되었다. 이를 위해 한 강좌가 VOD 유형, WBI 유형, 텍스트 유형의 각기 다른 제시방식으로 개발되었다. 동질성이 검증된 세 집단에게 본 연구에서 개발된 콘텐츠들을 학습하게 하였고, 집단별로 학업성취도, 만족도, 학습자가 인식하는 학습결과를 비교분석 하였다. 본 연구결과는 e-Learning 콘텐츠 개발 유형 선택 기준에 대한 시사점을 제공할 수 있을 것이며, 나아가 추후 다양한 과목 성격에 따라 어떤 제시유형이 효과적인지 제안할 수 있는 확장된 연구의 기반으로 기여할 수 있을 것이다.

• 대한한의학원전학회지
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• 제31권2호
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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.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 추계종합학술대회
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• pp.687-690
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• 2007

최근 웹2.0기술의 보급 확대로 개방과 참여, 공유를 의미하는 블로그, UCC 등과 함께 2.0시리즈가 대중화되고 있다. 그동안의 e-Learning이 강사주도형, 지식전달형으로 많이 치우쳐 이러닝에 학습자들의 참여가 저조한 실정이었던 반면, 웹2.0기술을 활용한 e-Learning 2.0은 이러한 한계를 뛰어 넘을 수 있는 환경을 제공하고 있다. e-Learning도 웹2.0을 잘 적용해 학습자들의 참여와 공유를 바탕으로 이러닝 2.0 시스템의 개발의 필요성이 대두되고 있다. e-Learning 2.0은 참여와 공유를 바탕으로 학습방법이나 노하우, 정보, 요약 노트를 주고받으면서 획기적으로 공부의 효율성을 서로 높일 수 있을 것이다.

• 공학교육연구
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• 제16권5호
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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.