• 비교문화연구
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• 제38권
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• pp.461-500
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• 2015

This study aims to investigate the effective method to improve the competence of the Vocabulary by the image and listening towards the ELF. In the first part, we observed the problems and point improvement on learning vocabulary based on learner survey. In the second part, we analyzed two remarkable studies: - consistent and adapt method, communicational context - method based on the lexical, morphological semantical, notional and thematic field Then we proposed effective methods that are applicable to the vocabulary's learning in the class : - learning vocabulary by combining the words - learning vocabulary based on the meaning field - learning vocabulary as concrete characters - learning vocabulary by the descriptive character - learning vocabulary with the type "who am I?" - learning vocabulary by listening For teachers, one of the difficulties to the conduct of vocabulary course is that learners take passive position. Specifically, it is the teachers who play an important role because it runs in the direction of the course. However, learners do not show the active attitude for vocabulary lessons despite the course to take to improve their vocabulary skills. Therefore, teachers must prepare course materials that can both improve the competence of the vocabulary of learners and cause their interest or desire on the current vocabulary. This is why teachers should exploit various materials depending on the skill level of the learner vocabulary.

• 한국보육지원학회지
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• 제18권2호
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• pp.1-18
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• 2022

Objective: A teacher learning community was developed in order to implement a play-centered curriculum at a child care center, and teachers' experiences during the process were explored. Methods: The teacher learning community was carried out for a total of 23 sessions. One researcher and six teachers participated in this study. Data including the transcripts of recordings of the teacher learning community, transcripts of individual teachers' interview recordings, teachers' reflective journals, and social media posts were collected. Data were analyzed according to the qualitative data analysis procedure. Results: The teachers recognized their experiences of the teacher learning community as follows: (1) encouraging and empowering each other to find a way together, (2) self-reflection, communication and sharing with experiences, (3) becoming a teacher who practices change. Conclusion/Implications: The results of this study show the importance and effectiveness of managing the teacher learning community in a way that teachers interact with each other in a collaborative manner within the community based on initiative and spontaneity, and to provide help to each other in the process of understanding and practicing the play-centered curriculum. The teacher learning community supports the professionalism of teachers for the practice of a play-centered curriculum.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제25권3호
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• pp.201-225
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• 2022

This paper details case study vignettes that focus on enhancing the teaching and learning of geometry and measurement in the elementary grades with attention to pedagogical practices for teaching through problem solving with rigor and centering equitable teaching practices. Rigor is a matter of equity and opportunity (Dana Center, 2019). Rigor matters for each and every student and yet research indicates historically disadvantaged and underserved groups have more of an opportunity gap when it comes to rigorous mathematics instruction (NCTM, 2020). Along with providing a conceptual framework that focuses on the importance of equitable instruction, our study unpacks ways teachers can leverage their deep understanding of geometry and measurement learning trajectories to amplify the mathematics through rigorous problems using multiple approaches including learning by doing, challenged-based and mathematical modeling instruction. Through these vignettes, we provide examples of tasks taught through rigorous problem solving approaches that support conceptual teaching and learning of geometry and measurement. Specifically, each of the three vignettes presented includes a task that was implemented in an elementary classroom and a vertically articulated task that engaged teachers in a professional learning workshop. By beginning with elementary tasks to more sophisticated concepts in higher grades, we demonstrate how vertically articulating a deeper understanding of the learning trajectory in geometric thinking can add to the rigor of the mathematics.

• 대한후두음성언어의학회지
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• 제33권3호
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• pp.142-155
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• 2022

Diagnosis using voice is non-invasive and can be implemented through various voice recording devices; therefore, it can be used as a screening or diagnostic assistant tool for laryngeal voice disease to help clinicians. The development of artificial intelligence algorithms, such as machine learning, led by the latest deep learning technology, began with a binary classification that distinguishes normal and pathological voices; consequently, it has contributed in improving the accuracy of multi-classification to classify various types of pathological voices. However, no conclusions that can be applied in the clinical field have yet been achieved. Most studies on pathological speech classification using speech have used the continuous short vowel /ah/, which is relatively easier than using continuous or running speech. However, continuous speech has the potential to derive more accurate results as additional information can be obtained from the change in the voice signal over time. In this review, explanations of terms related to artificial intelligence research, and the latest trends in machine learning and deep learning algorithms are reviewed; furthermore, the latest research results and limitations are introduced to provide future directions for researchers.

• 한국멀티미디어학회논문지
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• 제25권4호
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• pp.600-609
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• 2022

In supervised learning, labeling of all data is essential, and in particular, in the case of object detection, all objects belonging to the image and to be learned have to be labeled. Due to this problem, continual learning has recently attracted attention, which is a way to accumulate previous learned knowledge and minimize catastrophic forgetting. In this study, a continaul learning model is proposed that accumulates previously learned knowledge and enables learning about new objects. The proposed method is applied to CenterNet, which is a object detection model of anchor-free manner. In our study, the model is applied the knowledge distillation algorithm to be enabled continual learning. In particular, it is assumed that all output layers of the model have to be distilled in order to be most effective. Compared to LWF, the proposed method is increased by 23.3%p mAP in 19+1 scenarios, and also rised by 28.8%p in 15+5 scenarios.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2019년도 제60차 하계학술대회논문집 27권2호
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• pp.277-280
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• 2019

본 논문에서는 화학분야 특허 문서에 존재하는 도표(TABLE) 데이터를 인공지능 기술을 활용하여 자동으로 추출하고 정형화된 형태로 가공하는 방법을 제안한다. 특허 문서에서 도표 데이터는 실시예에서 실험결과나 비교결과를 간결하고 가시적으로 표현하기 위하여 주로 사용되나, 셀의 속성을 정의하는 헤더부분과 수치가 표현되는 값 부분의 경계가 모호하여 구조화하는데 어려움이 있다. 본 논문에서 제안하는 방법은 소량의 학습데이터를 구축하고 기계학습을 통해 도표에 존재하는 셀의 속성을 예측하고, 예측된 속성을 토대로 조성과 물성 정보를 자동으로 구분하여 추출하는 방법을 제시한다. 제시된 방법을 활용하여 화학 분야 조성물 특허의 도표데이터에 시뮬레이션 결과 각 항목별 98.17%의 속성 예측 정확도를 나타내었으며 기존 규칙기반 연구보다 작업난이도, 예측정확도에서 우수한 성과를 보인다.

• ETRI Journal
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• 제44권6호
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• pp.955-965
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• 2022

A low Earth orbit (LEO) satellite constellation could be used to provide network coverage for the entire globe. This study considers multi-beam frequency reuse in LEO satellite systems. In such a system, the channel is time-varying due to the fast movement of the satellite. This study proposes an efficient power and bandwidth allocation method that employs two linear machine learning algorithms and take channel conditions and traffic demand (TD) as input. With the aid of a simple linear system, the proposed scheme allows for the optimum allocation of resources under dynamic channel and TD conditions. Additionally, efficient projection schemes are added to the proposed method so that the provided capacity is best approximated to TD when TD exceeds the maximum allowable system capacity. The simulation results show that the proposed method outperforms existing methods.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 춘계학술발표대회
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• pp.447-450
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• 2021

시뮬레이션을 비롯한 많은 다중 에이전트 환경에서는 중앙 집중 훈련 및 분산 수행(centralized training with decentralized execution; CTDE) 방식이 활용되고 있다. CTDE 방식 하에서 중앙 집중 훈련 및 분산 수행 환경에서의 다중 에이전트 학습을 위한 상태 행동 가치 기반(state-action value; Q-value) 다중 에이전트 알고리즘들에 대한 많은 연구가 이루어졌다. 이러한 알고리즘들은 Independent Q-learning (IQL)이라는 강력한 벤치 마크 알고리즘에서 파생되어 다중 에이전트의 공동의 상태 행동 가치의 분해(Decomposition) 문제에 대해 집중적으로 연구되었다. 본 논문에서는 앞선 연구들에 관한 알고리즘들에 대한 분석과 실용적이고 일반적인 도메인에서의 실험 분석을 통해 검증한다.

• 한국군사과학기술학회지
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• 제25권1호
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• pp.30-44
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• 2022

The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.

• 센서학회지
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• 제31권1호
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• pp.24-30
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• 2022

Distributed fiber-optic acoustic·vibration sensing technology is becoming increasingly popular in many industrial and academic areas such as in securing large edifices, exploring underground seismic activity, monitoring oil well/reservoir, etc. Long-range perimeter intrusion detection exemplifies an application that not only detects intrusion, but also pinpoints where it happens and recognizes kinds of threats made along the perimeter where a single fiber cable was installed. In this study, we developed a distributed fiber-optic sensing device that measures a distributed acoustic·vibration signature (pattern) for intrusion detection. In addition, we demontrate the proposed deep learning algorithm and how it classifies various intrusion events. We evaluated the sensing device and deep learning algorithm in a practical testbed setup. The evaluation results confirm that the developed system is a promising intrusion detection system for long-distance and seamless recognition requirements.