• 한국인터넷방송통신학회논문지
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• 제20권5호
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• pp.57-62
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• 2020

30GHz~300GHz 대역의 스펙트럼을 사용하는 밀리미터파는 높은 주파수로 인해 파장이 짧아서 기지국에 더 많은 안테나를 장착할 수 있어 Massive MIMO 시스템에 적합하다. 하지만 안테나 당 RF chain이 요구되기 때문에 안테나의 수가 증가되면 하드웨어 비용 및 전력 소비가 증가하는 문제점을 갖는다. 따라서 본 논문에서는 이러한 문제점을 해결하기 위해 안테나 선택 기법을 조사한다. 기존 철저한 조사 기반 안테나 선택 기법에서 높은 계산 복잡도를 가지는 문제를 해결하기 위해 딥러닝 기술을 적용하는 방안을 제안한다. 멀티 클래스를 분류할 수 있는 DNN 모델을 사용하여 최적의 안테나 조합을 예측한다. 시뮬레이션을 통해 기존 안테나 선택 기법들과 제안하는 딥러닝 기반 안테나 선택 기법을 비교하여 평가한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 춘계학술발표대회
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• pp.346-349
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• 2011

In this paper, we design a perceptually invisible dot pattern layout and its recognition scheme, and we apply the recognition scheme into a smart self learning aid for interactive learning aid. To increase maximum information capacity and also increase robustness to the noises, we design a ECC (error correcting code) based dot pattern with directional vector indicator. To make a smart self-learning aid, we embed the micro dot pattern (20 information bit + 15 ECC bits + 9 layout information bit) using K ink (CMYK) and extract the dot pattern using IR (infrared) LED and IR filter based camera, which is embedded in the smart pen. The reason we use K ink is that K ink is a carbon based ink in nature, and carbon is easily recognized with IR even without light. After acquiring IR camera images for the dot patterns, we perform layout adjustment using the 9 layout information bit, and extract 20 information bits from 35 data bits which is composed of 20 information bits and 15 ECC bits. To embed and extract information bits, we use topology based dot pattern recognition scheme which is robust to geometric distortion which is very usual in camera based recognition scheme. Topology based pattern recognition traces next information bit symbols using topological distance measurement from the pivot information bit. We implemented and experimented with sample patterns, and it shows that we can achieve almost 99% recognition for our embedding patterns.

• Journal of Information Technology Applications and Management
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• 제22권4호
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• pp.1-37
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• 2015

Micro small business start-ups are receiving financial and marketing support from government or public authority, but business closure rate is very high and it causes a hugh burden to households and national economy. This study aims to verify the causal relationship between the self-determination characteristic of small business start-ups and entrepreneurial intention through learning orientation and innovation ability. The man's intrinsic motivation of self-determination theory is expected to have an impact on the entrepreneurial intention of small business start-ups. The self-determination theory includes perceived autonomy, perceived competence, perceived relationship as independent variables. This study presented a research model for explaining the entrepreneurial intention of small business start-ups, and collected 260 survey responses from the small business start-ups In order to validate the proposed research model, PLS analysis is performed with valid 246 questionnaires. By PLS technique, the measurement reliability and validity of research variables are tested and the path analysis is conducted to do the hypothesis test. Path analysis shows that perceived autonomy does not significantly affect the learning orientation and innovation ability. On the other hand, perceived competence significantly influences learning orientation and innovation ability. Perceived relationships had significant influence on learning orientation. It is found that the parameters of learning orientation and innovation ability significantly influence the dependent variable of entrepreneurial intention. Based on the results, a policy and strategy for supporting small business start-up are presented.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.87-106
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• 2024

Vehicle-to-Home (V2H) and Home Centralized Photovoltaic (HCPV) systems can address various energy storage issues and enhance demand response programs. Renewable energy, such as solar energy and wind turbines, address the energy gap. However, no energy management system is currently available to regulate the uncertainty of renewable energy sources, electric vehicles, and appliance consumption within a smart microgrid. Therefore, this study investigated the impact of solar photovoltaic (PV) panels, electric vehicles, and Micro-Grid (MG) storage on maximum solar radiation hours. Several Deep Learning (DL) algorithms were applied to account for the uncertainty. Moreover, a Reinforcement Learning HCPV (RL-HCPV) algorithm was created for efficient real-time energy scheduling decisions. The proposed algorithm managed the energy demand between PV solar energy generation and vehicle energy storage. RL-HCPV was modeled according to several constraints to meet household electricity demands in sunny and cloudy weather. Simulations demonstrated how the proposed RL-HCPV system could efficiently handle the demand response and how V2H can help to smooth the appliance load profile and reduce power consumption costs with sustainable power generation. The results demonstrated the advantages of utilizing RL and V2H as potential storage technology for smart buildings.

• 정보교육학회논문지
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• 제22권2호
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• pp.231-238
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• 2018

소프트웨어 교육이 교육 현장에서 다루어지기 시작했지만 현장 교사가 활용할 수 있는 피지컬 컴퓨팅 교구 및 수업 가이드라인이 부족한 실정이다. 따라서 본 연구에서는 마이크로비트라는 피지컬 컴퓨팅 교구를 활용한 창의 컴퓨팅 교육 프로그램을 제시하였다. 교육 프로그램에 적용된 동시따응발(MDIAP) 교수학습모형은 동기 유발, 시연하기, 따라하기, 응용하기, 발표하기의 5단계로 구성되었다. 교수-학습 과정은 마이크로비트의 기본 센서와 추가적인 센서, 구동장치를 활용한 메이커 학습으로 구성하여 나선형으로 제시하였다. 이러한 일련의 교수 학습 활동을 통하여 학생들의 창의 컴퓨팅 사고력을 키울 수 있을 것으로 기대한다.

• 대한전자공학회논문지TE
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• 제37권4호
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• pp.47-56
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• 2000

수학능력이 상대적으로 낮은 전문대학 학생들의 학습의용을 고취시키기 위하여 학생의 수학능력과 학습의욕을 고려한 교육과정의 연구를 수행하였다. 즉 전문대학 전자과의 교육과정을 세분야(전자통신기기 분야, 마이크로프로세서 분야, 집적회로설계 분야)로 편성하여, 전자통신기기 및 마이크로프로세서의 기본 원리를 습득하는데 중점을 두고, 국제적인 추세인 집적회로설계 분야를 이해하게 된다. 특히 국내의 빈약한 반도체 설계 분야를 활성화하기 위하여 설계기술을 집중적으로 훈련시키고 특성화하여, 반도체 설계분야의 기능화된 인력을 공급할 수 있도록 교육과정을 편성하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.

• International journal of advanced smart convergence
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• 제9권1호
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• pp.63-69
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• 2020

The rapid development in modern industrialization pollutant the water and atmospheric air across the globe that have a major impact on the human and livings health. In worldwide, every country government increasing the importance to improve the outdoor air pollution monitoring and control to provide quality of life and prevent the citizens and livings life from hazard disease. We proposed the environmental dust level detection method for outdoor facilities using sensor fusion technology to measure precise micro-dust level and monitor in realtime. In this proposed approach use the camera sensor and commercial dust level sensor data to predict the micro-dust level with data fusion method. The camera sensor based dust level detection uses the optical flow based machine learning method to detect the dust level and then fused with commercial dust level sensor data to predict the precise micro-dust level of the outdoor facilities and send the dust level informations to the outdoor air pollution monitoring system. The proposed method implemented on raspberry pi based open-source hardware with Internet-of-Things (IoT) framework and evaluated the performance of the system in realtime. The experimental results confirm that the proposed micro-dust level detection is precise and reliable in sensing the air dust and pollution, which helps to indicate the change in the air pollution more precisely than the commercial sensor based method in some extent.

• 한국융합학회논문지
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• 제13권2호
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• pp.13-20
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• 2022

본 연구는 딥러닝 기법을 활용하여 범죄 수사 도메인에 특화된 개체명 인식 모델을 개발하는 연구이다. 본 연구를 통해 비정형의 형사 판결문·수사 문서와 같은 텍스트 기반의 데이터에서 자동으로 범죄 수법과 범죄 관련 정보를 추출하고 유형화하여, 향후 데이터 분석기법을 활용한 범죄 예방 분석과 수사에 기여할 수 있는 시스템을 제안한다. 본 연구에서는 범죄 수사 도메인 텍스트를 수집하고 범죄 분석의 관점에서 필요한 개체명 분류를 새로 정의하였다. 또한 최근 자연어 처리에서 높은 성능을 보이고 있는 사전학습 언어모델인 KoELECTRA를 적용한 제안 모델은 본 연구에서 정의한 범죄 도메인 개체명 실험 데이터의 9종의 메인 카테고리 분류에서 micro average(이하 micro avg) F1-score 99%, macro average(이하 macro avg) F1-score 96%의 성능을 보이고, 56종의 서브 카테고리 분류에서 micro avg F1-score 98%, macro avg F1-score 62%의 성능을 보인다. 제안한 모델을 통해 향후 개선 가능성과 활용 가능성의 관점에서 분석한다.

• International Journal of Contents
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• 제18권2호
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• pp.47-57
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• 2022

e-Learning content can be defined as digital content to achieve educational goals. Since it is an educational material that can be distributed in offline, online, and mobile environments, it is important to create content that meets the learner's education environment and educational goals. In particular, if the learner is a public official, the vision, philosophy, and characteristics of each local government must reflect. As non-face-to-face online education expands further due to the COVID-19 pandemic, local governments that have relied on onsite education in the past urgently require developing strong basic competency education and special task competency content that reflect regional characteristics. Such e-learning content, however, hardly exists and the ability to independently develop them is also insufficient. In this circumstance, this case study describes the process of self-production of e-learning content suitable for Busan's characteristics by the Human Resource Development (HRD) Institute of Busan City, a local government. The field of instructional design and instructional technology is always evolving and growing by blending technological innovation into instructional platform design and adapting to the changes in society. Busan HRD Institute (BHI), therefore, tried to implement blended learning by developing content that reflected the recent trend of micro-learning in e-learning through a detailed analysis. For this, an e-learning content developer with certain requirements was selected and contracted, and the process of developing content through a collaboration between the client and developer was described in this study according to the ADDIE model of Instructional Systems Development (ISD).