Resource Allocation for NOMA based D2D System Using Genetic Algorithm with Continuous Pool

Resource allocation is a very crucial part of non-orthogonal multiple access(NOMA)-based device-to-device(D2D) systems. It must be fast, effective and flexible because devices, especially vehicles in the system move at high speed. In this paper, we propose a resource allocation method for NOMA-based D2D communication systems based on a genetic algorithm(GA) approach. We have designed a novel concept of genetic algorithm that is suitable for resource allocation in NOMA-based D2D systems. The proposed method quickly maximizes the total throughput of paired devices including cellular user equipment(CUE) and moving vehicles(V). The proposed method is a faster and more reasonable way to converge than an exhaustive search method, which requires up to factorial time complexity.This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No. 2018-0-00969, Full duplex non-orthogonal multiple access (NOMA) optimization technologies using deep learning for 5G based autonomous vehicular networks

Upright Adjustment of Casual Videos and its Combination with Stabilization

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Comparison of Nursing Workforce Supply and Employment in South Korea and Other OECD Countries

This study aims to report on and compare the conditions of practicing nurses and nursing graduates in Korea and other OECD countries to suggest policy to improve nurse staffing in Korea. Methods: Data on nurses and nursing graduates from 34 OECD countries in 2015 (or the nearest year) were analyzed. The proportion of practicing nurses among nurses who were licensed to practice and nursing graduates per population and per the number of practicing nurses were examined. Results: The number of practicing nurses per 1,000 population in Korea was 5.9 and, in Korea, only 31.0% of licensed nurses were practicing, whereas the OECD average was 69.5%. Korea had the highest number of nursing graduates (109.0) per 100,000 population and the highest number of nursing graduates (183.5) per 1,000 practicing nurses in the OECD countries. Skill-mix analysis indicated that 52.2% of the practicing nurses in Korea were professional practicing nurses, which was the second-lowest among the OECD countries. The ratio of nurses wages to those of physician specialists was 0.43 in the OECD countries. Conclusion: Nurse staffing and skill-mix in Korea were very low in comparison to other OECD countries. Policies for retention of nurses via improved working conditions are required

디지털 영상의 모션 블러 제거

DoctorMotion blur is a common artifact that produces disappointing blurry images with inevitable information loss. It is caused by the nature of imaging sensors that accumulate incoming lights for an amount of time to produce an image. During exposure, if the camera sensor moves or objects move, a motion blurred image will be obtained.Deblurring is to find the latent sharp image from a given blurred image. Formally, motion blur has been often modeled using a convolution based blur model, where a blurred image is the convolution result between a latent sharp image and a blur kernel. Then, deblurring becomes a deconvolution problem. In non-blind deconvolution, the motion blur kernel is given, and the problem is to recover the latent image from a blurry version using the kernel. In blind deconvolution, the kernel is unknown and the problem is to estimate the blur kernel as well as the latent image.While deblurring has been studied extensively my many researchers, it is still very challenging. The major difficulties can be summarized as follows: first there is missing information. Motion blur causes irreversible information loss. Moreover, the blur kernel is unknown in most cases, which makes the problem more difficult. Second, while the convolution based blur model has been widely used, it often does not hold in practice. In real cases, images are usually non-uniformly blurred, e.g., due to object motions and rotational camera shakes. Noise and outliers, e.g., saturated pixels and sensor defects, also severely degrade the performance of deblurring methods.In this thesis, we present software-based solutions to overcome the difficulties mentioned above. Specifically, the thesis includes the following topics:Fast uniform motion deblurring from a single blurred image: Previous blind deblurring methods need a huge computation time. In this work, we propose a fast blind deconvolution method, which produces a deblurring result in only a few seconds. The experimental results show that our method is not only faster but also more reliable than previous methods.Handling outliers in non-blind image deconvolution: Outliers, such as sensor defects and saturated pixels, cause severe artifacts in previous methods. In this work, we explicitly model outliers and derive an Expectation-Maximization based non-blind deconvolution method.Blind deconvolution methods for non-uniform motion blur: Although the uniform blur model has been widely used, real-photographed images usually have non-uniform blur caused by object motions and rotational camera shakes. In this work, we propose two methods for handling non-uniform motion blur.Video motion deblurring: Motion blurred video can significantly degrades the performance of computer vision algorithms. We use unblurred frames to achieve very reliable video deblurring results

360 비디오 감상을 위한 사용자 인터랙티브 자동 재생 시스템

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적응형 채널 어텐션 모듈을 활용한 복합 열화 복원 네트워크

자율 주행 자동차나 소방 로봇과 같은 시스템에서 영상을 얻을 때 다양한 요인들로 인해 잡음, 블러와 같은 열화가 발생한다. 이런 열화된 영상에 직접 영상 분류와 같은 기술을 적용하기 어려워 열화 제거가 불가피하나 이러한 시스템들은 영상의 열화를 인식할 수 없어서 열화된 영상을 복원하는데 어려움이있다. 본 논문에서는 영상에 적용된 열화를 인지하지 못하는 상황에서 여러 방법들로 열화된 영상으로부터 자연스럽고 선명한 영상을 복원하는 방법을 제안한다. 우리가 제안한 방법은 딥러닝 모델에 채널 어텐션 모듈과 스킵 커넥션을 사용하여 영상에 적용된 열화에 따라 복원에 필요한 채널에 높은 가중치를 적용해복합열화영상의복원을진행한다.이방법은다른복합열화복원방법에비해학습이간단하고기존의다른방법들에 비해 높은 복합 열화 복원 성능을 낸다. The image obtained from systems such as autonomous driving cars or fire-fighting robots often suffer from several degradation such as noise, motion blur, and compression artifact due to multiple factor. It is difficult to apply image recognition to these degraded images, then the image restoration is essential. However, these systems cannot recognize what kind of degradation and thus there are difficulty restoring the images. In this paper, we propose the deep neural network, which restore natural images from images degraded in several ways such as noise, blur and JPEG compression in situations where the distortion applied to images is not recognized. We adopt the channel attention modules and skip connections in the proposed method, which makes the network focus on valuable information to image restoration. The proposed method is simpler to train than other methods, and experimental results show that the proposed method outperforms existing state-of-the-art methods.22Nkc

Minimax 분석을 통한 센서 배치 최적화와 비용절감에 관한 연구

학위논문 (석사)-- 서울대학교 대학원 : 화학생물공학부, 2014. 8. 윤인섭.복합건축물의 수가 많이 밀집되어 있는 한국의 경우, 초기의 부적절한 대응으로 인한 초대형 재난으로 이루어질 확률이 높으며, 이때 조기 경보를 통한 재난전조 감시용 센서의 역할은 매우 중요하다. 사고감지에 있어 센서는 필수적이며, 실시간으로 유해물질의 농도를 기록하고 분석하여 사고를 예측 및 대응 할 수 있는 도구이다. 명백히, 센서의 설치 개수가 증가 할수록 더욱 정확하고 빠른 감지가 가능해진다. 하지만 이는 센서 당 감지 효율적인 측면과 설치 개수의 따른 비용증가를 초래하며, 감지가 중복이 되는 문제점이 발생함으로 적절치 못하다. 이에, 전체 비용을 줄이면서 센서의 감지 율을 높이기 위한 센서 배치의 최적화에 관련된 연구가 필요하다. 본 연구에서는 대기확산모델을 바탕으로 한 유해물질 별 확산도를 바탕으로 한 센서의 감지 확률을 구함으로서 센서 배치 최적화를 수치적인 계산으로 결정할 수 있는 방법에 관해 연구하였다. 이밖에 여러 참고 문헌을 바탕으로 단순히 수치적인 확률을 이용한 센서의 배치로 인한 단점을 경험적 지식을 포함하여 보완 하고자 했다. 이 모든 것을 바탕으로 최소의 비용으로 최대의 감지효과를 얻을 수 있는 최적의 센서 배치를 구할 수 있는 방법론은 formulation을 통하여 완성하는 것을 최종 목표로 잡았다. 또한 Case study를 통하여 그 방법론의 전체적인 논리의 타당성을 확인하였다. 이 방법은 실제 화학공장에서도 추가적인 요인을 첨가하면 복합건축물과 유사하게 적용 가능하며, 센서의 위치뿐만이 아닌 설치 개수까지 전체 비용을 바탕으로 선정할 수 있어 매우 유용하게 기여할 수 있을 것으로 기대된다.목 차 제 1 장 서론 1 제 1 절 연구배경 및 목적 1 제 2 장 Background Theory 3 제 1 절 센서 배치의 고려 대상 3 1. 물질 특성 3 2. 가스 누출 가능성 6 3. 환경적 요인 6 4. 접근성과 유지 관리 문제 7 5. 관련 기준 참조 7 제 2 절 센서 감지 기준 제시 및 경보단계 설정 8 제 3 절 농도에 따른 물질별 위험도 10 1. 일산화탄소 (CO) 10 2. 이산화질소 (NO2) 12 3. 이산화황 (SO2) 14 4. 암모니아 (NH3) 16 제 3 장 Methodology 18 제 1 절 Hazard Identification 19 제 2 절 Local Information 21 제 3 절 Atmospheric Dispersion Model 22 1. Sensor location Problem (SELP) 25 2. Mini-maximization Of Undetected Probability 29 3. Constraint: Maintenance Zone 31 제 4 절 Cost Minimization 32 1. Objective Function 32 2. Probability of Accident 33 3. Average Loss Per Accident 33 제 5 절 Formulation of Gas Sensor Location 34 제 4 장 Case Studies 35 제 1 절 정사각형 부지에서의 센서 배치 최적화 35 1. 센서 개수 별 가스 감지 확률의 변화 36 2. Cost Minimization 47 제 2 절 직사각형 부지에서의 센서 배치 최적화 50 1. 센서 개수 별 가스 감지 확률의 변화 50 2. Cost Minimization 62 제 5 장 결론 65 참고문헌 67 Abstract 68Maste

우리나라 병원의 질 향상 사업에 관한 연구

학위논문(석사)--서울대학교 보건대학원:보건관리학과 보건학전공,1997.Maste