• 한국환경복원기술학회지
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• 제17권2호
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• pp.125-136
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• 2014

Urban Green Area has ecologically deteriorated along with quantitative loss, being developed as a dot itself rather than connected to forests and green networks around the park. The present study aims to propose a connected plan on Urban Ecological Network establishment through 'assessment of the connectivity of the entire urban parks' in accordance with distance of forest and river and 'assessment of trends in connection fragmentation of urban parks' in accordance with the past change of forest and river. According to the result of this study, criteria based on previous research was "directly linked type is less than 300m, conceptually linked type is between 300m to 1km, the isolated type is greater than 1km". And the result of 'assessment of the connectivity of the entire urban parks' is analyzed as the rate of park and green network, 41.7% in Suwon, 80.0% in Seongnam, 88.9% in Namyangju on the basis of office and field investigation. Also, according to the result of 'assessment of trends in connection fragmentation of urban parks', consideration for connection to the original forest is insufficient.

• 반도체디스플레이기술학회지
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• 제15권4호
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• pp.61-66
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• 2016

This paper presents the implementation for a network structure of railway cars using a point to point communication. Most of network's representative specifications for a train are the FIP (Field Bus), MVB (Multifunction Vehicle Bus), CAN and WTB (Wire Train Bus) which is used by ALSOM, SIEMENS and BOMBADIER as major in this field. These networks in a physical layer use a multi-drop method, connected from $1^{st}$ car to $n^{th}$ car of a train through a cable without any extra services such as an electric part, amplifier. However waveforms which is passed through a long cable in the multi-drop are distorted by a capacitance or resistance of the cable or environments. Also since using a cable connected directly from $1^{st}$ car to $n^{th}$ car, if over two trains make double head, it isn't easy to distinguish ID for each railway cars. So by using the point to point network per each car, it is able to reduce a distortion. Also since reducing distortion, this communication speed can be been higher and transmit and receive any packets more stably. Using proposed token in a packet, this can make ID per each railway car automatically. Finally experimental results show the good performance and effectiveness of the proposed method.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2008년도 춘계 종합학술대회 논문집
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• pp.555-559
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• 2008

임베디드 시스템이 발전함에 따라 다양한 플랫폼을 가진 임베디드 디바이스에서 플랫폼 독립성을 위한 자바 기술이 급속도로 발전하고 있다. SUN 사의 CDC(Connected Device Configuration)에 정의된 CVM(Classic Virtual Machine)은 이러한 플랫폼 독립적인 자바 환경을 제공한다. 특히 셋톱박스나 스마트폰과 같은 임베디드 시스템에서는 네트워크 기능을 위해 CDC의 기본 프로파일인 FP(Foundation Profile)를 사용한다. 본 논문에서는 실시간 운영체제 iRTOS에서 네트워크 기능을 구현하기 위한 네트워크 API인 FP의 네이티브 메소드에 대해 설계 및 구현한 내용을 기술한다.

• 한국정보전자통신기술학회논문지
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• 제12권1호
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• pp.92-98
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• 2019

본 논문에서는 몰포러지 연산을 기본으로 하는 몰포러지 신경망(MNN: Morphological Neural Network) 기반 딥러닝 시스템을 제안하였다. 딥러닝에 사용되는 레이어는 몰포러지 레이어, 풀링 레이어, ReLU 레이어, Fully connected 레이어 등이다. 몰포러지 레이어에서 사용되는 연산은 에로전, 다이레이션, 에지검출 등이다. 본 논문에서 새롭게 제안한 MNN은 기존의 CNN(Convolutional Neural Network)을 이용한 딥러닝 시스템과는 달리 히든 레이어의 수와 각 레이어에 적용되는 커널 수가 제한적이다. 레이어 단위 처리시간이 감소하고, VLSI 칩 설계가 용이하다는 장점이 있으므로 모바일 임베디드 시스템에 딥러닝을 다양하게 적용할 수 있다. MNN에서는 제한된 수의 커널로 에지와 형상검출 등의 연산을 수행하기 때문이다. 데이터베이스 영상을 대상으로 행한 실험을 통해 MNN의 성능 및 딥러닝 시스템으로의 활용 가능성을 확인하였다.

• 한국항공우주학회지
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• 제49권3호
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• pp.197-204
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• 2021

유도탄의 비행 시험 중 고장 또는 비정상적인 기동이 발생하는 경우 비행을 계속하지 않도록 의도적으로 자폭한다. 이때 파편이 발생하며 안전 지역을 벗어났는지 여부를 실시간으로 추정하는 것이 중요하다. 본 논문에서는 Fully-Connected Neural Network(FCNN)를 이용하여 실시간으로 파편의 예상 낙하 영역 및 낙하 시간을 추정하는 방법을 제안한다. 많은 양의 학습 데이터 생성을 위해 Unscented Transform(UT)를 적용하였으며 신뢰도 확보를 위해 Monte-Carlo(MC) 시뮬레이션과 비교하여 파라미터를 선정하였다. 또한 제안한 방법의 추정 결과를 MC와 비교하여 성능을 분석하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2882-2903
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• 2022

Image super-resolution (SR) processing is of great value in the fields of digital image processing, intelligent security, film and television production and so on. This paper proposed a densely connected deep learning network based on cascade architecture, which can be used to solve the problem of super-resolution in the field of image quality enhancement. We proposed a more efficient residual scaling dense block (RSDB) and the multi-channel cascade architecture to realize more efficient feature reuse. Also we proposed a hybrid loss function based on L₁ error and L_∞ error to achieve better L_∞ error performance. The experimental results show that the overall performance of the network is effectively improved on cascade architecture and residual scaling. Compared with the residual dense net (RDN), the PSNR / SSIM of the new method is improved by 2.24% / 1.44% respectively, and the L_∞ performance is improved by 3.64%. It shows that the cascade connection and residual scaling method can effectively realize feature reuse, improving the residual convergence speed and learning efficiency of our network. The L_∞ performance is improved by 11.09% with only a minimal loses of 1.14% / 0.60% on PSNR / SSIM performance after adopting the new loss function. That is to say, the L_∞ performance can be improved greatly on the new loss function with a minor loss of PSNR / SSIM performance, which is of great value in L_∞ error sensitive tasks.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.119-126
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• 2024

Disease caused by the coronavirus (COVID-19) is sweeping the globe. There are numerous methods for identifying this disease using a chest imaging. Computerized Tomography (CT) chest scans are used in this study to detect COVID-19 disease using a pretrain Convolutional Neural Network (CNN) ResNet50. This model is based on image dataset taken from two hospitals and used to identify Covid-19 illnesses. The pre-train CNN (ResNet50) architecture was used for feature extraction, and then fully connected layers were used for classification, yielding 97%, 96%, 96%, 96% for accuracy, precision, recall, and F1-score, respectively. When combining the feature extraction techniques with the Back Propagation Neural Network (BPNN), it produced accuracy, precision, recall, and F1-scores of 92.5%, 83%, 92%, and 87.3%. In our suggested approach, we use a preprocessing phase to improve accuracy. The image was enhanced using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, which was followed by cropping the image before feature extraction with ResNet50. Finally, a fully connected layer was added for classification, with results of 99.1%, 98.7%, 99%, 98.8% in terms of accuracy, precision, recall, and F1-score.

• 한국통신학회논문지
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• 제30권5B호
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• pp.274-286
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• 2005

본 논문에서는 이동 에드 혹 네트워크에서 효율적인 전력 사용 및 신뢰성 있는 데이터 전송을 보장하는 연결형 dominating set 기반의 라우팅 프로토콜을 제안하였다. 연결형 dominating set 기반의 라우팅 알고리즘에서 잦은 dominating set의 재구성은 루트 손실로 인한 전송 에러를 발생시키기 때문에, 노드의 잔여 전력량과 이동성을 고려하여 게이트웨이 노드를 선택하여야 한다. 또한 같은 지역에 노드가 집중되어 있다면 매체를 공유하는 무선네트워크의 특성상 병목으로 인한 충돌 및 지연 등을 야기 시킬 가능성이 크다. 따라서 본 논문에서는 노드의 잔여전력량 및 이동성, 이웃 노드수의 가중 가산 값에 비례하여 이웃 구성 통보 메시지 (neighbor set advertisement message)의 브로드캐스팅을 지연시키는 방법을 통해 연결형 dominating set의 재구성을 최소화 하면서도 신뢰성 있고 효율적인 데이터 전송을 보장하는 새로운 연결형 dominating set 구성 방법을 제안하고 다양한 상황에서의 실험을 통해 그 성능을 비교 평가하였다.

• ETRI Journal
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• 제39권1호
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• pp.116-123
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• 2017

Message-locked encryption (MLE) is a widespread cryptographic primitive that enables the deduplication of encrypted data stored within the cloud. Practical client-side contributions of MLE, however, are vulnerable to a poison attack, and server-side MLE schemes require large bandwidth consumption. In this paper, we propose a new client-side secure deduplication method that prevents a poison attack, reduces the amount of traffic to be transmitted over a network, and requires fewer cryptographic operations to execute the protocol. The proposed primitive was analyzed in terms of security, communication costs, and computational requirements. We also compared our proposal with existing MLE schemes.

• Journal of Power Electronics
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• 제9권1호
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• pp.85-92
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• 2009

This paper studies the method to predict voltage variation that can be presented in the case of operating a small-sized wind turbine in grid connection to the isolated small-sized power system. In order to do this, it makes up the simplified simulation model of the existing power plant connected to the isolated system, load, transformer, and wind turbine on the basis of PSCAD/EMTDC and compares them with the operating characteristics of the actual established wind turbine. In particular, it suggests a simplified model formed with equivalent impedance of the power system network including the load to analytically predict voltage variation at the connected point. It also confirms that the voltage variation amount calculated by the suggested method accords well with both simulation and actually measured data. The results can be utilized as a tool to ensure security and reliability in the stage of system design and preliminary investigation of a small-sized grid connected wind turbine.