• 한국의학물리학회지:의학물리
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• 제6권2호
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• pp.93-101
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• 1995

본 연구에서는 개인용 컴퓨터에서 디지털 의료 영상을 관리하고 특정 영상을 외부의 다중 모니터에 출력할 수 있는 시스템을 설계하였다. 본 시스템의 주화면은 크기는 1280$\times$1024로 256$\times$256$\times$8bits의 영상을 8개까지 디스플레이 할 수 있으며 이 중 영상 처리가 필요한 영상을 마우스로 선택할 경우 512$\times$512$\times$8bits 혹은 1024$\times$1024$\times$8bits로 디스플레이 할 수 있게 하였고, 정밀한 관찰을 원하는 영상을 마우스로 선택할 경우 외부 모니터로 출력할 수 있게 하였다. 저장된 모든 영상을 효율적인 검색과 저장을 위해 B＋TREE 구조를 사용하여 데이터베이스를 구축하였다. 이상과 같은 모든 기능은 IBM-PC 386 이상 어느 기종에서도 작동하며, 사용자 편의 환경을 구현하기 위해서 모든 기능들은 마우스로 작동하게 하였다.

• 한국지리정보학회지
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• 제10권3호
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• pp.22-30
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• 2007

항공사진을 이용해 산지토사재해 구간의 DTM(Digital Terrain Model)을 제작할 경우 수목이 재해구간을 가리는 경우가 많아 연속적이고 정밀한 DTM을 얻기가 매우 어렵다. 본 연구에서는 이러한 어려움을 해결해 보고자 디지털항공사진을 이용하여 moving window 형태의 필터링방법을 제시하였으며 DTM을 추출하였다. 또한 산지토사재해 구간에서 적정한 필터링의 window 크기에 따른 수목영향제거의 효과를 알아보기 위해 상용 수치사진측량 S/W인 Socet set의 Adaptive 필터링한 DTM과 LiDAR(Light Detection And Ranging)에서 획득한 DTM을 비교하였다. 그 결과 LiDAR에서 획득한 DTM보다는 정확도가 낮았지만 Adaptive 필터링보다는 정확도가 약 6m 정도 높은 결과를 얻을 수 있었다.

• 한국농림기상학회지
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• 제6권1호
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• pp.49-60
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• 2004

Northern Gyeonggi Province(NGP), consisting of 3 counties, is the northernmost region in South Korea adjacent to the de-militarized zone with North Korea. To supplement insufficient spatial coverage of official climate data and climate atlases based on those data, high-resolution digital climate models(DCM) were prepared to support weather- related activities of residents in NGP Monthly climate data from 51 synoptic stations across both North and South Korea were collected for 1981-2000. A digital elevation model(DEM) for this region with 30m cell spacing was used with the climate data for spatially interpolating daily maximum and minimum temperatures, solar irradiance, and precipitation based on relevant topoclimatological models. For daily minimum temperature, a spatial interpolation scheme accommodating the potential influences of cold air accumulation and the temperature inversion was used. For daily maximum temperature estimation, a spatial interpolation model loaded with the overheating index was used. Daily solar irradiances over sloping surfaces were estimated from nearby synoptic station data weighted by potential relative radiation, which is the hourly sum of relative solar intensity. Precipitation was assumed to increase with the difference between virtual terrain elevation and the DEM multiplied by an observed rate. Validations were carried out by installing an observation network specifically for making comparisons with the spatially estimated temperature pattern. Freezing risk in January was estimated for major fruit tree species based on the DCMs under the recurrence intervals of 10, 30, and 100 years, respectively. Frost risks at bud-burst and blossom of tree flowers were also estimated for the same resolution as the DCMs.

• 디지털콘텐츠학회 논문지
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• 제18권4호
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• pp.739-745
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• 2017

본 논문에서는 GPU 병렬 처리 하드웨어 아키텍처 내 최소 물리적 스레드 실행 단위(warp) 내에서 shifted sort 기반 k개 최근접 이웃 검색 기법을 구현하는 방법을 논의하고 일반적으로 동일한 목적으로 널리 사용되는 GPU 기반 kd-tree 및 CPU 기반 ANN 라이브러리와 비교한 결과를 제시한다. 또한 많은 애플리케이션에서 k가 비교적 작은 값이 필요한 경우가 많다는 사실을 고려해서 k가 warp 내부에서 직접 처리 가능한 2, 4, 8, 16개일 때 최적화에 집중한다. 구현 세부에서는 사용한 CUB 공개 라이브러리의 루프 내 메모리 관리 방법, GPU 하드웨어 직접 명령 적용 방법 등의 최적화 방법을 논의한다. 실험 결과, 제안하는 방법은 기존의 GPU 기반 유사 방법에 비해 데이터 지점과 질의 지점의 개수가 각각 $2^{23}$개 일 때 16배 이상의 빠른 처리 속도를 보였으며 이러한 경향은 처리해야 할 데이터의 크기가 커지면 더욱 더 커지는 것으로 판단된다.

• 대한전자공학회논문지TC
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• 제45권4호
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• pp.22-30
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• 2008

본 논문은 웨이브릿 변환을 이용하여 사전정보 없이 입사하는 디지털 신호의 변조타입 자동식별 방법에 관한 것이다. 변조인식에 사용되는 특징(key features)은 변조타입에 대한 민감도가 우수하고, SNR에 대한 변화가 적은 속성을 가져야 한다. 잡음에 대한 변화가 적은 속성을 가진 웨이브릿 변환 계수에서 변조인식을 위해 4개의 특징(key features)을 선정하였다. 또한 선정된 특징들을 이용하여 총 8종의 디지털변조 신호를 분류하기 위해 시뮬레이션을 수행하였다. 소프트웨어 라디오의 변조인식 모듈 탑재를 고려하여, 3 타입의 변조인식기에 대한 인식 정확도 및 수행시간을 비교 분석하였다. 시뮬레이션 결과 전체 인식시간은 MDC(Minimum Distance Classifier)와 DTC(Decision Tree Classifier)가 빠르게 수행되었고, 인식정확도는 MDC와 SVMC(Support Vector Machine Classifier)가 우수하게 제시되었다.

• 산업경영시스템학회지
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• 제46권1호
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.34-36
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• 2006

In this paper, we will treat the diagnosis problem to accurately determine fault types. The judgement of fault types is accomplished by observing the cluster newly formed with faults and clustering the input current waveforms to intrinsically show the conditions with the dignet that is a clustering algorithm. The types of input current waveforms are, however, constrained during normal operation, though it considers the load character. In case of faults. new clusters are generated outside the clusters. which appear during normal operation, because the input current waveforms of the induction motor are generated by the type which is not observed in case of faults. The diagnosis about the types of faults is essential to building a fault tree about the induction motor, and it removes the causes of the faults using a fuzzy logic. We, first, constitute a fault tree, which connects with the parts and the entire system of the induction motor, and investigate fault modes which can be generated from the fault tree and the relationship of the cause and the effect of each part (of the motor). Also, we distinguish the faults of each part by means of inducing the said of fuzzy relation equations encapsulating the relationship of the fault modes and each part.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 추계종합학술대회
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• pp.838-841
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• 2007

본 연구에서는 두 개의 17비트 오퍼랜드를 radix-4 Booth's algorithm을 이용하여 곱셈 연산을 수행하는 곱셈기를 설계하였다. 속도를 빠르게 하기 위하여 2단 파이프라인 구조로 설계하였고 Wallace tree 부분의 레이아웃을 규칙적으로 하기 위해서 4:2 덧셈기를 사용하였다. 회로를 평가하기 위해 Hynix 0.6-um CMOS 공정으로 MPW 칩을 제작하였다. 회로를 효율적으로 테스트하기 위한 방법을 제안하고 고장 시뮬레이션을 수행하였다. 설계된 곱셈기는 9115개의 트랜지스터로 구성되며 코어 부분의 레이아웃 면적은 약 $1135^*1545$ mm2 이다. 칩은 전원전압 5V에서 24-MHz의 클럭 주파수로 동작하였음을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권9호
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• pp.3377-3395
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• 2015

Recently, several approaches to share road conditions and/or digital contents through VANETs have been proposed, and such approaches have generally considered the radial distance from the information source as well as the TTL to provision an ephemeral, geographically-limited information sharing service. However, they implement general MANETs and have not been tailored to the constrained movement of vehicles on roads that are mostly linear. In this paper, we propose a novel application-level mechanism that can be used to share road conditions, including accidents, detours and congestion, through a VANET. We assign probabilities to roads around each of the intersections in the neighborhood road network. We then use the graph representation of the road network to build a spanning tree of roads with the information source as the root node. Nodes below the root represent junctions, and the edges represent inter-connecting road segments. Messages propagate along the branches of the tree, and as the information propagates down the branches, the probability of replication decreases. The information is replicated until a threshold probability has been reached, and our method also ensures that messages are not delivered to irrelevant vehicles, independently of their proximity to the source. We evaluated the success rate and performance of this approach using NS-3 simulations, and we used IDM car following and MOBIL lane change models to provide realistic modeling of the vehicle mobility.

• 대한원격탐사학회지
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• 제21권5호
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• pp.383-392
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• 2005

Vegetation classes, especially grass and tree classes, are often confused in classification when conventional spectral pattern recognition techniques are used to classify urban areas. This paper reports on a study to improve the classification results by using an automated process of considering height information in separating urban vegetation classes, specifically tree and grass, using three-band, high-spatial resolution, digital aerial imagery. Height information was derived photogrammetrically from stereo pair imagery using cross correlation image matching to estimate differential parallax for vegetation pixels. A threshold value of differential parallax was used to assess whether the original class was correct. The average increase in overall accuracy for three test stereo pairs was $7.8\%$, and detailed examination showed that pixels reclassified as grass improved the overall accuracy more than pixels reclassified as tree. Visual examination and statistical accuracy assessment of four test areas showed improvement in vegetation classification with the increase in accuracy ranging from $3.7\%\;to\;18.1\%$. Vegetation classification can, in fact, be improved by adding height information to the classification procedure.