• 한국전자통신학회논문지
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• 제19권3호
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• pp.551-562
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• 2024

본 연구는 이동 객체 추출 및 추적 방법 및 장치에 관한 것으로, 특히 인접 영상 간의 차영상을 이용하여 객체를 추출하고, 추출된 객체의 위치정보를 지속적으로 전달함으로써 적어도 하나의 이동 객체의 정확한 위치정보를 토대로 이동 객체를 추적하는 이동 객체 추출 및 추적 방법 및 장치에 관한 것이다. 사람과 컴퓨터의 상호작용의 표현에서 시작된 사람추적은 로봇학습, 객체의 카운팅, 감시 시스템 등의 많은 응용분야에서 사용되고 있으며, 특히 보안 시스템분야에서 카메라를 이용하여 사람을 인식하고 추적하여 위법행위를 자동적으로 찾아낼 수 있는 감시 시스템 개발의 중요성이 나날이 커져 가고 있다.

• Journal of information and communication convergence engineering
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• 제12권1호
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• pp.14-18
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• 2014

A recent major development in computer technology is the advent of wearable computer systems. Wearable computer systems employ a wireless universal serial bus (WUSB), which refers to a combination of USB with the WiMedia wireless technical specifications. In this study, we focus on an integrated system of WUSB over wireless body area networks (WBANs) for wireless wearable computer systems. However, current WBAN MACs do not have well-defined quality of service (QoS) mapping and resource allocation mechanisms to support multimedia streams with the requested QoS parameters. To solve this problem, we propose a novel QoS-aware time slot allocation method. The proposed method provides fair and adaptive QoS provisioning to isochronous streams according to current traffic loads and their requested QoS parameters by executing a QoS satisfaction algorithm at the WUSB/WBAN host. The simulation results show that the proposed method improves the efficiency of time slot utilization while maximizing QoS provisioning.

• 전자공학회논문지B
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• 제29B권1호
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• pp.94-101
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• 1992

The multilayer perceptron, trained by the error back-propagation learning rule, has been known as a mapping network which can represent arbitrary functions. However depending on the complexity of a function and the initial weights of the multilayer perceptron, the error back-propagation learning may fall into a local minimum or a flat area which may require a long learning time or lead to unsuccessful learning. To solve such difficulties in training the multilayer perceptron by standard error back-propagation learning rule, the paper proposes a learning method which progressively enlarges the learning domain from a small area to the entire region. The proposed method is devised from the investigation on the roles of hidden nodes and connection weights in the multilayer perceptron which approximates a function of one variable. The validity of the proposed method was illustrated through simulations for a function of one variable and a function of two variable with many extremal points.

• 한국지리정보학회지
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• 제11권3호
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• pp.92-106
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• 2008

이 연구는 부산광역시 강서구 지역을 대상으로 토지이용을 파악하고 비오톱지도를 작성 및 평가하는데 그 목적이 있다. 비오톱지도에 사용되는 평가도구는 헤메로비등급(Hemeroby)을 사용하였는데, 헤메로비란 생태계에 대한 인간의 영향정도를 파악하는 등급이다. 강서구는 부산광역시에서 두 번째로 큰 구이며 강한 개발압력의 영향을 받고 있다. 현장조사에 앞서 수치지도, 항공사진, 위성영상 등을 이용하여 비오톱유형을 사전에 구분하였으며, 비오톱유형의 구분 방법은 2000년 서울특별시에서 수행된 방법론을 수정하여 적용하였다. 현장조사에서는 종합적인 비오톱 지도작성법이 사용되었으며, 그 결과 강서구 비오톱의 전체 면적은 $172,620,207m^2$이었으며, 13,631개 폴리곤으로 이루어진 29개 비오톱유형으로 구분되었다. 전체 22.6%가 도시화지역이었으며, 나머지는 산림과 오픈스페이스지역이었는데, 산림과 오픈스페이스지역 중 22.6%는 산림, 35.6%는 경작지 및 기타지역이었다. 강서구의 헤메로비지수는 54.7이었는데, 이는 강서구 지역이 현재까지는 광범위하게 개발되지 않았음을 의미하며, 체계적인 발전을 위해서는 장기보전 및 개발계획이 수립되어야 할 것으로 사료된다.

• 한국지리정보학회지
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• 제3권4호
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• pp.73-86
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• 2000

본 연구에서는 다시기 위성영상과 GIS(geographic information system)를 이용하여 과수재배분포도 작성에 있어 다양한 분류기법을 적용하여 보다 효율적인 기법도출에 그 목적을 두고 있다. 이를 위해 다시기별 Landsat TM영상과 현지 조사자료 및 기존 과수재배 면적 통계자료를 활용하여 각 분류기법에 대한 시기별 및 과수별 분포 특성과 비교 분석함으로서 과수재배분포도 작성에 있어 효과적인 분류기법을 도출하였다. 다시기 Landsat TM 영상을 이용한 과수재배 분포도작성을 위해서는 초가을 영상으로 MLC(maximum likelihood classification)기법을 적용하는 것이 가장 효율적인 것으로 나타났다. 또한 GIS를 통한 공간분석으로 행정별 과수재배의 면적을 효과적으로 추출함과 동시에 과수재배분포의 형태를 효율적으로 파악 할 수 있음을 규명하였다.

• ALGAE
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• 제23권1호
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• pp.83-90
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• 2008

Eelgrass beds are very productive and provide nursery functions for a variety of fish and shellfish species. Management for the conservation of eelgrass beds along the Korean coasts is critical, and requires comprehensive strategies such as vegetation mapping. We suggest a mapping method to spatial distribution and quantify of eelgrass beds using a digital echosounder. Echosounding data were collected from the northeast part of Kwangyang Bay, on the south of Korea, in March, 2007. A transducer was attached to a boat equipped with a DGPS. The boat completed a transect survey scanning whole eelgrass beds of 11.7 km2 with a speed of 1.5-2 m s-1 (3-4 knot). The acoustic reflectivity of eelgrass allowed for detection and explicit measurements of canopy cover and height. The results showed that eelgrass bed was distributed in depth from 1.19 to 3.6 m (below MSL) and total dry weight biomass of 4.1 ton with a vegetation area of 4.05 km2. This technique was found to be an effective way to undertake the patch size and biomass of eelgrass over large areas as nondestructive sampling.

• 한국측량학회지
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• 제39권2호
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• pp.103-111
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• 2021

MMS (Mobile Mapping System) is being used for HD (High Definition) map construction because it enables fast and accurate data construction, and it is receiving a lot of attention. However, research on the use of MMS in the construction field is insufficient. In this study, road surveying and inspection of construction structures were performed using MMS. Through data acquisition and processing using MMS, point cloud data for the study site was created, and the accuracy was evaluated by comparing with traditional surveying methods. The accuracy analysis results showed a maximum of 0.096m, 0.091m, and 0.093m in the X, Y, and H directions, respectively. Each RMSE was 0.012m, 0.015m, and 0.006m. These result satisfy the accuracy of topographic surveying in the general survey work regulation, indicating that construction surveying using MMS is possible. In addition, a 3D model was created using the design data for the underpass road, and the inspection was performed by comparing it with the MMS data. Through inspection results, deviations in construction can be visually confirmed for the entire underground roadway. The traditional method takes 6 hours for the 4.5km section of the target area, but MMS can significantly shorten the data acquisition time to 0.5 hours. Accurate 3D data is essential data as basic data for future smart construction. With MMS, you can increase the efficiency of construction sites with fast data collection and accuracy.

• Nuclear Engineering and Technology
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• 제54권12호
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• pp.4684-4692
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• 2022

Radionuclide identification is an important part of the nuclear material identification system. The development of artificial intelligence and machine learning has made nuclide identification rapid and automatic. However, many methods directly use existing deep learning models to analyze the gamma-ray spectrum, which lacks interpretability for researchers. This study proposes an explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping. This method shows the area of interest of the neural network on the gamma-ray spectrum by generating a class activation map. We analyzed the class activation map of the gamma-ray spectrum of different types, different gross counts, and different signal-to-noise ratios. The results show that the convolutional neural network attempted to learn the relationship between the input gamma-ray spectrum and the nuclide type, and could identify the nuclide based on the photoelectric peak and Compton edge. Furthermore, the results explain why the neural network could identify gamma-ray spectra with low counts and low signal-to-noise ratios. Thus, the findings improve researchers' confidence in the ability of neural networks to identify nuclides and promote the application of artificial intelligence methods in the field of nuclide identification.

• 로봇학회논문지
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• 제13권3호
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• pp.190-197
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• 2018

Geomagnetic field signals have potential for use in underwater navigation and geophysical surveys. To map underwater geomagnetic fields, we propose a method that exploits an autonomous surface vehicle. In our system, a magnetometer is rigidly attached to the vehicle and not towed by a cable, minimizing the system's size and complexity but requiring a dedicated calibration procedure due to magnetic distortion caused by the vehicle. Conventional 2D methods can be employed for the calibration by assuming the horizontal movement of the magnetometer, whereas the proposed 3D approach can correct for horizontal misalignment of the sensor. Our method does not require a supporting crane system to rotate the vehicle, and calibrates and maps simultaneously by exploiting data obtained from field operation. The proposed method has been verified experimentally in inland waters, generating a magnetic field map of the test area that is of much higher resolution than the public magnetic field data.

• Communications for Statistical Applications and Methods
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• 제17권6호
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• pp.845-852
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• 2010

The outcomes of counts commonly occur in the area of disease mapping for mortality rates or disease rates. A Poisson distribution is usually assumed as a model of disease rates in conjunction with a gamma prior. The small area typically refers to a small geographical area or demographic group for which very little information is available from the sample surveys. Under this situation the model-based estimation is very popular, in which the auxiliary variables from various administrative sources are used. The empirical Bayes estimator under Poissongamma model has been considered with its accuracy measures. An accuracy measure using a bootstrap samples adjust the underestimation incurred by the posterior variance as an estimator of true mean squared error. We explain the suggested method through a practical dataset of hitters in baseball games. We also perform a Monte Carlo study to compare the accuracy measures of mean squared error.