• 한국염색가공학회지
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• 제9권6호
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• pp.51-57
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• 1997

The lightness, chroma and hue tolerances with respect to the standard colour position in the CIELAB space have been studied in detail using the various existing data sets and the set form this study. The lightness tolerance showed a clear dependency upon the metric lightness for medium to light colour, but in the case of dark colours there was a discrepancy between the data sets. Both the chroma and hue tolerances showed dependency upon both the chroma and hue-angle and not the single dependency upon the metric chroma, as assumed in the CIE94 formula. New weighting functions were derived from the above experimental evidence, and finally a new formula, LCD(Leeds Colour Difference) was proposed. The LCD formula is nearly as simple and flexible as CIE94 but smoothes the individual weighting functions, especially for lightness tolerances for light colours and chromaticity discrimination near the blue region.

• 대한기계학회논문집A
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• 제32권4호
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• pp.347-353
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• 2008

Reflected light can be decomposed into specular and diffuse components according to the light reflectance theory and experiments. The specular component appears in smooth surfaces mainly, while the diffuse one is visible in rough surfaces mostly. Therefore, each component can be used in forming their correlations to a surface roughness. However, they cannot represent the whole surface roughness seamlessly, because each formulation is merely validated in their available surface roughness regions. To solve this problem, new approaches to properly blend two light components in all regions are proposed in this paper. First is the weighting function method that a blending zone and rate can be flexibly adjusted, and second is the neural network method based on the learning from the measurement data. Simulations based on the light reflectance theory were conducted to examine its performance, and then experiments conducted to prove the enhancement of the measurement accuracy and reliability through the whole surface roughness regions.

• 대한원격탐사학회지
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• 제25권6호
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• pp.547-557
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• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• Spatial Information Research
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• 제23권1호
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• pp.41-48
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• 2015

BIM 기술은 기존 2D 기반 도면처리에서 나아가 3D 모델링을 통한 시설물의 전 생애주기에 발생하는 데이터를 포함한다. 이러한 특성상 하나의 건물은 그 데이터의 방대한 양으로 엄청난 크기의 파일을 생산한다. 대표 표준포맷인 IFC가 그 예로, 객체 기반의 형상정보 및 속성정보를 기반으로 상당한 데이터를 포함한 대용량 처리에 대한 이슈가 종종 발생하고 있다. 이는 렌더링 속도를 증가시키고, 그래픽 카드 용량을 많이 차지하기 때문에, 화면 가시화 측면에서 비효율적이다. 대용량 데이터의 경량화 문제는 프로그램의 프로세스와 품질 측면에서 필수적으로 해결되어야 한다. 본 연구는 국내 및 해외 연구사례에서 경량화에 관련된 다양한 시도를 확인하였다. 이를 기반으로 대용량 BIM 데이터를 효과적으로 컨트롤하고 가시화하기 위해, BIM 특성을 고려하여 최대한 활용할 수 있는 데이터의 경량화 기법을 제안하고 검증하였다. 이는 웹 기반 GIS 플랫폼 상에서 대용량 시설물 데이터를 운용하는데, 최적의 시설물 유형을 분석하고 객체 기반의 IFC 특성을 최대한 활용하여 사용자 측면에서 화면전환의 품질을 확보하고 프로세스 측면에서 효과적인 메모리 운영을 확인하였다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 춘계학술대회논문집
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• pp.719-723
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• 2002

The purpose of this study is to suggest the correlation of single-number ratings for sound insulation by floor impact. As a assessment method of impact sound insulation. we selected the IIC contour of ISO, A weighted sound level. Inverse A-weighting curve and L-Index of japanese industrial standard. And we estimated the single-number ratings by application the measured data of impact sound level to each method. The results showed that the coefficients of determination between each two single-number ratings were very high (more than 0.9169). And In the condition of same assessment method, the coefficient of determination for light-weight impact sound was higher than that for heavy-weight impact sound.

• 대한원격탐사학회지
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• 제40권1호
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• pp.81-91
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• 2024

전기 에너지는 현대 문명의 핵심 에너지원으로 발전량, 전력 소모량의 변화 추이는 산업 및 생활 전반과 밀접한 관계가 존재한다. 본 연구에서는 남한 지역의 발전량과 야간광 값 사이의 상관관계를 파악하고 이를 이용하여 북한 지역의 월간 발전량 추이를 예측하였다. 연구 결과 서울시의 경우 야간광과 발전량 사이에는 0.34의 낮은 피어슨(Pearson) 상관계수를 보였으나 월 평균 기온을 이용하여 서울시의 가중치가 적용된 야간광 값과 발전량 사이에는 0.79의 높은 피어슨 상관계수를 보였다. 평양시의 월 평균 기온을 이용한 서울시의 가중치가 적용된 야간광 값을 이용하여 북한 지역의 월간 발전량 추이를 예측한 결과 2020년, 2021년 12월의 전월 대비 발전량 상승폭보다 2022년 12월의 전월 대비 발전량 상승폭이 약 60% 높은 것으로 나타났다. 본 연구의 결과는 월간 발전량 자료가 존재하지 않아 기민한 산업 동향 등의 파악이 힘든 지역의 월간 발전량의 추이를 예측하는데 도움이 될 것으로 기대된다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.334-334
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• 2022

Improvement of old-fashioned rain gauge systems for automatic, timely, continuous, and accurate precipitation observation is highly essential for weather/climate prediction and natural hazards early warning, since the occurrence frequency and intensity of heavy and extreme precipitation events (especially floods) are recently getting more increase and severe worldwide due to climate change. Although rain gauge accuracy of 0.1 mm is recommended by the World Meteorological Organization (WMO), the traditional rain gauges in both weighting and tipping bucket types are often unable to meet that demand due to several existing technical limitations together with higher production and maintenance costs. Therefore, we aim to introduce a newly developed and cost-effective hybrid rain gauge system at 0.1 mm accuracy that combines advantages of weighting and tipping bucket types for continuous, automatic, and accurate precipitation observation, where the errors from long-term load cells and external environmental sources (e.g., winds) can be removed via an automatic drainage system and artificial intelligence-based data quality control procedure. Our rain gauge system consists of an instrument unit for measuring precipitation, a communication unit for transmitting and receiving measured precipitation signals, and a database unit for storing, processing, and analyzing precipitation data. This newly developed rain gauge was designed according to the weather instrument criteria, where precipitation amounts filled into the tipping bucket are measured considering the receiver's diameter, the maximum measurement of precipitation, drainage time, and the conductivity marking. Moreover, it is also designed to transmit the measured precipitation data stored in the PCB through RS232, RS485, and TCP/IP, together with connecting to the data logger to enable data collection and analysis based on user needs. Preliminary results from a comparison with an existing 1.0-mm tipping bucket rain gauge indicated that our developed rain gauge has an excellent performance in continuous precipitation observation with higher measurement accuracy, more correct precipitation days observed (120 days), and a lower error of roughly 27 mm occurred during the measurement period.

• 한국정보통신학회논문지
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• 제18권3호
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• pp.735-741
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• 2014

에지는 영상에서 화소 간의 명암 차이가 큰 경우에 나타나며, 대상의 크기, 위치, 방향 등의 정보를 포함한다. 에지검출 방법에는 Sobel, Roberts, Laplacian, LoG(Laplacian of Gaussian) 등이 있으며, 이러한 방법들은 AWGN(additive white Gaussian noise)이 첨가된 영상에서 그 특성이 미흡하다. 따라서 이러한 특성을 개선하기 위하여 본 논문에서는 거리에 따른 가중치와 주변 화소의 평균에 의한 추정 마스크를 적용하는 알고리즘을 제안하였다. 그리고 제안한 방법의 성능을 확인하기 위하여 평가 척도는 처리 영상 및 PFOM(Pratt's figure of merit)을 사용하여 기존의 방법들과 비교하였다.

• 제어로봇시스템학회논문지
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• 제6권3호
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.729-733
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• 1997

Due to the variety of disturbance, it is not ease to accomplish the in-process detection of weld line with non-contact sensor. To get around this difficulties problem develop an automatic seam tracking weld system, the reliable signal processing algorithm has been recommanded. In this research, laser displacement sensor is applied as a seam finder in the automatic tracking system. The sensor is controlled by a dc servo motor which is mounted at X-Y moving table. X-Y moving table manipulated by an ac servo motor controls the position and velocity of the welding torch. First, X-Y table moves to Y-axis to search the welding joint feature before starting the welding, and welding joint is from the scanning data and weighting factor for each other. Second, weld line is determined using proposed signal processing algorithm during welding process. Form the experimental results, we could see the possibility that laser displacement sensor with procesed algorithm can be used as a seam finder in welding process under the severe noise (spatter,arc light etc.) condition