• 환경영향평가
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• 제3권2호
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• pp.15-24
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• 1994

환경영향평가 연구의 목표(국가환경정책법에 명시)는 개발사업에 대한 여러가지 인자들(생태적, 경제적, 사회적)을 사업이 결정되기 전에 고려하여 사업이 진행되도록 허용하는 것을 확보하는데 있다. 말하자면, 목표는 환경영향평가를 계획과 정책과정에 통합하는데 있다. 국가환경정책법이 시행된 지 25년이 된 오늘날에 그러한 통합의 방향으로 진행의 정도에 관해서 문의하는 것은 적절하다. 이글은 미국의 대평원지역에서의 자원개발사업과 관련하여 계획과 정책과정에서의 환경영향평가에 대한 역할을 검토한다. 특히 에너지 추출 및 변환사업과 관련한 사회경계적 영향과 사업평가, 지방과 지역계획, 주정책개발에서의 환경영향평가에 대한 역할을 설명한다.

• Bulletin of the Korean Chemical Society
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• 제31권7호
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• pp.1976-1980
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• 2010

A simple and reliable method has been developed to selectively separate and concentrate trace amounts of Fe(III) ions from water and food samples by using flame atomic absorption spectrometry. A new reagent, 5-hydroxy-4-ethyl-5,6-di-pyridin-2-yl-4,5-dihydro-2H-[1,2,4] triazine-3-thione, was synthesized and characterized by using FT-IR spectroscopy and elemental analysis. Effects of pH, concentration and volume of elution solution, sample flow rate, sample volume and interfering ions on the recovery of Fe(III) were investigated. The optimum pH was found to be 5. Eluent for quantitative elution was 10 mL of 2 M HCl. The preconcentration factor of the method, detection limit (3s/b, ${\mu}gL^{-1}$) and relative standard deviation values were found to be 25, 4.59 and 1%, respectively. In order to verify the accuracy of the method, two certified reference materials (TMDA 54.4 lake water and SRM 1568a rice flour) were analyzed. The results obtained were in good agreement with the certified values. The method was successfully applied to the determination of Fe(III) ions in water and food samples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.363-372
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• 2017

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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• pp.4419-4441
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• 2016

A cause of vehicle accidents is the reduced visibility due to bad weather conditions such as fog. Therefore, an onboard vision system should take visibility detection into account. In this paper, we propose a simple and effective approach for measuring the visibility distance using a single camera placed onboard a moving vehicle. The proposed algorithm is controlled by a few parameters and mainly includes camera parameter estimation, region of interest (ROI) estimation and visibility computation. Thanks to the ROI extraction, the position of the inflection point may be measured in practice. Thus, combined with the estimated camera parameters, the visibility distance of the input foggy image can be computed with a single camera and just the presence of road and sky in the scene. To assess the accuracy of the proposed approach, a reference target based visibility detection method is also introduced. The comparative study and quantitative evaluation show that the proposed method can obtain good visibility detection results with relatively fast speed.

• 방송공학회논문지
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• 제9권1호
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• pp.64-73
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• 2004

본 논문에서는 3차원 메쉬 모델에 적용되는 강인한 워터마킹 알고리듬을 제안한다. 제안하는 알고리듬에서 워터마크는 3차원 모델로부터 추출된 2차원 영상에 삽입된다. 이 2차원 영상의 화소 값은 정해진 기준점들로부터 3차원 모델의 표면까지의 거리이며, 이를 거리 영상이라 한다. 워터마크는 거리 영상에 삽입되며, 워터마크된 거리 영상을 이용하여 3차원 모델의 꼭지점 좌표를 변경하면 워터마크가 삽입된 3차원 모델을 얻는다. 워터마크의 추출은 워터마크가 삽입된 모델로부터 거리영상을 얻고, 이 영상에서 워터마크를 추출한다. 워터마크 추출에는 원본 모델이 필요하며 원본과의 정합이 필요하다. 실험을 통해 제안하는 알고리듬이 회전, 병진, 비례조절, 가우스 잡음, 메쉬 간략화, 꼭지점 양자화에 강인함을 확인하였다.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2006년도 춘계학술발표회 논문집
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• pp.181-185
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• 2006

Image co-registration is the process of overlaying two images of the same scene, one of which represents a reference image, while the other is geometrically transformed to the one. In order to improve efficiency and effectiveness of the co-registration approach, the author proposed a pre-qualified area matching algorithm which is composed of feature extraction with canny operator and area matching algorithm with cross correlation coefficient. For refining matching points, outlier detection using studentized residual was used and iteratively removes outliers at the level of three standard deviation. Throughout the pre-qualification and the refining processes, the computation time was significantly improved and the registration accuracy is enhanced. A prototype of the proposed algorithm was implemented and the performance test of 3 Landsat images of Korea showed: (1) average RMSE error of the approach was 0.436 Pixel (2) the average number of matching points was over 38,475 (3) the average processing time was 489 seconds per image with a regular workstation equipped with a 3 GHz Intel Pentium 4 CPU and 1 Gbytes Ram. The proposed approach achieved robustness, full automation, and time efficiency.

• Toxicological Research
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• 제31권3호
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• pp.265-271
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• 2015

This study was carried out to investigate contents of 8 polycyclic aromatic hydrocarbons (PAHs) from frequently consumed seafood and dairy products and to evaluate their chemical analysis methods. Samples were collected from markets of 9 cities in Korea chosen as the population reference and evaluated. The methodology involved saponification, extraction with n-hexane, clean-up on Sep-Pak silica cartridges and gas chromatograph-mass spectrometry analysis. Validation proceeded on 2 matrices. Recoveries for 8 PAHs ranged from 86.87 to 103.57%. The limit of detection (LOD) 8 PAHs was $0.04{\sim}0.20{\mu}g/kg$, and limit of quantification (LOQ) of 8 PAHs was $0.12{\sim}0.60{\mu}g/kg$. The mean concentration of benzo[a]pyrene (BaP) was $0.34{\mu}g/kg$ from seafood and $0.34{\mu}g/kg$ from dairy products. The total PAHs concentration was $1.06{\mu}g/kg$ in seafood and $1.52{\mu}g/kg$ in dairy products.

• Mass Spectrometry Letters
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• 제4권1호
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• pp.18-20
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• 2013

Headspace solid phase micro-extraction gas chromatography/flame ionization detection (HS-SPME-GC/FID) method was compared with headspace gas chromatography/mass selective detection (HS-GC/MS). Organic solvent-spiked urine as well as urine samples from workspace was analyzed under optimal condition of each method. Detection limit of each compound by HS-SPME-GC/FID was $3.4-9.5{\mu}g/L$, which enabled trace analysis of organic solvents in urine. Linear range of each organic solvent was $10-400{\mu}g/L$, with fair correlation coefficient between 0.992 and 0.999. The detection sensitivity was 4 times better than HS-GC/MS in selected ion monitoring (SIM) mode. Accuracy and precision was confirmed using commercial reference material, with accuracy around 90% and precision less than 4.6% of coefficient of variance. Among 48 urine samples from workplace, toluene was detected from 45 samples in the range of $20-324{\mu}g/L$, but no other solvents were found. As a method for trace analysis, SPME HS GC/FID showed high sensitivity for biological monitoring of organic solvent in urine.

• 대한한의학원전학회지
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• 제25권4호
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• pp.105-113
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• 2012

Objectives : Acquiring information from symptoms is one of the important method to gain clinically available information in korean medicine. Therefore, up to now, study of symptom terms was frequently implemented in promotion of various information project. In data extraction methods using symptom information from DB, information search using synonym and method using ontology is studied and utilized. However, considering concept of symptom has essential information of appeared body area and phenomenon we think that extending synonym and ontology relationship in symptom terms can be useful for search and set to this study. Methods : We collect terms relevant to human body area and structure described in Donguibogam. Synonymous relationship between collected terms is organized. Relationship between collected terms is build to human-body-knowledge table which has form of Concept+Relation+Concept. Type of relationship is limited on a range of expressing content about parts of human body. Result & Conclusion : Search condition is generated automatically using relationship of the upper area in knowledge table contents. Information of next and previous acupuncture point, upper and lower acupuncture point, left and right acupuncture point can be searched using information of acupuncture point location, order, relative position in area, direction in knowledge table contents.