• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.734-739
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• 2018

Fingerprint recognition and iris recognition, which are one of the biometric methods, are easily influenced by external factors such as sunlight. Recently, finger vein recognition is used as a method utilizing internal features. However, for accurate finger vein recognition, it is important to clearly separate vein and background regions. However, it is difficult to separate the vein region and background region due to the abnormalized illumination, and a method of separating the vein region and the background region after normalized the illumination of the input image has been proposed. In this paper, we proposed a method to enhance the quality improvement and improve the processing time compared to the existing finger vein recognition system binarization and labeling method of the image including the image stretching process based on the existing illumination normalization method.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.87-94
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• 2008

In this paper, we propose a new vision-based fire detection method for a real-life application. Most previous vision-based methods using color information and temporal variation of pixel produce frequent false alarms because they used a lot of heuristic features. Furthermore there is also computation delay for accurate fire detection. To overcome these problems, we first detected candidated fire regions by using background modeling and color model of fire. Then we made probabilistic models of fire by using a fact that fire pixel values of consecutive frames are changed constantly and applied them to a Bayesian Network. In this paper we used two level Bayesian network, which contains the intermediate nodes and uses four skewnesses for evidence at each node. Skewness of R normalized with intensity and skewnesses of three high frequency components obtained through wavelet transform. The proposed system has been successfully applied to many fire detection tasks in real world environment and distinguishes fire from moving objects having fire color.

• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.595-600
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• 2013

In order to spatially interpolate the near-surface temperature (Ta) values, satellite and reanalysis methods were used from previous studies. Accuracy of reanalysis Ta was generally better than that of satellite-based Ta, but spatial resolution of reanalysis Ta was large to use at local scale studies. Our purpose is to evaluate accuracy of reanalysis Ta and satellite-based Ta according to elevation from April 2011 to March 2012 in Northeast Asia that includes various topographic features. In this study, we used reanalysis data that is ERA-Interim produced by European Centre for Medium-Range Weather Forecasts (ECMWF), and estimated satellite-based Ta using Digital Elevation Meter (DEM), Normalized Difference Vegetation Index (NDVI), difference between brightness temperature of $11{\mu}m$ and $12{\mu}m$, and Land Surface Temperature (LST) data. The DEM data was used as auxiliary data, and observed Ta at 470 meteorological stations was used in order to evaluate accuracy. We confirmed that the accuracy of satellite-based Ta was less accurate than that of ERA-Interim Ta for total data. Results of analyzing according to elevation that was divided nine cases, ERA-Interim Ta showed higher accurate than satellite-based Ta at the low elevation (less than 500 m). However, satellite-based Ta was more accurate than ERA-Interim Ta at the higher elevation from 500 to 3500 m. Also, the width of the upper and lower quartile appeared largely from 2500 to 3500 m. It is clear from these results that ERA-Interim Ta do not consider elevation because of large spatial resolution. Therefore, satellite-based Ta was more effective than ERA-Interim Ta in the regions that is range from 500 m to 3500 m, and satellite-based Ta was recommended at a region of above 2500 m.

• Childhood Kidney Diseases
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• v.10 no.2
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• pp.119-124
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• 2006

Purpose : The prognosis of acute poststreptococcal glomerulonephritis(APSGN) has been reported to be favorable. However, several studies have reported that patients with nephrotic range proteinuria in the acute phase or persistent proteinuria may progress to chronic renal failure. To elucidate this further, we analyzed the features of proteinuria and its prognosis in pediatric patients with APSGN. Methods : A total of 48 children with APSGN admitted to our hospital between Jan. 2000 and Dec. 2004 were included. After discharge from the hospital, patients were regularly followed up every month by clinical evaluations and laboratory tests including routine urinalysis and quantification of proteinuria. Results : Age of the patients ranged from 3 to 15 years(median 5.8 years) at the time of disease onset. Proteinuria was present in 34(70.8%) patients and 5 of them showed heavy proteinuria. Proteinuria normalized within one month in most patients(82.3%) and there was no one with proteinuria after 6 months. Cyclosporine A(5 mg/kg/day in two divided doses) was given to 3 patients with massive proteinuria that lasted longer than 2 months and the result was complete remission within 4 months. Conclusions : Our data indicated that the prognosis of APSGN during childhood is excellent. Children with severe proteinuria or subnormal renal function in poststreptococcal glomerulonephritis had favorable prognosis without chronic renal failure, and children with crescentic formation also had favorable prognosis. Three patients who continued to have heavy proteinuria for more than 2 months received cyclosporine A and remission of proteinuria was achieved within a couple of months.

• CELLMED
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• v.5 no.1
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• pp.5.1-5.5
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• 2015

In this first double-blind-placebo-controlled investigation of the Aeon Patch, electrocardiographic data were acquired from 50 well-hydrated volunteers [21 females and 29 males, age: 19-79, weight: 117-334 lbs, height: 5'-6',3"], under 3 different conditions for a total duration of 15 min (5 min/condition). Condition1: Control (C) - while wearing no Patch, Condition 2: Placebo (P) or Active (A) - after wearing either the Placebo or the Aeon Patch for 20 min, and Condition 3: Active (A) or Placebo (P) - after wearing either the Aeon or the Placebo Patch for 20 min. There was a washout period of 15 min between Conditions 2 and 3. All involved in the investigation were blind to the (A) and (P) Patches as they looked exactly the same and were assigned in a random fashion. The hypothesis to be tested was: Wearing the Aeon Patch for 20 min reduces stress. Data were first quality assured and those subjects who showed a parasympathetic response after wearing the Patch within 20 min were identified as early responders. Thirty subjects (60%) achieved a relaxed state after wearing the Aeon Patch as early as 20 min. Statistical analysis (one-sample inference) was used to compare the spectral features of the responders. The normalized LF/HF decreased significantly ($24%{\pm}9%$ after 20 min) in condition (A) compared to condition (P) with a p-value < 0.047 (n = 30) in responders. Therefore, the hypothesis that wearing the Aeon Patch for 20 min reduces stress was accepted as true.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.237-245
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• 2010

Nerve conduction studies (NCS) are the most objective measure of nerve function and essential for the diagnosis of sub-clinical neuropathy in diabetes mellitus and diabetic polyneuropathy (DPN). This study evaluates the characteristic of electrophysiological abnormalities in DPN. Electrodiagnostic data from 120 patients with diabetic polyneuropathies and 77 control subjects were reviewed. Motor nerve conduction velocities (MNCV), distal motor latencies (DML), compound muscle action potential (CMAP) amplitudes, No potential frequency and conduction block were analyzed. Data were normalized based on normative reference values, and the proportion of nerves with abnormal values in the lower and upper limbs were evaluated. DPN was systemic demyelinating peripheral polyneuropathy and more severe abnormal nerve conduction was found in lower limbs than in upper limbs. The abnormal degree was more severe in peroneal nerve. It was no statistically significant difference of conduction block in control and DPN group. Our findings suggest that DPN had more common and severe peroneal nerve involvement in the motor nerve conduction studies (MNCS). These findings have important implications for the electrophysiological evaluation of DPN.

• The Journal of the Korea Contents Association
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• v.4 no.1
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• pp.18-23
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• 2004

In this paper, we propose a new visual feature extraction method for content-based image retrieval(CBIR) based on wavelet transform which has both spatial-frequency characteristic and multi-resolution characteristic. We extract visual features for each frequency band in wavelet transformation and use them to CBIR. The lowest frequency band involves spacial information of original image. We extract L feature vectors using fuzzy homogeneity in the wavelet domain, which consider both the wavelet coefficients and the spacial information of each coefficient. Also, we extract 3 feature vectors wing the energy values of high frequency bands, and store those to image database. As a query, we retrieve the most similar image from image database according to the 10 largest homograms(normalized fuzzy homogeneity vectors) and 3 energy values. Simulation results show that the proposed method has good accuracy in image retrieval using 90 texture images.

• The Journal of the Petrological Society of Korea
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• v.28 no.4
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• pp.299-306
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• 2019

We estimated the provenance of the obsidian artifacts from Samgeori site at Yeoncheon, one of the prehistoric sites in South Korea. Pyroxene microlites are of hedenbergite to augite compositions, and intergrown and/or overgrown with Fe-oxides showing poikilitic texture. Major oxides contents for the matrix of the obsidian artifacts exhibit a narrow compositional range, especially SiO₂ contents being 73.0~75.5 wt.% of acidic rhyolitic composition. Also, rare earth element (REE) contents are relatively constant in the obsidian artifacts, and the chondrite-normalized REE patterns show a strong Eu negative anomaly. These mineralogical and geochemical features of the Samgeori obsidian artifacts were compared with those from both the Baekdusan obsidians and Japanese Kyushu obsidians which have been thought to be two major obsidian provenances around South Korea. It is suggested that the Samgeori obsidian artifacts were possibly originated from the Baekdusan obsidians.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.3
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• pp.90-98
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• 2011

The recognition and classification of paddy rice disease are of major importance to the technical and economical aspect of agricultural industry over the world. Computer vision techniques are used to diagnose rice diseases and to efficiently manage crops. Segmentation of lesions is the most important technique to detect paddy rice disease early and accurately. A new Gaussian Mean (GM) method was proposed to segment paddy rice diseases in various color spaces. Different color spaces produced different results in segmenting paddy diseases. Thus, this empirical study was conducted with the motivation to determine which color space is best for segmentation of rice disease. It included five color spaces; NTSC, CIE, YCbCr, HSV and the normalized RGB(NRGB). The results showed that YCbCr was the best color space for optimal segmentation of the disease lesions with 98.0% of accuracy. Furthermore, the proposed method demonstrated that diseases lesions of paddy rice can be segmented automatically and robustly.