• KIISE Transactions on Computing Practices
• /
• v.22 no.4
• /
• pp.189-194
• /
• 2016

A trajectory is the motion path of a moving object. The advances in IT have made it possible to collect an immeasurable amount of various type of trajectory data from a moving object using location detection devices like GPS. The trajectories of moving objects are widely used in many different fields of research, including the geographic information system (GIS) field. In the GIS field, several attempts have been made to automatically generate digital maps of roads by using the vehicle trajectory data. To achieve this goal, the method to cluster the trajectories on the same road is needed. Usually, the $Fr{\acute{e}}chet$ distance measure is used to calculate the distance between a pair of trajectories. However, the $Fr{\acute{e}}chet$ distance measure requires prolonged calculation time for a large amount of trajectories. In this paper, we presented a fast heuristic algorithm to distinguish whether the trajectories are in close distance or not using the discrete $Fr{\acute{e}}chet$ distance measure. This algorithm trades the accuracy of the resulting distance with decreased calculation time. By experiments, we showed that the algorithm could distinguish between the trajectory within 10 meters and the distant trajectory with 95% accuracy and, at worst, 65% of calculation reduction, as compared with the discrete $Fr{\acute{e}}chet$ distance.

• Korean Journal of Weed Science
• /
• v.5 no.2
• /
• pp.96-102
• /
• 1985

A study on the competition-ecological classification of the 10 prominent paddy weed species around bulrush (Scirpus juneoides) to simplify the weed problem concept for the rice production. A serial assessments on the competition ability in space and dry matter production(nutrient depletion) of respective weed species and paddy rice, and the data were used to compute the phenotypic similarity by Single Link Clustering method. Both growth response of weed species in mono- and under the paddy rice standing was very similar (r = 0.969), but the reduction rate as affected by paddy rice standing was negatively correlated with the ability in space-competition(r=-0.513). Dendrogram of 10 weed species based on the phenotypic similarity computed in 4 characters in mono- and under the paddy rice standing was also similar, as Echinochloa c., Ludwigia p., Cyperus s., and Scirpus m. in I-group, Eleocharis k., Scirpus j, in II-group, and Juncus e., Potamogeton d. in III-group, respectively. Also, that of paddy rice to 10 weed species showed Fimbristylis m., Scirpus j., Eleocharis k., Scirpus m., Juncus e. in I-group, and Ludwigia p., Potamogeton d., Monochoria v. in II-group, respectively. The integrated dendrogram by the above two data indicate the I-group with Fimbristylis m., Scirpus j., Eleocharis k. and Juncus e., as higher growth response with relatively lower competition ability to paddy rice, II-group with Cyperus s., Echinochloa c., Potamogeton d., and Ludwigia p., as higher both in growth and competition, and the last, III-group with Monochoria v., and Scirpus m., as lower growth but higher competition, respectively.

• Horticultural Science & Technology
• /
• v.19 no.2
• /
• pp.153-158
• /
• 2001

Leaf spots in Saintpaulia leaves are caused by temperature drop shock (TDS). This TDS-mediated leaf injury has not been reported in other plants besides Saintpaulia. To investigate how many and what kinds of plants are susceptible to temperature drop shock, Gesneriaceae and Acanthaceae plants were treated with TDS (from $30^{\circ}C$ to $15^{\circ}C$ or $5^{\circ}C$). Yellow or brown spots were found in 26 species or cultivars of 10 genuses of Gesneriaceae plants and in 8 species or cultivars of 7 genuses of Acanthaceae plants. Morphologically and anatomically no similarity was observed among the plants susceptible to TDS. Some plants have very thin and hard leaves, whereas other plants have thick and soft leaves. In spite of this non-similarity, the injury was restricted only to palisade cells as those of Saintpaulia leaves. Also the rapid and irreversible reduction of chlorophyll fluorescence was observed soon after TDS treatment in those plants. These results indicate that leaf injury induced by TDS is a more widespread leaf injury than has previously been thought.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.3
• /
• pp.25-33
• /
• 2023

Noise generated during the acquisition and transmission of CT images acts as a factor that degrades image quality. Therefore, noise removal to solve this problem is an important preprocessing process in image processing. In this paper, we remove noise by using a deformable convolutional autoencoder (DeCAE) model in which deformable convolution operation is applied instead of the existing convolution operation in the convolutional autoencoder (CAE) model of deep learning. Here, the deformable convolution operation can extract features of an image in a more flexible area than the conventional convolution operation. The proposed DeCAE model has the same encoder-decoder structure as the existing CAE model, but the encoder is composed of deformable convolutional layers and the decoder is composed of conventional convolutional layers for efficient noise removal. To evaluate the performance of the DeCAE model proposed in this paper, experiments were conducted on CT images corrupted by various noises, that is, Gaussian noise, impulse noise, and Poisson noise. As a result of the performance experiment, the DeCAE model has more qualitative and quantitative measures than the traditional filters, that is, the Mean filter, Median filter, Bilateral filter and NL-means method, as well as the existing CAE models, that is, MAE (Mean Absolute Error), PSNR (Peak Signal-to-Noise Ratio) and SSIM. (Structural Similarity Index Measure) showed excellent results.

• Journal of the Korean Society of Radiology
• /
• v.17 no.2
• /
• pp.191-199
• /
• 2023

This study aimed to propose minimized radiation doses with an optimized abdomen x-ray image, which realizes a Deep Blind Image Super-Resolution Generative adversarial network (BSRGAN) technique. Entrance surface doses (ESD) measured were collected by changing exposure conditions. In the identical exposures, abdominal images were acquired and were processed with the BSRGAN. The images reconstructed by the BSRGAN were compared to a reference image with 80 kVp and 320 mA, which was evaluated by mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). In addition, signal profile analysis was employed to validate the effect of the images reconstructed by the BSRGAN. The exposure conditions with the lowest MSE (about 0.285) were shown in 90 kVp, 125 mA and 100 kVp, 100 mA, which decreased the ESD in about 52 to 53% reduction), exhibiting PSNR = 37.694 and SSIM = 0.999. The signal intensity variations in the optimized conditions rather decreased than that of the reference image. This means that the optimized exposure conditions would obtain reasonable image quality with a substantial decrease of the radiation dose, indicating it could sufficiently reflect the concept of As Low As Reasonably Achievable (ALARA) as the principle of radiation protection.

• Journal of the Korean Society of Radiology
• /
• v.17 no.5
• /
• pp.679-684
• /
• 2023

Venous puncture is widely used to obtain blood samples for pathological examination. Because the invasive venipuncture method using a needle is repeatedly performed, the pain suffered by the patient increases, so our research team pre-developed a miniaturized near-infrared (NIR) imaging system in advance. To improve the image quality of the acquired NIR images, this study aims to model the non-local means (NLM) algorithm, which is well known to be efficient in noise reduction, and analyze its applicability in the system. The developed NIR imaging system is based on the principle that infrared rays pass through dichroic and long-pass filters and are detected by a CMOS sensor module. The proposed NLM algorithm is modeled based on the principle of replacing the pixel from which noise is to be removed with a value that reflects the distances between surrounding pixels. After acquiring an NIR image with a central wavelength of 850 nm, the NLM algorithm was applied to segment the final vein area through histogram equalization. As a result, the coefficient of variation of the NIR image of the vein using the NLM algorithm was 0.247 on average, which was an excellent result compared to conventional filtering methods. In addition, the dice similarity coefficient value of the NLM algorithm was improved by 62.91 and 9.40%, respectively, compared to the median filter and total variation methods. In conclusion, we demonstrated that the NLM algorithm can acquire accurate segmentation of veins acquired with a NIR imaging system.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.5
• /
• pp.21-29
• /
• 2024

Reducing the radiation dose during CT scanning can lower the risk of radiation exposure, but not only does the image resolution significantly deteriorate, but the effectiveness of diagnosis is reduced due to the generation of noise. Therefore, noise removal from CT images is a very important and essential processing process in the image restoration. Until now, there are limitations in removing only the noise by separating the noise and the original signal in the image area. In this paper, we aim to effectively remove noise from CT images using the wavelet transform-based GAN model, that is, the WT-GAN model in the frequency domain. The GAN model used here generates images with noise removed through a U-Net structured generator and a PatchGAN structured discriminator. To evaluate the performance of the WT-GAN model proposed in this paper, experiments were conducted on CT images damaged by various noises, namely Gaussian noise, Poisson noise, and speckle noise. As a result of the performance experiment, the WT-GAN model is better than the traditional filter, that is, the BM3D filter, as well as the existing deep learning models, such as DnCNN, CDAE model, and U-Net GAN model, in qualitative and quantitative measures, that is, PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure) showed excellent results.

• Journal of Microbiology
• /
• v.45 no.6
• /
• pp.505-509
• /
• 2007

An obligately aerobic bacterium, strain KOPRI $20902^T$, was isolated from a marine sediment in Ny-${\AA}$lesund, Spitsbergen Islands, Norway. Cells were irregular rods and motile with polar monotrichous flagellum. The optimum growth temperature was $17-22^{\circ}C$. Cells grew best in pH 7.0-10.0 and 3-4% sea salts (corresponding to 2.3-3.1% NaCl). The novel strain required $Ca^{2+}$ or $Mg^{2+}$ in addition to NaCl for growth. Sequence analysis of 16S rRNA gene revealed that the Arctic isolate is distantly related with established species (<92.4% sequence similarity) and formed a monophyletic group with Cellvibrio, which formed a distinct phylogenetic lineage in the order Pseudomonadales. Predominant cellular fatty acids [$C_{16:1}\;{\omega}7c/15:0$ iso 2OH (45.3%), $C_{16:0}$ (18.4%), ECL 11.799 (11.2%), $C_{10:0}$ 3OH (10.4%)]; DNA G+C content (37.0 mol%); nitrate reduction to nitrogen; absence of aesculin hydrolysis, N-acetyl-${\beta}$-glucosaminidase and esterase; no assimilation of arabinose, galactose, glucose, lactose, maltose, and trehalose differentiated the strain from the genus Cellvibrio. Based on the phylogenetic and phenotypic characteristics, Dasania marina gen. nov., sp. nov. is proposed in the order Pseudomonadales. Strain KOPRI $20902^T$ (=KCTC $12566^T$=JCM $13441^T$) is the type strain of Dasania marina.

• The Journal of the Korea Contents Association
• /
• v.14 no.4
• /
• pp.97-109
• /
• 2014

Social media is a general term for online services by which users share opinions, perspectives, and experiences. It supports interactions between users in sharing contents on it and weak ties among them play an important role in the process. This exploratory study attempts to identify crucial factors of establishing weak ties between social media users in the perspective of social network theory and uncertainty reduction theory. We collected data through diary study and in-depth interview and analyzed it following grounded theory approach. As a result, social media users more actively interacted each other or shared contents based on weak ties, compared to strong ties. In addition, similarity, self-disclosure, and relevance appeared to facilitate establishment of weak ties, by reducing psychological distance between social media users and perceived uncertainty of them.

• The KIPS Transactions:PartB
• /
• v.12B no.6 s.102
• /
• pp.715-720
• /
• 2005

This paper presents a hybrid recognition method of first moment of face image and principal component analysis(PCA). First moment is applied to reduce the dimension by shifting to the centroid of image, which is to exclude the needless backgrounds in the face recognitions. PCA is implemented by single layer neural network which has a teaming rule of Foldiak algorithm. It has been used as an alternative method for numerical PCA. PCA is to derive an orthonormal basis which directly leads to dimensionality reduction and possibly to feature extraction of face image. The proposed method has been applied to the problems for recognizing the 48 face images(12 Persons $\ast$ 4 scenes) of 64$\ast$64 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed method has a superior recognition performances(speed, rate). The negative angle has been relatively achieved more an accurate similarity than city-block or Euclidean.